Technical Report #502

PART A

Structural MRI Laboratory Manual

MR Image Acquisition and Image Processing Tools

And Neuroanatomical Regions of Interest (ROI)

and

Part B

Diffusion MRI Laboratory Manual

DTI Acquisition and Image Processing Tools

And Methods of Analyses

Martha E. Shenton, Ph.D., Marek Kubicki, M.D., Ph.D.,

and Robert W. McCarley, M.D.

http://pnl.bwh.harvard.edu/pub/pdfs/TR_502_Shenton.pdf http://www.spl.harvard.edu/pages/Special:PubDB_View?dspaceid=1263

© Copyright 2008 Shenton, Kubicki, and McCarley

PART A

Structural MRI Laboratory Manual MR Image Acquisition and Image Processing Tools

And Neuroanatomical Regions of Interest (ROI) Table of Contents Overview Page 3-4 Section I. Image Acquisition and Image Processing Tools. Pages 4-9 Section 2. Regions of Interest. Pages 9-28 I. Neocortical Gray Matter ROI Page 9 II. Temporal Lobe ROI. Pages 9-14 III. Frontal and Prefrontal Cortex ROI. Pages 14-20 IV. Parietal Lobe ROI. Pages 20-22 V. Basal Ganglia and Thalamus ROI. Pages 22-23 VI. Cerebellar and Brainstem ROI. Pages 23-25 VII. Orbito-Frontal Sulcal-Gyral Pattern and Cortex ROI Pages 25-27 VIII. Occipital Lobe ROI Page 27-28 IX. Power Analyses Pages 28 Section 3. References. Pages 28-34 Figures: Pages 35-56

Part B

Diffusion Tensor Imaging Laboratory Manual DTI Acquisition and Image Processing Tools

And Methods of Analyses

Table of Contents Page 57 I. Overview of DTI and Methods used to Analyze DTI Images Page 58-64 II. Scan Parameters for Current Research Studies Page 64-66 III. Specific Regions of Interest Page 66-70

1. Uncinate Fasciculus Page 66-67 2. Cingulate Bundle Page 67-68 3. Arcuate Fasciculus Page 68-69 4. Fornix Page 69 5. Corpus Callosum Page 69 6. Anterior Limb of the Internal Capsule Page 69-70

IV. Comparison of 1.5T and 3T DTI Data Page 70-71 V. Statistical Analyses Page 71 VI. Power Analyses Page 72 VII. References Page 72-73 VIII. Further Reading Page 73-74 IX. Figures Page 74-85

OVERVIEW This Technical Report provides information on details of image acquisition and image processing tools in addition to the region of interest (ROI) definitions for brain regions often used in schizophrenia research. It is a “snapshot” of current image acquisition and processing procedures and our intended alterations in the near future as we move to use of 3 T scanners at all sites. The ROI definitions are based on several different studies that span over many years. Of note, with improved spatial resolution in the images, and with improved segmentation procedures for classifying different tissues, the reliability and accuracy of our measurements have increased over time. Training on ROIs for small brain ROI, are, however, nonetheless still labor intensive and time consuming, and are based on the use of specific MR data sets that are used for reliability purposes and included in our ROI library. These training ROIs are from our most state-of-the-art images, using the most state-of-the-art segmentation procedure, and as such, are periodically updated based on changes in either spatial resolution and/or segmentation algorithms. Of note, this Appendix is considered multipurpose in our two laboratories, and is used by several investigators for their own individual grant applications. It was originally written by Drs. Shenton and McCarley, but multiple individuals have contributed to the ROI definitions, most of which are now published, and the appendix has been revised multiple times with the help of many investigators. Voxel-based morphometry (VBM) and manually drawn ROI. It is probably worth commenting on the current state of VBM and its utility in our subject population vs. manually drawn ROI, which are the current standard (see review in Shenton et al., 2001). We recently did a systematic comparison of VBM results vs. manually drawn ROI in our first episode schizophrenic and manic subjects and their controls (Kubicki et al., 2002). A simple summary is that VBM and the manual ROI were congruent in some comparisons but not in others. We are seeking to understand the basis of the discrepancies, but, in the meantime, we would see methodological problems arising with the exclusive use of VBM in populations of subjects with schizophrenia and bipolar disorder. VBM will likely prove very useful in suggesting possible abnormalities in regions not covered by manual ROI, and, indeed a VBM finding of an insula abnormality in schizophrenia was subsequently confirmed by manual ROI analysis (Kasai et al., 2003). This Technical Report is considered multipurpose in our laboratories and is used by several investigators. The web-publishing was done to facilitate access both within the lab and also for workers outside our laboratory who wish to have a convenient reference to our definitions. Drs. McCarley and Shenton originally began it, but now multiple individuals have contributed to the ROI definitions, most of which are now published in peer-reviewed journals. We emphasize to the reader that this Technical Report has been revised now several times, and will likely continue to be revised in the future as our methodology advances. The work that involves image processing and measuring regions of interest will partly take place at the Brockton VA and partly in the Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, directed by Dr. Shenton (see http://pnl.bwh.harvard.edu), which is closely linked to the Surgical Planning Laboratory (SPL) also at Brigham and Women’s Hospital (BWH). We also note scans for the first episode sample will be acquired at McLean Hospital while those for Brockton VA patients will be acquired at BWH, an acquisition arrangement that has been has been in use more than 8 years and has proved fruitful in terms of publications documenting scientific advances. What is new in our acquisition protocol from the last technical report is the use of a GE 3T scanner just begun at BWH and use of a 3T Siemens scanner at McLean scheduled to begin within the near future, once we have determined the equivalence of MRI data acquired at McLean to that acquired at the 3 T scanner at BWH. We went through the same determination of equivalence of data for the 1.5 T scanners when we began to use the McLean site and the BWH used a 1.5 T scanner. In Section I, we describe the MR acquisition protocol as well as recent additions to the image processing tools used to measure specific ROI. In Section 2, we describe specific ROI and

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neuroanatomical landmarks. The ROI defined in this Technical Report include: I. Neocortical Gray Matter ROI, followed by II. Temporal Lobe ROI, III. Frontal and Prefrontal Lobe ROI, IV. Parietal Lobe ROI, V. Basal Ganglia and Thalamus ROI, VI. Cerebellar and Brainstem ROI, VII. Orbitofrontal Sulco-Gyral Pattern and Cortex ROI, and, VIII. Occipital Lobe ROI. Some of these ROI definitions have been published, including: many of the Temporal Lobe ROI (Shenton et al., 1992; Kwon et al., 1999; Hirayasu et al., 2000), Prefrontal Lobe ROI for whole gray and white matter (Wible et al., 1995; 1997), Parietal Lobe ROI (Niznikiewicz et al., 2000; Nierenberg et al., 2005), Basal Ganglia ROI (Hokama et al., 1995), and, Cerebellar and Brain Stem ROI (Levitt et al., 1999). A description of the Thalamus ROI is also published (Portas et al., 1998). We also review criteria used for defining orbital frontal sulcal gyral patterns. In Section 3 we provide references for the technical descriptions in Sections 1 and 2. SECTION I: MR Acquisition Protocol and Image Processing Tools. 3T MRI Acquisition Protocol at BWH. Structural MRI (sMRI). 3T. For the Structural MRI volume measurements, images will be acquired using a 3T whole body MRI Echospeed system (General Electric Medical Systems, Milwaukee) at BWH in Boston, MA. We will use an 8 Channel coil in order to perform parallel imaging using ASSET (Array Spatial Sensitivity Encoding techniques, GE) with a SENSE-factor (speed-up) of 2. The structural MRI acquisition protocol will include two MRI pulse sequences. The first results in contiguous spoiled gradient-recalled acquisition (fastSPGR) with the following parameters; TR=7.4ms, TE=3ms, TI=600, 10 degree flip angle, 25.6cm2 field of view, matrix=256x256. The voxel dimensions are 1x1x1 mm. The second- XETA (eXtended Echo Train Acquisition) produces a series of contiguous T2-weighted images (TR=2500ms, TE=80ms, 25.6 cm2 field of view, 1 mm slice thickness). Voxel dimensions are 1x1x1 mm. This latter sequence is used as the additional channel of information for brain segmentation. Total scan time for the structural protocol is 11 minutes. Artifact Reduction. For both the XETA and fastSPGR acquisitions, flow compensation and presaturation of a slab inferior to the head will be used to reduce flow related artifacts and to obtain low intra-arterial signal intensity. These parameters have been optimized for our application so that, in combination with our specialized image filtering, they afford the best trade-off between high spatial resolution and high SNR. Together, these two acquisition sequences thus provide the technical benefit of high spatial resolution, covering the whole brain, combined with the clinical benefit of a short time in the magnet (about 20 minutes including set up time). MRI information will be transferred onto a network of 18 SUN workstations and two supercomputers for processing. MRI Acquisition Protocol at McLean. 1.5 T. The current protocol follows exactly the protocol that was previously in use for the 1.5 T scanner at BWH. 3D Fourier Transform Spoiled Gradient-Recalled Acquisition in Steady State (3DFT SPGR) Images. This pulse sequence affords excellent gray and white matter contrast for evaluation of brain structures. Imaging parameters will be: TR=35-msec, TE=5-msec, one repetition, 45 degree nutation angle, 24-cm field of view, 1.0 NEX, matrix=256 X 256 (192 phase encoding steps) X 124. Voxel dimensions will be: 0.9375 X 0.9375 X 1.5-mm. Data will be stored and analyzed as 124 1.5-mm thickness coronal slices. Dual Echo, Spin Echo T2 Weighted Sequence. The imaging parameters are: Repetition Time (TR)=3000ms, Echo Times (TE)=30 and 80ms, 24-cm field of view, four interleaved acquisitions with 3 mm slice thickness. This will result in a series of contiguous double echo (proton density and T2-weighted) images (52 levels/104 slices). Voxel (volume of pixel) dimensions will be 1X1X1X3-mm. Scan duration is 25 min, total duration, including setup, is 34 min. McLean 3T scanner (to be placed into use in the near future.) For the Structural MRI volume measurements, images will be acquired using a 3T whole body MRI Siemens Trio 3T scanner (Siemes Medical Solutions USA, Inc., Malvern, PA). Protocol parameters will follow as

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much as possible those used at BWH. We will use an 8 Channel coil in order to perform parallel imaging using GRAPPA (Generalized Autocalibrating Partially Parallel Acquisition) with acceleration factor of 2. The structural MRI acquisition protocol will include two MRI pulse sequences. The first results in contiguous gradient-echo acquisition (MP-RAGE- Magnetization Prepared Rapid Gradient Echo) with the following parameters: TR=7.4ms, TE=2.7ms, T1=600, 10 degree flip angel, 25.6cm2 field of view, matrix=256x256. The voxel dimensions are 1X1X1-mm. The second- SPACE (Sampling Perfection with Application optimized Contrasts using different flip angle Evolution) produces a series of contiguous T2-weighted images (TR=2500ms, TE=80ms, 25.6cm2 field of view, 1-mm slice thickness, voxel dimensions: 1X1X1-mm. Scanner Variability Over Time and Scanner Compatibility. Of note, we have scanned and rescanned 5 individuals over a short period of time (< 1 week) and we have found that geometrically complicated and large ROI like the STG have no more inter-scan variation with the same rater than the intra-rater reliability done on the same scan rescored 6 months later (sufficiently long for the rater to forget the original scan scoring). Signal-to-Noise-Ratio (SNR), Resolution, Contrast, and Field Inhomogeneities. At both scanner sites, BWH and McLean, programs from GE, including Top Level Tests (TLT) are performed every day to check the SNR, resolution, contrast, and field Inhomogeneities. The field Inhomogeneities are monitored using cylindrical water filled phantoms. Additionally, image geometric linearity is monitored daily with a 100 mm square cross phantom. Similar Siemens programs will be used for the Siemens 3T scanner at McLean. Image Processing Tools and Procedures McLean Data Transfer. Each MR image data set will then be transferred to CD and maintained and archived in duplicate copy at the McLean Laboratory of Dr. Salisbury (our collaborator in first episode studies). This information in DICOM format will be transferred to the BWH PNL for our processing, as we have successfully done for many years for the McLean GE 1.5 T data. McLean Data Processing for Siemens 3T data. Because of the interoperability of the Slicer (www.slicer.org), our analysis tool described below, these images will be directly readable by it, although we will save them in NRRD (Nearly Raw Raster Data), our preferred file format prior to processing. Using the slicer we have successfully processed Siemens MRI data from Mass. General Hospital. Thus the image processing steps described below will also be applicable to the Siemens 3T data from McLean. Image Processing Tools.

The 3D Slicer (www.slicer.org) is a freely available, open-source software for visualization, registration, segmentation, and quantification of medical neuroimaging data, and was developed in a collaboration between the MIT Artificial Intelligence Lab and the Surgical Planning Lab at BWH, with input from investigators McCarley and Shenton on desirable features for MRI analysis of images from schizophrenia and schizophrenia spectrum subjects and their controls. The newest version of slicer was released in 2007, version 2.7. (A completely new version of slicer, “Slicer3”, will be released soon. 3D Slicer is natively designed to be available on multiple platforms, including Windows, Linux and Mac OS X. Slicer's capabilities include: 1) interactive visualization of images, 2) manual editing, 3) fusion and co-registering of data, 4) automatic segmentation, 5) analysis of diffuse tensor imaging data, and 6) visualization of tracking information for image-guided surgical procedures. Slicer has been used for processing and analysis of the MRI data from schizophrenia and spectrum subjects and their controls for more than a decade. Segmentation Tools: Expectation-Maximization (EM) Algorithm. For segmentation, the iterative expectation-maximization (EM) algorithm combines the statistical classification of tissue classes with the automatic identification of intensity inhomogeneities in the images. Accurate tissue

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segmentation in MR images is a difficult problem because of the spatial inhomogeneities in pixel intensity. For example, a pixel representing white matter in the upper left of an image is often much brighter than a pixel representing white matter in the lower right side of the same slicing. The EM segmenter alternates two computational stages. In one stage, the spatial intensity inhomogeneities are estimated, and then in a second stage, this estimate is used to improve the accuracy of the tissue classification. An initial semi-automated segmentation (the algorithm used for segmentation in our previous papers) is used as a starting point or as input to the EM segmenter, and then the algorithm improves the segmentation in several iterations of the two steps (see Wells et al., 1994 for a detailed discussion). This algorithm allows for the use of the same statistical model or semi-automated segmentation (the input or starting point) to be used for all of the scans of a given acquisition type, hence eliminating error due to differences between users with regard to tissue classification. More specifically, earlier a label maps for tissue classification were created for each individual subject, and this can lead to errors based on individual differences among the individuals creating the label maps. With the new algorithm, label maps are created once for all cases in a study and these maps are used as the input or starting point. Further, the addition of the estimation of inhomogeneities allows for a more consistent segmentation of scans across magnet upgrades, and/or across different imaging sites. The segmentations computed using this algorithm were more consistent in estimating tissue classes than the semi-automated segmentation procedures when the segmentation was compared among 5 raters (Wells et al., 1994). In previous papers (e.g., Wible et al., 1995), the measurement of the gray/white matter volume in a cortical region required slice by slice editing of the boundary on each slice. The segmentations obtained using the EM segmenter are more accurate for the whole brain, and require less editing. This segmenter has made it possible to measure cortical regions faster, allowing us to accurately measure more regions in a greater number of subjects.

A new segmentation method is now in use, which uses the EM Segmenter, described above, in conjunction with a statistical framework that combines the registration of an atlas with the segmentation of magnetic resonance images. We use the same Expectation Maximization-based algorithm to find a solution within the model, which simultaneously estimates image inhomogeneities, anatomical labelmap, and a mapping from the atlas to the image space. An example of the approach is given in a recent publication for a brain structure-dependent affine mapping approach (see Pohl et al., 2005a). The algorithm produces high quality segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 22 magnetic resonance images. In addition, we show that the approach performs better than similar methods, which separate the registration and segmentation problems.

The segmentation method developed by Pohl et al. (2004) has recently been applied to MR images from subjects with schizophrenia and schizophrenia spectrum disorders and their controls in order to partition the images into the major three tissue classes: gray matter, white matter, and CSF (Koo et al., 2006; Nakamura et al., 2007). The method, as noted above, is based on use of an expectation-maximization (EM) algorithm, which simultaneously estimates the inhomogeneities in the images and segments the images into the three major tissue classes. The algorithm analyzes both SPGR and T2-weighted MR images (Pohl et al., 2004), and uses spatial priors (Guimond et al., 2001) to increase the accuracy of the approach. Spatial priors capture the probability of a certain tissue class being present at a certain location in the 3D volume. When compared to other state-of-the-art algorithms (Bouix et al. 2004), the method produces highly accurate segmentations of the three major tissue classes as it combines prior information, image inhomogeneity correction, and dual channel analysis. The final step measures the volume of the different tissue classes using the medical imaging software 3D-Slicer (Pieper et al., 2004). The voxel volumes of gray and white matter and CSF are summed yielding the total intracranial contents (ICC). Koo et al. (2006), using

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this method, were able to discern smaller neocortical gray matter and larger sulcal CSF volumes in neuroleptic-naïve females diagnosed with schizotypal personality disorder. This kind of algorithm development is important as small differences between groups, such as those investigated in schizophrenia and schizophrenia related disorders require that tools be sufficiently sensitive to detect very subtle volume differences, which while small, may nonetheless be quite important in the etiology of these disorders. Different segmentation algorithms have been compared by a computer scientist, Dr. Sylvain Bouix, who works with Dr. Shenton in the Psychiatry Neuroimaging Laboratory, who found our segmentation program performed well in comparison with others in the field (Bouix et al., 2007). The reader is also referred to publications cited in the references at the end of this Appendix (see Bouix et al., 2004; Liu et al., 2004; Martin-Fernandez et al., 2005; Pohl et al., 2002, 2005a, 2005b).

FSL software. We used the FSL software for bias field correction for the 3T images. FSL is the product of the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB), and FSL is shorthand for FMRIB’s Software Library (overview in Smith et al., 2004) and includes tools for structural MRI analysis (http://www.fmrib.ox.ac.uk/fsl/index.html). The particular FSL implementation for bias field correction for the 3T images was developed by Zhang et al. (2001). It is based on a Markov Field Model using a modification of the expectation-maximization algorithm of Wells et al (1996) for bias field correction. A number of new image processing tools have been developed in our image-processing laboratory, and they are currently implemented in our studies. 3D volume editor The presence of supercomputers at the Brigham and Women’s laboratory site aids and speeds image processing. For example, one tool that is useful is called the 3D volume editor, incorporated into slicer 2007. This program displays coronal, sagittal, and axial planes of any image series automatically. The slice number is orthogonal for each of the 3 planes, so that each can be viewed at any anterior/posterior or dorsal/ventral level. A segmentation file can also be viewed as an overlay onto the MR image, and some editing subroutines are available. Changing to a new slice, editing a volume of segmentation, or segmentation of a volume using thresholding are all computations that can be done instantaneously in this editor. In addition, any changes to the segmentation are immediately updated in a window in which the segmentation is rendered in 3D. The 3D rendering can also be viewed in any orientation. This editor contains routines for line drawing of ROI, connectivity, dilation and erosion, island removal and magnification. Realignment and Reslicing programs were developed to compensate for head tilt and rotation during MR acquisition, and for realigning the brain along any chose axes, currently implemented in Slicer. Head tilt or rotation can interfere with identifying landmarks and midline structures while constructing ROI. A plane that minimizes the square distance error is fitted to a set of user chosen axes to align the brain, and then the image is resampled into isotropic voxels. The voxel size is set to the smallest dimension of the original voxels, in this case 1 mm. Cubic interpolation is used to determine the intensity values of the resampled scans for MR Images. The intensity of a voxel in the resulting scan is set to a linear combination of the intensity values of the voxel’s eight nearest neighbors with the weights linearly decreasing when the distance between the voxel centers. Segmented images and ROI can also be realigned and resampled. For the segmented slices, the interpolation scheme had to be modified, as the original tri-linear interpolation algorithm produces a scan with label values that did not exist in the original scan. It assumes continuous range of values in the images, and therefore is not applicable in this case. It was modified to what

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is called “tri-linear voting”, where the weights are computed identically to the original method, but are used as “votes” for the corresponding label values, rather than as weights for computing the linear combination of the labels. The resulting label is the label that receives the highest vote. Using this method, resampling after automatic tissue segmentation preservers the segmentation obtained in the original, unaligned image, with no variation in intracranial contents and minimal change in gray (1%), white (none) and CSF (5%) classification, the same order of difference observed when manual classification is performed by different raters. Summary of 3T Processing Protocol Prior to ROI Analysis.

1. First, the T2 image will be realigned to the T1 using a rigid body registration algorithm (Slicer). Note that both the T1 & T2 images have voxels of 1x1x1mm

2. FSL will be used to extract the intracranial contents (ICC) mask, which will be further edited by human raters. The ICC mask has skull, skin and non-CNS elements stripped from the MRI image.

3. Both realigned T1 and T2 images will then be corrected for bias field inhomogeneities using FSL.

4. Finally, the two images will be used as input to Slicer EMAtlasBrainClassifier designed by Kilian Pohl (see above), with slightly modified parameters better suited to the 3T.

COMPARISON OF SEGMENTATION OF IMAGES FROM GE 1.5 T and GE 3.0T SCANNER AT BWH. Separation of brain tissue into distinct tissue classes of gray matter, white matter and CSF in segmentation depends on the signal intensity differences of these tissue classes. This contrast is lessened by partial volumes, where different classes of tissue are mixed in one voxel. The larger the voxel, the greater the partial volume effect. The 3T scanner has smaller voxels (1x1x1mm) than the 1.5 T scanner (1.5x.9375x.9375mm); the 3T scanner thus lessens the partial volume effect and increases the accuracy of segmentation. However, for the 3T acquisitions, segmentation is complicated by larger inhomogeneities in the magnetic field, referred to as a bias field, with the bias field being greater for the 3T than the 1.5 T scanner, necessitating a bias field correction, as described above. For our comparison we scanned 5 subjects with schizophrenia and 5 age-matched subjects both on the 1.5 T scanner used previously in our studies and on the 3 T scanner. Segmentation and bias field correction was as described above. To illustrate features of the 1.5T vs. 3T we chose scans of the same subject with schizophrenia. As shown in Figure 1A, 3T shows superior resolution with less partial volume effect, especially evident in the better segmentation—seen in more detail of white matter in basal ganglia and in cortex. A coronal slice view of Heschl’s gyrus and the superior temporal gyrus in Figure 1B also reveals greater detail in the raw images and greater accuracy of segmentation in the 3T scanner. To compare 1.5T vs. 3T quantitatively across all subjects we evaluated neocortical gray matter and white matter and supratentorial CSF (subcortical nuclei and cerebellum/brain stem were very poorly done in 1.5 T and were not compared). While the sample is much too small for statistical evaluation and for final conclusions, the preliminary data are interesting. Overall in our 10 subject comparison the superior quality of the 3T scanner with bias correction compared with the 1.5 T scans (3T-1.5T values) let to a mean increase in gray matter of .7%, a decrease in CSF of 1.1% and an increase in white matter of 6.3%. Interestingly, the healthy controls and schizophrenic patients showed reverse trends in gray matter and CSF for the 3T-1.5T differences, with 3T gray matter in controls increasing by 2.9% and decreasing in schizophrenics by 1.5%, while 3T CSF showed controls decreasing by 4% and schizophrenics increasing by 8.8%. White matter increased in both groups, 3.8% in controls and 8.8% in schizophrenics. These data suggest that

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the differences between controls and schizophrenics we observed using 1.5T images in neocortical comparisons (Nakamura et al., 2007) would have shown a more pronounced reduction in gray matter and an increase in CSF in schizophrenics compared with controls, and thus the 3T data confirms, with even stronger emphasis, our findings using 1.5T.

Insert Figure 1 About Here SECTION 2: REGIONS OF INTEREST (ROI). I. NEOCORTICAL GRAY MATTER. The procedure for isolating neocortex is described elsewhere (Koo et al., 2006). Briefly, neocortical ROI delineation included all six-layered neocortex and excluded the major portion of nonneocortical cortex, including limbic cortical areas (with the exception of the pyriform cortex) and most of paralimbic cortex, with the exception of portions of cingulate, insula, and temporal pole (for anatomic description: Mesulam, 1985). We describe this ROI as neocortical gray matter (NCGM) because the included regions of non-six-layer cortex comprise less than 5% of the GM volume in the ROI. Consistent with exclusion of medial temporal gray matter structures, the LV ROI did not include the very small temporal horn portion. NCGM was manually parcellated into three lobar ROI, frontal, temporal, and parieto-occipital (Figure 2). This parcellation mainly used sulcal boundaries because these are more faithful to brain anatomy than a purely geometric parcellation. The frontal lobe was separated from the parieto-occipital lobe by the central sulcus on the convexity, a boundary that is constant, easily identifiable, and traceable with little interindividual variation (Ono et al., 1990). The central sulcus was initially traced on the axial plane, and subsequently these trace lines were used on the coronal plane to separate frontal and parietal lobes. For frontal GM on the medial wall, the posterior terminus was the most posterior coronal slice containing corpus callosum. The frontal lobe was clearly separated from the temporal lobe by the Sylvian fissure and circular insular sulcus. The posterior temporal lobe terminus was geometrically defined as the most posterior coronal slice where the fornix could be clearly seen along the lateral ventricles, as in our previous studies (Hirayasu, et al., 2000). Occasionally, especially in the right hemisphere, the Sylvian fissure steeply ascended posteriorly through the parieto-occipital region. In this case, the superior boundary separating the temporal lobe from the parieto-occipital lobe was defined as the most superior axial slice where Heschl’s gyrus could be seen. The parieto-occipital lobe was automatically defined by its contiguous boundaries with the frontal and temporal lobes.

Insert Figure 2 About Here II. TEMPORAL LOBE ROI. Introduction. Table 1, below, defines the landmarks used for the regions of interest in our laboratory, and thus we include some ROIs that go beyond the ROIs proposed in the current application. We therefore list some regions not proposed because we think it important to have developed rules for ROI for brain regions that might prove interesting in terms of unhypothesized findings on fMRI scans done on subjects in this REAP, such as the thalamus and cerebellum. Daniels et al. (1987) was the primary anatomical MRI atlas used. Interrater reliability was high, and is discussed at the end of this section. Figure 3 provides a lateral view of the brain which includes many of the gyri that are measured, including superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, etc. (see Figure 3 at end of this Appendix).

Insert Figure 3 About Here

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Table 1

Region Most Anterior Slice

Most Posterior slice

Anterior amygdala-hippocampus (amygdala)

White-matter tract (temporal stem) linking temporal lobe with the rest of brain

Last slice before the appearance of the mammillary bodies

Posterior amygdala-hippocampus (hippocampus)

First appearance of the mammillary bodies

Last appearance of fibers of the crux of the fornix

Superior temporal gyrus Anterior region Posterior region

Landmarks for anterior and posterior hippocampus Landmarks for amygdala - anterior hippocampus Landmarks for posterior

ippocampus h

Landmarks for anterior and posterior hippocampus Landmarks for amygdala - anterior hippocampus Landmarks for posterior

ippocampus h Middle temporal gyrus, Inferior temporal gyrus, Fusiform gyrus,

arahippocampal gyrus P

Landmarks for superior temporal gyrus

Landmarks for superior temporal gyrus

Temporal Lobe Anterior pole

Second slice showing temporal lobe

Slice immediately anterior to start of superior temporal gyrus & amygdala

(1) Amygdala-Hippocampus. This region was subdivided into anterior and posterior segments, with the subiculum included with each hippocampal subdivision. A) Anterior Amygdala-Hippocampal Complex (Amygdala weighted). The contiguous gray matter of the anterior hippocampus and the amygdala could not be reliably differentiated and so were grouped together. The most anterior slice of this ROI was objectively defined as the slice showing the temporal stem. As illustrated in Figure 151 (p. 366) of Crosby et al. (1962), this objectively definable fiber connection is nearly coextensive anteriorly with the less objectively definable (on MRI) onset of the anterior amygdala. Figure 4 depicts a coronal 1.5mm slice that shows an outline of the amygdala as well as an outline of the parahippocampal gyrus and the whole temporal lobe. Regions of interest (ROIs) were drawn on multiple slices that included the region of interest (see Figure 4).

Insert Figure 4 about Here B) Posterior Amygdala-Hippocampal Complex (Hippocampal weighted). The coronal slice that showed the onset of the mammillary bodies was used as the anterior landmark. The most posterior coronal section to be included was defined by the last appearance of the fibers of the fornix traveling dorsally from hippocampus along the medio-ventral border of the lateral ventricles; this slice also contained the splenium of the corpus callosum. A visual inspection of the MR scans of all cases indicated that these definitions included the entire amygdala/hippocampal complex within a precision equal to the slice thickness (i.e., +1.5-

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mm). To determine further the validity of the anterior and posterior landmarks, i.e., whether or not they included the entire extent of amygdala-hippocampal complex, the length of this complex for each subject was computed from the number of 1.5-mm slices used. Mean values (+S.D.) were: 43.95+4.38-mm (normal controls) and 44.40+3.26-mm (schizophrenics) for right hippocampus, and 40.65+2.81-mm (normal controls) and 40.5+2.90-mm (schizophrenics) for left hippocampus. These values did not differ from post-mortem data collected from 60 adult brains (Duvernoy, 1988), which showed an average length of 40-45-mm for the hippocampus. In addition, schizophrenics and normal controls in the New England Journal of Medicine study (Shenton et al., 1992) did not differ on entire hippocampal length; they also did not differ in the lengths of either the anterior or posterior segments. Posterior segment lengths were 31.8+3.93-mm (normal controls) and 32.70+2.13-mm (schizophrenics) on the right, and 30.71+2.84-mm (normal controls), and 31.31+2.19-mm (schizophrenics) on the left. Anterior segment lengths were 12.11+1.83-mm (normal controls), and 11.70+2.28-mm (schizophrenics) on the right, and 9.9+2.03-mm (normal controls) and 9.20+2.78-mm (schizophrenics) on the left. (2) Parahippocampal Gyrus (PHG) Gray Matter ROI. Non-subicular portions of this gyrus were defined laterally by the collateral sulcus and a demarcation line drawn across the narrow portion of the gyral isthmus at the deepest portion of the collateral sulcus. The anterior and posterior extent used the same landmarks as for the anterior and posterior amygdala-hippocampus. (3) Superior Temporal Gyrus (STG) Gray Matter ROI. In addition to measuring medial temporal lobe regions, the neocortical STG gray matter was also examined. This region has been reported to show abnormalities in schizophrenia and is also important because of its critical role in auditory and language processing (e.g., Geschwind and Levitsky, 1968; Galaburda et al., 1984; Steinmetz et al., 1989; Shenton et al., 1997; Shenton 2001). The medial extent of STG was defined by the limiting fissure of the insula; from here a line was drawn through the gray matter. For the quantitative measurements, the anterior and posterior borders of the STG were the same as for the amygdala-hippocampal complex. (4) Middle Temporal Gyrus. For the middle temporal gyrus (MTG), we used criteria similar to Kim et al. (1999) and Crespo-Facorro et al. (2000). Before tracing, we drew two guidelines on the sagittal slice in each hemisphere to assure the borders (see Figure 5). We identified STG and anterior occipital sulcus on the sagittal slice where STG could be seen most clearly. These gyri were the superior border of the MTG, and the inferior temporal gyrus (ITG) was used as the inferior border. Manual drawings of MTG were performed on the coronal plane referring the guidelines (slice A in Figure 5). On the slice of a transition area, MTG and ITG were divided referring the guidelines (slice B in Figure 5). The most anterior slice was the first one in which the white matter tract linking the temporal lobe with the rest of the brain (temporal stem) could be seen. The most posterior slice was determined by the anterior tip of parietoccipital sulcus in midsagittal plane. MTG was thus defined as the gyrus just inferior to the STG (the parallel and superior temporal sulci will serve as the boundaries). The ventral boundary of the gyrus, the middle temporal sulcus, was the most inferior sulcus on the lateral surface of the temporal lobe. Further work evaluating the Middle Temporal Gyrus in schizophrenia has been completed in our laboratory by Onitsuka et al. (2004) and by Kuroki et al. (2006). Reliabilities were > 0.95 ICC.

Insert Figure 5 About Here (5) Inferior Temporal Gyrus. The middle temporal sulcus constitutes the dorsal border of this gyrus on the lateral surface. The medial border constitutes the fusiform gyrus and lateral occipitotemporal sulcus, and the inferior temporal sulcus (ITS) constitutes the superior border for the inferior temporal gyrus (ITG). The occipitotemporal sulcus was used to determine the medial

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border. This sulcus is interrupted frequently (the proportion of a single continuous sulcus is 48% for the right side and 24% for the left side). In such interrupted cases, the border was decided as the prominent sulcus on the coronal and axial slices. The most anterior and posterior slice was the same as MTG. Manual drawings of ITG were also performed on the coronal plane. Figure 5 shows STG, MTG, ITG, fusiform gyrus (FG) in a coronal slice as well as 3-dimensional reconstructions. Further work evaluating the Inferior Temporal Gyrus in schizophrenia has been completed in our laboratory by Onitsuka et al. (2004) and by Kuroki et al. (2006). Reliabilities were > 0.95 (interclass correlation coefficient). (6) The Fusiform Gyrus. This gyrus is defined as the first gyrus lateral to the parahippocampal gyrus. Bordered medially by the collateral sulcus and laterally by the lateral occipitotemporal sulcus. The fusiform gyrus (FG) is a spindle-shaped structure that is coextensive with the length of the temporal lobe, at a lateral distance lateral to the parahippocampal gyrus. Anatomically, the collateral sulcus forms the medial border of FG along its entire length. The occipitotemporal sulcus forms the lateral border of FG along its entire length. In some anatomical definitions, the anterior and posterior transverse collateral sulci are used to define the anterior and posterior FG boundaries. However, the anterior and posterior borders are often hard to identify reliably on MR images, and, consequently, different landmarks have to be used for the segmentation of this structure. In the current study, we used criteria similar to the work of Kim et al. (2000) who provided detailed guidelines for FG measurement in the parcellation of the temporal lobe. Drawing for FG was performed on the coronal plane. We found it essential to refer to axial and sagittal orientations for cases in which the borders were ambiguous on coronal slices. The anterior landmark was reliably defined by one slice posterior to the appearance of the mamillary body. The posterior landmark was determined by the anterior tip of the parietoccipital sulcus in midsagittal plane. These landmarks were chosen because they were the most reliable for delineating FG, although small amounts of the anterior and posterior parts of FG were excluded. This approach prevented erroneous inclusion of parts of another structure in FG measurement. The collateral sulcus and occipitotemporal sulcus were used to determine medial and lateral FG borders respectively. In some cases, these sulci were interrupted or duplicated particularly in the posterior part near the preoccipital incisura. In these sections, the more laterally located sulcus was used as the border (see Figure 6 for a 3D view of the ventral surface of the brain and for a slice showing the ROI on a 1.5mm coronal slice; see also Lee et al., 2002). This gyrus has also been investigated in schizotypal personality disorder by our research group, where subjects with schizotypal personality disorder did not show reduced gray matter volume of the fusiform gyrus compared with normal controls (see Dickey et al., 2003).

Insert Figure 6 and Figure 7 About Here (7) Temporal Pole. For the delineation of the temporal pole, we used the same criteria as Kim et al. (2000) and Crespo-Facorro et al. (2000), which were similar to those of Gur et al. (2000). The posterior border of the temporal pole was defined as the coronal plane where there was no frontotemporal junction. The lateral, medial, superior, and inferior boundaries were defined simply by the natural limits of the temporal lobe, and the anterior boundary was the rostral end of the temporal lobe tissue adjacent to the sphenoid bone (see Figure 7).

(8) Whole Temporal Lobe ROI. The ROI included both white and gray matter volume. Two definitions were used. The first portion included that part of temporal lobe that was co-extensive with the anterior-posterior extent of the hippocampal and STG ROI. The second portion added the most anterior temporal pole region, a region we will refer to simply as the temporal pole and had the following definition: the most anterior portion started with the second slice on which temporal lobe appeared (the first slice included meninges) and the posterior portion was the slice anterior to the beginning of the anterior amygdala-hippocampal complex.

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Visual inspection of 3D reconstruction showed that for the right temporal lobe, the posterior landmark (the same as for STG and PHG) included almost all of the temporal lobe (in most cases, within + 1.5-mm) as defined by the extent of the Sylvian Fissure. However, in accord with LeMay (1990), the left temporal lobe, as defined by the extent of the Sylvian Fissure, extended beyond (was more posterior to) the slice defined by the end of the posterior hippocampus ROI. To allow objective comparisons of volumes based on the same slice definition, this most posterior extent of the left temporal lobe ROI was not included in the computation of volumes, and this restriction should be kept in mind. It should be emphasized, nevertheless, that the volume definition used did include most of the left planum temporale (PT) and almost all of Heschl’s gyrus. Use of this objective and reliable scheme also resulted in anterior and posterior landmarks that applied equally to left and right temporal lobes and to normal controls and schizophrenics. For the temporal lobe boundary, a line was drawn along the cortical gray matter surface of the temporal lobe, following the Sylvian Fissure up to the Sylvian point, and then a line was drawn diagonally from the Sylvian point to the upper most portion of the amygdala-hippocampal complex and then medially to the lateral ventricle if this was present, or along the amygdala-hippocampal complex, back to the temporal lobe. All areas of CSF within the temporal lobe were excluded and only the gray and white matter values were included in the volumetric analyses. Left and right were computed separately. (9) Localization of Structures Within STG- Heschl's Gyrus and PT. For the measurement of Heschl's gyrus we will use Pfeifer's definition of Heschl's gyrus, Heschl's sulcus, and the anterior border of the PT (translated by Steinmetz et al., 1989). The anterior border of the PT is defined by Heschl's sulcus, and, if needed, a line is drawn that extends its trajectory to the lateral surface of the STG. In cases where there are two transverse gyri (frequent on the right), we will follow "Pfeifer's norm". That is, if the two gyri come from a common stem, then they will be both classified as Heschl's gyrus. The posterior border of the PT will be the same as for the STG. In accordance with the criteria of Barta et al., 1995, Heschl’s gyrus extends from the posterior margin of the insula near the opercular branch of the postcentral gyrus, transverses the superior aspect of the temporal lobe and terminates in the lateral border of the superior temporal gyrus. (For more details see Kwon et al., 1999; see also Figure 8).

Insert Figure 8 About Here

Axial images (reformatted from coronal scans) were first used to mark the outline of Heschl’s gyrus in order to accurately indicate the location of Heschl’s gyrus on coronal images. The images were then converted back to the coronal plane and the markers were used as a guide to outline the gray matter. As a last step, ROI were checked on sagittal images to confirm the accuracy of the boundaries. This subdivision of posterior STG is under development, and is not proposed in the current application. Heschl’s gyrus and planum temporale has also been evaluated in schizotypal personality disorder (Dickey et al., 2002) as well as in first episode patients diagnosed with schizophrenia (Kasai et al., 2003a). In the Kasai et al. study, progression of volume reduction was reported at follow up in both Heschl’s gyrus and planum temporale in patients diagnosed with schizophrenia but not in bipolar first episode patients or controls. (10) Temporal Horn. This includes all CSF within the segment of the lateral ventricle running approximately in the plane of the temporal lobe, with the most posterior slice being the 2nd slice anterior to the most posterior slice of the hippocampus (this definition excludes the portion of the lateral ventricle running dorso-ventrally).

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Interrater Reliability. For our interrater reliability studies we use the intraclass correlation coefficient, and independent raters trace regions of interest, blind to diagnostic group. For our original study (Shenton et al., 1992), Dr. Martha Shenton measured the temporal lobe regions of interest, blind to diagnosis. A second rater, also blind to diagnosis, measured the temporal regions for three normal controls and three schizophrenic patients selected randomly from each group. Additionally, four raters, blind to diagnosis, rated superior frontal gyrus for one case selected randomly and segmented into four regions (12 slices each). The average intraclass correlation was ri=0.86. Since this time we have conducted further reliability studies, for both intra- and interrater reliability, and results have all been ri >0.90. For interrater reliability for the temporal pole, three raters, blinded to group membership, independently drew ROIs. Ten cases were selected at random and the raters edited every other slice. The intraclass correlation coefficient was 0.99/0.98 for left/right temporal pole gray matter, and 0.99/0.99 for left/right temporal pole white matter, respectively. For Heschl’s gyrus and planum temporale, interrater reliability was based on three independent raters who drew ROIs for 10 cases selected randomly. Interrater reliability was ri=0.92 for left Heschl’s gyrus, ri=0.90 for right Heschl’s gyrus, and ri=0.93 for left planum temporale and ri=0.91 for right planum temporale (see Kwon et al., 1999). In a later study by Hirayasu et al. (2000), interrater reliability was also computed by three independent raters for ten cases selected randomly. For Heschl’s gyrus ri=0.88 for left Heschl’s gyrus, ri=0.88 for right Heschl’s gyrus, and ri=0.99 for left planum temporale and ri=0.95 for right planum temporale. Interrater reliability for the middle and inferior temporal gyrus, based on three independent raters and 10 cases, was: ri=0.98 for left MTG, ri=0.98 for right MTG, ri=0.964 for left ITG, and ri=0.97 for right ITG. Interrater reliability for the fusiform gyrus was computed by 3 independent raters for ten cases that were selected randomly. Interrater reliability for the three raters was: 0.979 for left FG, 0.985 for right FG (see Lee et al., 2002). III. PREFRONTAL CORTEX ROI. Overview. The prefrontal ROI are described below, beginning with total gray and white matter of the prefrontal cortex, and ending with ROI definitions for the individual gyri of the prefrontal lobe. Reliability is high, and is discussed in conjunction with each subdivision of this section. (1) Total Prefrontal Gray Matter and White Matter. The boundaries for the prefrontal region have been described in detail elsewhere (Wible et al., 1995; see Figure 9). Briefly, the gray matter measurements extended from the most anterior slice containing gray matter to three slices anterior to the temporal stem. The posterior landmark was determined by first locating the most anterior slice that contained the temporal stem (the white matter tract connecting the temporal and frontal lobes), then moving anteriorly three slices. This landmark was chosen because it was reliable and it controlled for any difference between schizophrenics and controls in lateral asymmetries (reliability for all anterior-posterior landmarks is discussed below).

Insert Figure 9 About Here The prefrontal cortex gray matter measure stops a few slices anterior to the most inferior aspect of the precentral sulcus, and the posterior bound differed slightly on the left and right, reflecting the different anteroposterior onset of left and right temporal stems. Thus, the gray matter volume compared in this study excluded Brodmann’s area 4 (motor cortex) and at least parts of area 6 (supplementary and premotor cortices). Reliability. To ensure a high degree of reliability, different anteroposterior landmarks were

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used for the prefrontal white matter volume than for the prefrontal gray matter volume. Anteriorly, the white matter was measured beginning with the first slice that contained white matter and extended posteriorly to the slice immediately anterior to the slice that contained the lateral ventricles. These landmarks were chosen for the white matter because it is invaded posteriorly by the gray matter structures of basal ganglia and the claustrum, both of which were not accurately segmented by the present semi-automated segmentation procedures and would have required extensive manual editing. Two different raters segmented data on 29 cases, each working on half of the cases. Interrater reliability was assessed by having each rater segment a random case that was initially processed by the other rater. A third rater also segmented the two cases, resulting in three estimates of volume (from three raters) for the two cases (one control and one schizophrenic patient). The intraclass correlation for three raters (Drs. Wible and Hokama, and Research Assistant I-han Chou) was ri=0.98. The inter-rater reliability of the first two raters was of interest, since they performed the segmentation. The average percent error for the first two raters was computed by subtracting the volumes obtained on the same case by the two raters for each category of tissue measured (i.e., white and gray matter on the left and right) and dividing the difference by the first rater’s volume score; the average percentage difference between raters was 1.75% for the schizophrenic case and 3.63% for the control case. More recently, interrater reliability was computed by three independent raters based on ten cases where Intraclass correlation coefficients were: 0.996 for left prefrontal gray, 0.998 for right prefrontal gray, 0.999 for left prefrontal white, and 0.999 for right prefrontal white (Hirayasu et al., 2001). Intrarater reliability was obtained for the two raters who segmented each of the brains. After the segmentation was completed for all cases, inter-rater reliability was determined by having each of the two raters reapply the image processing stages to a randomly selected case that this rater had previously segmented. This procedure produced two estimates of all four prefrontal ROI (left and right gray and white matter) for two cases. The average percentage difference between the first and second segmentations of the case was 3.25% for rater CGW, and the correlation between segmentations was r=0.98. For rater HH, the percentage difference was 4.25%, and the correlation between segmentations was r=0.99. Intra-rater and inter-rater reliability for the identification of the landmarks used to delineate gray and white matter boundaries were also assessed. Three cases for each of the two raters were chosen randomly from those initially processed by that rater, resulting in a total of six cases that were used for landmark reliability. The landmarks for gray and white matter on the left and right were judged blindly for all six cases by the two raters, resulting in landmark values for each case from the original segmentation, a second judgment from the original rater, and a third judgment from the rater who had not originally processed the case. The intraclass correlation for landmark reliability over six cases each rated three times was ri=0.99. For further information the reader is referred to Wible et al. (1995). (2) Parcellation of the Prefrontal Cortex into ROI. The prefrontal cortex was divided into insular, orbital, inferior, middle, superior, cingulate, and polar portions. The landmarks and methods used to parcellate the prefrontal cortex will be described for each of these areas. The delineating sulci used to define the ROI will be discussed in terms of three anterior-posterior levels of the prefrontal cortex (designated anterior, intermediate, and posterior); for some regions the boundaries changed at these transition points. The dorsolateral and ventromedial boundaries will be described only for the orbital region, and the dorsolateral boundary will be described for each remaining region. The other regions will be described in turn from the most ventral to the most dorsal. Each region's dorsal boundary was the same as the ventral boundary of the region

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immediately superior to it. A. Insular Region. The insular region was determined by visualizing sagittal views of the brain. It was bounded dorsally and ventrally by the circular insular sulcus. In the most posterior extent, the insula was divided from the orbital cortex by designating cortex on the ventral surface orbital cortex, and on the lateral surface, insular cortex. In the most anterior extent, it consisted of a small gyrus between the orbital and inferior gyrus regions). Kasai et al. (2003b) illustrate the definition.

B. Orbital Region. Most recently, we have focused on delineating the orbital frontal region from other portions of the prefrontal cortex. The ventral surface of the frontal lobe, traditionally referred to as the orbitofrontal cortex (OFC), extends from the frontal pole rostrally to the anterior perforated substance caudally. The frontal operculum and the ventromedial margin of the cerebral hemisphere form its lateral and medial boundaries, respectively. Greater variability exists among the sulcal and gyral patterns of the human OFC. In order to specify OFC more specifically and more anatomically, we classified OFC into three subregions primarily based on sulcal information (Chiavaras and Petrides 2000), and including: Gyrus Rectus (GR); Middle Orbital Gyri (MiOG); and Lateral Orbital Gyrus (LOG). Of note, Medial, Anterior, and Posterior Orbital Gyri (MOG, AOG, and POG, respectively) were combined into MiOG, to ensure reliability of ROI definition in view of the extreme variability of “H-shaped” sulci dividing MiOG. The detailed boundary definition is next described. Figure 10 provides an overview of the ROI definitions and Figure 11 shows a 3D reconstruction. Boundary Definitions (Based on sulci). (1) GR (Gyrus Rectus). Anterior: the most anterior slice where olfactory sulcus can be seen clearly. Posterior: GR disappear itself before olfactory trigone and subcallosal gyrus appear. Lateral: olfactory sulcus. Medical: supraorbital sulcus.

(2) MidOG (Middle Orbital Gyrus)

Anterior: One slice posterior to the slice at one fourth anterior point between the most anterior slice of brain parenchyma and the most anterior slice where corpus callosum are separately seen above and below the septum. Posterior: MidOG (POG and MOG) disappear. Lateral:-Anterior part: lateral portion of H-shaped sulci

-Transitional part: some obscure slices (up to 4~5 coronal 1.5mm slices) between anterior and -posterior parts -Posterior part (where POG disappears): circular insular sulcus -Medial: olfactory sulcus

(3) LOG (Lateral Orbital Gyrus)

Anterior: the most anterior slice where both lateral orbital sulcus and the lateral ramus of H-shaped sulci can be seen clearly. Posterior: the most posterior slice where lateral ramus of H-shaped sulci can be seen clearly. Lateral: lateral orbital sulcus Medial: lateral portion of H-shaped sulci

These parcellations can be seen in Figure 10, and are adapted from Chiavaras and Petrides (2000).

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Insert Figure 10 and Figure 11 About Here

C. Inferior Frontal Gyrus. At all levels, the dorsal boundary of this gyrus was the inferior frontal sulcus, which was identified primarily from 3D reconstructions. At an intermediate level, the inferior frontal gyrus includes the pars orbitalis, which was visible on the slice as a small gyrus situated immediately inferior to the circular insular gyrus. The pars orbitalis was difficult to distinguish from the insular cortex on coronal slices, and therefore was identified primarily from 3-D reconstruction. Reformatting the scans in the sagittal plane can also aid in identification of the pars orbitalis, where the gyrus is a C shaped structure bordered by the insular cortex. Note also that the true border of the inferior frontal gyrus often lies on the lateral, not ventral surface. However, we found the lateral orbital sulcus to be difficult to consistently identify, and so at levels anterior to the pars orbitalis, we chose to use the most ventral and lateral orbital sulcus as the boundary. The middle and superior frontal gyri extend more anteriorly than the inferior frontal gyrus, so at the most anterior level, the gyrus usually occupied a relatively small part of the brain. In the 3-D reconstruction, the inferior frontal gyrus was identified by the appearance of the pars opercularis, triangularis, and orbitalis. If the border of the inferior frontal gyrus in the most anterior end of the measured region was unclear, then the superior boundary of the gyrus was determined by extending the last clear boundary horizontally to the anterior most end of the measured region. In a more recent method, Kawashima, et al. (in preparation) also segmented the inferior frontal gyrus, along with the medial frontal and superior frontal gyri, as part of a prefrontal cortex parcellation (see Figure 12). The posterior boundary of the inferior frontal gyrus (IFG) was the most anterior slice that contained the genu of corpus callosum as same as that of SFG and MFG. The inferior frontal sulcus represented the superior boundary of IFG. The guidelines to be followed when the inferior frontal sulcus existed as an interrupted sulcus were described under tracing guidelines of the MFG. The anterior limit of the IFG was defined by the most anterior slice that contained the inferior frontal sulcus. The inferior boundary of the IFG consisted of the lateral orbital sulcus anteriorly and the superior circular sulcus of insula posteriorly. When both the lateral orbital sulcus and the superior circular sulcus were visualize on each coronal slice, the superior circular sulcus was chosen as a boundary. The guidelines to be followed when the lateral orbital sulcus could not be visualized in consecutive coronal slices were described under tracing guidelines of the IFG. D. Middle Frontal Gyrus. The middle frontal gyrus was the most difficult to identify, and was primarily segmented using 3D reconstructions. In the coronal plane, the gyrus was often split into an inferior and superior portion by the middle frontal sulcus, and so consisted of at least two separate gyri with a deep sulcus between them. The middle frontal gyrus was usually defined after first identifying the superior and inferior frontal gyri. The middle frontal gyrus was also part of Kawashima's segmentation (see Figure 12). The superior frontal sulcus formed the superior boundary of the MFG. The guidelines to be followed when the superior frontal sulcus existed as an interrupted sulcus are described under tracing guidelines of the SFG. Inferiorly, the inferior frontal sulcus formed the boundary of the MFG. When the inferior frontal sulcus was interrupted into two or three segments, the most-superior one was chosen as a boundary on each coronal slice. More anteriorly, the lateral orbital sulcus constituted the inferior boundary. When the lateral orbital sulcus could not be visualized in consecutive coronal slices, the tracing from the coronal section that last contained the sulcus was copied onto the neighboring slices. If the inferior frontal sulcus joined the lateral orbital sulcus, and these sulci intersected at a coronal plane anterior to FP, the inferior frontal sulcus also formed the

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inferior boundary for MFG in the anterior part. We did not use the frontomarginal sulcus as a landmark because it was highly variable and made reliable definition problematic. The posterior boundary of MFG was formed by the most-anterior slice that contained the genu of corpus callosum as same as that of SFG. The anterior limit of the MFG was determined by the FP. E. Superior Frontal Gyrus. This gyrus was also identified primarily from the 3D reconstructions. At posterior levels, the gyrus often consisted of a large single gyrus in the superior aspect that was bounded medially by the cingulate gyrus. At anterior levels, anterior to the corpus callosum, the superior frontal gyrus was arbitrarily defined as tissue occupying most of the medial aspect of the brain. The genu of the cingulate gyrus was included in the measurement at this level; the suborbital sulcus was the inferior boundary. Near the frontal pole, a transverse component often invaded the space usually occupied by the middle frontal gyrus; these transverse gyri were included in the superior region. The superior frontal gyrus was defined to consist of the first large gyrus on the superior aspect of the brain, although occasionally it bifurcated into two or more branches. Other gyri were included if they joined the most superior gyrus at points between the precentral sulcus and the frontal pole, and if the two gyri appeared to be parallel to each other. In the coronal plane, especially at anterior levels, the middle frontal sulcus was relatively deep and gave the appearance of grouping gyri above and below it into two groups. However, it is important to note that the gyrus above the middle frontal sulcus most often consists of the superior portion of the middle frontal gyrus, not the superior frontal gyrus. Kawashima et al. also segmented the superior frontal gyrus (see Figure 12). The posterior border of the superior frontal gyrus (SFG) was determined by the most posterior anterior slice that contained the genu of corpus callosum. On the lateral aspect of the cerebral hemisphere, the inferior boundary was the superior frontal sulcus. When the sulcus was interrupted into two or three segments, the most-inferior one was chosen as a boundary on each coronal slice. Infero-medially, the cingulate sulcus formed the boundary of the SFG. In case of a double-parallel type of cingulate sulcus, the most-inferior one was selected as a boundary on each coronal slice. The paracingulate sulcus, if present, was considered as part of the SFG. More anteriorly, on the medial aspect, the superior rostral sulcus constituted the inferior boundary. If the superior rostral sulcus was unconnected to the cingulate sulcus, the inferior boundary of the SFG was completed by extending the posterior aspect of the superior rostral sulcus to intersect the cingulate sulcus on coronal slice. Anteriorly, the SFG was limited by the posterior extent of the FP.

Insert Figure 12 About Here F. Cingulate Gyrus. The cingulate gyrus, defined as the one or two gyri superior to the

corpus callosum, was outlined manually on a workstation (see Figure 13). The cingulate gyrus was bounded superiorly by the cingulate sulcus, and inferiorly by the callosal sulcus on each of the coronal slices. The anatomical landmark for dividing the cingulate gyrus into anterior and posterior cingulate regions was a vertical line (Bush et al., 1999) passing through the anterior commissure point in the mid-sagittal slice. Within the anterior cingulate gyrus, further parcellations were made forming subgenual (Drevets et al., 1997), affective (antero-rostral; Bush, Luu, and Posner, 2000; Crespo-Facorro et al., 1999), and cognitive (antero-caudal; Bush, Luu, and Posner, 2000) subregion ROIs.

G. Subgenual Cingulate Gyrus. We defined this region according to Drevets et al., (1997), who reported it as significantly reduced in patients with affective disorder. Accordingly, we defined the subgenual subregion as the cingulate area under the corpus callosum, bounded anteriorly by

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the line passing through the anterior margin of the genu of corpus callosum, and posteriorly one slice anterior to the internal capsule that divides the striatum. The affective subregion (Bush, Luu, and Posner, 2000) was bounded anteriorly by the cingulate sulcus and posteriorly above the corpus callosum by a line (Crespo-Facorro et al., 1999) passing through the most anterior point of the inner surface of the genu of the corpus callosum, and anterior to the subgenual division below the corpus callosum. The cognitive subregion (Bush, Luu, and Posner, 2000) was defined as the remaining ACC between the affective subregion and posterior cingulate gyrus. The posterior cingulate subregion extended to the line passing through the most posterior end of corpus callosum (Noga et al., 1995). We did not include the most posterior part of the posterior cingulate division, often termed the retrosplenial cortex (Vogt, Absher, and Bush, 2000; Maddock, 1999), since there are no clear MRI boundaries to define it.

Some, but not all, brains contained a paracingulate sulcus, parallel to the cingulate sulcus.

The paracingulate sulcus was judged as present if it measured at least 20-mm in length in a sagittal view, and if the paracingulate gyrus was clearly independent from the cingulate and superior frontal gyri on coronal slices. When the paracingulate sulcus was present, the paracingulate gyrus, which comprises approximately Brodmann area 32, was excluded from the cingulate gyrus measurement. In order to examine the effect of differential presence of the paracingulate gyrus, the numbers of cases with paracingulate sulci present were compared among groups. Since the paracingulate sulcus can be present on one hemisphere but not on the other, its presence or absence was examined in both hemispheres.

Insert Figure 13 About Here

H. Frontal Pole. The frontal pole measurement was arbitrarily defined as the anterior-most 10 slices of brain. This grouping was done because the fusing of gyri makes reliable differentiation in this region problematic. In Kawashima's method, the frontal pole (FP) (see Figure 12, above) measurement was arbitrarily defined as the anterior-most 15 slices of brain (equivalent to 14.0625 mm), because the fusing of gyri makes reliable differentiation problematic (e.g., when the lateral surfaces of the frontal lobe reach the frontal pole, the longitudinally oriented frontal gyri are interrupted by transversal folds: the transverse frontopolar gyri). The FP extends onto the lateral, orbital and medial surfaces of the cortex. Notes on parcellation. The rules for classifying difficult or unusual sulcal/gyral patterns. Long transverse gyri. Transverse gyri (with the exception of the superior frontopolar gyrus) were classified using 3D surface according to the region they occupied. For example, if a transverse gyrus from the middle frontal region invaded the inferior frontal region, that part of the gyrus would be classified as inferior frontal gyrus. Discontinuous or unclear sulcal boundaries. If a sulcal boundary was not present on a slice, the tissue was segmented so that the boundary between regions was a straight line between two regions where there were clear boundaries. Reliability. In our previous study (Wible et al., 1995), one rater (CGW) did the parcellation of prefrontal gray matter for all of the cases. The intra-rater reliability was assessed by having this rater blindly parcellate the gray matter for the two randomly chosen cases, one schizophrenic, and one control case. The second segmentations for each of the two cases were done months after the initial segmentation was completed. The percent error was calculated by taking the absolute value of the difference score between the initial and second segmentation results for rater CGW. The volumes for each individual prefrontal region, left and right hemisphere separately, were used in

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the calculation. The average percent error was 9.5% and 3.8% for the schizophrenic and control cases respectively. The intraclass correlation for the intrarater reliability for the two cases for two segmentations was ri=.97. The intraclass correlations for the intrarater reliability calculated separately for the two cases for two segmentations were ri=.95 and ri=.99 for the schizophrenic and control cases, respectively. Inter-rater reliability was assessed by having 2 raters parcellate 2 cases (one control, one schizophrenic). These raters were instructed with the rules for parcellation in one short session. The percent error was calculated by taking the absolute value of the difference scores between the volumes for each rater’s segmentation and the average value of the three segmentations. The average percent error was 6.7% for both the schizophrenic and control cases. The intraclass correlations for the three raters were ri=.97 and ri=.96 for the schizophrenic and control cases, respectively. Reliability was also done for Kawashima's method. All editing for definition of ROI was done blind to diagnosis. Interrater reliability for each region of interest was evaluated in three randomly selected cases assessed by three independent raters (T.K., M.N., and D.C.) blind to diagnosis. Intraclass correlation coefficients were 0.99 for the left FP, 0.99 for the right FP, 0.98 for the left SFG, 0.97 for the right SFG, 0.96 for the left MFG, 0.97 for the right MFG, 0.94 for the left IFG, and 0.95 for the right IFG. IV. PARIETAL LOBE ROI. Initial Steps. Several steps were followed for the delineating the regions of interest (ROI) within the parietal lobe. The first editing step made use of a re-slice editing algorithm that constructed sagittal and axial images of each brain from the original coronal images. Specific markings were made on several sagittal slices to define certain boundaries for the ROI (described below). These markings then appeared on the coronal slices, where the ROI outlining was performed by manual tracing (with a computer pointing device). The final editing step made use of a surface rendering algorithm (Cline, 1991), which made possible a three-dimensional view of the relevant structures. The three-dimensional images of the ROI could then be viewed individually or within the context of the entire cortex, and images could be rotated around x, y, and z axes, to achieve the best possible visualization of each ROI. After examining the three-dimensional images, the coronal slices were then reassessed and any necessary corrections were made on the original editing. Once the editing was complete, volumetric measurements of the ROI were automatically derived (as with the whole brain data) by summing the voxels for each ROI across all relevant slices (see Figure 14).

Insert Figure 14 About Here

The boundaries for the ROI in this study were determined with the help of an anatomical atlas (Duvernoy, 1991). All ROI definitions were identical for each hemisphere. For all boundaries that involved cutting planes (see below), we corrected for head rotation (tilt) around all three axes (this was done prior to our head alignment program but would still have been necessary depending upon individual landmarks). Head rotation about the fronto-occipital axis was measured by a line drawn perpendicular to the interhemispheric fissure on a coronal slice at the level of the parietal lobe. Head rotation about the vertical (z) axis was measured by a line drawn perpendicular to the interhemispheric fissure on an axial slice at the level of the parietal lobe. Head rotation about the bitemporal axis was measured by a line drawn from the most anterior point of the corpus callosum to the most posterior point on a midsagittal slice. This reference line was more reliably determined than an anterior to posterior commissure line, which was verified to be virtually parallel with the callosal line (mean difference angle < 3 degrees) for the 30 cases reported here. After correction for rotation about the fronto-occipital and vertical axes, all brain rotation about the bitemporal axis was corrected to match the brain with the least rotation (brain with the callosal line most nearly horizontal).

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Medial Surface. The parietal lobe is bounded by the frontal lobe, occipital lobe, and cingulate gyrus. The fronto-parietal border was defined by the central sulcus and the marginal ramus of the cingulate sulcus and, since the sulci do not intersect, by a vertical line extending from the most posterior portion of the central sulcus to the cingulate sulcus. This line was extended laterally in the coronal plane (perpendicular to the sagittal plane). The parieto-occipital fissure was a clear anatomical boundary separating the parietal and occipital lobes. The parietal lobe and cingulate gyrus were bounded anteriorly by the subparietal sulcus. In the absence of a clear anatomical division, we defined the posterior and ventral parieto-cingulate border by a vertical line extending from the subparietal sulcus to the occipito-parietal fissure. This line was extended laterally in the coronal plane (perpendicular to the sagittal plane). Lateral Surface. Anteriorly, the central sulcus is seen as the parieto-frontal lobe boundary. The Sylvian fissure bounded the parietal and temporal lobes anteriorly. More posteriorly, the ventral bound of the parietal lobe was defined by the three cutting planes, which were all perpendicular to the sagittal plane. Plane A began at the dorsal level of the Sylvian fissure on the most posterior coronal slice of the postcentral gyrus, and continued posteriorly using the same vertical (z) position, for 15 mm (10 coronal slices). Plane B of the ventral parietal boundary was defined by two parallel lines. The first line was drawn on a midsagittal slice from the most anterior point of the corpus callosum extending posteriorly at a 9 degree angle to the callosal reference line. The second line defining this plane was drawn on a more lateral sagittal slice, using the same coordinates as the first boundary line; these lines defined the cutting plane B. The 9 degree angle between the reference line and the boundary line was selected so as to include the maximum amount of parietal lobe gray matter without including any (or at least only minimal amounts of) temporal or occipital lobe tissue. The posterior boundary of the parietal lobe was the cutting plane C, defined by two parallel lines. The first line was drawn through the parieto-occipital fissure on a midsagittal slice. The second line was drawn on a more lateral sagittal slice using the same coordinates as the first line. These lines defined the cutting plane C. Left and the interhemispheric fissure separated right parietal hemispheres. Parcellation of the Parietal lobe. The ROI within the parietal lobe included the postcentral gyrus (PCG), the superior parietal gyrus (SPG), and the inferior parietal lobule (IPL), comprised of the angular gyrus and the supramarginal gyrus. The PCG was separated from the SPG and the IPL by the postcentral sulcus. The IPL was separated from the SPG by the intra-parietal sulcus. The IPL was further subdivided into the angular gyrus (AG) and the supramarginal gyrus (SMG). In the absence of a clear, consistent anatomical boundary between the AG and the SMG, the bound between these two IPL structures was defined by the coronal slice midway between the most posterior and most anterior coronal slices of the IPL. Reliability. The investigators were "blind" to subject diagnosis throughout the entire image processing stage, and remained blind for reliability testing. Both inter- and intra-rater reliability was measured for each of the parietal regions using intraclass r. For inter-rater reliability, three judges measured each of the parietal regions on 10 slices coronal (2 sets of 5 contiguous slices) on three randomly selected brains, thus producing 6 measures for each parietal region (i.e. a left and right measure for each of three brains). Using these six measures for each parietal region, intraclass r estimates of reliability were determined to be 0.96 for the IPL, 0.96 for the SPG, and 0.97 for the PCG. Intra-rater reliability was computed using all of the slices from one randomly selected brain measured by the primary investigator (RD) at two separate times (approximately one year apart). For intra-rater reliability, intraclass r values for the parietal regions were 0.97 for the IPL, 0.98 for the SPG and 0.94 for the PCG. Thus, all reliability measures were very satisfactory. This work has

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been published in Niznikiewicz et al. (2000). Newer criteria have also been developed by Nierenberg et al. (2005). V. BASAL GANGLIA AND THALAMUS ROI. (1) Basal Ganglia Overview of ROI Definition. These ROI are described in detail, together with illustrations of their boundaries on MRI sections in: Hokama H, Shenton ME, Nestor PG, Kikinis R, Levitt JJ, Metcalf D, Wible CG, O'Donnell BF, Jolesz FA, McCarley RW. (1995) Caudate, putamen, and globus pallidus volume in schizophrenia: A quantitative MRI study. Psychiatry Research: Neuroimaging. 61:209-229. We here provide a brief summary for the reader of this application. Crosby et al. (1962), Carpenter (1978), and Duvernoy (1991) were used as primary anatomical references. Throughout the development of the ROI we were quite conscious of partial volume (PV) constraints on reliability: when voxels include more than one tissue component, such as both gray and white matter, reliability is greatly reduced. Our rule was that if reliable tracing of the boundaries of a portion of a ROI could not be performed, this portion was excluded from analysis (such as most of the tail of the caudate--see ROI descriptions below). The basic definitions of landmarks used for the basal ganglia ROI (caudate, putamen, and globus pallidus) are described below, where the entire extent of ROI on coronal slices for one case. A. Caudate Nucleus. This ROI included the head & body of the caudate and the tail portion as it curved ventrally abutting the lateral portion of the atrium of the ventricle. Tracing of the tail portion stopped when the tail portion turned to course anteriorly, since, even with our small voxels, PV effects rendered more extensive tracing unreliable. The caudate ROI also included most of the nucleus accumbens; the accumbens is ontogenetically and phylogenetically related to the caudate-putamen and cannot be reliably differentiated on MRI images (see Figure 15).

Insert Figure 15 About Here B. Putamen. This included its ventral extension, termed the peduncle of the lentiform nucleus. C. Globus Pallidus (GP). The medial and lateral GP are separated by a very thin white matter layer (medial medullary lamina), which, because of its thinness and consequent PV effects, is lumped together with medial, and lateral GP to form the GP ROI. Reliability. This was assessed in several ways. Anterior-Posterior boundaries. Each of 3 raters was within + one 1.5 mm slice for all 3 ROI. Interrater reliability of manual definition of ROI within the AP bounds was assessed on 10 coronal slices from 2 randomly selected cases; left and right sides of all three basal ganglia ROI were delineated by three separate raters (HH, MES, CGW). This interrater procedure follows that previously used by us and by the literature on basal ganglia measurements (e.g., Elkashef et al., 1994), and focuses on the main source of variation, that of manual tracing of ROI by individual raters. [Parenthetically, a multiple slice assessment is preferable to a single slice assessment because it captures the range of difficulty inherent in tracing structures whose boundaries and ease of definition differ from slice to slice.] Mean intraclass correlation coefficients, computed using the fixed rater model of Shrout and Fleiss (1979), were high for caudate (0.955), putamen (0.918), and globus pallidus (0.967). Intrarater reliability. HH measured all cases. Reliability was assessed by duplicate measurements, 6 months apart, by HH on the entire data set of two randomly selected cases.

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There was an excellent agreement for caudate (4.4 and 4.3% volume differences on the two cases for the two measurements), putamen (2.0% and 3.2% differences), and globus pallidus (0.5% difference for both cases). Segmentation reliability. The excellent reliability measurements for the segmentation of total gray and white matter, and of CSF on the double echo spin echo images have been described elsewhere (Kikinis et al., 1992; Shenton et al., 1992). (2) Thalamus. Overview of ROI Definition. These ROI are described in detail, together with illustrations of their boundaries on MRI sections in Portas et al. (1998). We here provide a brief summary. Thalamic Boundary Definition. The automated segmentation procedures produced the separation of gray and white matter based on differences in signal intensity values (Kikinis et al., 1990; Cline et al., 1990). Manual segmentation of the thalamus occurred in 20-21 consecutive slices (out of an average of 120 slices over the entire brain). In order to overcome the problem of partial volume (PV) effects, we decided to include 50% of the PV area. The definitions of the landmarks used for the thalamus are described as follows. Since the most anterior boundary was difficult to resolve objectively and reliably, we chose to use a clear anatomical landmark as the anterior bound, the mammillary bodies of the hypothalamus. The ventralis anterior nucleus is just dorsal to the hypothalamus, bounded laterally by the internal capsule, dorsally by the lateral ventricle, and medially by the third ventricle. The posterior boundary was defined on the slice showing the thalamus merging under the crus fornix. The thalamus was medially defined using the third ventricle. The inferior border was defined as the point of merger with the brainstem; the superior border was defined by the main body of the lateral ventricle. Duvernoy, 1991; De Armond 1989; Roberts, 1971; Haines, 1991, were used as primary anatomical references. Intrarater and Interrater Reliability. Intra- and interrater reliability was conducted by three raters (CP, IF, RD). Since CP measured all cases, intrarater reliability on the thalamic segmentation was assessed six months apart on three randomly selected cases. The volume difference between the first and the second measurement was negligible in all three cases (< 1%). Interrater reliability, estimated by intraclass correlation coefficients, for three randomly selected cases across three raters was: .93 for total thalamic volume, .93 for right thalamic volume and .91 for left thalamic volume. VI. Cerebellar and Brainstem ROI. (1) Cerebellar (ROI) Definitions: Cerebellum, Vermis and Brainstem ROI. The cerebellum was masked from the rest of the brain prior to segmentation. The gray and white matter of the combined cerebellum/brainstem ROI (defined below), derived from the segmented SPGR images of the whole brain, initially were converted into a single pixel value. Once the cerebellum was separated and masked from the rest of the brain (as defined below), it was then re-segmented into gray and white matter using the automatic segmenter of Wells et al. (1994). The masked cerebellum gray-scale image was then segmented into gray and white matter, and the segmentation was manually edited in 3 planes, (both the MR image and the segmented image can be formatted into sagittal, axial and coronal planes), and finally checked using a 3-D reconstruction program. Duvernoy (1995), Angevine et al. (1961), and DeArmond et al. (1976) were the primary anatomical atlases used to assist in the neuroanatomical ROI definitions. A. The Combined Cerebellum/Brainstem ROI: The rostral and dorsal boundaries of this combined structure were composed of the rostral plane of the brainstem, as defined below, and the tentorium cerebelli. The ventral and inferior bounds were defined by the subarachnoid cerebrospinal (CSF) cisterns and the caudal plane of the brainstem, as defined below; the lateral bounds were formed by subarachnoid CSF cisterns and the traverse sinuses (see Figure 16).

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Insert Figure 16 About Here

B. The Brainstem of This Combined Cerebellum/Brainstem Structure. In the sagittal MR image, this ROI was positioned with the long axis coinciding with the perpendicular axis of the screen. The rostral plane bounding the brainstem was defined by one ventral and two dorsal points in order to correct for any head tilt and angulation of the brain stem with respect to the MR axis: a) the height of the ventral point was defined on the midsagittal slice (at the level of the cerebral aqueduct) by the voxel immediately rostral to the point of deepest penetration of CSF into the mammillary body arch; the ventral extent of this point was defined as the most ventral coronal slice where both mammillary bodies and crus cerebri were present, and b) the dorsal points were defined, on a coronal slice, as the voxels immediately rostral to the right and left superior colliculi at the point of their maximal dorsal extent. The caudal boundary of the brainstem was defined as the points representing the most superior aspect of the odontoid process (visualized on coronal and sagittal slices); the caudal plane passes through these points and was perpendicular to the long axis of the brainstem. The ventral, dorsal and lateral bounds of the brainstem were formed by the surrounding subarachnoid CSF cisterns, blood vessels and cranial nerves. Manual editing, determined in coronal slices, was used to separate the brainstem crus cerebri from these adjacent structures; and its lateral extent was defined, bilaterally, as the most lateral sagittal slice in which they were still present. C. The Cerebellum ROI. The cerebellum, using manual tracing, was separated from the combined cerebellum/brainstem structure by cutting perpendicularly to the direction of the fiber tracts of the cerebellar peduncles using an axial view. The separated cerebellar segmented image was then merged with an MR gray scale image of the whole brain, using a merger program, resulting in a separate gray scale MR cerebellar image which will then be re-segmented into grey and white matter using an automated segmentation algorithm as described above (Wells et al., 1994). D. The Brainstem ROI. The above separation of the cerebellum automatically yields a separated brainstem. Because of the complexity of gray and white matter spatial organization in the brainstem, no effort at this time was made to segment the brainstem into gray and white matter tissue types; rather, a total brainstem volume was acquired. E. The Vermis ROI. The cerebellar vermis were then separated from the cerebellar hemispheres using manual tracing in all 3 planes with sagittal slices offering the clearest view. The vermis was seen in about 10 or 11 1mm thick reformatted sagittal slices. The posterior bounds of the more lateral sagittal slices will require separation from the hemispheres (as both the natural twisting of the cerebellar vermis, Latin for worm, and head tilt result in overlapping of vermian and hemispheric structures on sagittal view); this separation was facilitated by the characteristic radial sulci pattern of the vermis in sagittal slices, which differs from the hemispheric sulci pattern, and by the use of reformatting of the image into 3 planes. The anterior inferior bound of the vermis was formed by the hemispheric tonsils which require manual separation; the tonsils characteristic horizontal sulci pattern contrasting with the radial sulci pattern of the vermis, together with reformatting of the image into 3 planes, permit a precise separation. The lateral extent of the vermis was difficult to define objectively. We chose to define it by using a combination of criteria; on sagittal view its maximum extent was defined by: 1) that sagittal slice prior to the slice where the prepyramidal fissure no longer was visualized (the prepyramidal fissure was restricted to the vermis (Angevine et al., 1961); 2) that most lateral sagittal slice where the corpus medullare retains a characteristic vermian shape (that is, the primary and secondary branches emanating from a relatively sparse corpus medullare core were still clearly discernable; the characteristic shape and size of the corpus medullare in the vermis vs in the hemispheres was described by Press et al.,

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1989 and Courchesne et al., 1989). Additionally, on coronal and axial views, the previously traced prepyramidal fissure can be well visualized, as can the lateral extent of the vermis, at certain levels, offering a further check on our definition. We give such great emphasis to defining the lateral extent of the vermis objectively because (this has not been done in previous studies of the vermis) and the total vermian volume so depends on the way this boundary was defined. F. Gray and White Matter Cerebellar Hemisphere ROI. With the delineation of the vermis, the left and right gray and white matter hemisphere ROI were automatically defined. G. Gray and White Matter Vermis ROI. The vermis was manually parcellated into 3 gray matter regions and a single total vermis white matter region. The gray matter regions were parcellated using the sagittal plane: 1) vermian lobules I-V, (the lingula, central and culmen); 2) vermian lobules V-VII (the declive, folium and tuber vermis); and 3) vermian lobules VIII-X ( the pyramid, uvula and nodulus). The boundary between vermian lobules I-V and vermian lobules V-VII was defined by tracing, in all sagittal vermian slices, the primary fissure from its point of connection to the surface of cerebellar cortex to its point of connection to the corpus medullare which surrounds the roof of the 4th ventricle; the boundary between vermis lobules Vl-Vll and lobules Vlll-X were similarly defined in all sagittal vermian slices by tracing along the prepyramidal fissure to the corpus medullare surrounding the roof of the 4th ventricle (See Fig 4; Courchesne et al. (1994), in their influential studies in autism, traced the same fissures to define 3 vermian regions but did so, only, in one unsegmented 5mm vermian sagittal slice, yielding a combined gray and white matter measure for their vermian lobule regions). All manual tracings were done under magnification. The total vermis white matter region was generated automatically by the segmentation program; all white matter pixels lying within the vermis were manually labeled as vermis white matter. Interrater Reliability. Interrater reliability was computed for all ROI, and, in our pilot data, was ri >.90 (intraclass correlation). These ROI are under development, and acceptable reliability for future work will continue to be ri >.90. If it is less than .90, further training will take place until all raters show a better than .90 interrater reliability. Intrarater reliability is in the process of being computed. This work has been published in Levitt et al. (1999). VII. Orbito-Frontal Cortex (OFC): A. Sulcal Gyral Pattern Identification: Sulcal Pattern Identification. We based our sulcal pattern identification on previous work by Chiavaras and Petrides (2000). These investigators classified the OFC sulco-gyral pattern into 3 types (type I, type II, type III) in each hemisphere. This visual classification was based on the continuity of the Medial and Lateral Orbital Sulci (MOS, LOS, respectively) (See Figure 17 and Figure 18). In type I, rostral and caudal portions of the LOS were connected, while the MOS were clearly interrupted between rostral and caudal portions of MOS. In type II, rostral and caudal portions of both the MOS and LOS were connected, and continuous MOS and LOS were jointed by the horizontally oriented Transverse Orbital Sulcus (TOS). In type III, rostral and caudal portions of both MOS and LOS were interrupted. To evaluate the sulcal pattern precisely and consistently, neighboring sulci including the Olfactory Sulcus (Olf), Intermediate Orbital Sulcus (IOS), Posterior Orbital Sulcus (POS), and Sulcus Fragmentosus (Fr) were also identified as a landmark. Of note, Chiavaras and Petrides reported that IOS was identified in all of 100 observed hemispheres where 19% showed double IOS (medial and lateral). POS was observed in 77%, and Fr was observed in only 10 % of the 100 hemispheres. Three-dimensional information was also used in this study to provide reliable classification of the OFC sulco-gyral pattern, using a software package for medical image analysis [3D slicer: http://www.slicer.org] on a workstation.

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The sulco-gyral pattern identification was done using mainly axial slices. At first, the most inferior level (axial plane) where Olf can be seen clearly was identified, and then moving up to the superior level where Olf could be seen discontinuously. At this level, caudal portions of MOS and LOS can be seen connected by TOS. It is important to identify this sulcal complex, because these three sulcal portions are always connected. At this point, POS could be identified, if present. For the next step, continuity between rostral and caudal MOS was examined at the intersection with TOS, observing several axial slices (0.9375mm). If the rostral and caudal MOS were separated, the sulco-gyral pattern could be type I or III, and if not, type II. If MOS could be seen discontinuously distant from the TOS intersection level, its anterior fragment was identified as IOS or Fr, and the rostral and caudal MOS were considered as continued (type II). For the last step, the continuity between rostral and caudal LOS was examined observing several axial slices. If they were connected, the sulco-gyral pattern could be type I, if not, type III. The rostral LOS can be seen most laterally in the axial plane, however, short sulcus, which is oriented vertical rather than parallel to Olf, was not regarded as rostral LOS. At this step, one or two IOS could be identified between MOS and LOS. It should be emphasized that proper and consistent realignment of brain images are very important for reliable identification of the sulco-gyral pattern. The sulco-gyral pattern classification in each hemisphere of the 100 subjects was done by one rater (Dr. Motoaki Nakamura) blinded to subject group. (See Figure 17 and Figure 18.) Inter-Rater Reliability. For assessing interrater reliability, two raters (Motoaki Nakamura, Toshiro Kawashima), blinded to diagnoses, independently evaluated the sulcal pattern for 25 random cases. The intraclass correlation coefficients were 0.842 for left hemisphere and 0.836 for right hemisphere. Figure 17 depicts the landmarks used for defining the sulcal-gyral pattern.

Insert Figure 17, 18 and 19 about Here B. Orbito-Frontal Cortex Volume: We also used the above landmarks to help evaluate volume in the orbito-frontal cortex (Figure 19). Table 2 shows the landmarks used to define the Gyrus Rectus (GR), Middle Orbital Gyrus (MiOG), and Lateral Orbital Gyrus (LOG).

Table 2

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Inter-Rater Reliability: All manual delineations were performed by Motoaki Nakamura, who was blind to subject group. For assessing interrater reliability, three raters (Motoaki Nakamura, Adam Cohen and Toshiro Kawashima), also blind to subject diagnosis, independently delineated left and right GR, MiOG and LOG for seven randomly selected cases. The intraclass correlation coefficients were 0.95 (left GR), 0.96 (right GR), 0.99 (left MiOG), 0.96 (right MiOG), 0.96 (left LOG) and 0.99 (right LOG). VIII. OCCIPITAL LOBE. Figure 20 shows the medial and lateral view of the three-dimensional reconstruction of the occipital lobe. The following steps were used to define the occipital lobe. First, the parietooccipital sulcus (POS) was identified on the midsagittal plane for each hemisphere. Second, the anterior tip of the POS was identified, as well as the posterior tip of the POS that corresponds to the parietooccipital fissure (see Figure 20). The occipital lobe was defined as beginning at one slice posterior to the plane that contains the anterior tip of the POS, identified on the midsagittal plane, and ending in the last slice in the coronal plane, including the posterior tip of occipital lobe. For the medial surface, the boundary between the parietal and occipital lobe was the POS. For the lateral surface, the rater (Toshiaki Onitsuka) drew a guideline connecting the parietooccipital fissure and the superior temporal sulcus, or anterior occipital sulcus, on the first, beginning slice of occipital lobe. This guideline was defined as the boundary between the parietal and occipital lobe for the lateral surface. This guideline was seen as a point on each coronal image (see Figure 21c). The parietal and occipital lobe were divided operationally by extending the guideline across the tissue bridge of white matter, horizontally and medially up to the intraparietal sulcus (see Figure 21 c&d). The primary visual area (PVA) was defined as the area between one gyrus above the calcarine fissure and one gyrus below the sulcus on each coronal image. The rater drew two guidelines at 3-5 slices laterally from the medial surface to determine the gyri above and below calcarine fissure (see Figure 21b). The lines were drawn extending the sulcal course across the tissue bridge of white matter. These guidelines were seen as points on each coronal image and the rater delineated the primary visual area referring to the lines (see Figure 21c). Manual drawings of the ROIs were then performed on the realigned and resampled coronal slices (see Figure 21d).

Insert Figure 20 & 21 About Here Inter-Rater Reliability. Interrater reliability was computed for the ROIs by 3 independent raters (Toshi Onitsuka, Noriomi Kuroki, and Susan S. Demeo), who were blinded to diagnostic group membership. Six cases were selected randomly for interrater reliability. Three raters measured the occipital lobe on every third slice. An intraclass correlation coefficient was used to compute interrater reliability. For the three raters, the intraclass correlations were: 0.93 for the left PVA, 0.90 for the right PVA, 0.98 for the left VAA, 0.98 for the right VAA. IX. Power Analyses. Statistical power is related to the probabilities of Type I and Type II errors; to estimate power the sample size, the effect size, and the significance levels are all important (Cohen, 1977). We have pilot data from which we have been able to estimate effect sizes using projected sample sizes of n=15 to n=25, and significance levels of p=0.05. Based on our MRI data, the projected power for detecting differences between groups for left posterior STG is >98% (mean difference 0.8 ml, sd=0.86 ml), and for left anterior hippocampus-amygdala the power is >99% (mean difference 0.5 ml, sd=0.6ml), and this was with an “n” of 15 subjects. Shape measures of hippocampus were also based on small n’s (n=15 in schizophrenic group) and differences were detected. We therefore think that the subject “ns” are sufficient to detect differences between groups if differences are present. Additionally, we note that the expected magnitude of our clinical correlations (e.g., SAPS and TDI) has ranged from 0.41 to .81 with our MR measures, with a power of 97% for n=15. We thus believe that the sample size proposed here

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will be sufficient to detect associations/differences between variables. We note, however that with multivariate analyses the power magnitudes will diminish and, unfortunately, as noted by Harris (1985), there are no simple, well accepted formulae for multivariate analyses. However, we have no reason to believe that the magnitude of the effects will be markedly different in the studies proposed here than in our previous studies where an n=15 showed excellent empirical power for many of the ROIs, though an increase in sample size is clearly indicated in order to detect differences in an increasing number of ROIs. Section 3: References. Angevine, J.B., Mancall, E.L., Yakovlev, P.I. (1961). The Human Cerebellum: An Atlas of Gross

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Figure 1

Figure 1. Part A. Comparison of raw images (top row) and segmented images (bottom row), 1.5 T scanner, 3T scanner uncorrected, and 3T scanner with Bias Field Correction, same subject with schizophrenia. SPGR pulse sequence acquisition, axial slices at approximately the same level. In the top row, note that 1.5 T has more partial volume effect than 3T, evident in neocortical region gray-white borders and in basal ganglia. In 3T uncorrected image (top,middle) posterior white matter has higher signal (“whiter”) than anterior white matter with a corresponding posterior> anterior white matter segmentation bias. The Bias Field correction nullifies this signal intensity bias and the corresponding segmentation inhomogeneity. Note that for the Bias Field Correction non-brain components have been stripped.

Figure 1. Part B. Coronal section through superior temporal gyrus (STG, box) from 1.5 T and 3T scan of same subject with schizophrenia, showing raw and segmented images. Note less “blurring” in 3T image = less partial volume effect and greater detail of white-gray matter junction, especially in Heschl’s gyrus, the medial “bump” on STG.

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Figure 2

Figure 2. Expectation-maximization atlas tissue segmentation and regions of interest (ROI). Top, left: Examples of spoiled-gradient-recalled images and tissue segmentation. Tissue has been segmented and parcellated into ROI of neocortical gray matter (NCGM, green on right, blue on left hemisphere), cerebral white matter (CWM, yellow on right, beige on left hemisphere), sulcal cerebrospinal fluid (SCSF, red on right, brown on left side), and lateral ventricles (LV, dark blue on right and purple on left side). Note the exclusion of subcortical nuclei, the medial temporal region, and all infratentorial tissue. Top, right: Three-dimensional reconstructions of brain tissue ROI. Bottom: Upper portion shows lobar parcellation of NCGM into frontal lobe (green on right, blue on left hemisphere), temporal lobe (purple on right, pink on left hemisphere), and parieto-occipital lobe (red on right, brown on left hemisphere). Lower part is a three-dimensional reconstruction. L, left; R, right; A, anterior; P, posterior.

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Figure 3

Figure 3. A lateral view of the brain shows the major sulci and gyri in the brain. The ROIs that we have delineated include many of the gyri depicted here (i.e., superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, superior frontal gyrus, supramarginal gyrus, angular gyrus, etc.). (From Carpenter and Sutin, 1983, Human Neuroanatomy: Williams & Wilkins).

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Figure 4

Figure 4. Coronal 1.5mm slice showing medial temporal and neocortical structures. The region bordering the Sylvian fissure on the right (subject left) is the superior temporal gyrus. The almond-shaped region in the medial portion of the temporal lobe is the amygdala and the region demarcated beneath is the parahippocampal gyrus. The whole temporal lobe is outlined on the left (subject right). [From Shenton et al., 1992, courtesy of The New England Journal of Medicine.]

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Figure 5 PANEL A.

PANEL B.

Panel A. Sagittal and coronal MR images showing delineation of MTG and ITG. A red line signifies the STS. A yellow line signifies the ITS. The vertical dotted lines A and B correspond to the slices of A and B, respectively. On the slice A, the gray matter of MTG is shown in orange and the gray matter of ITG is shown in purple (subject left). Below is a magnified view that corresponds to the red square. On the slice of a transition area (slice B), MTG and ITG were delineated referring the guidelines (red and yellow dots). Panel B. Delineation of temporal subregions in a coronal image. The gray matter of MTG is shown in orange (subject left) and light blue (subject right). The gray matter of ITG is shown in purple (subject left) and yellow (subject right). The gray matters of left FG, right FG, left STG, right STG are shown in light green, red, blue, green, respectively. (b) Frontal view tilted to the foreground of left temporal lobe subregions of a three-dimensional reconstruction. (c) Ventral view of bilateral temporal lobe subregions. (d) Left lateral view of temporal lobe subregions.

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Figure 6

PANEL A

PANEL B

Panel A. Three dimensional reconstruction of the ventral surface of the brain. The fusiform gyrus region of interest is red on subject left and yellow on subject right. Panel B. Coronal SPGR resampled image with isotropic voxels (.9375 mm) showing the outline of the fusiform gray matter ROI on subject left (yellow) and subject right (blue).

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Figure 7

Figure 7. 2-D and 3-D presentation of the paralimbic regions of interest. This figure is based on MRI data of a control subject. The gray matter of the anterior insular cortex is colored blue on subject left and orange on subject right. The gray matter of the posterior insular cortex is colored light blue on subject left and yellow on subject right. The gray matter of the temporal pole is colored purple on subject left and green on subject right. The white matter of the bilateral temporal pole is colored ivory.

Panel A, B, and C: Delineation of the paralimbic regions of interest on coronal slices. Panel A represents the rostal part of the anterior insular cortex adjacent to the orbital cortex, and the temporal pole. Panel B represents the caudal portion of the anterior insular cortex. Panel C represents the middle portion of the posterior insular cortex.

Panel D: Sagittal view of the insular cortex and temporal pole in the left hemisphere. The coronal lines A, B, and C correspond to the planes of Panel A, B, and C, respectively.

Panel E and F: 3-D reconstruction of insular cortex (E) and temporal pole (F) gray matter superimposed on the axial plane.

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Figure 8

Figure 8. Panel A. Delineation of Heschl’s gyrus and planum temporale in a coronal slice, based on MRI data of a control subject. The gray matter of Heschl’s gyrus is labeled dark blue on subject left and green on subject right. The gray matter of planum temporale is light blue on subject left and yellow on subject right. Panel B. 3-D reconstruction of Heschl’s gyrus and planum temporale gray matter superimposed on the axial plane. Each region is labeled using the same color as that in Panel A.

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Figure 9

Figure 9. Gray matter of the prefrontal cortex was measured starting anteriorly from the first slice that contained brain tissue. The posterior land mark was determined by first locating the most anterior slice that contained the temporal stem (the white matter tract connecting the temporal and frontal lobes), then moving anteriorly three slices. Anteriorly, the white matter was measured beginning with the first slice that contained white matter and extended posteriorly to the slice immediately anterior to the slice that contained the lateral ventricles. Prefrontal segmented images on a coronal slice are shown in Fig. 1A, and Fig. 1B, C, and D illustrate the anterior-posterior boundaries of the gray and white matter regions of interest (ROI). Panel A. Coronal slice (1.5 mm) through the prefrontal region of a normal control subject. Prefrontal gray matter is outlined in red. Panel B. Three-dimensional reconstruction of prefrontal white matter (yellow) with semi-transparent gray matter (gray matter) on a midsagittal MR slice viewed from the left side. This illustrates the anterior-posterior extent of gray and white matter ROI. Panel C. Top-down view of a three-dimensional reconstruction of prefrontal gray matter (beige) on an axial MR slice. Panel D. Top-down view of a three-dimensional reconstruction of prefrontal white matter (yellow) on an axial MR slice.

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Figure 10

Figure 10. (Adapted and Modified from Chiavaras and Petrides, 2000) OFC Subregions and Neighboring Sulci. Abbreviations: Olf, olfactory sulcus; MOS, medial orbital sulcus (-r: rostral, -c: caudal); TOS, transverse orbital sulcus; LOS, lateral ramus of H-shaped sulci (-r: rostral, -c: caudal); IOS, intermediate orbital sulcus (-m: medial, -l: lateral); POS, posterior orbital sulcus; Fr, sulcus fragmentosus.

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Figure 11

Figure 11. The 3-D Reconstructed ROI of OFC Subregions. Abbreviations: GR, Gyrus Rectus; MiOG, Middle Orbital Gyri; LOG, Lateral Orbital Gyrus.

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Figure 12

Figure 12. Regions of interest (ROI). Top: The frontal pole (FP), the superior (SFG), middle (MFG), and inferior frontal gyrus (IFG) are shown on a coronal slice (left) and a sagittal slice (right). Bottom: Three-dimensional reconstructions of ROIs and other grey matter in coronal view (left) and sagittal view (right).

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Figure 13

Figure 13. Cingulate gyrus subregions Regions of Interest. Three-dimensional reconstruction of the cingulate gyrus gray matter according to subregions (subgenual, affective, cognitive, and posterior divisions), seen in sagittal and coronal views. On sagittal view of left cingulate gyrus, subgenual division is color-coded by yellow, affective division by pink, cognitive division by blue, and posterior division by green.

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Figure 14

Figure 14. A 3-D surface rendering of the cortex (gray), with the gyri of the parietal lobe color coded as follows: postcentral gyrus (blue), superior parietal gyrus (green), supramarginal gyrus (red), and angular gyrus (yellow). (See text for detailed description of boundaries).

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Figure 15

Figure 15. Three dimensional renderings of left and right head of the caudate nucleus (shaded blue) and left and right posterior caudate nucleus (shaded red) superimposed on MRI coronal and axial slices in a normal control (top) and SPD subject (bottom).

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Figure 16

Regions of Interest in Gray and White Matter of the Cerebellum and Vermis of One Subject in an MRI Comparison of Patients With Schizophrenia and Healthy

Comparison Subjects

Figure 16. For the coronal image (left side), a combination of criteria were used to define objectively the indistinct lateral extent of the vermis: the last sagittal slice bilaterally where the prepyramidal fissure was visualized (the prepyramidal fissure is restricted to the vermis) and where the corpus medullare retained a “characteristic” vermian shape. Additionally, using a surface rendering program, we created a three-dimensional reconstruction of the cerebellar hemispheric and vermian white matter, alone, facilitating their separation. For the sagittal image (right side), the vermis was parcellated into three gray matter regions (lobules I–V, VI–VII, and VIII–X) by tracing the primary and prepyramidal fissures in the sagittal plane in turn, defining total vermis white matter.

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Figure 17

“H-shaped” sulcus and its variation in human brain. A. Schema of orbitofrontal sulci and major gyri. “H-shaped” sulcus is traced by red dotted line, dividing orbitofrontal cortex into 4 gyri of medial, anterior, posterior, and lateral orbital gyri. B. Example of three sulcal pattern. Three main orbitofrontal sulco-gyral types are defined based on the continuity of the medial and lateral orbital sulci. Type I expresses most frequently and type III expresses least frequently in healthy population. C. Schema of major three types of sulcal patterns of “H-shaped” sulcus. Abbreviations: Olf, olfactory sulcus; MOS, medial orbital sulcus (-r: rostral, -c: caudal); TOS, transverse orbital sulcus; LOS, lateral orbital sulcus (-r: rostral, -c: caudal); IOS, intermediate orbital sulcus (-m: medial, -l: lateral); POS, posterior orbital sulcus; Fr, sulcus fragmentosus; R, right hemisphere, L, left hemisphere. Panel A, B, C were adapted and modified from the previous paper (Chiavaras and Petrides, 2000).

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Figure 18

MRI images of major three types of “H-shaped” sulcus. Examples of the major three sulco-gyral patterns from six different subjects. On the axial plane

of SPGR (spoiled gradient-recalled images), sulci of type I, II, III are delineated with green, blue and pink color, respectively. Upper and lower column demonstrate left and right hemisphere. At this level, olfactory sulcus cannot be observed in most cases. Abbreviations: L, left hemisphere; R, right hemisphere.

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Figure 19 (Panel A) A

MR Images of Three Orbitofrontal Subregions. Panel A: 3D reconstruction of the three orbitofrontal subregions of Gyrus Rectus (GR; left: blue, right: green), Middle Orbital Gyri (MiOG; left: brown, right: red), and Lateral Orbital Gyrus (LOG; left: purple, right: light green), superimposed on axial plane of SPGR image. Panel B: Orbitofrontal ROIs in axial and coronal planes of SPGR images. See method section for their boundary definition.

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Figure 19 (Panel B) B

MR Images of Three Orbitofrontal Subregions. Panel A: 3D reconstruction of the three orbitofrontal subregions of Gyrus Rectus (GR; left: blue, right: green), Middle Orbital Gyri (MiOG; left: brown, right: red), and Lateral Orbital Gyrus (LOG; left: purple, right: light green), superimposed on axial plane of SPGR image. Panel B: Orbitofrontal ROIs in axial and coronal planes of SPGR images. See method section for their boundary definition.

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Figure 20

Figure 20. Delineation of PVA and VAA in occipital lobe. PVA is shown in purple, and VAA is

shown in blue. For the medial surface (left), the border between parietal lobe and occipital lobe is the parietooccipital sulcus. PVA is defined as the area including one gyrus above and one gyrus below the calcarine fissure. For the lateral surface (right), the border between the two lobes is delineated by a guideline connecting the parietooccipital fissure and the superior temporal sulcus on the most anterior slice of occipital lobe (The guideline is shown in red).

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Figure 21

Figure 21. Sagittal and coronal MR images showing delineation of PVA and VAA. (a) The rater identifies the parietooccipital sulcus and the calcarine fissure on the midsaggital plane. (b) The yellow lines are the guidelines extending the sulcal courses used to delineate PVA and VAA. (c) On a coronal slice PVA and VAA are delineated by referring to the guidelines (yellow dots). In part B, here shown as yellow dots. On the lateral surface, the parietal lobe and occipital lobe are operationally separated by extending the guideline (the red dot) horizontally and medially across the tissue bridge of white matter horizontally and medially up to the intraparietal sulcus. (d) A coronal view of PVA and VAA delineation. White matter and gray matter were shown in light yellow and light blue respectively. The gray matter of PVA is shown in orange (subject left) and purple (subject right). The gray matter of VAA is shown in red (subject left) and blue (subject right).

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Technical Report#502

Part B

Diffusion Tensor Imaging

Table of Contents Page 57 I. Overview of DTI and Methods used to Analyze DTI Images Page 58-64 II. Scan Parameters for Current Research Studies Page 64-66 III. Specific Regions of Interest Page 66-70

1. Uncinate Fasciculus Page 66-67 2. Cingulate Bundle Page 67-68 3. Arcuate Fasciculus Page 68-69 4. Fornix Page 69 5. Corpus Callosum Page 69 6. Anterior Limb of the Internal Capsule Page 69-70

IV. Comparison of 1.5T and 3T DTI Data Page 70-71 V. Statistical Analyses Page 71 VI. Power Analyses Page 72 VII. References Page 72-73 VIII. Further Reading Page 73-74 IX. Figures Page 74-85

http://pnl.bwh.harvard.edu/pub/pdfs/TR_502_Shenton.pdf

http://www.spl.harvard.edu/pages/Special:PubDB_View?dspaceid=1263 © Shenton, Kubicki, and McCarley, 2008

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I. Overview. This overview section reviews the principles underlying diffusion tensor imaging. The Phenomena of Diffusion: The phenomenon of water diffusion, known as Brownian motion, is named after the botanist, Robert Brown, who, in 1827, observed the movement of plant spores floating in water. The first satisfactory theoretical treatment of Brownian motion, however, was not conducted until much later by Albert Einstein in 1905. We now appreciate the fact that molecular motion is affected by the properties of the medium in which it occurs, and that diffusion within biological tissues reflects both tissue structure and architecture at the microscopic level. An example and early use of diffusion is the study of anisotropic media such as crystals, textile fibers, and polymer films. Here, diffusion is not the same (isotropic) in all directions, as it would be, for example, in cerebral spinal fluid (CSF), where the medium does not restrict the molecular motion. In brain tissue, the motion of water molecules can also be restricted at many levels, as for instance by interacting with tissue components, such as cell membranes, macromolecules, fibers and fiber tracts. Within white matter, for example, the mobility of water is restricted in directions that are perpendicular to the axons that are oriented along the fiber tracts. This anisotropic diffusion is due to tightly packed multiple myelin membranes encompassing the axon. And, although myelination is not essential for diffusion anisotropy of nerves, see for example studies of non-myelinated garfish olfactory nerves and studies of neonate brains prior to the appearance of myelin, myelin is generally assumed to be the major barrier to diffusion in myelinated fiber tracts. Anisotropic diffusion is, in fact, best described by an ellipsoid where the radius defines the diffusion in a particular direction The widely accepted analogy between symmetric 3X3 tensors and ellipsoids makes such tensors natural descriptors for diffusion. Moreover, the geometric nature of the diffusion tensors can quantitatively characterize the local structure in tissues such as muscle and white matter in the brain. The first attempts to detect diffusion within the brain in vivo led to the development of diffusion weighted imaging (DWI), and more recently, to the development of multidimensional assessment of diffusion data in vivo, known as Diffusion Tensor Imaging (DTI). With current, conventional MRI, we can easily identify gray matter, white matter, and CSF in the brain, although for white matter, the appearance is homogeneous and fiber tract directions cannot be observed or quantified. With the advent of diffusion imaging, however, anisotropic diffusion in the brain can be explored, and the white matter fiber tract directions in the brain can be appreciated. Diffusion MR Imaging and DTI: Diffusion MR Imaging was introduced in 1986 by Le Bihan (Le Bihan 1986). From the beginning, however, the widespread application of this technique to clinical studies was greatly impeded by technical constraints, the most important being motion sensitivity, which can cause severe ghosting artifacts or complete signal loss. In order to observe molecular displacement in micrometers, it is no surprise that any motion, even unavoidable involuntary head motion or physiological, blood-pressure related pulsations of the brain tissue, interfere with the measurements. The problem is even more serious when scans must be obtained from disoriented and confused stroke victims, who move their head excessively. These limitations were a major incentive for the development of faster sequences that are more robust to bulk motion. The development of diffusion sensitive pulse sequences basically followed two directions: echo-planar imaging methods, which acquire a complete image within a single shot, and navigator methods, which acquire images in multiple shots, but for each shot employ so called navigator MR signals to detect and correct the bulk motion. While single shot methods are extremely robust, the

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elevated sensitivity to magnetic field inhomogeneities may lead to image distortion artifacts in areas exhibiting large variations in magnetic susceptibility, e.g., at interfaces between air, bone, and brain tissue. Moreover, spatial resolution is limited and signal averaging may be necessary. Navigator methods, on the other hand, permit excellent spatial resolution with minimal image distortion artifacts and high signal-to-noise ratio, but are not as robust and require acquisition times in the order of ten minutes or more. Furthermore, cardiac gating must be used, which makes the technique less attractive in a routine clinical setting. Recently several new techniques have been proposed to decrease artifacts caused by echo-planar techniques. These applications include multiecho techniques such as turbo spin-echo, HASTE (half Fourier single shot turbo spin echo), PROPELLER (Periodically Rotated Overlapping Parallel Lines with Enchanced Reconstruction), and GRASE (Gradient and Spin Echo) –all reducing susceptibility artifacts, but suffering from low resolution and motion artifacts. Other new applications, which avoid susceptibility and chemical shift artifacts but allow for higher than EPI resolution include: Diffusion Weighted Radial Acquisition of Data (DIFRAD), an MR diffusion imaging method that acquires multiple radial lines using spin echo refocusing, Line Scan Diffusion Imaging (LSDI), an MR diffusion imaging method that acquires images line-by-line in multiple independent single-shot acquisitions and its modification, the Slab Scan Diffusion Imaging (SSDI), which combines the advantages of LSDI with multiple spin echoes, thus decreasing scan time. As noted previously, the measurement of water diffusion in tissues is based on probing the movement of water molecules within the tissue environment. In pure liquids, such as water, individual water molecules are in constant motion in every direction due to random motion. In tissues, however, the presence of various tissue components (larger molecules, intracellular organs, membranes, cell walls, etc.), restrict the Brownian motion. In many tissues, when averaged over the macroscopic scale of image voxels, this restriction is identical in every direction, i.e., the diffusion is isotropic. In some very structured tissues, however, such as muscle or cerebral white matter, cellular arrangement shows a preferred direction of water diffusion that is largely uniform across the entire voxel, i.e., the diffusion is anisotropic. In the brain and spinal cord, the diffusion anisotropy is attributed to the presence of myelinated white matter fiber tracts. Across the multiple layers of myelin membranes, which surround the axons, the motion of tissue water is restricted. At the same time water molecules are relatively free to move along the longitudinal direction of the axons. To avoid fiber tract direction dependent signal variations, image data of several acquisitions with diffusion weighting along different directions must be combined. The description of multidirectional tissue water diffusion requires a more complex formalism in order to characterize it accurately. This more complex formalism uses the concept of a diffusion tensor and was introduced to the field of MR diffusion imaging by Basser in 1994. This technique is described below, under Diffusion Tensor Imaging (Basser 1994). The term "tensor" is a construct adapted from physics and engineering, where it was introduced to describe tension forces in solid bodies with an array of three-dimensional vectors. The particular tensors used to describe diffusion can be further conceptualized and visualized as ellipsoids. The three main axes of the ellipsoid describe an orthogonal coordinate system. The directions of the main axes represent the so-called eigenvectors and their length the so-called eigenvalues of the tensor. In MR diffusion tensor imaging (DTI), a tensor that describes diffusion in all spatial directions is calculated for each voxel. To correctly assess the diffusion tensor, data from at least six independent diffusion encoding directions is acquired for each voxel. The longest main axis of the diffusion ellipsoid represents value and direction of maximum diffusion, whereas the shortest axis denotes value and direction of minimum diffusion. If the three eigenvalues are equal, then the diffusion is said to be

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isotropic and the diffusion tensor can be visualized as a sphere. DTI acquisitions are generally performed on high field, high performance gradient systems where 3-4 Tesla magnets are becoming more and more popular in clinical research. However, the 1.5 Tesla magnet is still the preferred MR field strength for clinical imaging. Lower field strengths systems require longer acquisition times (due to the increased number of repetitions to compensate for the decreased signal to noise ratio). Higher field strengths, on the other hand, allow for potentially faster acquisitions and higher s/n ratios, but they are not free of limitations. Such limitations, in fact, include (depending on the acquisition technique), eddy current distortions, susceptibility and chemical shift artifacts as well as geometrical distortions of the images, which are, at least in the case of the faster techniques, much higher that on lower field strength machines. Nonetheless, the development of acquisition tools such as high order shimming, which make possible better quality images, as well as post-processing tools, which minimize image distortions and artifacts, should all help to ensure that clinical research can be moved to higher field magnets. To return, however, to the image acquisition, and as mentioned previously, in order to calculate the tensor, at least 6 non-colinear measurements of diffusion are required. Some recent schizophrenia studies, however, use even more diffusion directions (Wang et al., 2003; Sun et al., 2003). Of note, multiple direction acquisitions have numerous advantages, including the fact that multiple direction acquisitions allow for the creation of more symmetrical tensors, they minimize noise, as well as make the guiding of fiber tracking more precise. Moreover, higher angular data can be used to solve the problem of fiber crossings, and to guide fiber tracking in areas where the anisotropy is low, and where uncertainty exists with respect to the major direction of diffusion (Tuch et al., 2002; Frank 2002). This approach, however, requires complicated mathematical models such as in the recent work by Hagman and coworkers (Hagman et al., 2004). In this work the authors show tractography results of fiber crossing in the centrum semiovale. We note, however, that while the direction of this work is promising, it needs further development and is not validated at this time. White matter fiber tracts consist of a large number of densely packed myelinated axons. Within this myelinated white matter the movement of water molecules is substantially restricted in directions that are perpendicular to the longitudinal axis of the axons. Consequently, in white matter tracts, the longest main axis of the diffusion ellipsoid is much larger than the other two and coincides with the direction of the fibers. Following Westin's geometrical classification of the diffusion tensor, this type of anisotropic restricted diffusion is termed linear diffusion. The term planar diffusion is used, if diffusion is restricted along one direction only and unrestricted along the other two directions, e.g., between layers of tissue sheets. An example of anisotropic and isotropic diffusion is illustrated in Figure 1 at the end of this appendix. A further example of a tensor map can be seen in Figure 2, also at the end of this Technical Report.

Insert Figure 1 and Figure 2 About Here The above-mentioned basic ellipsoid model is idealized and does not necessarily reflect the true diffusion behavior encountered in real tissues. For example, at nerve fiber tract crossings the ellipsoid tensor model fails, since each fiber tract constitutes a preferred diffusion direction. Acquisition protocols that measure diffusion along a large number of directions, however, allow for a better description of the complex directional diffusion behavior in fiber tract crossings and in other heterogeneously organized tissue structures. Data from DTI can be analyzed in several ways. The most general approach is to characterize the overall displacement of the molecules (average ellipsoid size) by calculating the

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mean diffusivity. To do so the trace (Tr) of the diffusion tensor, which is calculated as the sum of the eigenvalues of the tensor, has been introduced. The mean diffusivity is then given by Tr(D)/3. To describe the anisotropy of diffusion, several scalar indices have been introduced. To be useful these indices had to be rotationally invariant, i.e. not dependent on the absolute orientation of the diffusion tensor ellipsoid, and thus give the ultimate value for a specific tissue type. The most commonly used indices (proposed by Basser et al., 1994; 1996; 2000) are: (1) Relative Anisotropy (RA), a normalized standard deviation representing the ratio of the anisotropic part of the tensor to its isotropic part; (2) Fractional Anisotropy (FA), a measure of the fraction of the magnitude of the tensor that can be ascribed to the anisotropic diffusion; and, (3) volume ratio (VR), a measure representing the ratio of the ellipsoid volume to the volume as a sphere of radius. Other anisotropy indices include measures of intervoxel coherence (Pfefferbaum et al., 2000), and the geometric nature of diffusion and intravoxel coherence (Westin et al., 2002). Similar to anisotropy indices, several methods have also been proposed for visualizing 3D information contained in DT-MRI data. These include: (1) the use of the octahedral icon for each pixel (Reese et al., 1995); (2) color maps, where different intensities of the three colors indicate the size and the ADC in each of the three Cartesian directions (Makris et al., 1997); (3) rendered ellipsoids to visualize diffusion data in a slice, and (Pierpaoli et al., 1996); (4) the use of lines to represent the in-plane component of the principal diffusion direction, along with a color-coded out-of-plane component (Peled et al., 1998). Region of Interest (ROI) and Voxel Based (VBM) Methods: Finally, multiple methods have been introduced for the quantitative analysis of diffusion. The most popular approach to data analysis in clinical studies is to use a fixed size (square or circle) ROI method. This approach has been used to place large ROIs within anatomically non-specific white matter regions of the brain (i.e., frontal white matter), as well as much smaller ROIs, placed within the specific white matter fiber tracts. These two methods are distinct, as one is averaged over a bigger area, and thus is less sensitive to noise and artifacts, and more statistically conservative (Lim et al. 1999), while the other is more anatomically specific. Also, in terms of underlying pathology, anisotropy differences detected by large ROIs point to anomalies in coherence and/or organization of the white matter structures within the analyzed region, while differences detected by small ROIs, placed in the middle of the fiber tract, demonstrate abnormalities that could be due to the number/density of the fibers traveled within the fiber tract, as well as their myelin content. While the latter method relates more directly to the question of anatomical connectivity subserved by the measured fiber tract, it still has many limitations. Specifically, small ROI measurements are characterized by larger errors, due to the noise inherent in DTI images. Also, due to the various sizes of the structures across subjects, the sampled region is not always comparable. Lastly, but not the least limitation of the small ROI method, is the partial volume effect. This effect, due to the anisotropic size of voxels used in most of studies so far, is much higher in the slice direction, than in-plane. What this means is, that in order to minimize the partial volume effect, and thus to get the most reliable measurements of the small fiber tract, one should consider (if an acquisition of high resolution images with isotropic voxels is not an option) using an acquisition that is perpendicular to the fiber bundle of interest. An alternative approach for diffusion data analysis is to define the ROI based on the directional information obtained from the DTI. While this method is more specific to the fiber tracts compared with the small ROI approach (this method extracts cross section of the whole fiber bundle, as opposed to the fixed square that includes the center of the fiber bundle as in the fixed, small ROI approach), it also has numerous limitations. For example, similar to the small fixed ROI method, the directional threshold should be only applied to the fibers that are perpendicular to the plane, which limits the number of possible bundles tested. Moreover, the approach uses eigenvalues and eigenvectors to define the ROI, and these same variables are then used to calculate the FA within the segmented structure. Also, torque of the brains, or any shape

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asymmetries, that could exist between studied groups, can affect the main direction of the bundle, threshold, and thus effect the anisotropy results. As opposed to the ROI approach, where there is a driving clinical hypothesis prior to the measurements, a voxel-based strategy is more exploratory and is suitable for identifying unpredicted/unhypothesized areas of abnormal white matter morphology. This method requires data to be spatially warped (or normalized) to the atlas template. Spatial normalization involves the registration of images and the generation of a stereotaxic atlas that represents the statistical distribution of the group at each voxel (Guimond et al., 2001; Friston et al., 1995; Mazziotta et al., 1995; Thompson et al., 1997). The registration of diffusion tensor images is usually performed in a similar way to the registration of T1-weighted or SPGR MR images. That is, T2-weighted MR images are used to estimate deformation fields or transformation functions in order to minimize the intensity difference between the template and the normalized image. With the estimated transformation function, the morphology of the diffusion images is then deformed to fit a stereotaxic space. Since T2 structural images do not include information relevant to the orientation and direction of the fiber tracts, the registration of these types of images to the atlas template usually performs poorly, with significant residual miss-registration error due to differences in fiber tracts location, shape and size, as well as anatomical variability that can not be captured with information provided by structural images. For this reason, voxel-based quantification of diffusion tensor images requires more sophisticated spatial normalization that takes advantage of the directional information provided by the full tensor (Park et al., 2003). Fiber Tractography: Since, as noted previously, the major advantage of DTI over any other in vivo imaging methods is the fact that now white matter fiber tracts can be visualized and quantified, we are interested in extracting full features of white matter tracts. Presented above, methods such as ROI and VBM approaches, can detect local diffusion differences, but are unable to follow the entire fiber bundle, and thus measurements along the tract are quite difficult. Thus, in addition to ROI and voxel based methods, we will be also using fiber tractography. Fiber tractography is a very promising new method to visualize bundles of brain white matter fibers. The method follows the fiber paths along the principal diffusion direction in small steps, creating long fiber tracts that connect anatomically distant brain regions. The performance of the fiber tractography depends on many factors, including data resolution, noise, image distortions, and partial volume effects caused by multiple tracts in a voxel. At today’s image resolutions, this method does not detect water behavior within the individual axons, but instead describes the estimated local diffusion properties in the tensor field, and thus tractography should be regarded as a visualization of features in this field. It is, however, remarkable, that several publications describe the similarity of fiber tracts obtained with DTI tractography to anatomically defined white matter fiber bundles (Conturo et al., 1999; Mori 2002). Of further note, white matter fiber diameters are on the order of micrometers. The current resolution of diffusion-weighted images produced by MRI is thus roughly three orders of magnitude lower (currently, vowels with side length of ~1 mm or more). Consequently, while fiber tracking algorithms give the impression that individual fibers are being extracted, this is not the case and does not correspond to physical reality. At the scanner resolution level, many fibers will be contained within an individual vowel. We thus use the term “fiber bundles” to describe the data. With this in mind, tractography algorithms provide important information. To initialize tracking, starting points need to be defined. These can be individual (user prescribed) points or points contained in regions/volumes of interest defined by manual delineations in vowel space. Tractography results depend on the number and location of these seed points, a dependency that may not be desirable. Quantities such as the number of computed (tracked) fiber bundles (subject

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to the stopping criteria of the tracking algorithm employed) are only of limited utility, because they depend on the initial seeding. Since the introduction of fiber tractography, several different methods have been proposed to delineate anatomically distinct fiber tracts. So far, the most frequently used method for this purpose is the multiple region of interest (ROI) method. This is a guided method that performs fiber tractography starting from seeding points within the first predefined ROI, and then preserves only traces that touch the other predefined ROI. The main advantage of this method is that it displays only the fiber tract that is of interest to the user. We are currently using this method to delineate several fiber bundles of the human brain, including uncinate fasciculus (See Figure 3, demonstrating fibers connecting red and yellow ROIs), inferior occipito-frontal fasciculus, fornix, cingulum bundle and internal capsule. After fiber tracts are defined, we use them as segmented regions of interest, and calculate mean Fractional Anisotropy and mean Diffusivity for each individual subject, using these data for group comparison. This approach will be also used in current proposal.

Insert Figure 3 About Here

The main limitation of the multiple ROI method is the fact that because of low DTI

resolution, it is usually difficult to define more than one ROI for a single bundle. In addition, the method can be applied only to tracts with well-documented anatomy, and the method may fail where tracts are displaced due to some pathological process. Also, this procedure excludes shorter fibers that, though they run in the same direction, do not touch two ROIs. Thus instead of this guided method, we propose and test fiber clustering approach, which is fully automatic, unguided, and takes advantage of the similarity of the fiber tracts. The fiber clustering method analyzes a collection of paths in 3D, and automatically separate them into bundles, or clusters, that contain paths with similar shape and spatial position. In this paper, we demonstrated the ability of the clustering algorithm to separate several fiber tracts, which are otherwise not easy to define (left and right fornix, uncinate fasciculus and inferior occipito-frontal fasciculus, and corpus fibers). The continuation of this method is to apply fiber clustering to the entire population of brain fiber traces. This work has been started in our lab, and Figure 3 shows promising results of whole brain fiber tractography clustering. [See O’Donnell et al. Clinical applications of fiber clustering, AJNR 2006].

Conventional tractography (otherwise known as Principle Diffusion Direction (PDD),

Streamline or Deterministic Tractography) estimates fibers by tracing the direction of maximum water diffusivity. A main limitation of this traditional approach is that it gives an impression of being very precise. However, in practice there are several factors that introduce uncertainty in the tracking procedure. Noise, splitting and crossing fibers, head motion and image artifacts are all examples of factors that cause variability in the estimated fibers. To address this uncertainty we have been working on stochastic tractography methods, which aim to quantify and visualize the uncertainty associated with the estimated fibers. We developed a propagation model based on stochastics and regularization, allowing paths originating at one point to branch and return a probability distribution of possible paths. Figure 4 demonstrates the ability of this method to generate fiber bundles originating from one seed point. We plan to use this method to estimate the probability of anatomical connectivity between different brain regions, which can be then compared between populations. This method will be applied also in this proposal, especially to the tracts that cannot be extracted with regular “deterministic” tractography, such as the arcuate fasciculus, or internal capsule. [See Freeman and Westin. Uncertainty in White Matter Tractography, 2005]

Insert Figure 4 About Here

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In addition to measures of Fractional Anisotropy and Mean Diffusivity, indices used routinely by us as well as others to measure diffusion within living tissues, we are developing new measurement matrices that more precisely describe shape, size and skewness of the diffusion anisotropy (see Figure 5). Figure 5 demonstrates how diffusion properties can differ within the tissues characterized by the same Fractional Anisotropy, and Diffusivity. Four new, normalized measures have been developed to describe diffusion anisotropy- Spherical, Linear and Planar Diffusion (represented on the graph as three vertices of the triangle- upper- spherical, lower left- planar, lower right- linear), and Mode, which changes along the lines of the same Fractional Anisotropy[See Westin et al., 1997] .

Insert Figure 5 About Here

In our preliminary study, looking at the previously defined Cingulum Bundle ROI, Fractional Anisotropy that was previously shown to be decreased in schizophrenia was strongly correlated with Mode, Linear and Spherical, but not Planar Diffusion. In addition, Linear Diffusion and Mode were much stronger than the FA and correlated with age in patients with schizophrenia. These results demonstrate the utility of proposed methods. II. Scan Parameters for Current Research Studies. DTI Acquisition Parameters to Be Used: McLean Hospital. In the proposed research studies, FE patients and NC will be scanned at McLean Hospital initially using the 1.5T GE magnet. We will use Line Scan Diffusion Imaging (LSDI). Unlike the single-shot echo planar imaging (EPI) and navigated echo pulsed gradient spin echo imaging, the most commonly used MR diffusion imaging techniques, the LSDI is composed of a series of parallel columns lying in the image plane. The sequential collection of this line data in independent acquisitions makes the sequence largely insensitive to bulk motion artifact since no phase encoding is used and shot-to-shot phase variations are fully removed by calculating the magnitude of the signal. We have used this protocol at both McLean Hospital and at Brigham and Women’s Hospital since we began our DTI studies in schizophrenia and thus have experience with this acquisition protocol. MR scans will be performed with a quadrature head coil on a 1.5 Tesla GE Echospeed system (General Electric Medical Systems, Milwaukee, WI), which permits maximum gradient amplitudes of 40 mT/m. We will begin with a set of three orthogonal T1-weighted images used as localizers (sagittal, axial oblique aligned to the anterior commissure-posterior commissure (AC-PC) line and another sagittal oblique aligned to the interhemispheric fissure). From the last sagittal oblique T1W image, the LSDI sequence in the coronal orientation will then aligned to the AC-PC line. For each line, six images with high (1000 s/mm2 ) diffusion-weighting along six non-collinear directions will be collected. For low (5 s/mm2 ) diffusion-weighting we will collect only two images, since diffusion related signal changes are minimal. The following scan parameters will be used: rectangular FOV (field of view) 220x165mm; 128x128 scan matrix (256x256 image matrix); slice thickness 4-mm; interslice distance 1-mm; receiver bandwidth +/-4kHz; TE (echo time) 64ms; effective TR (repetition time) 2592ms; scan time 60 seconds/slice section. We will acquire a total of 31-35 coronal slices covering the entire brain, depending upon brain size. The total scan time will be 31-35 minutes. After reconstruction, the diffusion-weighted images will be transferred to a SUN workstation, where eigenvalue, eigenvector and fractional anisotropy (FA) maps of the diffusion tensor will be calculated. Motion-related artifact maps will also be constructed. Even though LSDI sequence gives us relatively undistorted, high signal-to-noise ratio DTI images, their spatial (5mm slice thickness) and directional (6 diffusion directions) resolution is much lower than today standards, thus within the first year of the grant, and hopefully prior to starting this grant, we will transition to the 3T Siemens Trio magnet at McLean Hospital, where we

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will first test and then apply to the clinical population the sequence similar to the one we have already transitioned to at BWH. We note that, importantly, we have NC group matched to the FE SZ and FE BP at McLean Hospital, as we do for the CSZ and NC at BWH. We further note that we have now transitioned from the 1.5T to the 3T magnet at BWH and we do not anticipate any problems with transitioning from the 1.5T at McLean Hospital to the 3T at McLean. We are working actively now to make this transition but we want to be conservative in estimating the time and although we are confident this will be completed prior to funding based on the current application, we want to give ourselves additional time and that is why we state that we will be ready at least within the first year of active funding. The proposed parameters for this new sequence are as follows: Dual Echo Spin Echo Echo Planar Imaging with 8 Channel head coil that will allow us to perform parallel imaging using GRAPPA reconstruction and speed up acquisition by the factor of 2. We will acquire 50 directions with b=900, 8 baseline scans with b=0. TR 9000 ms, TE 78 ms, FOV 24 cm, 144x144 encoding steps, 1.9 mm slice thickness. We will acquire 80 axial slices parallel to the AC-PC line covering the whole brain. In addition, Bo field inhomogeneity maps will be collected and calculated (as described below). Total scan time will be 9 minutes. Brigham and Women’s Hospital: For BWH, CSZ and their respective NC will be scanned on a 3T GE Echospeed system, using an echo planar imaging (EPI) DTI Tensor sequence. We will use a double echo option to reduce eddy-current related distortions (Heid 2000; Alexander 1997). To reduce impact of EPI spatial distortion, we will use an 8 Channel coil that will allow us to perform parallel imaging using ASSET (Array Spatial Sensitivity Encoding Techniques, GE) with a SENSE-factor (speed-up) of 2. We will acquire 51 directions with b=900, 8 baseline scans with b=0. The original GE sequence has been modified to increase spatial resolution, and to further minimize image artifacts. The following scan parameters will be used: TR 17000 ms, TE 78 ms, FOV 24 cm, 144x144 encoding steps, 1.7 mm slice thickness. We will acquire 85 axial slices parallel to the AC-PC line covering the whole brain. In addition, Bo field inhomogeneity maps will be collected and calculated (as described below). Total scan time will be 17 minutes. As with MRI scans, we will monitor artifact in the images to ensure quality. In case of movement artifacts, images will be redone. Artifact Reduction: Both of our new DTI protocols are implemented based on EPI. The major advantage here is speed, although, unfortunately, EPI is very susceptible to various artifacts, especially geometric distortions. Specifically, EPI based data are affected by geometric distortions in two ways. First, the eddy current effect due to varying diffusion sensitizing gradients results in inconsistent distortions in serial EPI measurements, and thus the calculated diffusion quantities may be affected by edge artifact. As mentioned previously, it has been shown that the eddy current related edge artifact can be minimized by using a double-echo diffusion-weighted EPI pulse sequence, which we are proposing to do in this application. In addition, we will be registering each individual gradient direction scan to the baseline, further reducing eddy current artifact. Second, Bo field inhomogeneity and subject induced susceptibility field gradients result in image distortions. The susceptibility related distortions are nonlinear, which may not be effectively removed with pure post-processing algorithms. It has been shown that the nonlinear susceptibility related distortions may be effective reduced, if the susceptibility field gradients are first measured with MRI field mapping technique and the measured information is applied to convert EPI data from distorted to non-distorted coordinates. We developed a field mapping protocol, based on multiple asymmetric spin-echo EPI, to measure the susceptibility field inhomogeneities. In our implementation, six asymmetric spin-echo EPI with different levels of T2*-weightings (0, 2, 4, 6, 8 and 10msec) are acquired with the inter-k line echo spacing time identical to that in actual scans. Bo off-resonance values can be calculated by first unwrapping the pixel-wise phase values in time-domain, followed by linearly fitting the phase gradient slope. The calculated Bo field inhomogeneity maps are then

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used to convert EPI data from distorted to non-distorted coordinates, using the previously reported phase modulation method. Application of the developed geometric correction technique to human brain EPI data is shown in Figure 6, where left and right images in each display panel are EPI data before and after distortion correction, respectively. It can be seen that the nonlinear image-domain deformation can be reduced with our correction technique, even in critical brain regions affected by pronounced susceptibility field gradients.

Insert Figure 6 and 7 About Here Subject’s movement in the scanner, as well as bulk motion, can affect diffusion weighted EPI images, and lead to erroneous tensor estimation. Single affected images can be excluded from tensor estimation, however it unevenly reduces the number of gradient orientations used for the analysis, and can skew the tensor estimation as well. In this application, we plan to use a novel post-processing method that deals with such artifacts on a voxel-by-voxel basis. In the first step, we compute the curvature profile of the apparent diffusion coefficients over the sphere induced by the gradient directions. Obvious outliers correspond to high curvature magnitudes. Measurements identified as outliers are removed (on a voxel-by-voxel basis) from the set of diffusion-weighted images. Neighborhood-based Laplace-Beltrami interpolation is performed to reconstruct the rejected outlier measurements. This methodology improves the quality of the acquired diffusion weighted images by rejecting obvious outliers. Since no tensor model is used for outlier rejection and reconstruction the processed diffusion weighted images can still represent non-Gaussian diffusion profiles (e.g., at locations of fiber crossings). Figure 7 illustrates images before (upper row), and after (lower row) outlier rejection. Note significant improvement in middle slice tensor estimation. III. Specific Regions of Interest. Based on our pilot projects, we will evaluate four major fronto-temporal connections (Uncinate Fasciculus, Cingulum Bundle, Fornix and Arcuate Fasciculus), Corpus Callosum, largest white matter fiber tract in the human brain, interconnecting right and left hemispheres, and thalamo-cortical projections traveling through the anterior limb of the internal capsule. Since each tract is unique in terms of its shape, anatomical location and appearance, segmentation of each requires slightly different segmentation methods. For 1.5T data, due to limited image resolution and number of diffusion directions, we will be mostly use slice based (2D) ROI methods, described also in our previous publications (Kubicki et al., 2002, 2003; Kuroki et al., 2005; Nakamura et al., 2006). These methods will be complemented with tractography methods that are described below. We will use two algorithms for tractography, both part of freely available software, called the Slicer3 package (slicer.org). The first method, called “Deterministic Tractography” will be used in combination with either multiple ROI and/or our clustering method to separate out UF, Fornix, CB and its segments, Corpus Callosum and its segments and Internal Capsule. The second method, called “Stochastic Tractography” will be used with a multiple ROI extraction method in order to segment Arcuate Fasciculus and Internal Capsule, but we will also apply it to other tracts of interest. Regions of interests are reviewed below. (1) Uncinate Fasciculus. To quantify diffusion within the uncinate fasciculus (UF) on 1.5T scans, we will use one coronal slice, perpendicular to the AC-PC line that cuts the UF in the temporal stem-the most dense portion of this fiber tract. The UF in this location is parallel to the AC-PC line and appears visible only on one slice in the segmentation defined by the maximum diffusivity (i.e., the largest eigenvalue of the diffusion tensor). All measurements will be done blind to diagnostic group. For all cases, a point centered within each fiber tract (separately for left and right side) will be selected, and FA (which describes deviation from the isotropic diffusion) will be calculated. As we are interested in the extent to which anisotropy differences between groups

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reflect the degree of connectivity between frontal and temporal lobes, FA over the area derived from the maximum diffusivity segmentation (1x10�³mm²/s will be used as the fixed threshold for all the cases) will be averaged for each case. Additionally, as the manual selection of a point could potentially bias the measurement, the point with the maximal FA value within the UF will also automatically calculated. We have successfully used these methods in a recent paper (Kubicki et al., 2002).

In addition, to quantify diffusion anisotropy within the entire UF, we will use fiber tractography and a multiple ROI method applied to 3T data. First, white matter will be segmented from T2W scans (average of 5 B0 images obtained as part of the DTI acquisition). Then, whole brain fiber bundles will be reconstructed from seed points assigned to all voxels inside the white matter mask. Because starting from every voxel would be computationally very expensive, we will reduce the number of seed points to 50,000. Thus 50,000 traces, or streamlines will be reconstructed, beginning at randomly assigned voxels within the white matter, and terminated at the voxels that fulfilled predefined stopping criteria: a low fractional anisotropy (FA) (0.15) and a rapid change of trace direction (20° per 1 mm). Fiber extraction and data analysis will be performed as follows: First, we will draw large ROI that include the area of the temporal stem. The ROI definitions will be similar to our previous investigations (as described above), except that in order to avoid excluding any fibers of interest, instead of a conservative directional threshold used in previous investigations, we will draw much more over-inclusive ROIs based on the FA map. After the algorithm automatically excludes fibers that do not travel through this ROI, second ROI will be drawn using the tracking visualization tool in slicer3. The second, larger ROI will be drawn within the temporal pole. The last step involves calculating mean FA, mean length, mean angle and total number of fiber traces. Only FA will be used as a dependent variable in our investigation, but other measures will ensure that we are comparing same features across groups. Reliability will be established by two independent raters on nine randomly selected cases. Each rater will manually delineate two ROIs on each side, and then fiber tracts connecting these ROIs will be generated automatically. Finally, mean FA for each tract will be calculated, and compared between raters. Repeated-measures analysis of variance (ANOVA) with group as a between-subjects factor, and side as a within-subjects factor, will be used to test for group differences in UF diffusion. In the case of a significant group by side interaction, independent t-tests will be used to compare group differences, separately for the right and left hemispheres, and paired t-tests will be used within each group to test for hemispheric asymmetry differences in diffusion.

(2) Cingulate Bundle. The portion of the CB that contains the most dense concentration of

parallel fibers lies dorsal to the body of the corpus callosum. Throughout its extent, fibers of variable length join the bundle and branches form connections between prefrontal and temporal regions in more anterior portions and between parietal and temporal regions in more posterior areas. The curving geometry of the CB, along with the branching of the fibers prohibits reliable measurement of anisotropy in the most anterior and posterior portions, and for this reason we will concentrate our analyses in the portion of the tract dorsal to the body of the corpus callosum. The measurement includes 8 slices starting caudally with the first coronal slice posterior to the genu of the corpus callosum. The resulting 40 mm section of the CB covers most of the fibers dorsal to the body of the corpus callosum. For all the cases and all the slices, same automatic threshold from the maximum diffusivity segmentation (1x10�³mm²/s) as for the UF will be used on 1.5T data to define part of the CB localized above the body of the CC. As we are interested in the extent to which anisotropy differences between groups reflect the degree of connectivity between different brain regions

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interconnected by CB, we will measure the mean FA of the entire ROI. Again, all measurements will be done blind to group membership. In addition, we will calculate the degree of motion related artifacts within the segmented ROI. This measure was defined as the number of line segments with abnormal low signal in the raw LSDI data (6 directional diffusion images). To quantify diffusion anisotropy within the entire CB, we will also use fiber tractography and a multiple ROI method applied to 3T data. The procedure will be, again, similar to the UF tractography segmentation. We will use the multiple ROI method to define tracts of interest, using the method described above, and then calculate mean FA for all the fiber tracts traveling through defined ROIs. In addition, since the information provided by 3T DTI scans is much richer than that from 1.5T, we will be able to separate out fibers that travel within the cingulum bundle, but fan out of the bundle to interconnect neighborhood gyri (such as anterior and posterior cingulate gyrus, posterior cingulate gyrus and medial parietal lobe, parietal and occipital lobes, and parietal and medial temporal structures). For this purpose, besides using multiple ROI method to extract fibers of interest belonging to CB, we will use a fiber clustering approach, which is fully automatic, unguided, and takes advantage of the similarity of the fiber tracts. Our fiber clustering method analyzes a collection of paths in 3D space, and separates them into bundles, or clusters, that contain paths with similar shape and spatial position. With this fully automatic procedure, fiber traces are grouped according to a pairwise similarity function, which takes into account the shape and connectivity of fiber traces (our group is the first to apply this method to DTI data--see Brun et al., 2004). We believe that this method will be useful for the further segmentation of fiber trace data, not only with respect to differentiating separate anatomical fiber tracts, but also to further divide single fiber bundles into anatomically and functionally relevant fiber clusters. This approach has been pioneered for CB using our new, high resolution, multiangular DTI data (see Preliminary Data and Figure 8 here), and demonstrates promise in differentiating distinct subdivisions of cingulum fibers.

Insert Figure 8 About Here

(3) Arcuate Fasciculus. The arcuate fasciculus is the short fiber tract existing exclusively

in humans, and it connects Broca’s area with Wernicke’s area bilaterally. As it is heavily involved in language processing, we predict that the diffusion along these fiber tracts will be distributed asymmetrically, with much higher diffusion anisotropy on the left side in control subjects. For patients with schizophrenia, on the other hand, we predict lower diffusion anisotropy compared to controls, in both hemispheres, though we predict that the left side will be more affected than the right side.

Because of the nature of this fiber tract (not as compact and thick as UF or CB) we were unsuccessful in extracting this tract using our 1.5T data. Fiber tractography when using deterministic tractography was also mostly not successful, even with our newly acquired high resolution 3T DTI data. Thus this tract will be extracted only using stochastic tractography, a Bayesian approach to estimating fiber tracts from DWMRI (Diffusion Weighted Magnetic Imaging) images. A Bayesian framework provides a measure of confidence regarding estimated tracts, as this measure of confidence allows the algorithm to generate tracts, which pass through regions with uncertain fiber directions, thus revealing more details about structural connectivity than non-Bayesian tractography algorithms. Using the generated tracts, we can derive distributions of tracts averaged FA, as well as estimate the fraction of fibers connecting two ROIs to the total number of sampled fibers. Finally, the connectivity probability map generated using the tracts can be thresholded at a required P value to generate an ROI that includes voxels that have a higher probability of being part of the tract. Figure 9 demonstrates how this approach can be used to

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investigate connections between Broca and Wernicke’s regions within the language network.

Insert Figure 9 About Here (4) Fornix. The fornix is a compact bundle of white matter fibers projecting from the

hippocampus to the septum, anterior nucleus of the thalamus and the mamillary bodies. This structure is involved in important brain functions such as spatial memory (Gaffan 1994; Parker and Gaffan 1997), memory retrieval (Calabrese et al., 1995) and verbal memory (Calabrese et al., 1995; Mc Mackin et al., 1995). These are also all functions that are disturbed in schizophrenia, thus characterizing disruptions in fornix integrity might further our understanding of this disorder. Fornix extraction and measurements will be obtained using in house software (slicer3), and diffusion tensor tractography. Because the fornix is a small white matter structure and its fibers run closely to other structures such as the corpus callosum and the anterior commissure, its segmentation is not trivial. In order to ensure precise segmentation of the structure 5 separate ROIs will be defined to tract the desired fibers. Only those pathways passing through all 5 ROIs will be retained for analysis (Catani et al., 2002; Conturo et al., 1999). ROIs will be manually drawn on the FA map blind to diagnosis. The first ROI will be placed on the most anterior coronal plane where the body of fornix is visible on the FA map, and then two more ROIs on the next two consecutive slices using the corpus callosum, the contours of the lateral ventricles and the third ventricle, as landmarks. Finally, two additional ROIs, one on each side, will be drawn on the two more posterior slices including the hippocampus (tail), parahippocampal gyrus and the crus fornicis. After fornix tracts are extracted, averaged values of FA and mean diffusivity for the entire tract separately for each side will be calculated, and subjected to the statistical analysis.

(5) Corpus Callosum. The CC is a midline brain structure that is the largest white matter

fiber tract in the brain. This structure interconnects left and right hemispheres, and plays a primary role in sensory, as well as high-level cognitive integration (Seymour et al., 1994); (Gazzaniga, 2000). In order to measure diffusion properties within CC, we will utilize a novel method for the computation of a probabilistic subdivision of CC, which is described in detail in (Styner et al., 2005) and (Cascio et al., 2006). The procedure involves four separate steps: 1) automatic segmentation of the entire CC using SPGR data; 2) further separation of the CC ROI (still using structural scan) into 4 separate segments using a previously created atlas; 3) co-registration of the structural scan (along with CC segments) to DTI, and 4) calculation of the area and mean FA for each CC segment. The segmentation of CC into 4 smaller anatomical sections (step 3) will be performed using a probabilistic atlas/model (Styner et al., 2005); (Cascio et al., 2006), which was generated in a separate study (Cascio et al., 2006). Its creation involves tractography of the entire CC, its further division using automatic lobar clustering segmentation, and back-mapping of these divisions onto midsagittal plane. The last step of this process results in midsagittal plane labels of pre-frontal, frontal, parietal and temporo-occipital callosal segments corresponding to the probability of callosal fibers projecting to each of these four brain regions. The entire process will be automatic and fully reproducible, i.e., no manual measurements will be conducted.

(6) Anterior Limb of the Internal Capsule. AL-IC is the final step for all higher cognitive

function frontal-subcortical feedback loops, both cognitive and limbic, the white matter fiber tract carrying thalamic projections, primarily from the dorsomedial nucleus of the thalamus, to the prefrontal cortex. Furthermore, the AL-IC also contains fibers connecting the anterior nuclei of the thalamus with the anterior cingulate cortex. Based on the literature (e.g., Byne et al., 2002; Bhatia and Marsden, 1994; Uranova et al., 2001), we predict an increase in diffusivity and reduced anisotropy in CSZ and FESZ compared with a contrast group of FE BP and NCs within this

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structure. To quantify white matter integrity within the AL-IC, on 1.5T data, we will use manual drawing ROI approach. We will use the Slicer3, a program developed in the Surgical Planning Laboratory, to manually draw AL-IC on the coronal plane using the following criteria: The AL-IC span approximately four coronal slices. The anterior boundary of the AL-IC is the coronal slice posterior to the emergence of the rostrum of the corpus callosum. The superior boundary of the AL-IC is formed by the line connecting the most medio-dorsal aspect of the putamen and the dorso-lateral aspect of the caudate nucleus where it is contiguous with the corpus callosum (easier to visualize using DTI FA Maps). The medial and lateral boundaries are formed by the lateral aspect of the caudate nucleus and the medial aspect of the lentiform nucleus, respectively. The inferior boundary is defined by a gray matter area that include the ventral striatum, basal forebrain nuclei and the globus pallidus. Lastly, the posterior boundary is formed by the most anterior slice where the column of the fornix is present. Mean FA will be calculated for each subject, for AL-IC separately for the left and the right sides, and subjected to the statistical analysis (see below).

For the 3T data, we will use both available methods of tractography, streamline (i.e., deterministic) as well as stochastic. We will use multiple ROI method to segment fiber tract of interest, and will use in both cases manually drawn ROIs for the seeding region. For the second ROI, we will use entire white matter area of the first coronal slice in front of the genu of the corpus callosum. Because of the anatomical vicinity of the AL-IC and callosal fibers fanning out of the genu of the corpus callosum, and thus multiple fiber crossings affecting FA values along the tract, we hope to preserve more anatomically correct internal capsule fibers using stochastic tractography, as this method does not use stopping criteria for tractography termination (see preliminary data section for more details).

IV. Comparisons of 1.5T and 3T DTI Data.

While we may still collect some DTI data for FESZ using 1.5T scanner at McLean Hospital within the first year of the grant, we will switch to the 3T Siemens Trio scanner either before the grant starts, or within the first year. All DTI scans at Brigham and Women’s Hospital will be conducted on the 3T magnet (GE). We are convinced that despite higher distortions and susceptibility artifacts in Echo-Planar Imaging (EPI) sequences on the 3T scanners, much higher signal-to-noise ratio (twofold) and much higher spatial and directional resolution are clear advantages over lower magnet strengths. By switching to a 3T magnet, we are now able to achieve unprecedented image resolution (1.7 mm cubic), which was not possible on the 1.5T magnet (see Figure 10a for an example of an FA map acquired on a 1.5T magnet-left, and a 3T magnet-right). Thus unlike with structural MRI protocol, where it was unclear to us that the move from 1.5T to 3T would be beneficial, it was only after thorough experiments that we were convinced that that both contrast to noise ratio and segmentation results are much more favorable using 3T data. In fact the difference is quite striking. More specifically, data resolution is improved not by 1.5 fold, as is the case for structural MRI, but by 4 fold, making it possible now to investigate small, functionally relevant fiber tracts (examples of such advantage can be seen with Preliminary Data regarding parts of the cingulum bundle, or corpus callosum). In addition, an 8 Channel coil that we use to obtain DTI data further decreases scan time, increases signal-to-noise ratio (21 on 3T versus 11 on 1.5T magnet), and minimizes image distortions. Finally, we use distortion correction techniques that further minimize artifacts related to high magnetic field. Here, we demonstrate the advantages of using high magnet strength with fiber tractography (Figure 10b), where we see averaged fiber tracts for a group of 10 NC scanned on 3T (left image), and 1.5 T (right image). Because of low reproducibility of the data and high noise, much more smoothing is needed for the 1.5T data, in order to align the large fiber bundles. In effect, small bundles are smoothed out, and disappear. It is thus clear that fiber tracking, which is a major part of this proposal, will perform much better on data from the 3T magnet.

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Insert Figure 10 About Here

Lastly, in order to understand the consequences of our switch from 1.5T to 3T, we conducted a study where we chose a subset of subjects (CSZ and NC) that were scanned on both the 1.5T and the 3T, and used the same method of automatic ROI segmentation that was used in our previous published reports, in order to extract Cingulum Bundle in 8 CSZ and 8 NC. Figure 11 represents pictorially a mid-sagittal view of the brain with CB (in green) above the CC (red) (left 1.5T, right 3T), group comparison using automatic ROI segmentation of the CB (left- 1.5T, middle 3T), and correlations between FA values obtained from 1.5T and 3T ROI analysis. Analyses on both magnet strengths resulted in similar group differences (P=0.024 for left CB mean FA obtained from 1.5T data; P=0.008 for left CB mean FA obtained from 3T data). In addition, correlations between 1.5T and 3T mean FA within the CB was 0.591.

Insert Figure 11 About Here

V. Statistical Analyses.

Repeated measures analysis of variance (ANOVA) with group as the between-subjects factor, and side as the within-subjects factor, will be used to test for group differences in anisotropy of diffusion. In addition, in tracts that can be further separated into functional segments, such as CB and CC, tract segments will be used as additional within-subject factors. In the case of a significant group by side interaction, independent post-hoc Scheffe tests will be used to compare group differences, separately for the left and right hemispheres. Finally, we will correlate the diffusion findings with the clinical and behavior measures for the pilot projects, as discussed in the Main Text.

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VI. Power Analyses.

White Matter Fiber Tract

Statistical Power

N=50

Significance Level

Set at p=0.05

Effect Size Based on Subject N

From Preliminary Data

UF (ROI 1.5T) >99% p=0.05 0.93 (asymmetry) 10 NC, 10 CSZ

UF (Tractography 1.5T) 9% p=0.05 0.04 27 NC, 34 CSZ

CB (ROI 1.5T) >99% p=0.05 0.63 8 NC, 8 CSZ

CB (Tractography 1.5T) >99% p=0.05 0.41 8 NC, 8 CSZ

CB (ROI 3T) >99% p=0.05 0.60 8 NC, 8 CSZ

CB (Tractography 3T) >99% p=0.05 0.46 8 NC, 8 CSZ

AF (Tractography 3T) 99% p=0.05 0.39 10 NC, 10 CSZ

Fornix (ROI 1.5T) 98% p=0.05 0.37 31 NC, 24 CSZ

Fornix (Tractography 1.5T) 91% p=0.05 0.31 36 NC, 35 CSZ

CC (Tractography 1.5T) >99% p=0.05 0.60 21 NC, 21 CSZ

CC (Tractography 3T) 91% p=0.05 0.31 10 NC, 10 CSZ

AL-IC (ROI 1.5T) >99% p=0.05 0.45 16 NC, 19 CSZ

AL-IC (Tractography 1.5T) >99% p=0.05 0.48 19 NC, 22 CSZ

Gray Matter Regions

Temporal Pole >99% p=0.05 1.19 27 NC, 27FESZ, 26FEBP

STG >99% p=0.05 1.1 (Left STG) 28 NC, 23 CSZ

Inferior Parietal Lobule 99% p=0.05 0.60 14 NC, 14 CSZ

Hippocampus 94% p=0.05 0.75 31 NC, 24 CSZ

Orbital Frontal Cortex

Middle Orbital Frontal Gyrus

Left SZ & NC

Right SZ & NC

>90%

>99%

p=0.05

p=0.05

0.96

0.89

25 NC, 24 CSZ

25 NC, 24 CSZ

Thalamus 94% p=0.05 0.50 15 NC, 15 CSZ

Prefrontal Gray Matter >99% p=0.05 0.67 17 NC, 17 CSZ

Cingulate Gray Matter >99% p=0.05 0.88 20 NC, 7FESZ, 14 FEBP

Power calculations are related to the probabilities of Type I and Type II errors detection. We

have set the significance level for all tests at 0.05. From the pilot data we estimated the effect sizes based on our preliminary results of diffusion anisotropy (measure of organization and integrity of the fiber tracts) within the cingulate fasciculus, uncinate fasciculus and corpus callosum. VII. References in Text. Basser, P.J., Mattiello, J., LeBihan, D. (1994). “MR diffusion tensor spectroscopy and imaging.” Biophysical Journal 66:259-67. Basser, P., Pajevic, S., Pierpaoli, C., Duda, J., Aldroubi, A. (2000). “In vivo fiber tractography using DT-MRI data.” Magnetic Resonance in Medicine 44:625-32.

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Basser, P.J., Pierpaoli, C. (1996). “Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI.” Journal of Magnetic Resonance, Series B 111:209-19. Brun, A., Knutsson, H., Park, H.J., Shenton, M.E., Westin, C-F. (2004). “Clustering fiber traces using normalized cuts.” Proceedings of the 7th International Conference on Medical Image Computing and Computer Assisted Intervention MICCAI (1)368-75. Friman, O., Westin, C-F. (2005). “Uncertainty in fiber tractography.” Eighth International Conference on Medical Image Computing and Computer-Assisted Intervention MICCAI 107-14. Kubicki, M., Westin, C-F., Maier, S., Frumin, M., Nestor, P.G., Salisbury, D., Kikinis, R., Jolesz, F.A., McCarley, R.W., Shenton, M.E. (2002). “Uncinate fasciculus findings in schizophrenia: a magnetic resonance diffusion tensor imaging study.” American Journal of Psychiatry 159:813-20. Kubicki, M., Westin, C-F., Maier, S.E., Mamata, H., Frumin, M., Ersner-Hershfield, H., Kikinis, R., Jolesz, F.A., McCarley, R.W., Shenton, M.E. (2002). “Diffusion tensor imaging and its application to neuropsychiatric disorders.” Harvard Review of Psychiatry 10(6):324-36. Le Bihan, D., Breton, E., Lallemand, D., Grenier, P., Cabanis, E., Laval-Jeantet, M. (1986). “MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders.” Radiology 161:401-7. O’Donnell, L., Kubicki, M., Shenton, M.E., Dreusicke, M., Grimson, W.E.L., Westin, C-F. (2006). “A Method for Clustering White Matter Fiber Tracts.” American Journal of Neuroradiology. In Press. Park, H.J., Kubicki, M., Westin, C-F., Talos, I.F., Brun, A., Pieper, S., Kikinis, R., Jolesz, F.A., McCarley, R.W., Shenton, M.E (2004). “Method for combining information from white matter fiber tracking and gray matter parcellation.” American Journal of Neuroradiology 25(8):1318-24. Park, H.J., Westin, C-F., Kubicki, M., Maier, S.E., Niznikiewicz, M., Baer, A., Frumin, M., Kikinis, R., Jolesz, F.A., McCarley, R.W., Shenton, M.E. (2004). “White matter hemisphere asymmetries in healthy subjects and in schizophrenia: A diffusion tensor MRI study.” NeuroImage 23(1):213-23. Westin, C-F., Maier, S., Khidhir, B., Everett, P., Jolesz, F., Kikinis, R. (1999). “Image Processing for Diffusion Tensor Magnetic Resonance Imaging.” Medical Image Computing and Computer-Assisted Intervention 441-52. Westin, C-F., Maier, S.E., Khidhir, B., Everett, P., Jolesz, F.A., Kikinis, R. (1999). Image Processing for Diffusion Tensor Magnetic Resonance Imaging. In Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention (pp. 441-52). Westin, C-F., Maier, S.E., Mamata, H., Nabavi, A., Jolesz, F.A., Kikinis, R. (2002). “Processing and visualization of diffusion tensor MRI.” Medical Image Analysis 6(2):93-108. VIII. Further Reading Beaulieu, C. Allen, P. (1994). “Determinants of Anisotropic Water Diffusion in Nerves.” Magnetic Resonance in Medicine 31:394-400. Conturo, T.E., Lori, N.F., Cull, T.S., Akbuda, E., Snyder, A.Z., Shimony, J.S., McKinstry, R.C., Burton, H., Raichle, A.E. (1999). “Tracking neuronal fiber pathways in the living human brain.”

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Neurobiology 96:10422-27. Jones, D.K., Simmons, A., Williams, S.C.R., Horsfield, M.A. (1999). “Non-invasive assessment of axonal fiber connectivity in the human brain via diffusion tensor MRI.” Magnetic Resonance in Medicine 42:37-41. Gudbjartsson, H., Maier, S.E., Mulkern, R.V., Morocz, I.A., Patz, S., Jolesz, F.A. (1996). “Line scan diffusion imaging.” Magnetic Resonance in Medicine 36:509-19. Huppi, P.S., Maier, S.E., Peled, S., Zientara, G.P., Jolesz, F.A. (1998). “Microstructural development of human newborn cerebral white matter assessed in vivo by diffusion tensor magnetic resonance imaging.” Pediatric Research 44:584-90. Le Bihan, D. (1991). “Molecular diffusion nuclear magnetic resonance imaging.” Magnetic Resonance Quarterly 7:1-30. Maier, S.E. (2001). “Slab scan diffusion imaging.” Magnetic Resonance in Medicine 46:1136-43. Ordidge, R.J., Helpern, J.A., Qing, Z.X., Knight, R.A., Nagesh, V. (1994). “Correction of motional artifacts in diffusion-weighted MR images using navigator echoes.” Magnetic Resonance Imaging 12:455-60. Pierpaoli, C., Jezzard, P., Basser, P.J., Barnett, A., DiChiro, G. (1996). “Diffusion tensor MR imaging of the human brain.” Radiology 201:637-48. Pipe, J.G., Farthing, V.G., Forbes, K.P. (2002). “Multishot diffusion-weigted FSE using PROPELLER MRI.” Magnetic Resonance in Medicine 47:42-52. Shick, F. (1997). “SPLICE: sub-second diffusion-sensitive MR imaging using a modified fast spin-echo acquisition mode.” Magnetic Resonance in Medicine 38:638-44. Trouard, T.P., Theilmann, R.J., Altbach, M.I., Gmitro, A.F. (1999). “High-resolution diffusion imaging with DIFRAD-FSE (diffusion-weighted radial acquisition of data with fast spin-echo) MRI.” Magnetic Resonance in Medicine 42:11-18. Turner, R., Le Bihan, D., Maier, J., Vavrek, R., Hedges, L.K., Pekar, J. (1990). “Echo-planar imaging of intravoxel incoherent motion.” Radiology 177:407-14. Uhl, M., Hauer, M.P., Allmann, K.H., Gufler, H., Laubenberger, J., Hennig, J. (1998). “Recent developments and applications of MRI sequence technique. I: turbo spin echo, HASTE, turbo inversion recovery, turbo gradient echo, turbo gradient spin sequences.” Aktuelle Radiol 8:4-10. Wimberger, D.M., Roberts, T.P., Barkovich, A.J., Prayer, L.M., Moseley, M.E., Kucharczyk, J. (1995). “Identification of premyelination by diffusion-weighted MRI.” Journal of Computer Assisted Tomography 19:28-33.

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Figure 1

Figure 1. The diffusion ellipsoid on the left shows unrestricted diffusion, or isotropic diffusion, where the diffusion is more or less equal in all directions. The diffusion ellipsoid on the right shows a restriction of diffusion resulting from the surrounding tissue, in this case a myelin sheet, which restricts the movement of diffusion to directions parallel to the axons. Note that the arrows go in two directions only.

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Figure 2

Figure 2. A coronal tensor map with the blue lines showing the white matter fiber tracts. Note the thicker and more coherent lines in the corpus callosum. The red areas reflect the out of plane direction of the fibers.

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Figure 3

Figure 3. Fiber tractography of UF extracted using multiple ROI method (red and yellow).

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Figure 4

Figure 4. Tractography with combination with fiber clustering method applied to CB tractography. Separate parts of the CB are displayed.

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Figure 5

Figure 5. Figure represents results of stochastic tractography originating from one seed point within the corpus callosum. Colors represent different degrees of connectivity between the point, and other brain areas.

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Figure 6

321

21

λλλλλ++

−=lC

321

32 )(2λλλ

λλ++

−=pC

Cs =

3λ3

λ1 + λ2 + λ3

Figure 6. Visualization of the geometric diffusion indices introduced in Westin et al., (1997).

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Figure 7

Figure 7. EPI B0 distortion correction. Images before (left) and after (right) distortion correction.

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Figure 8

Figure 8. Method correcting for bulk motion. Images before (upper row) and after (lower row) outlier rejection.

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Figure 9

Figure 9. Arcuate Fasciculus obtained with stochastic tractography method.

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Figure 10

Figure 10. Comparison between 1.5T (left) and 3T (right) DTI data. Upper segment: FA axial maps obtained with two field strengths. Note the increased resolution in the 3T data on the right. Lower segment: fiber tractography on the average tensor data obtained from 10 controls on 1.5 Tesla magnet (left) and the 3 Tesla magnet (right). Note that the small tracts are being washed out by registration problems due to the higher noise levels and larger partial volume effects.

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Figure 11

Figure 11. Midsaggital view of the brain with CB (in green) above the corpus callosum (red) (left 1.5T, right 3T), group comparison using automatic ROI segmentation of the CB (left- 1.5T, middle 3T), and correlations between FA values obtained from 1.5T and 3T ROI analysis.

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