41 citations · 56 across the 17 of their papers we have counts for
18 papers
Bayesian brain mapping: a population-informed framework for personalized functional network topography and connectivity
Diego Derman, Nohelia Da Silva Sanchez, Damon D. Pham +3
The spatial topography of functional brain organization is increasingly recognized to play an important role in cognition and disease. Accounting for individual differences in func…
Fast Bayesian estimation of brain activation with cortical surface and subcortical fMRI data using EM
Daniel Spencer, David Bolin, Mary Beth Nebel +1
Analysis of brain imaging scans is critical to understanding the way the human brain functions, which can be leveraged to treat injuries and conditions that affect the quality of l…
Less is more: balancing noise reduction and data retention in fMRI with data-driven scrubbing
Damon Pham, Daniel McDonald, Lei Ding +2
Artifacts in functional MRI (fMRI) data cause deviations from common distributional assumptions, introduce spatial and temporal outliers, and reduce the signal-to-noise ratio of th…
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 Joint Network Optimization Framework to Predict Clinical Severity from Resting State Functional MRI Data
Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas Wymbs +2
We propose a novel optimization framework to predict clinical severity from resting state fMRI (rs-fMRI) data. Our model consists of two coupled terms. The first term decomposes th…
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 +4
We propose a novel integrated framework that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tr…
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 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 +4
We propose an integrated deep-generative framework, that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imag…
Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data
Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas Wymbs +2
We propose a unified optimization framework that combines neural networks with dictionary learning to model complex interactions between resting state functional MRI and behavioral…
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…
A Generative-Discriminative Basis Learning Framework to Predict Clinical Severity from Resting State Functional MRI Data
Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas Wymbs +2
We propose a matrix factorization technique that decomposes the resting state fMRI (rs-fMRI) correlation matrices for a patient population into a sparse set of representative subne…
Effects of Scan Length and Shrinkage on Reliability of Resting-State Functional Connectivity in the Human Connectome Project
Amanda F. Mejia, Mary Beth Nebel, Anita D. Barber +2
In this paper, we use data from the Human Connectome Project (N=461) to investigate the effect of scan length on reliability of resting-state functional connectivity (rsFC) estimat…
PCA leverage: outlier detection for high-dimensional functional magnetic resonance imaging data
Amanda F. Mejia, Mary Beth Nebel, Ani Eloyan +2
Outlier detection for high-dimensional (HD) data is a popular topic in modern statistical research. However, one source of HD data that has received relatively little attention is…
Improving Reliability of Subject-Level Resting-State fMRI Parcellation with Shrinkage Estimators
Amanda F. Mejia, Mary Beth Nebel, Haochang Shou +5
A recent interest in resting state functional magnetic resonance imaging (rsfMRI) lies in subdividing the human brain into anatomically and functionally distinct regions of interes…
Homotopic Group ICA for Multi-Subject Brain Imaging Data
Juemin Yang, Ani Eloyan, Anita Barber +5
Independent Component Analysis (ICA) is a computational technique for revealing latent factors that underlie sets of measurements or signals. It has become a standard technique in…