Search Results for author: Vince D. Calhoun

Found 31 papers, 13 papers with code

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.

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.

Representation Learning

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 Electroencephalogram (EEG)

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

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

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

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

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 Multi-Task Learning

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

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.

Graph Embedding Time Series Analysis

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

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

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

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.

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

1 code implementation1 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

Latent Similarity Identifies Important Functional Connections for Phenotype Prediction

1 code implementation30 Aug 2022 Anton Orlichenko, Gang Qu, Gemeng Zhang, Binish Patel, Tony W. Wilson, Julia M. Stephen, Vince D. Calhoun, Yu-Ping Wang

Significance: We propose a novel algorithm for small sample, high feature dimension datasets and use it to identify connections in task fMRI data.

Computational Efficiency Metric 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.

Self-Supervised Learning

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

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.

Explainable Artificial Intelligence (XAI)

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.

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.

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.

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 Time Series +1

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.

feature selection

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