Distinguishing bipolar and major XXXX by brain structural morphometry: a pilot study.

@entity1 @entity162 @entity308 @entity74
BACKGROUND: The clinical presentation of common symptoms during @entity308 episodes in @entity162 ( @entity162 ) and @entity308 ( @entity308 ) poses challenges for accurate diagnosis. @entity74 may aid the development of reliable discrimination between these two clinical conditions. METHODS: For our sample of 16 @entity162 @entity1 , 19 @entity308 @entity1 and 29 healthy volunteers, we adopted vertex-wise cortical based brain imaging techniques to examine cortical thickness and surface area, two components of cortical volume with distinct genetic determinants. Based on specific characteristics of neuroanatomical features, we then used support vector machine (SVM) algorithm to discriminate between @entity1 with @entity162 and @entity308 . RESULTS: Compared to @entity308 @entity1 , @entity162 @entity1 showed significantly larger cortical surface area in the left bankssts, precuneus, precentral, inferior parietal, superior parietal and the right middle temporal gyri. In addition, larger volumes of subcortical regions were found in @entity162 @entity1 . In SVM discriminative analyses, the overall accuracy was 74.3  %, with a sensitivity of 62.5  % and a specificity of 84.2  % (p   =   0.028). Compared to controls, larger surface area in the temporo-parietal regions were observed in @entity162 @entity1 , and thinner cortices in fronto-temporal regions were observed in @entity308 @entity1 , especially in the medial orbito-frontal area. CONCLUSIONS: These findings have demonstrated distinct spatially distributed variations in cortical thickness and surface area in @entity1 with @entity162 and @entity308 , suggesting potentially varying etiological and neuropathological processes in these two conditions. The employment of multimodal classification on disorder-specific biological features has shed light to the development of potential classification tools that could aid diagnostic decisions.
