Search Results for author: Vince D. Calhoun

Found 49 papers, 14 papers with code

MoRE-Brain: Routed Mixture of Experts for Interpretable and Generalizable Cross-Subject fMRI Visual Decoding

no code implementations21 May 2025 Yuxiang Wei, Yanteng Zhang, Xi Xiao, Tianyang Wang, Xiao Wang, Vince D. Calhoun

Consequently, MoRE-Brain marks a substantial advance towards more generalizable and interpretable fMRI-based visual decoding.

Mixture-of-Experts

Unified Cross-Modal Attention-Mixer Based Structural-Functional Connectomics Fusion for Neuropsychiatric Disorder Diagnosis

no code implementations21 May 2025 Badhan Mazumder, Lei Wu, Vince D. Calhoun, Dong Hye Ye

Gaining insights into the structural and functional mechanisms of the brain has been a longstanding focus in neuroscience research, particularly in the context of understanding and treating neuropsychiatric disorders such as Schizophrenia (SZ).

Diagnostic Multimodal Deep Learning

Physics-Guided Multi-View Graph Neural Network for Schizophrenia Classification via Structural-Functional Coupling

no code implementations21 May 2025 Badhan Mazumder, Ayush Kanyal, Lei Wu, Vince D. Calhoun, Dong Hye Ye

Clinical studies reveal disruptions in brain structural connectivity (SC) and functional connectivity (FC) in neuropsychiatric disorders such as schizophrenia (SZ).

Functional Connectivity Graph Neural Network

4D Multimodal Co-attention Fusion Network with Latent Contrastive Alignment for Alzheimer's Diagnosis

no code implementations23 Apr 2025 Yuxiang Wei, Yanteng Zhang, Xi Xiao, Tianyang Wang, Xiao Wang, Vince D. Calhoun

Multimodal neuroimaging provides complementary structural and functional insights into both human brain organization and disease-related dynamics.

Diagnostic

Functional Correspondences in the Human and Marmoset Visual Cortex During Movie Watching: Insights from Correlation, Redundancy, and Synergy

no code implementations19 Mar 2025 Qiang Li, Ting Xu, Vince D. Calhoun

The world of beauty is deeply connected to the visual cortex, as perception often begins with vision in both humans and marmosets.

A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis

no code implementations3 Feb 2025 Yipu Zhang, Likai Wang, Kuan-Jui Su, Aiying Zhang, Hao Zhu, Xiaowen Liu, Hui Shen, Vince D. Calhoun, Yuping Wang, HongWen Deng

Resting-state functional magnetic resonance imaging (rs-fMRI) and its derived functional connectivity networks (FCNs) have become critical for understanding neurological disorders.

Contrastive Learning Disentanglement +5

A Deep Spatio-Temporal Architecture for Dynamic Effective Connectivity Network Analysis Based on Dynamic Causal Discovery

no code implementations31 Jan 2025 Faming Xu, Yiding Wang, Chen Qiao, Gang Qu, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson, Yu-Ping Wang

Dynamic effective connectivity networks (dECNs) reveal the changing directed brain activity and the dynamic causal influences among brain regions, which facilitate the identification of individual differences and enhance the understanding of human brain.

Causal Discovery

Correlation of Correlation Networks: High-Order Interactions in the Topology of Brain Networks

no code implementations1 Nov 2024 Qiang Li, Jingyu Liu, Vince D. Calhoun

Moreover, after applying topological network analysis to the correlation of correlation networks, we observed that some high-order interaction hubs predominantly occurred in primary and high-level cognitive areas, such as the visual and fronto-parietal regions.

Generative forecasting of brain activity enhances Alzheimer's classification and interpretation

no code implementations30 Oct 2024 Yutong Gao, Vince D. Calhoun, Robyn L. Miller

Understanding the relationship between cognition and intrinsic brain activity through purely data-driven approaches remains a significant challenge in neuroscience.

Data Augmentation Deep Learning +2

Integrated Brain Connectivity Analysis with fMRI, DTI, and sMRI Powered by Interpretable Graph Neural Networks

no code implementations26 Aug 2024 Gang Qu, Ziyu Zhou, Vince D. Calhoun, Aiying Zhang, Yu-Ping Wang

Our approach incorporates a masking strategy to differentially weight neural connections, thereby facilitating a holistic amalgamation of multimodal imaging data.

Functional Connectivity

Multi-modal Imaging Genomics Transformer: Attentive Integration of Imaging with Genomic Biomarkers for Schizophrenia Classification

no code implementations28 Jul 2024 Nagur Shareef Shaik, Teja Krishna Cherukuri, Vince D. Calhoun, Dong Hye Ye

Schizophrenia (SZ) is a severe brain disorder marked by diverse cognitive impairments, abnormalities in brain structure, function, and genetic factors.

Diagnostic

Generalized Dynamic Brain Functional Connectivity Based on Random Convolutions

no code implementations24 Jun 2024 Yongjie Duan, Vince D. Calhoun, Zhiying Long

In this study, we propose a generalized approach to dynamics via a multi-dimensional random convolution (RandCon) DFC method that is able to effectively capture time-varying DFC at arbitrary time scales by extracting different local features from fMRI time series using a number of multi-dimensional random convolution kernels without the need for learning kernel weights.

Functional Connectivity

An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease

no code implementations19 Jun 2024 Giorgio Dolci, Federica Cruciani, Md Abdur Rahaman, Anees Abrol, Jiayu Chen, Zening Fu, Ilaria Boscolo Galazzo, Gloria Menegaz, Vince D. Calhoun

% in two distinct tasks, dealing with also missing data.\\ \textbf{Approach:} We propose a multimodal DL-based classification framework where a generative module employing Cycle Generative Adversarial Networks was introduced in the latent space for imputing missing data (a common issue of multimodal approaches).

Multimodal MRI Accurately Identifies Amyloid Status in Unbalanced Cohorts in Alzheimer's Disease Continuum

no code implementations19 Jun 2024 Giorgio Dolci, Charles A. Ellis, Federica Cruciani, Lorenza Brusini, Anees Abrol, Ilaria Boscolo Galazzo, Gloria Menegaz, Vince D. Calhoun

Amyloid-$\beta$ (A$\beta$) plaques in conjunction with hyperphosphorylated tau proteins in the form of neurofibrillary tangles are the two neuropathological hallmarks of Alzheimer's disease.

Diffusion MRI Hippocampus +1

Spectral Introspection Identifies Group Training Dynamics in Deep Neural Networks for Neuroimaging

no code implementations17 Jun 2024 Bradley T. Baker, Vince D. Calhoun, Sergey M. Plis

Neural networks, whice have had a profound effect on how researchers study complex phenomena, do so through a complex, nonlinear mathematical structure which can be difficult for human researchers to interpret.

Cross-Modality Translation with Generative Adversarial Networks to Unveil Alzheimer's Disease Biomarkers

no code implementations8 May 2024 Reihaneh Hassanzadeh, Anees Abrol, Hamid Reza Hassanzadeh, Vince D. Calhoun

Additionally, the T1 images generated by our model showed a similar pattern of atrophy in the hippocampal and other temporal regions of Alzheimer's patients.

Diagnostic Functional Connectivity +2

Multiscale Neuroimaging Features for the Identification of Medication Class and Non-Responders in Mood Disorder Treatment

no code implementations12 Feb 2024 Bradley T. Baker, Mustafa S. Salman, Zening Fu, Armin Iraji, Elizabeth Osuch, Jeremy Bockholt, Vince D. Calhoun

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 difficulties in providing fast and reliable treatment when standard diagnostic and prescription methods are used.

Diagnostic feature selection

Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis

no code implementations11 Dec 2023 Yutong Gao, Charles A. Ellis, Vince D. Calhoun, Robyn L. Miller

The high dimensionality and complexity of neuroimaging data necessitate large datasets to develop robust and high-performing deep learning models.

Data Augmentation Deep Learning +2

Constrained Independent Vector Analysis with Reference for Multi-Subject fMRI Analysis

no code implementations8 Nov 2023 Trung Vu, Francisco Laport, Hanlu Yang, Vince D. Calhoun, Tulay Adali

Independent vector analysis (IVA) generalizes ICA to multiple datasets, i. e., to multi-subject data, and in addition to higher-order statistical information in ICA, it leverages the statistical dependence across the datasets as an additional type of statistical diversity.

The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)

no code implementations11 Sep 2023 Russell A. Poldrack, Christopher J. Markiewicz, Stefan Appelhoff, Yoni K. Ashar, Tibor Auer, Sylvain Baillet, Shashank Bansal, Leandro Beltrachini, Christian G. Benar, Giacomo Bertazzoli, Suyash Bhogawar, Ross W. Blair, Marta Bortoletto, Mathieu Boudreau, Teon L. Brooks, Vince D. Calhoun, Filippo Maria Castelli, Patricia Clement, Alexander L Cohen, Julien Cohen-Adad, Sasha D'Ambrosio, Gilles de Hollander, María de la iglesia-Vayá, Alejandro de la Vega, Arnaud Delorme, Orrin Devinsky, Dejan Draschkow, Eugene Paul Duff, Elizabeth Dupre, Eric Earl, Oscar Esteban, Franklin W. Feingold, Guillaume Flandin, anthony galassi, Giuseppe Gallitto, Melanie Ganz, Rémi Gau, James Gholam, Satrajit S. Ghosh, Alessio Giacomel, Ashley G Gillman, Padraig Gleeson, Alexandre Gramfort, Samuel Guay, Giacomo Guidali, Yaroslav O. Halchenko, Daniel A. Handwerker, Nell Hardcastle, Peer Herholz, Dora Hermes, Christopher J. Honey, Robert B. Innis, Horea-Ioan Ioanas, Andrew Jahn, Agah Karakuzu, David B. Keator, Gregory Kiar, Balint Kincses, Angela R. Laird, Jonathan C. Lau, Alberto Lazari, Jon Haitz Legarreta, Adam Li, Xiangrui Li, Bradley C. Love, Hanzhang Lu, Camille Maumet, Giacomo Mazzamuto, Steven L. Meisler, Mark Mikkelsen, Henk Mutsaerts, Thomas E. Nichols, Aki Nikolaidis, Gustav Nilsonne, Guiomar Niso, Martin Norgaard, Thomas W Okell, Robert Oostenveld, Eduard Ort, Patrick J. Park, Mateusz Pawlik, Cyril R. Pernet, Franco Pestilli, Jan Petr, Christophe Phillips, Jean-Baptiste Poline, Luca Pollonini, Pradeep Reddy Raamana, Petra Ritter, Gaia Rizzo, Kay A. Robbins, Alexander P. Rockhill, Christine Rogers, Ariel Rokem, Chris Rorden, Alexandre Routier, Jose Manuel Saborit-Torres, Taylor Salo, Michael Schirner, Robert E. Smith, Tamas Spisak, Julia Sprenger, Nicole C. Swann, Martin Szinte, Sylvain Takerkart, Bertrand Thirion, Adam G. Thomas, Sajjad Torabian, Gael Varoquaux, Bradley Voytek, Julius Welzel, Martin Wilson, Tal Yarkoni, Krzysztof J. Gorgolewski

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities.

Predictive Sparse Manifold Transform

no code implementations27 Aug 2023 Yujia Xie, Xinhui Li, Vince D. Calhoun

PSMT incorporates two layers where the first sparse coding layer represents the input sequence as sparse coefficients over an overcomplete dictionary and the second manifold learning layer learns a geometric embedding space that captures topological similarity and dynamic temporal linearity in sparse coefficients.

Looking deeper into interpretable deep learning in neuroimaging: a comprehensive survey

no code implementations14 Jul 2023 Md. Mahfuzur Rahman, Vince D. Calhoun, Sergey M. Plis

Secondly, we discuss how multiple recent neuroimaging studies leveraged model interpretability to capture anatomical and functional brain alterations most relevant to model predictions.

Deep Learning Explainable Artificial Intelligence (XAI)

New Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity Using Dictionary Learning

no code implementations10 Nov 2022 Fateme Ghayem, Hanlu Yang, Furkan Kantar, Seung-Jun Kim, Vince D. Calhoun, Tulay Adali

In this paper, we present a new method that leverages ICA and DL for the identification of directly interpretable patterns to discriminate between the HC and Sz groups.

Dictionary Learning

Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes

1 code implementation7 Sep 2022 Alex Fedorov, Eloy Geenjaar, Lei Wu, Tristan Sylvain, Thomas P. DeRamus, Margaux Luck, Maria Misiura, R Devon Hjelm, Sergey M. Plis, Vince D. Calhoun

Coarse labels do not capture the long-tailed spectrum of brain disorder phenotypes, which leads to a loss of generalizability of the model that makes them less useful in diagnostic settings.

Diagnostic Self-Supervised Learning

Persistent Homological State-Space Estimation of Functional Human Brain Networks at Rest

2 code implementations1 Jan 2022 Moo K. Chung, Shih-Gu Huang, Ian C. Carroll, Vince D. Calhoun, H. Hill Goldsmith

We introduce an innovative, data-driven topological data analysis (TDA) technique for estimating the state spaces of dynamically changing functional human brain networks at rest.

Clustering Graph Clustering +1

An AO-ADMM approach to constraining PARAFAC2 on all modes

1 code implementation4 Oct 2021 Marie Roald, Carla Schenker, Vince D. Calhoun, Tülay Adalı, Rasmus Bro, Jeremy E. Cohen, Evrim Acar

We also apply our model to two real-world datasets from neuroscience and chemometrics, and show that constraining the evolving mode improves the interpretability of the extracted patterns.

All

Algorithm-Agnostic Explainability for Unsupervised Clustering

1 code implementation17 May 2021 Charles A. Ellis, Mohammad S. E. Sendi, Eloy P. T. Geenjaar, Sergey M. Plis, Robyn L. Miller, Vince D. Calhoun

The methods are (1) easy to implement and (2) broadly applicable across clustering algorithms, which could make them highly impactful.

BIG-bench Machine Learning Clustering +2

Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI data

1 code implementation29 Mar 2021 Alex Fedorov, Eloy Geenjaar, Lei Wu, Thomas P. DeRamus, Vince D. Calhoun, Sergey M. Plis

We show that self-supervised models are not as robust as expected based on their results in natural imaging benchmarks and can be outperformed by supervised learning with dropout.

Out-of-Distribution Generalization Self-Supervised Learning

Peering Beyond the Gradient Veil with Distributed Auto Differentiation

no code implementations18 Feb 2021 Bradley T. Baker, Aashis Khanal, Vince D. Calhoun, Barak Pearlmutter, Sergey M. Plis

We introduce an innovative, communication-friendly approach for training distributed DNNs, which capitalizes on the outer-product structure of the gradient as revealed by the mechanics of auto-differentiation.

Quantization

Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction

no code implementations20 Jan 2021 Gang Qu, Li Xiao, Wenxing Hu, Kun Zhang, Vince D. Calhoun, Yu-Ping Wang

Methods: To take advantage of complementary information from multi-modal fMRI, we propose an interpretable multi-modal graph convolutional network (MGCN) model, incorporating the fMRI time series and the functional connectivity (FC) between each pair of brain regions.

Functional Connectivity Graph Embedding +1

On self-supervised multi-modal representation learning: An application to Alzheimer's disease

1 code implementation25 Dec 2020 Alex Fedorov, Lei Wu, Tristan Sylvain, Margaux Luck, Thomas P. DeRamus, Dmitry Bleklov, Sergey M. Plis, Vince D. Calhoun

In this paper, we introduce a way to exhaustively consider multimodal architectures for contrastive self-supervised fusion of fMRI and MRI of AD patients and controls.

General Classification Representation Learning

Distance Correlation Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies

no code implementations30 Sep 2020 Li Xiao, Biao Cai, Gang Qu, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, Yu-Ping Wang

Resting-state functional magnetic resonance imaging (rs-fMRI)-derived functional connectivity patterns have been extensively utilized to delineate global functional organization of the human brain in health, development, and neuropsychiatric disorders.

Connectivity Estimation Functional Connectivity +1

Whole MILC: generalizing learned dynamics across tasks, datasets, and populations

1 code implementation29 Jul 2020 Usman Mahmood, Md Mahfuzur Rahman, Alex Fedorov, Noah Lewis, Zening Fu, Vince D. Calhoun, Sergey M. Plis

In this paper we present a novel self supervised training schema which reinforces whole sequence mutual information local to context (whole MILC).

Feature Importance

Interpretable multimodal fusion networks reveal mechanisms of brain cognition

no code implementations16 Jun 2020 Wenxing Hu, Xianghe Meng, Yuntong Bai, Aiying Zhang, Biao Cai, Gemeng Zhang, Tony W. Wilson, Julia M. Stephen, Vince D. Calhoun, Yu-Ping Wang

Moreover, the estimated activation maps are class-specific, and the captured cross-data associations are interest/label related, which further facilitates class-specific analysis and biological mechanism analysis.

Object Recognition

Multidataset Independent Subspace Analysis with Application to Multimodal Fusion

1 code implementation11 Nov 2019 Rogers F. Silva, Sergey M. Plis, Tulay Adali, Marios S. Pattichis, Vince D. Calhoun

In the last two decades, unsupervised latent variable models---blind source separation (BSS) especially---have enjoyed a strong reputation for the interpretable features they produce.

blind source separation Combinatorial Optimization +1

Prediction of Progression to Alzheimer's disease with Deep InfoMax

no code implementations24 Apr 2019 Alex Fedorov, R. Devon Hjelm, Anees Abrol, Zening Fu, Yuhui Du, Sergey Plis, Vince D. Calhoun

Arguably, unsupervised learning plays a crucial role in the majority of algorithms for processing brain imaging.

General Classification

Tensor-Based Fusion of EEG and FMRI to Understand Neurological Changes in Schizophrenia

no code implementations7 Dec 2016 Evrim Acar, Yuri Levin-Schwartz, Vince D. Calhoun, Tülay Adalı

Neuroimaging modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) provide information about neurological functions in complementary spatiotemporal resolutions; therefore, fusion of these modalities is expected to provide better understanding of brain activity.

EEG

Deep learning for neuroimaging: a validation study

no code implementations20 Dec 2013 Sergey M. Plis, Devon R. Hjelm, Ruslan Salakhutdinov, Vince D. Calhoun

In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data.

Deep Learning Representation Learning

Block Coordinate Descent for Sparse NMF

1 code implementation15 Jan 2013 Vamsi K. Potluru, Sergey M. Plis, Jonathan Le Roux, Barak A. Pearlmutter, Vince D. Calhoun, Thomas P. Hayes

However, present algorithms designed for optimizing the mixed norm L$_1$/L$_2$ are slow and other formulations for sparse NMF have been proposed such as those based on L$_1$ and L$_0$ norms.

3D Object Classification

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