no code implementations • 15 Jan 2023 • Niharika Shimona D'Souza
In this work, we approach this problem from the lens of representation learning and bayesian modeling.
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 • 17 Nov 2020 • Naresh Nandakumar, Niharika Shimona D'Souza, Komal Manzoor, Jay J. Pillai, Sachin K. Gujar, Haris I. Sair, Archana Venkataraman
We present a novel deep learning framework that uses dynamic functional connectivity to simultaneously localize the language and motor areas of the eloquent cortex in brain tumor patients.
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.
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.
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.