The corpus callosum is the largest white matter tract in the brain, interconnecting the left and right hemispheres. In spite of its significance, its unusual shape has led to controversies regarding its morphological characteristics and their relevance. Dr. Ardekani’s group has introduced methodological improvements to characterizing corpus callosum morphology including the introduction of a new metric, circularity, into this field of study. Software ‘yuki’ for measurement of corpus callosum morphology has been developed by the group and made publicly available to the research community. Several papers have been published in high-impact journals by researchers utilizing this software. ‘yuki’ is currently believed to be the best corpus callosum segmentation software publicly available in terms of speed, accuracy, robustness, reliability and ease of use.
While considerable obstacles still remain towards a practical computer-based system for diagnosis of psychiatric disorders, the group has applied pattern recognition and machine learning methods for automated diagnosis of schizophrenia based on the patterns of water diffusion in the brain measure using diffusion tensor imaging. After training the algorithm on 50 cases with known diagnoses (25 schizophrenia and 25 normal controls), the algorithm was presented with 50 new cases without specifying the diagnoses. The program was able to classify 49 cases correctly. Dr. Ardekani’s group has also published research that used a Support Vector Machine algorithm to classify cognitively normal individuals from AD patients with 97% accuracy. In addition, a Random Forest classifier was trained to predict conversion from mild cognitive impairment to AD with over 80% accuracy.
The hippocampus is a component of the medial temporal lobe limbic system and plays a central role in the formation, consolidation and retention of recent (or declarative) memory. Atrophy of the hippocampus occurs early in the pathogenesis of AD, which can be detected by structural MRI. Thus, hippocampus atrophy above age expectation has been proposed as a core neuroimaging biomarker of AD. However, measurement of hippocampal atrophy has been challenging in the past. Dr. Ardekani’s group has developed ‘kaiba’ a fully automatic and rapid technique for measuring an index of hippocampal volumetric integrity (HVI) from 3D T1-weighted MRI scans. The group has shown that the bilateral HVI and its time rate of change can be used to reliable differentiate between normal aging, mild cognitive impairment and AD.