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Editorial
. 2010 Jun;49(6):533-8.
doi: 10.1016/j.jaac.2010.03.010.

Form determines function: new methods for identifying the neuroanatomical loci of circuit-based disturbances in childhood disorders

Bradley S Peterson
Editorial

Form determines function: new methods for identifying the neuroanatomical loci of circuit-based disturbances in childhood disorders

Bradley S Peterson. J Am Acad Child Adolesc Psychiatry. 2010 Jun.
No abstract available

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Figures

Figure 1
Figure 1. The Anatomical Heterogeneity of the Brain and Its Subregions
Left: On the top is a surface rending of the cerebral cortex, viewed from above (frontal cortex is at the top of the image). Beneath is a subdivision of the cortex by gyri, each of which has function that differs from the functions of other gyri. The overall volume of the brain only rarely differs between groups of patients and healthy comparison subjects, even though several gyri may differ in volume substantially between groups. Therefore the overall volume of the brain obviously is unlikely either to discriminate groups or to reveal the neural circuits involved in pathogenesis of the disorder under study. Middle and Right: Similar difficulties plague the use of overall volumes of brain subregions. Even relatively small subregions, such as the amygdala and hippocampus (middle) have numerous subregions, each of which has a specific and unique function that differs from the functions of other subregions (the drawing of the right amygdala in the bottom middle images is coronal section, viewed from the front; the drawing of the left hippocampus adjacent to it is show from above). Even regions such as the thalamus, which have a quite uniform appearance on anatomical images (green in top-right most image), is anatomically and functionally highly heterogeneous (green outlines in bottom-right most image). Therefore, the overall volumes of these regions are also unlikely to discriminate groups or reveal the neural circuits involved in the disorder.
Figure 2
Figure 2. Finer-Grained Analyses of the Morphological Features of the Hippocampus
Left: Shown is a whisker plot for the overall volumes of the right hippocampus in 81 persons with schizophrenia and in 30 healthy comparison participants. Although average overall volumes are smaller in the schizophrenia group than in the healthy controls, the distributions of volumes overlap substantially, making overall volume a poor diagnostic discriminator across groups. Right: Morphological features are compared across these same groups of participants at each point along the surface of the right hippocampus. The reduction in overall volume of this region can be seen to derive from highly significant local reductions in volume (represented in purple, p’s<0.001) over the head and tail of the hippocampus. These local reductions in volume are somewhat offset, however, by significant increases in local volume (red, p’s<0.001) over the anterior body of the hippocampus. Local volumes in the midbody of the hippocampus do not differ significantly across groups (green) (B. Peterson and B. Wexler, in preparation). These differing morphological effects in differing subregions of the hippocampus likely represents differing involvement of these subregions, and the neural circuits of which they are a part, in the pathophysiology of schizophrenia.
Figure 3
Figure 3. Brain Deformations Used in Fine-Grained Comparisons of Morphology Across Diagnostic Groups
This figure depicts one of several common strategies used to compare morphological features across diagnostic groups at each point on the surface of the brain. Similar procedures are used for analyses of brain subregions, such as the amygdala, hippocampus, thalamus, and basal ganglia. Left: The brain of each participant in the study is first brought into rough approximation with the template brain using a linear rigid body transformation (not shown). Then a nonlinear transformation warps each point of the participant’s brain (lower left) to corresponding points of the template brain (upper middle). The warping affects each portion of the brain differently and is visualized here by showing the effects on a rectangular grid overlaid on an outline of the unwarped participant’s brain (upper left). When warped using this nonlinear transformation, the participant’s brain is identical in appearance to the template brain (lower middle), permitting each point in the template brain (shown in the numbers placed at the surface of the brain) to be mapped to the corresponding point in the participant’s brain. The nonlinear warping that was applied to the participant’s brain is now precisely reversed (represented by the undistorted rectangular grid in the upper right of this panel) to bring the participant’s brain back to its original shape, but bringing along during the reverse warping all of the template labels assigned to each point in the participant’s brain (lower right). Right: The corresponding labeled points are shown at the contours representing the surfaces of the participant’s (white) and template (red) brains in their original shapes after reversal of the nonlinear warping that was used to coregister the two brains. At some points, the surface of the participant’s brain will protrude out from the surface of the template brain, yielding a positive numerical distance, and at other it will be indented relative to the surface of the template brain, yielding a negative numerical distance from the template. When these same procedures are applied to the brains of all the participants in a study, each point at the surface will have associated with it a population of positive and negative numbers, one for each participant in the study. Conventional statistical analyses are then used to compare those values across diagnostic groups to indicate whether the surfaces of the brains from one diagnostic group on average protrudes significantly relative to the surfaces of the brains from the other diagnostic group (significantly more positive values of distances from the template brain at that point) or whether the surfaces of the first group are significantly indented relative to the surfaces of the other group (significantly more negative values of distances from the template brain). Additionally, the numerical values for these distances from the template surface can be correlated with clinical or demographic measures within a diagnostic group to determine how local morphological features vary systematically with that clinical or demographic variable.
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References

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