Validating cortical surface analysis of medial prefrontal cortex.
This paper describes cortical analysis of 19 high resolution MRI subvolumes of medial prefrontal cortex (MPFC), a region that has been implicated in major depressive disorder. An automated Bayesian segmentation is used to delineate the MRI subvolumes into cerebrospinal fluid (CSF), gray matter (GM), white matter (WM), and partial volumes of either CSF/GM or GM/WM. The intensity value at which there is equal probability of GM and GM/WM partial volume is used to reconstruct MPFC cortical surfaces based on a 3-D isocontouring algorithm. The segmented data and the generated surfaces are validated by comparison with hand segmented data and semiautomated contours, respectively. The L(1) distances between Bayesian and hand segmented data are 0.05-0.10 (n = 5). Fifty percent of the voxels of the reconstructed surface lie within 0.12-0.28 mm (n = 14) from the semiautomated contours. Cortical thickness metrics are generated in the form of frequency of occurrence histograms for GM and WM labelled voxels as a function of their position from the cortical surface. An algorithm to compute the surface area of the GM/WM interface of the MPFC subvolume is described. These methods represent a novel approach to morphometric chacterization of regional cortex features which may be important in the study of psychiatric disorders such as major depression.
Ratnanather
J T
JT
Center for Imaging Science, The Johns Hopkins University, Baltimore, Maryland 21218-2686, USA.
Botteron
K N
KN
Nishino
T
T
Massie
A B
AB
Lal
R M
RM
Patel
S G
SG
Peddi
S
S
Todd
R D
RD
Miller
M I
MI
D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D002508:Cephalometry; D002555:Cerebrospinal Fluid / Q000502:physiology; D003865:Major Depressive Disorder / Q000473:pathology*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging; D017397:Prefrontal Cortex / Q000473:pathology*; D012016:Reference Values; D012680:Sensitivity and Specificity