This study was supported by a subcontract (PI: Dan Iosifescu) to NIH grant U54 LM008748 (PI: Isaac Kohane)
LIMBIC STRUCTURES IN CHRONIC DEPRESSION: A STUDY USING PRE-EXISTING CLINICAL AND MRI DATA
Wouter S. Hoogenboom, MS1, 2, Roy H. Perlis, MD1, Jordan W. Smoller, MD, ScD1, Qing Zeng-Treitler, PhD3, Vivian S. Gainer, MS4, Shawn N. Murphy, MD, PhD4, Susanne E. Churchill, PhD5, Isaac Kohane, MD, PhD5, Martha E. Shenton, PhD2, 6, and Dan V. Iosifescu, MD, MS1
from the 1Depression Clinical and Research Program, Massachusetts General Hospital & Harvard Medical School, the 2Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital & Harvard Medical School, the 3Decision Systems Group, Brigham and Women’s Hospital, the 4Laboratory of Computer Science, Massachusetts General Hospital & Harvard Medical School, the 5i2b2 National Center for Biomedical Computing, Brigham and Women’s Hospital, and the 6Clinical Neuroscience Division, Laboratory of Neuroscience, VA Boston Healthcare System, Brockton Division & Harvard Medical School
CONCLUSIONS
REFERENCES1. Nierenberg A.A., & Alpert A.J. (2000). Depressive breakthrough. Psychiatr. Clin. North Am.
23(4): 731-42.2. Sheline Y.I., Gado, M.H., & Kraemer, H.C. (2003). Untreated depression and hippocampal
volume loss. Am J Psychiatry. 160(8):1516-8.3. Caetano S.C. et al. (2006). Smaller cingulate volumes in unipolar depressed patients,
Biological Psychiatry. 59: 702–706. 4. Frodl T.S., Koutsouleris N., Bottlender R., et al. (2008). Depression-related variation in brain
morphology over 3 years: effects of stress? Arc Gen Psychiatry. 65(10):1156-65.5. Fennema-Notestine C. et al. (2007). Feasibility of multi-site clinical structural neuroimaging
studies of aging using legacy data. Neuroinformatics. 5(4):235-45.6. Zeng Q.T., Goryachev S., Weiss S., et al. (2006). Extracting principal diagnosis, co-morbidity
and smoking status for asthma research: evaluation of a natural language processing system. BMC Medical Informatics and Decision Making. 6(30).
7. Fischl B. et al. (2004). Automatically parcellating the human cerebral cortex. Cereb. Cortex. 14: 11- 22.
8. Fischl, B. et al. (2002). Whole brain segmentation: automated labeling of neuroanatomicalstructure in the human brain. Neuron. 33: 341-355. .
9. Desikan R.S. et al. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 31(3):968-80.
10. Koo M.S., Levitt J.J., Salisbury D.F., et al. (2008). A cross-sectional and longitudinal magnetic resonance imaging study of cingulate gyrus gray matter volume abnormalities in first-episode schizophrenia and first-episode affective psychosis. Arch Gen Psychiatry. 65(7): 746-60.
i2b2: Informatics for Integrating Biology & the Bedside. [http://www.i2b2.org/]Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital. [http://pnl.bwh.harvard.edu/]For more information please contact Wouter Hoogenboom: [email protected]
METHODS
RESULTS
INTRODUCTION
TABLE 3. Absolute and relative volume measures of limbic structures.
0.500.012 ± 0.0040.20 ± 0.060.013 ± 0.0040.24 ± 0.080.010 ± 0.0050.17 ± 0.08Right
ANOVAUnremitted (n = 6)Improvement (n = 6)Remission (n = 8)
0.770.42 ± 0.077.11 ± 1.480.43 ± 0.067.85 ± 1.070.45 ± 0.097.41 ± 1.38Total0.410.24 ± 0.063.99 ± 1.080.20 ± 0.043.66 ± 0.730.23 ± 0.043.80 ± 0.73Left
Anterior cingulate cortex (ACC)
0.180.18 ± 0.023.11 ± 0.480.23 ± 0.044.19 ± 0.670.22 ± 0.053.61 ± 0.74Right
aRemission > Unremitted, p = 0.026 (LSD), and Improvement > Unremitted, p = 0.017 (LSD).
0.015 ± 0.007
0.10 ± 0.020.15 ± 0.05
0.07 ± 0.01
0.07 ± 0.01
0.086 ± 0.020.084 ± 0.01
0.24 ± 0.030.23 ± 0.04
relative
0.015 ± 0.008
0.14 ± 0.020.14 ± 0.03
0.07 ± 0.02
0.05 ± 0.01
0.086 ± 0.010.083 ± 0.02
0.25 ± 0.050.25 ± 0.04
relative
0.015 ± 0.006
0.14 ± 0.030.16 ± 0.04
0.08 ± 0.02
0.07 ± 0.02
0.099 ± 0.010.093 ± 0.01
0.25 ± 0.030.26 ± 0.04
relative
1.00
0.034a
0.50
0.92
0.11
0.110.32
0.690.54
1.45 ± 0.301.49 ± 0.071.54 ± 0.17Left1.51 ± 0.421.55 ± 0.161.65 ± 0.24Right
4.07 ± 0.784.51 ± 0.374.13 ± 0.50Right4.01 ± 0.734.48 ± 0.724.24 ± 0.66Left
absoluteabsoluteabsoluteHippocampus
1.19 ± 0.121.31 ± 0.211.23 ± 0.26RightRostral
2.50 ± 0.962.48 ± 0.612.69 ± 0.72Left1.73 ± 0.542.64 ± 0.542.28 ± 0.45Right
Subgenual
0.25 ± 0.100.28 ± 0.150.24 ± 0.09Left
1.24 ± 0.190.90 ± 0.121.10 ± 0.37Left
Caudal
Amygdala
OBJECTIVESTo test whether chronic unremitted MDD is
associated with changes in limbic structures compared to MDD subjects improving with treatment or to those achieving full remission.
To use NLP for identifying patient cohorts.
To use existing clinical and MRI data collected as part of routine clinical treatment.
Chronic unremitted depression may be associated with changes in brain morphology, in particular volume reductions in the right rostral anterior cingulate, compared to depressed individuals who achieve remission.
Such changes in brain morphology may play a role in the pathophysiology of chronic unremitted depression.
Our findings support the feasibility of using neuroimaging data collected during routine clinical treatment as a promising and cost-effective alternative for answering specific research questions.
STUDY DESIGN Natural Language Processing (NLP) tools were used
to identify cohorts of MDD subjects. N = 20 MDD subjects (see table 1) were meeting
criteria as outlined in figure 2. 1.5-T coronal T1-weighted brain MRIs, acquired at
Massachusetts General Hospital since 2000 or later. All patients on long-term anti-depressant regimens.
Major depression is chronic and recurrent A large proportion of patients with major depressive
disorder (MDD) do not improve adequately with antidepressant treatment or experience depressive relapse and recurrence after initial improvement (Nierenberg & Alpert, 2000).
Morphological changes in limbic structures For example:
Sheline et al. (2003) found that hippocampal volume loss is associated with the length of untreated depression.
Caetano et al. (2006) reported reduced cingulate volumes in unmedicated MDD vs. controls.
In a longitudinal study of MDD vs. controls, Frodl et al. (2008) found widespread decrease of gray matter density in limbic and frontal cortical brain regions, including hippocampus, amygdala, and anterior cingulate.
Applications of advances in biomedical computing
Meaningful re-analyses of existing (legacy) data mayenable cost-effective quantitative clinical andimaging studies (Fennema-Notestine et al., 2007).
Natural Language Processing (NLP) tools can be used to extract large amounts of specific clinical data from electronic medical records (Zeng et al., 2006).
Automated brain-segmentation software, such as Freesurfer, can perform subcortical segmentation (Fischl et al., 2002), and parcellation of surface areas (Fischl et al., 2004), that is proven to be fast, and anatomically valid and reliable (Desikan et al., 2006).
i2b2
3D MODELS OF REGIONS OF INTEREST
Figure 3. Coronal view with 3DSlicer generated models of Freesurfer segmentation.
Figure 4. Medial view of right hemisphere. Freesurfer reconstruction of pialmatter with an overlay of anterior cingulate parcellation.
Amygdala
Hippocampus
Caudal anterior cingulate
Rostral anterior cingulate
Subgenual anterior cingulate
Volume distributions across groups
NS620 ± 158587 ± 55508 ± 73Total white matter (mL)
NS189 ± 26196 ± 21193 ± 21Subcortical GM (mL)
NS400 ± 123425 ± 65436 ± 46Neocortical GM (mL)
NS1726 ± 1861858 ± 3781660 ± 184Intra Cranial Volume (ICV)
NS589 ± 131621 ± 84629 ± 57Total gray matter (mL)
NSa3/3 (50%) 4/2 (33%) 4/4 (50%) Gender M/F (% female)
TABLE 1. Sample characteristics.
Note: Means ± SD or n (%)aChi-square
NS46 ± 2545 ± 1739 ± 13 Age (yrs)
ANOVAp-values
Unremitted (n=6) (UR)
Improvement (n=6) (IMP)
Remitted (n=8) (REM)
amygdalahippocampus
Figure 1. Visualization and segmentation tools. Left: 3DSlicer interface with T1-scan in different cross-sectional views. Right: FreeSurfer subcortical segmentation and surface parcellation.
TABLE 2. Reliability between 3DSlicer and FreeSurfer(manual vs. automatic)
RightLeftRightLeft
0.95 0.72
Amygdala
0.85
ICV
0.94
Total GM
0.94
Total WM
0.87
Hippocampus
0.85Intra-class correlation coefficient (ICC)**p-values < 0.05
ROI (n=10)
Figure 2. Patient enrollment flowchart.
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