Search Results for author: Vince Calhoun

Found 26 papers, 6 papers with code

Coupled CP tensor decomposition with shared and distinct components for multi-task fMRI data fusion

no code implementations25 Nov 2022 Ricardo Augusto Borsoi, Isabell Lehmann, Mohammad Abu Baker Siddique Akhonda, Vince Calhoun, Konstantin Usevich, David Brie, Tülay Adalı

Discovering components that are shared in multiple datasets, next to dataset-specific features, has great potential for studying the relationships between different subjects or tasks in functional Magnetic Resonance Imaging (fMRI) data.

Tensor Decomposition

MultiCrossViT: Multimodal Vision Transformer for Schizophrenia Prediction using Structural MRI and Functional Network Connectivity Data

no code implementations12 Nov 2022 Yuda Bi, Anees Abrol, Zening Fu, Vince Calhoun

Vision Transformer (ViT) is a pioneering deep learning framework that can address real-world computer vision issues, such as image classification and object recognition.

Image Classification Multimodal Deep Learning +1

CommsVAE: Learning the brain's macroscale communication dynamics using coupled sequential VAEs

no code implementations7 Oct 2022 Eloy Geenjaar, Noah Lewis, Amrit Kashyap, Robyn Miller, Vince Calhoun

To analyze communication, the brain is often split up into anatomical regions that each perform certain computations.


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

no code implementations15 Sep 2022 Yuda Bi, Anees Abrol, Zening Fu, Jiayu Chen, Jingyu Liu, Vince Calhoun

Prior work has demonstrated that deep learning models that take advantage of the data's 3D structure can outperform standard machine learning on several learning tasks.

Gender Prediction

Pipeline-Invariant Representation Learning for Neuroimaging

no code implementations27 Aug 2022 Xinhui Li, Alex Fedorov, Mrinal Mathur, Anees Abrol, Gregory Kiar, Sergey Plis, Vince Calhoun

Using 2000 human subjects from the UK Biobank dataset, we demonstrate that both models present unique advantages, in particular that MPSL can be used to improve out-of-sample generalization to new pipelines, while PXL can be used to improve predictive performance consistency and representational similarity.

Representation Learning

Explainable AI (XAI) in Biomedical Signal and Image Processing: Promises and Challenges

no code implementations9 Jul 2022 Guang Yang, Arvind Rao, Christine Fernandez-Maloigne, Vince Calhoun, Gloria Menegaz

This paper aims at providing an overview on XAI in biomedical data processing and points to an upcoming Special Issue on Deep Learning in Biomedical Image and Signal Processing of the IEEE Signal Processing Magazine that is going to appear in March 2022.

Spatio-temporally separable non-linear latent factor learning: an application to somatomotor cortex fMRI data

no code implementations26 May 2022 Eloy Geenjaar, Amrit Kashyap, Noah Lewis, Robyn Miller, Vince Calhoun

Our approach is evaluated on data with multiple motor sub-tasks to assess whether the model captures disentangled latent factors that correspond to each sub-task.

Constraint-Based Causal Structure Learning from Undersampled Graphs

no code implementations18 May 2022 Mohammadsajad Abavisani, David Danks, Vince Calhoun, Sergey Plis

Graphical structures estimated by causal learning algorithms from time series data can provide highly misleading causal information if the causal timescale of the generating process fails to match the measurement timescale of the data.

Informativeness Time Series Analysis

Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series

no code implementations4 Feb 2022 Usman Mahmood, Zening Fu, Vince Calhoun, Sergey Plis

To bridge this gap, we developed dynamic effective connectivity estimation via neural network training (DECENNT), a novel model to learn an interpretable directed and dynamic graph induced by the downstream classification/prediction task.

Connectivity Estimation Link Prediction +1

A deep learning model for data-driven discovery of functional connectivity

1 code implementation7 Dec 2021 Usman Mahmood, Zening Fu, Vince Calhoun, Sergey Plis

Functional connectivity (FC) studies have demonstrated the overarching value of studying the brain and its disorders through the undirected weighted graph of fMRI correlation matrix.

Brain dynamics via Cumulative Auto-Regressive Self-Attention

no code implementations1 Nov 2021 Usman Mahmood, Zening Fu, Vince Calhoun, Sergey Plis

The supervised training of the model as a classifier between patients and controls results in a model that generates directed connectivity graphs and highlights the components of the time-series that are predictive for each subject.

Time Series Analysis

Multi network InfoMax: A pre-training method involving graph convolutional networks

no code implementations1 Nov 2021 Usman Mahmood, Zening Fu, Vince Calhoun, Sergey Plis

Since almost every DL model is an ensemble of multiple networks, we take our high-level embeddings from two different networks of a model --a convolutional and a graph network--.

Graph Classification

Variational voxelwise rs-fMRI representation learning: Evaluation of sex, age, and neuropsychiatric signatures

no code implementations29 Aug 2021 Eloy Geenjaar, Tonya White, Vince Calhoun

The VAE is trained on voxelwise rs-fMRI data and performs non-linear dimensionality reduction that retains meaningful information.

Dimensionality Reduction regression +2

Attend to connect: end-to-end brain functional connectivity estimation

no code implementations ICLR Workshop GTRL 2021 Usman Mahmood, Zening Fu, Vince Calhoun, Sergey Plis

Functional connectivity (FC) studies have demonstrated the benefits of investigating the brain and its disorders through the undirected weighted graph of fMRI correlation matrix.

Connectivity Estimation

Improved Differentially Private Decentralized Source Separation for fMRI Data

no code implementations28 Oct 2019 Hafiz Imtiaz, Jafar Mohammadi, Rogers Silva, Bradley Baker, Sergey M. Plis, Anand D. Sarwate, Vince Calhoun

In this work, we propose a differentially private algorithm for performing ICA in a decentralized data setting.

Tracing Network Evolution Using the PARAFAC2 Model

1 code implementation23 Oct 2019 Marie Roald, Suchita Bhinge, Chunying Jia, Vince Calhoun, Tülay Adalı, Evrim Acar

For instance, how spatial networks of functional connectivity in the brain evolve during a task is not well-understood.

Improving Classification Rate of Schizophrenia Using a Multimodal Multi-Layer Perceptron Model with Structural and Functional MR

no code implementations4 Apr 2018 Alvaro Ulloa, Sergey Plis, Vince Calhoun

We propose the use of a multimodal multi-layer perceptron model to enhance the predictive power of structural and functional magnetic resonance imaging (sMRI and fMRI) combined.

General Classification

Almost instant brain atlas segmentation for large-scale studies

1 code implementation1 Nov 2017 Alex Fedorov, Eswar Damaraju, Vince Calhoun, Sergey Plis

Complexity of the task increases even further when segmenting structural MRI of the brain into an atlas with more than 50 regions.

Kernel Method for Detecting Higher Order Interactions in multi-view Data: An Application to Imaging, Genetics, and Epigenetics

no code implementations14 Jul 2017 Md. Ashad Alam, Hui-Yi Lin, Vince Calhoun, Yu-Ping Wang

In this study, we tested the interaction effect of multimodal datasets using a novel method called the kernel method for detecting higher order interactions among biologically relevant mulit-view data.

FDR-Corrected Sparse Canonical Correlation Analysis with Applications to Imaging Genomics

1 code implementation11 May 2017 Alexej Gossmann, Pascal Zille, Vince Calhoun, Yu-Ping Wang

Here we propose a way of applying the FDR concept to sparse CCA, and a method to control the FDR.

End-to-end learning of brain tissue segmentation from imperfect labeling

1 code implementation3 Dec 2016 Alex Fedorov, Jeremy Johnson, Eswar Damaraju, Alexei Ozerin, Vince Calhoun, Sergey Plis

Segmenting a structural magnetic resonance imaging (MRI) scan is an important pre-processing step for analytic procedures and subsequent inferences about longitudinal tissue changes.

Spatio-temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural Networks

no code implementations3 Nov 2016 R. Devon Hjelm, Eswar Damaraju, Kyunghyun Cho, Helmut Laufs, Sergey M. Plis, Vince Calhoun

We introduce a novel recurrent neural network (RNN) approach to account for temporal dynamics and dependencies in brain networks observed via functional magnetic resonance imaging (fMRI).

Learning Schizophrenia Imaging Genetics Data Via Multiple Kernel Canonical Correlation Analysis

no code implementations15 Sep 2016 Owen Richfield, Md. Ashad Alam, Vince Calhoun, Yu-Ping Wang

Kernel and Multiple Kernel Canonical Correlation Analysis (CCA) are employed to classify schizophrenic and healthy patients based on their SNPs, DNA Methylation and fMRI data.

Classification General Classification

Rate-Agnostic (Causal) Structure Learning

no code implementations NeurIPS 2015 Sergey Plis, David Danks, Cynthia Freeman, Vince Calhoun

That is, these algorithms all learn causal structure without assuming any particular relation between the measurement and system timescales; they are thus rate-agnostic.

Time Series Analysis

Iterative Refinement of the Approximate Posterior for Directed Belief Networks

1 code implementation NeurIPS 2016 R. Devon Hjelm, Kyunghyun Cho, Junyoung Chung, Russ Salakhutdinov, Vince Calhoun, Nebojsa Jojic

Variational methods that rely on a recognition network to approximate the posterior of directed graphical models offer better inference and learning than previous methods.

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