2 citations · 4 across the 6 of their papers we have counts for
9 papers
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 +4
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 su…
Group Linear non-Gaussian Component Analysis with Applications to Neuroimaging
Yuxuan Zhao, David S. Matteson, Mary Beth Nebel +2
Independent component analysis (ICA) is an unsupervised learning method popular in functional magnetic resonance imaging (fMRI). Group ICA has been used to search for biomarkers in…
A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces
Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas Wymbs +2
The problem of linking functional connectomics to behavior is extremely challenging due to the complex interactions between the two distinct, but related, data domains. We propose…
A spatial template independent component analysis model for subject-level brain network estimation and inference
Amanda F. Mejia, David Bolin, Yu Ryan Yue +3
Independent component analysis is commonly applied to functional magnetic resonance imaging (fMRI) data to extract independent components (ICs) representing functional brain networ…
Neuropsychiatric Disease Classification Using Functional Connectomics -- Results of the Connectomics in NeuroImaging Transfer Learning Challenge
Markus D. Schirmer, Archana Venkataraman, Islem Rekik +23
Large, open-source consortium datasets have spurred the development of new and increasingly powerful machine learning approaches in brain connectomics. However, one key question re…
Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks using Big Data Population Priors
Amanda F. Mejia, Mary Beth Nebel, Yikai Wang +2
Large brain imaging databases contain a wealth of information on brain organization in the populations they target, and on individual variability. While such databases have been us…