Cortical surface modeling reveals gross morphometric correlates of individual differences

Quantification of brain structure is important for evaluating changes in brain size with growth and aging and for characterizing neurodegeneration disorders. Previous quantification efforts using ex vivo techniques suffered considerable error due to shrinkage of the cerebrum after extraction from the skull, deformation of slices during sectioning, and numerous other factors. In vivo imaging studies of brain anatomy avoid these problems and allow repetitive studies following progression of brain structure changes due to disease or natural processes. We have developed a methodology for obtaining triangular mesh models of the cortical surface from MRI brain datasets. The cortex is segmented from nonbrain tissue using a 2D region-growing technique combined with occasional manual edits. Once segmented, thresholding and image morphological operations (erosions and openings) are used to expose the regions between adjacent surfaces in deep cortical folds. A 2D region-following procedure is then used to find a set of contours outlining the cortical boundary on each slice. The contours on all slices are tiled together to form a closed triangular mesh model approximating the cortical surface. This model can be used for calculation of cortical surface area and volume, as well as other parameters of interest. Except for the initial segmentation of the cortex from the skull, the technique is automatic and requires only modest computation time on modern workstations.Though the use of image data avoids many of the pitfalls of ex vivo and sectioning techniques, our MRI-based technique is still vulnerable to errors that may impact the accuracy of estimated brain structure parameters. Potential inaccuracies include segmentation errors due to incorrect thresholding, missed deep sulcal surfaces, falsely segmented holes due to image noise and surface tiling artifacts. The focus of this paper is the characterization of these errors and how they affect measurements of cortical surface area and volume.

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“…al. [8]. In a related surface-based method, Jouandet [9] has proposed a method for projecting the cortical surface onto unfolded 2D maps.…”

Section: Introductionmentioning

confidence: 99%

<title>Sensitivity analysis of brain morphometry based on MRI-derived surface models</title>

et al. 1998

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“…Accordingly, many groups have noted the importance of probabilistic atlases and have made probability maps using different modalities and different methodologies. Probability maps for subregions of normal brains have been reported (Amunts et al, 1999(Amunts et al, , 2000Kennedy et al, 1998;Leonard et al, 1998;Loftus et al, 1995;Paus et al, 1996;Penhune et al, 1996;Rademacher et al, 2001aRademacher et al, ,b, 2002Thompson et al, 1996a;Tomaiuolo et al, 1999;Varnavas and Grand, 1999;Westbury et al, 1999;White et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

An MRI study of spatial probability brain map differences between first-episode schizophrenia and normal controls

Levitt¹,

Shenton²,

Salisbury³

et al. 2004

NeuroImage

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We created a spatial probability atlas of schizophrenia to provide information about the neuroanatomic variability of brain regions of patients with the disorder. Probability maps of 16 regions of interest (ROIs) were constructed by taking manually parcellated ROIs from subjects' magnetic resonance images (MRIs) and linearly transforming them into Talairach space using the Montreal Neurological Institute (MNI) template. ROIs included temporal, parietal, and prefrontal cortex subregions, with a principal focus on temporal lobe structures. Subject Ns ranged from 11 to 28 for the different ROIs. Our global measure of the spatial distribution of the transformed ROI was the sum of voxels with 50% overlap among subjects. The superior temporal gyrus (STG) and fusiform gyrus (FG) had lower values for schizophrenic subjects than for normal controls, suggestive of greater spatial variability for these ROIs in schizophrenic subjects. For the computation of statistical significance of group differences in portions of the ROI, we used voxel-wise comparisons and Fisher's exact test. First-episode schizophrenic patients compared with controls showed lower probability (P < 0.05) at dorso-posterior areas of planum temporale and Heschl's gyrus, lateral and anterior regions in the left hippocampus (HIPP), and dorsolateral regions of fusiform gyrus. Importantly, most ROIs of schizophrenic subjects showed a significantly lower spatial overlap than controls, even after nonlinear spatial normalization, suggesting a greater heterogeneity in the spatial distribution of ROIs. There is consequently a need for caution in neuroimaging studies where data from schizophrenic subjects are normalized to a particular stereotaxic coordinate system based on healthy controls. Apparent group differences in activation may simply reflect a greater heterogeneity of spatial distribution in schizophrenia.

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Mapping Mental Illness

Mazziotta¹

1996

Arch Gen Psychiatry

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Cortical surface modeling reveals gross morphometric correlates of individual differences

Cited by 14 publications

References 61 publications

<title>Sensitivity analysis of brain morphometry based on MRI-derived surface models</title>

<title>Sensitivity analysis of brain morphometry based on MRI-derived surface models</title>

An MRI study of spatial probability brain map differences between first-episode schizophrenia and normal controls

Mapping Mental Illness

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