The structural organization of the Brain
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Transcript of The structural organization of the Brain
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The structural organizationof the Brain
Gray matter: nerve cell bodies (neurons), glial cells, capillaries, and short nerve cell extensions (axons and
dendrites). Information processing
White matter: bundles of myelinated nerve cell axons, which connect various gray matter areas of the brain to each other, and carry nerve impulses between neurons. Information transmission
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Development of morphometric „in vivo“ techniques
volumetric analysis of Regions of Interest (ROIs)
Voxel-Based Morphometry
Cortical Thickness
Diffusion Tensor Imaging
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Volumetric analysis of ROIs/VOIs
Lenroot et al., 2007
Brain developmental trajectories
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Volumetric analysis cons:
• Require a priori assumptions about ROIs
• Detect only gross structural changes in GM, WM
• Manual segmentation of brain tissue (Gray-White matter) and definition of ROIs is time consuming and subject to errors
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• VBM is a voxel-based comparison of local tissue volumes within or across groups
• Whole-brain analysis, does not require a priori assumptions about ROIs; unbiased way of localising structural changes
• Can be automated, requires little user intervention compare to manual ROI tracing
Voxel-Based Morphometry
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GREY MATTERGREY MATTER WHITE MATTERWHITE MATTER CSF CSF
SPATIALLY SPATIALLY NORMALISED NORMALISED
IMAGE IMAGE
Voxel Based MorphometryAims to classify image as GM, WM or CSF
Two sources of information
a) Spatial prior probability maps
b) Intensity information in the image itself
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• Comparison of local tissue volumes: false positives due to misregistration of the images
• Lack of accuracy: differences detected only at macroscopic scale
• Poor understanding of the nature of GM/WM changing
VBM Cons
E.g. Increased gray matter volume could result from more folding as well as thicker gray matter
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Cortical Thickness
It allows not only to determine significant difference between groups but also to measure this difference (in mm)
Changes across the axes of the cortical columns.
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Cortical Thickness Measures the distance between outer and inner surfaces.
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Cortical Thickness
(Fischl and Dale, 2000 )
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Shaw, P. et al. J. Neurosci. 2008
Complexity of developmental trajectories throughout the
cerebral cortex
Lu, L. et al. Cereb. Cortex 2007
Improvement in motor skills or phonological processing results in thinning or thickening of
dedicated brain regions
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Cortical Thickness cons• Uncorrected measurements due to mis-registration of the two surfaces (outer and inner)
• Possible misclassification of GM/WM tissue (Partial volume effects) due to the low resolution of MRI
• Cortical thinning could be not entirely due to reduction in size or number of neuronal cell
bodies or their synaptic processes, but also in part due to an increase in the myelin coating of fibers (Sowell et al. 2007)
i.e. axons look like gray matter until they are myelinated, so measured gray matter decreases are observed in part as a result of myelination
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Diffusion Tensor Imaging by tracking the motion of water along the white matter fibers, gives a measure of the structural connectivity between brain regions
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Diffusion Tensor Imaging
isotropic anisotropic
Shows the path of less resistance of water diffusion.
This allows to reconstruct the pathway of the underlying fibre
Change in Fractional Anysotropy (FA - directionality of the water) or Mean diffusion (MD) are indicator of funcionally relevant variation in the pathway
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Diffusion Tensor Imaging
Cohen et al. 2008, Nature
Connectivity-based segregation of the human striatum predicts personality characteristics
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DTI cons
• Due to the low resolution of MRI images, the method is not efficient in region were there is high complexity or fibre crossing
• Not possible to differentiate anterograde or retrogade connections
• Inference only at macroscopic level
• The presence or absence of any
pathway should be interpreted with care