Lihong Wang

TL;DR: The 1000 Functional Connectomes Project (Fcon_1000) as discussed by the authors is a large-scale collection of functional connectome data from 1,414 volunteers collected independently at 35 international centers.

TL;DR: This study attempts to integrate information from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) for improving classification performance and indicates that the multimodality classification approach yields statistically significant improvement in accuracy over using each modality independently.

TL;DR: It was found that the reliability of volume measures including percent volume difference, percent volume overlap, and intraclass correlation coefficient, varied substantially across brain regions, and sample size estimates for detecting changes in brain volume for a range of likely effect sizes also differed by region.

TL;DR: This work proposes an effective network-based multivariate classification algorithm, using a collection of measures derived from white matter (WM) connectivity networks, to accurately identify MCI patients from normal controls and found that portions of the prefrontal cortex, orbitofrontal cortex, parietal lobe and insula regions provided the most discriminant features for classification, in line with results reported in previous studies.

TL;DR: Direct evidence of an alteration in the neural systems that interplay cognition with mood in MDD is provided, confirming a role of this region in coping with emotional distraction.