Effect of SSRIs on Resting-State Functional Brain Networks in Adolescents with Major Depressive Disorder.

Investigation of brain changes in functional connectivity and functional network topology from receiving 8-week selective serotonin reuptake inhibitor (SSRI) treatments is conducted in 12 unmedicated adolescents with major depressive disorder (MDD) by using wavelet-filtered resting-state functional magnetic resonance imaging (fMRI). Changes are observed in frontal-limbic, temporal, and default mode networks. In particular, topological analysis shows, at the global scale and in the 0.

Ranking Regions, Edges and Classifying Tasks in Functional Brain Graphs by Sub-Graph Entropy.

This paper considers analysis of human brain networks or graphs constructed from time-series collected from functional magnetic resonance imaging (fMRI). In the network of time-series, the nodes describe the regions and the edge weights correspond to the absolute values of correlation coefficients of the time-series of the two nodes associated with the edges. The paper introduces a novel information-theoretic metric, referred as sub-graph entropy, to measure uncertainty associated with a sub-graph.

Biomarkers for Adolescent MDD from Anatomical Connectivity and Network Topology Using Diffusion MRI.

Due to the high resistance (35%) to the current treatment methods in adolescent Major Depressive Disorder (MDD) and its tragic outcomes, the discovery of treatmentrelated responders is critical to developing effective treatments. In this paper, the permutation test is performed to identify statistically significant changes in anatomical characteristics during pairwise comparisons among the control group (n=27), treated MDD group (n=37), and untreated MDD group (n=15). The anatomical characteristics include: 1) anatomical connectivity defined using DTI metrics between a pair of brain regions, and 2) topological measurements of anatomical networks.

Classifying Treated vs. Untreated MDD Adolescents from Anatomical Connectivity using Nonlinear SVM.

Identification of the treatment-related responders for adolescent Major Depressive Disorder (MDD) is urgently needed to develop effective treatments. In this paper, machine learning based classifiers are used to reveal anatomical features as responders for distinguishing MDD patients who have received treatment from those who never received any treatment. The features are drawn from two sets of measurements: 1) anatomical connectivity defined by diffusion tensor imaging measurements between a pair of brain regions, and 2) topological measurements from anatomical networks.

Function-specific and Enhanced Brain Structural Connectivity Mapping via Joint Modeling of Diffusion and Functional MRI.

A joint structural-functional brain network model is presented, which enables the discovery of function-specific brain circuits, and recovers structural connections that are under-estimated by diffusion MRI (dMRI). Incorporating information from functional MRI (fMRI) into diffusion MRI to estimate brain circuits is a challenging task. Usually, seed regions for tractography are selected from fMRI activation maps to extract the white matter pathways of interest.