Dysfunctional "Insight" Network in BDD and AN
       
     
Connectomes, cognition, and Bipolar Disorder
       
     
       
     
PLACE and MDD
       
     
Constructing the dynamic phase-space from EEG connectomes
       
     
Phase Angle Spatial Embedding (PhASE)
       
     
Dysfunctional "Insight" Network in BDD and AN
       
     
Dysfunctional "Insight" Network in BDD and AN

Altered brain network community structure in body dysmorphic disorder and anorexia nervosa

Connectomes, cognition, and Bipolar Disorder
       
     
Connectomes, cognition, and Bipolar Disorder

Connectomic properties of Brodmann area 47 are correlated with cognitive measures in euthymic bipolar disorders

       
     
LLD Hubs

Connectomic studies in late-life depression demonstrate altered network hubs (centrality) in the default mode network and limbic brain regions.

PLACE and MDD
       
     
PLACE and MDD

Using our novel method for modularity,  Path Length Associated Community Estimation (PLACE), depressed subjects demonstrated altered community affiliation of the posterior default mode network (precuneus)..

Constructing the dynamic phase-space from EEG connectomes
       
     
Constructing the dynamic phase-space from EEG connectomes

Dynamic connectomes are generated from EEG data using a sliding window approach. Each connectome (from all subjects and all time points), mathematically defined as a 34 by 34 matrix, is mapped onto a point on a manifold that is embedded in a high dimensional space (here the manifold is depicted as a Swiss roll, a 2-dimensional structure that is rolled up and embedded in 3D). +-Thus, the distance in the high dimensional space (straight arrow) is not the manifold geodesic distance (curved arrow), which encodes the intrinsic geometric features of the manifold. This distance is used to create the neighborhood graph visualized as minimum spanning tree (MST).

Phase Angle Spatial Embedding (PhASE)
       
     
Phase Angle Spatial Embedding (PhASE)

Computing PhASE from rs-fMRI data. Connectivity data is acquired from multiple subjects, and the frequency of edge negativity is computed for each ROI to form a negative probability matrix (NPM). Then, the phase angle ( θ ) is computed for each ROI, which encodes each ROI’s “phase” with every other ROI in the network.