Support vector machine classification of Major Depressive Disorder using diffusion-weighted neuroimaging and graph theory

Recently there has been considerable interest in understanding brain networks in Major Depressive Disorder (MDD). Neural pathways can be tracked in the living brain using diffusion weighted imaging (DWI); graph theory can then be used to study properties of the resulting fiber networks. To date, global abnormalities have not been reported in tractography-based graph metrics in MDD, so we used a machine learning approach based on ‘support vector machines’ to differentiate depressed from healthy individuals based on multiple brain network properties. We also assessed how important specific graph metrics were for this differentiation. Finally, we conducted a local graph analysis to identify abnormal connectivity at specific nodes of the network. We were able to classify depression using whole-brain graph metrics. Small-worldness was the most useful graph metric for classification. The right pars orbitalis, right inferior parietal cortex, and left rostral anterior cingulate all showed abnormal network connectivity in MDD. This is the first use of structural global graph metrics to classify depressed individuals. These findings highlight the importance of future research to understand network properties in depression across imaging modalities, improve classification results, and relate network alterations to psychiatric symptoms, medication, and co-morbidities

Comparison of 9 Tractography Algorithms for Detecting Abnormal Structural Brain Networks in Alzheimer’s Disease

Alzheimer’s disease (AD) involves a gradual breakdown of brain connectivity, and network analyses offer a promising new approach to track and understand disease progression. Even so, our ability to detect degenerative changes in brain networks depends on the methods used. Here we compared several tractography and feature extraction methods to see which ones gave best diagnostic classification for 202 people with AD, mild cognitive impairment (MCI) or normal cognition, scanned with 41-gradient diffusion-weighted MRI as part of the ADNI project. We computed brain networks based on whole brain tractography with 9 different methods – 4 of them tensor-based deterministic (FACT, RK2, SL, and TL), two ODF-based deterministic (FACT, RK2), two ODF-based probabilistic approaches (Hough and PICo) and one ball-and-stick approach (Probtrackx). Brain networks derived from different tractography algorithms did not differ in terms of classification performance on ADNI, but performing PCA on networks helped classification in some cases. Small differences may still be detectable in a truly vast cohort, but these experiments help assess the relative advantages of different tractography algorithms, and different post-processing choices, when used for classification