Nicholas F. Wymbs

Publication Activity (10 Years)

Years Active: 2012-2021
Publications (10 Years): 20

Top Topics

Top Venues

IEEE J. Sel. Top. Signal Process.

Publications

Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti, Nicholas F. Wymbs, Joshua Robinson, Stewart Mostofsky, Archana Venkataraman
A Matrix Autoencoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes. CoRR (2021)
Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti, Joshua Robinson, Nicholas F. Wymbs, Stewart H. Mostofsky, Archana Venkataraman
Deep sr-DDL: Deep structurally regularized dynamic dictionary learning to integrate multimodal and dynamic functional connectomics data for multidimensional clinical characterizations. NeuroImage 241 (2021)
Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas F. Wymbs, Stewart Mostofsky, Archana Venkataraman
Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data. CoRR (2020)
Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas F. Wymbs, Stewart H. Mostofsky, Archana Venkataraman
A joint network optimization framework to predict clinical severity from resting state functional MRI data. NeuroImage 206 (2020)
Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti, Nicholas F. Wymbs, Joshua Robinson, Stewart Mostofsky, Archana Venkataraman
A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism. CoRR (2020)
Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti, Nicholas F. Wymbs, Joshua Robinson, Stewart Mostofsky, Archana Venkataraman
A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism. MICCAI (7) (2020)
Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas F. Wymbs, Stewart H. Mostofsky, Archana Venkataraman
A Joint Network Optimization Framework to Predict Clinical Severity from Resting State Functional MRI Data. CoRR (2020)
Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti, Nicholas F. Wymbs, Joshua Robinson, Stewart H. Mostofsky, Archana Venkataraman
Deep sr-DDL: Deep Structurally Regularized Dynamic Dictionary Learning to Integrate Multimodal and Dynamic Functional Connectomics data for Multidimensional Clinical Characterizations. CoRR (2020)
Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas F. Wymbs, Stewart Mostofsky, Archana Venkataraman
A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces. CoRR (2020)
Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas F. Wymbs, Stewart Mostofsky, Archana Venkataraman
A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces. IPMI (2019)
Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas F. Wymbs, Stewart Mostofsky, Archana Venkataraman
Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data. MICCAI (3) (2019)
Brian A. Mitchell, Nina Lauharatanahirun, Javier O. Garcia, Nicholas F. Wymbs, Scott T. Grafton, Jean M. Vettel, Linda R. Petzold
A Minimum Free Energy Model of Motor Learning. Neural Comput. 31 (10) (2019)
Pranav G. Reddy, Marcelo Gomes Mattar, Andrew C. Murphy, Nicholas F. Wymbs, Scott T. Grafton, Theodore D. Satterthwaite, Danielle S. Bassett
Brain state flexibility accompanies motor-skill acquisition. NeuroImage 171 (2018)
Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas F. Wymbs, Stewart Mostofsky, Archana Venkataraman
A Generative-Discriminative Basis Learning Framework to Predict Clinical Severity from Resting State Functional MRI Data. MICCAI (3) (2018)
Ankit N. Khambhati, Marcelo Gomes Mattar, Nicholas F. Wymbs, Scott T. Grafton, Danielle S. Bassett
Beyond modularity: Fine-scale mechanisms and rules for brain network reconfiguration. NeuroImage 166 (2018)
Marcelo Gomes Mattar, Nicholas F. Wymbs, Andrew S. Bock, Geoffrey Karl Aguirre, Scott T. Grafton, Danielle S. Bassett
Predicting future learning from baseline network architecture. NeuroImage 172 (2018)
Leah Goldsberry, Weiyu Huang, Nicholas F. Wymbs, Scott T. Grafton, Danielle S. Bassett, Alejandro Ribeiro
Brain signal analytics from graph signal processing perspective. ICASSP (2017)
Archana Venkataraman, Nicholas F. Wymbs, Mary Beth Nebel, Stewart Mostofsky
A Unified Bayesian Approach to Extract Network-Based Functional Differences from a Heterogeneous Patient Cohort. CNI@MICCAI (2017)
Weiyu Huang, Leah Goldsberry, Nicholas F. Wymbs, Scott T. Grafton, Danielle S. Bassett, Alejandro Ribeiro
Graph Frequency Analysis of Brain Signals. IEEE J. Sel. Top. Signal Process. 10 (7) (2016)
Firas Mawase, Nicholas F. Wymbs, Shintaro Uehara, Pablo Celnik
Reward gain model describes cortical use-dependent plasticity. EMBC (2016)
Weiyu Huang, Leah Goldsberry, Nicholas F. Wymbs, Scott T. Grafton, Danielle S. Bassett, Alejandro Ribeiro
Graph Frequency Analysis of Brain Signals. CoRR (2015)
Danielle S. Bassett, Nicholas F. Wymbs, M. Puck Rombach, Mason A. Porter, Peter J. Mucha, Scott T. Grafton
Task-Based Core-Periphery Organization of Human Brain Dynamics. PLoS Comput. Biol. 9 (9) (2013)
Alexander V. Mantzaris, Danielle S. Bassett, Nicholas F. Wymbs, Ernesto Estrada, Mason A. Porter, Peter J. Mucha, Scott T. Grafton, Desmond J. Higham
Dynamic network centrality summarizes learning in the human brain. J. Complex Networks 1 (1) (2013)
Danielle S. Bassett, Mason A. Porter, Nicholas F. Wymbs, Scott T. Grafton, Jean M. Carlson, Peter J. Mucha
Robust Detection of Dynamic Community Structure in Networks CoRR (2012)