Robust Detection of Dynamic Community Structure in Networks - Danielle S. Bassett; Mason A. Porter; Nicholas F. Wymbs; Scott T. Grafton; Jean M. Carlson; Peter J. Mucha (CoRR, 2012)

Dynamic network centrality summarizes learning in the human brain. - Alexander V. Mantzaris; Danielle S. Bassett; Nicholas F. Wymbs; Ernesto Estrada; Mason A. Porter; Peter J. Mucha; Scott T. Grafton; Desmond J. Higham (J. Complex Networks, 2013)

Task-Based Core-Periphery Organization of Human Brain Dynamics. - Danielle S. Bassett; Nicholas F. Wymbs; M. Puck Rombach; Mason A. Porter; Peter J. Mucha; Scott T. Grafton (PLoS Comput. Biol., 2013)

Reward gain model describes cortical use-dependent plasticity. - Firas Mawase; Nicholas F. Wymbs; Shintaro Uehara; Pablo Celnik (EMBC, 2016)

Graph Frequency Analysis of Brain Signals. - Weiyu Huang; Leah Goldsberry; Nicholas F. Wymbs; Scott T. Grafton; Danielle S. Bassett; Alejandro Ribeiro (IEEE J. Sel. Top. Signal Process., 2016)

Brain signal analytics from graph signal processing perspective. - Leah Goldsberry; Weiyu Huang; Nicholas F. Wymbs; Scott T. Grafton; Danielle S. Bassett; Alejandro Ribeiro (ICASSP, 2017)

A Unified Bayesian Approach to Extract Network-Based Functional Differences from a Heterogeneous Patient Cohort. - Archana Venkataraman; Nicholas F. Wymbs; Mary Beth Nebel; Stewart Mostofsky (CNI@MICCAI, 2017)

A Generative-Discriminative Basis Learning Framework to Predict Clinical Severity from Resting State Functional MRI Data. - Niharika Shimona D'Souza; Mary Beth Nebel; Nicholas F. Wymbs; Stewart Mostofsky; Archana Venkataraman (MICCAI (3), 2018)

Beyond modularity: Fine-scale mechanisms and rules for brain network reconfiguration. - Ankit N. Khambhati; Marcelo Gomes Mattar; Nicholas F. Wymbs; Scott T. Grafton; Danielle S. Bassett (NeuroImage, 2018)

Predicting future learning from baseline network architecture. - Marcelo Gomes Mattar; Nicholas F. Wymbs; Andrew S. Bock; Geoffrey Karl Aguirre; Scott T. Grafton; Danielle S. Bassett (NeuroImage, 2018)

Brain state flexibility accompanies motor-skill acquisition. - Pranav G. Reddy; Marcelo Gomes Mattar; Andrew C. Murphy; Nicholas F. Wymbs; Scott T. Grafton; Theodore D. Satterthwaite; Danielle S. Bassett (NeuroImage, 2018)

A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces. - Niharika Shimona D'Souza; Mary Beth Nebel; Nicholas F. Wymbs; Stewart Mostofsky; Archana Venkataraman (IPMI, 2019)

Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data. - Niharika Shimona D'Souza; Mary Beth Nebel; Nicholas F. Wymbs; Stewart Mostofsky; Archana Venkataraman (MICCAI (3), 2019)

A Minimum Free Energy Model of Motor Learning. - Brian A. Mitchell; Nina Lauharatanahirun; Javier O. Garcia; Nicholas F. Wymbs; Scott T. Grafton; Jean M. Vettel; Linda R. Petzold (Neural Comput., 2019)

A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism. - Niharika Shimona D'Souza; Mary Beth Nebel; Deana Crocetti; Nicholas F. Wymbs; Joshua Robinson; Stewart Mostofsky; Archana Venkataraman (MICCAI (7), 2020)

A joint network optimization framework to predict clinical severity from resting state functional MRI data. - Niharika Shimona D'Souza; Mary Beth Nebel; Nicholas F. Wymbs; Stewart H. Mostofsky; Archana Venkataraman (NeuroImage, 2020)

A Matrix Autoencoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes. - Niharika Shimona D'Souza; Mary Beth Nebel; Deana Crocetti; Nicholas F. Wymbs; Joshua Robinson; Stewart Mostofsky; Archana Venkataraman (CoRR, 2021)

Deep sr-DDL: Deep structurally regularized dynamic dictionary learning to integrate multimodal and dynamic functional connectomics data for multidimensional clinical characterizations. - Niharika Shimona D'Souza; Mary Beth Nebel; Deana Crocetti; Joshua Robinson; Nicholas F. Wymbs; Stewart H. Mostofsky; Archana Venkataraman (NeuroImage, 2021)