18 citations · 42 across the 15 of their papers we have counts for
16 papers
MSR-IVA: Masked Structural Residual Independent Vector Analysis for State-Aware Fusion of Structural MRI and Dynamic Functional Network Connectivity
Victor Solomon, Zening Fu, Rafal Angryk +2
Multimodal fusion of structural MRI (sMRI) and dynamic functional network connectivity (dFNC) can reveal how brain structure relates to changing functional states. When the same st…
An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease
Giorgio Dolci, Federica Cruciani, Md Abdur Rahaman +6
\textbf{Objective:} Alzheimer's disease (AD) is the most prevalent form of dementia worldwide, encompassing a prodromal stage known as Mild Cognitive Impairment (MCI), where patien…
An Interpretable Cross-Attentive Multi-modal MRI Fusion Framework for Schizophrenia Diagnosis
Ziyu Zhou, Anton Orlichenko, Gang Qu +4
Both functional and structural magnetic resonance imaging (fMRI and sMRI) are widely used for the diagnosis of mental disorder. However, combining complementary information from th…
Multiscale Neuroimaging Features for the Identification of Medication Class and Non-Responders in Mood Disorder Treatment
Bradley T. Baker, Mustafa S. Salman, Zening Fu +4
In the clinical treatment of mood disorders, the complex behavioral symptoms presented by patients and variability of patient response to particular medication classes can create d…
Self-Supervised Mental Disorder Classifiers via Time Reversal
Zafar Iqbal, Usman Mahmood, Zening Fu +1
Data scarcity is a notable problem, especially in the medical domain, due to patient data laws. Therefore, efficient Pre-Training techniques could help in combating this problem. I…
MultiCrossViT: Multimodal Vision Transformer for Schizophrenia Prediction using Structural MRI and Functional Network Connectivity Data
Yuda Bi, Anees Abrol, Zening Fu +1
Vision Transformer (ViT) is a pioneering deep learning framework that can address real-world computer vision issues, such as image classification and object recognition. Importantl…
Prediction of Gender from Longitudinal MRI data via Deep Learning on Adolescent Data Reveals Unique Patterns Associated with Brain Structure and Change over a Two-year Period
Yuda Bi, Anees Abrol, Zening Fu +3
Deep learning algorithms for predicting neuroimaging data have shown considerable promise in various applications. Prior work has demonstrated that deep learning models that take a…
Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series
Usman Mahmood, Zening Fu, Vince Calhoun +1
Recently, methods that represent data as a graph, such as graph neural networks (GNNs) have been successfully used to learn data representations and structures to solve classificat…
A deep learning model for data-driven discovery of functional connectivity
Usman Mahmood, Zening Fu, Vince Calhoun +1
Functional connectivity (FC) studies have demonstrated the overarching value of studying the brain and its disorders through the undirected weighted graph of fMRI correlation matri…
Multi network InfoMax: A pre-training method involving graph convolutional networks
Usman Mahmood, Zening Fu, Vince Calhoun +1
Discovering distinct features and their relations from data can help us uncover valuable knowledge crucial for various tasks, e.g., classification. In neuroimaging, these features…
Brain dynamics via Cumulative Auto-Regressive Self-Attention
Usman Mahmood, Zening Fu, Vince Calhoun +1
Multivariate dynamical processes can often be intuitively described by a weighted connectivity graph between components representing each individual time-series. Even a simple repr…
Fusing multimodal neuroimaging data with a variational autoencoder
Eloy Geenjaar, Noah Lewis, Zening Fu +3
Neuroimaging studies often involve the collection of multiple data modalities. These modalities contain both shared and mutually exclusive information about the brain. This work ai…
Whole MILC: generalizing learned dynamics across tasks, datasets, and populations
Usman Mahmood, Md Mahfuzur Rahman, Alex Fedorov +4
Behavioral changes are the earliest signs of a mental disorder, but arguably, the dynamics of brain function gets affected even earlier. Subsequently, spatio-temporal structure of…
Learnt dynamics generalizes across tasks, datasets, and populations
U. Mahmood, M. M. Rahman, A. Fedorov +3
Differentiating multivariate dynamic signals is a difficult learning problem as the feature space may be large yet often only a few training examples are available. Traditional app…
Transfer Learning of fMRI Dynamics
Usman Mahmood, Md Mahfuzur Rahman, Alex Fedorov +2
As a mental disorder progresses, it may affect brain structure, but brain function expressed in brain dynamics is affected much earlier. Capturing the moment when brain dynamics ex…
Prediction of Progression to Alzheimer's disease with Deep InfoMax
Alex Fedorov, R Devon Hjelm, Anees Abrol +4
Arguably, unsupervised learning plays a crucial role in the majority of algorithms for processing brain imaging. A recently introduced unsupervised approach Deep InfoMax (DIM) is a…