no code implementations • 11 Oct 2023 • Benjamin Cichy, Jamie Lukos, Mohammad Alam, J. Cortney Bradford, Nicholas Wymbs
While our present understanding of DNN usage for BCI is promising, we have little experience in deciphering neural events from dynamic freely-mobile situations.
1 code implementation • 30 May 2021 • Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti, Nicholas Wymbs, Joshua Robinson, Stewart Mostofsky, Archana Venkataraman
We propose a novel matrix autoencoder to map functional connectomes from resting state fMRI (rs-fMRI) to structural connectomes from Diffusion Tensor Imaging (DTI), as guided by subject-level phenotypic measures.
no code implementations • 27 Aug 2020 • Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas Wymbs, Stewart H. Mostofsky, Archana Venkataraman
Our model consists of two coupled terms.
no code implementations • 27 Aug 2020 • Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti, Nicholas Wymbs, Joshua Robinson, Stewart H. Mostofsky, Archana Venkataraman
The generative component is a structurally-regularized Dynamic Dictionary Learning (sr-DDL) model that decomposes the dynamic rs-fMRI correlation matrices into a collection of shared basis networks and time varying subject-specific loadings.
no code implementations • 3 Jul 2020 • Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas Wymbs, Stewart Mostofsky, Archana Venkataraman
The dictionary learning objective decomposes patient correlation matrices into a collection of shared basis networks and subject-specific loadings.
1 code implementation • 3 Jul 2020 • Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti, Nicholas Wymbs, Joshua Robinson, Stewart Mostofsky, Archana Venkataraman
The generative part of our framework is a structurally-regularized Dynamic Dictionary Learning (sr-DDL) model that decomposes the dynamic rs-fMRI correlation matrices into a collection of shared basis networks and time varying patient-specific loadings.
no code implementations • 3 Jul 2020 • Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas Wymbs, Stewart Mostofsky, Archana Venkataraman
We propose a coupled manifold optimization framework which projects fMRI data onto a low dimensional matrix manifold common to the cohort.