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Viewing Publication 1 - 100 from 626

2024

Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain NetworksGang Qu 0002, Anton Orlichenko, Junqi Wang, Gemeng Zhang, Li Xiao 0002, Kun Zhang 0012, Tony W. Wilson, Julia M. Stephen, Vince D. Calhoun, Yu-Ping Wang 0002. tmi, 43(4):1568-1578, April 2024. [doi]

A Novel Deep Subspace Learning Framework to Automatically Uncover Assessment-Specific Independent Brain NetworksIshaan Batta, Anees Abrol, Vince D. Calhoun. ciss 2024: 1-6 [doi]

Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal linksAlex Fedorov, Eloy Geenjaar, Lei Wu, Tristan Sylvain, Thomas P. DeRamus, Margaux Luck, Maria B. Misiura, Girish Mittapalle, R. Devon Hjelm, Sergey M. Plis, Vince D. Calhoun. neuroimage, 285:120485, January 2024. [doi]

Striatum- and Cerebellum-Modulated Epileptic Networks Varying Across States with and without Interictal Epileptic DischargesSisi Jiang, Haonan Pei, Junxia Chen, Hechun Li, Zetao Liu, Yuehan Wang, Jinnan Gong, Sheng Wang, Qifu Li, Mingjun Duan, Vince D. Calhoun, Dezhong Yao 0001, Cheng Luo. IJNS, 34(4), April 2024. [doi]

Intra-Atlas Node Size Effects on Graph Metrics in fMRI Data: Implications for Alzheimer's Disease and Cognitive ImpairmentSahithi Kolla, Haleh Falakshahi, Anees Abrol, Zening Fu, Vince D. Calhoun. sensors, 24(3):814, February 2024. [doi]

A Method to Estimate Longitudinal Change Patterns in Functional Network Connectivity of the Developing Brain Relevant to Psychiatric Problems, Cognition, and AgeRekha Saha, Debbrata K. Saha, Md Abdur Rahaman, Zening Fu, Jingyu Liu 0001, Vince D. Calhoun. brain, 14(2):130-140, 2024. [doi]

Maximum Classifier Discrepancy Generative Adversarial Network for Jointly Harmonizing Scanner Effects and Improving Reproducibility of Downstream TasksWeizheng Yan, Zening Fu, Rongtao Jiang, Jing Sui, Vince D. Calhoun. tbe, 71(4):1170-1178, April 2024. [doi]

2023

REGRESSION-ASSISTED INDEPENDENT VECTOR ANALYSIS: A SOLUTION TO LARGE-SCALE FMRI DATA ANALYSISHanlu Yang 0001, Ben Gabrielson, Vince D. Calhoun, Tülay Adali. acssc 2023: 1443-1447 [doi]

Constrained Independent Component Analysis Based on Entropy Bound Minimization for Subgroup Identification from Multi-subject fMRI DataHanlu Yang 0001, Fateme Ghayem, Ben Gabrielson, Mohammad A. B. S. Akhonda, Vince D. Calhoun, Tülay Adali. icassp 2023: 1-5 [doi]

A Multi-dimensional Joint ICA Model with Gaussian CopulaOktay Agcaoglu, Rogers F. Silva, Deniz Alaçam, Vince D. Calhoun. iciap 2024: 152-163 [doi]

Functional Network Connectivity Based Mental Health Category Prediction from Rest-fMRI DataMeenu Ajith, Vince D. Calhoun. isbi 2023: 1-5 [doi]

Federated Linear Mixed Effects Modeling for Voxel-Based MorphometrySunitha Basodi, Rajikha Raja, Harshvardhan Gazula, Javier Romero, Sandeep R. Panta, Vince D. Calhoun. isbi 2023: 1-4 [doi]

A Multimodal Deep Learning Approach for Automated Detection and Characterization of Distinctly Salient Features of Alzheimers DiseaseIshaan Batta, Anees Abrol, Vince D. Calhoun. isbi 2023: 1-4 [doi]

Multi-Subject Analysis for Brain Developmental Patterns Discovery via Tensor Decomposition of MEG DataIrina Belyaeva, Ben Gabrielson, Yu-Ping Wang 0002, Tony W. Wilson, Vince D. Calhoun, Julia M. Stephen, Tülay Adali. ni, 21(1):115-141, January 2023. [doi]

Correction to: Multi-Subject Analysis for Brain Developmental Patterns Discovery via Tensor Decomposition of MEG DataIrina Belyaeva, Ben Gabrielson, Yu-Ping Wang 0002, Tony W. Wilson, Vince D. Calhoun, Julia M. Stephen, Tülay Adali. ni, 21(1):143, January 2023. [doi]

MultiViT: Multimodal Vision Transformer for Schizophrenia Prediction using Structural MRI and Functional Network Connectivity DataYuda Bi, Anees Abrol, Zening Fu, Vince D. Calhoun. isbi 2023: 1-5 [doi]

Coupled CP Tensor Decomposition with Shared and Distinct Components for Multi-Task Fmri Data FusionR. A. Borsoi, Isabell Lehmann, Mohammad A. B. S. Akhonda, Vince D. Calhoun, Konstantin Usevich, David Brie, Tülay Adali. icassp 2023: 1-5 [doi]

Spatial Dynamic Propagation of Network Activity in Resting fMRI DataBiozid Bostami, Mukesh Dhamala, Tülay Adali, Vince D. Calhoun, Armin Iraji. isbi 2023: 1-4 [doi]

Deep Generative Transfer Learning Predicts Conversion To Alzheimer'S Disease From Neuroimaging Genomics DataGiorgio Dolci, Md Abdur Rahaman, Ilaria Boscolo Galazzo, Federica Cruciani, Anees Abrol, Jiayu Chen 0003, Zening Fu, Kuaikuai Duan, Gloria Menegaz, Vince D. Calhoun. icassp 2023: 1-5 [doi]

Objective Assessment of the Bias Introduced by Baseline Signals in XAI Attribution MethodsGiorgio Dolci, Federica Cruciani, Ilaria Boscolo Galazzo, Vince D. Calhoun, Gloria Menegaz. metroxraine 2023: 266-271 [doi]

How Does Aging Affect Whole-brain Functional Network Connectivity? Evidence from An ICA MethodYuhui Du, Yating Guo, Vince D. Calhoun. embc 2023: 1-4 [doi]

An Adaptive Semi-Supervised Deep Clustering and Its Application to Identifying Biotypes of Psychiatric DisordersYuhui Du, Fulin Wu, Ju Niu, Vince D. Calhoun. isbi 2023: 1-4 [doi]

Any-Way Independent Component Analysis with ReferenceKuaikuai Duan, Rogers F. Silva, Jingyu Liu 0001, Oktay Agcaoglu, Vince D. Calhoun. isbi 2023: 1-4 [doi]

Identifying Neuropsychiatric Disorder Subtypes and Subtype-Dependent Variation in Diagnostic Deep Learning Classifier PerformanceCharles A. Ellis, Robyn L. Miller, Vince D. Calhoun. isbi 2023: 1-4 [doi]

Neuropsychiatric Disorder Subtyping Via Clustered Deep Learning Classifier ExplanationsCharles A. Ellis, Robyn L. Miller, Vince D. Calhoun. embc 2023: 1-4 [doi]

A Convolutional Autoencoder-based Explainable Clustering Approach for Resting-State EEG AnalysisCharles A. Ellis, Robyn L. Miller, Vince D. Calhoun. embc 2023: 1-4 [doi]

A Novel Explainable Fuzzy Clustering Approach for fMRI Dynamic Functional Network Connectivity AnalysisCharles A. Ellis, Robyn L. Miller, Vince D. Calhoun. embc 2023: 1-4 [doi]

Novel Approach Explains Spatio-Spectral Interactions In Raw Electroencephalogram Deep Learning ClassifiersCharles A. Ellis, Abhinav Sattiraju, Robyn L. Miller, Vince D. Calhoun. icassp 2023: 1-5 [doi]

Novel methods for elucidating modality importance in multimodal electrophysiology classifiersCharles A. Ellis, Mohammad S. Eslampanah Sendi, Rongen Zhang, Darwin A. Carbajal, May D. Wang, Robyn L. Miller, Vince D. Calhoun. fini, 17, March 2023. [doi]

Network Differential in Gaussian Graphical Models from Multimodal Neuroimaging DataHaleh Falakshahi, Hooman Rokham, Robyn L. Miller, Jean Liu, Vince D. Calhoun. embc 2023: 1-6 [doi]

Joint Structural and Functional Connectivity Learning Based Independent Component AnalysisMahshid Fouladivanda, Armin Iraji, Lei Wu, Vince D. Calhoun. mlsp 2023: 1-5 [doi]

Independent Vector Analysis with Multivariate Gaussian Model: a Scalable Method by Multilinear RegressionBen Gabrielson, Mingyu Sun, Mohammad A. B. S. Akhonda, Vince D. Calhoun, Tülay Adali. icassp 2023: 1-5 [doi]

Interpretable LSTM model reveals transiently-realized patterns of dynamic brain connectivity that predict patient deterioration or recovery from very mild cognitive impairmentYutong Gao, Noah Lewis, Vince D. Calhoun, Robyn L. Miller. cbm, 161:107005, July 2023. [doi]

Federated Analysis in COINSTAC Reveals Functional Network Connectivity and Spectral Links to Smoking and Alcohol Consumption in Nearly 2,000 Adolescent BrainsHarshvardhan Gazula, Kelly Rootes-Murdy, Bharath Holla, Sunitha Basodi, Zuo Zhang, Eric Verner, Ross Kelly, Pratima Murthy, Amit Chakrabarti, Debasish Basu, Subodh Bhagyalakshmi Nanjayya, Rajkumar Lenin Singh, Roshan Lourembam Singh, Kartik Kalyanram, Kamakshi Kartik, Kalyanaraman Kumaran, Krishnaveni Ghattu, Rebecca Kuriyan, Sunita Simon Kurpad, Gareth J. Barker, Rose Dawn Bharath, Sylvane Desrivieres, Meera Purushottam, Dimitri Papadopoulos-Orfanos, Eesha Sharma, Matthew Hickman, Mireille Toledano, Nilakshi Vaidya, Tobias Banaschewski, Arun L. W. Bokde, Herta Flor, Antoine Grigis, Hugh Garavan, Penny Gowland, Andreas Heinz, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère-Martinot, Eric Artiges, Frauke Nees, Tomás Paus, Luise Poustka, Juliane H. Fröhner, Lauren Robinson, Michael N. Smolka, Henrik Walter, Jeanne Winterer, Robert Whelan, Jessica A. Turner, Anand D. Sarwate, Sergey M. Plis, Vivek Benegal, Gunter Schumann, Vince D. Calhoun. ni, 21(2):287-301, April 2023. [doi]

New Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity using Dictionary LearningFateme Ghayem, H. Yang, Furkan Kantar, S.-J. Kim, Vince D. Calhoun, Tülay Adali. icassp 2023: 1-5 [doi]

Extraction of One Time Point Dynamic Group Features via Tucker Decomposition of Multi-subject FMRI Data: Application to SchizophreniaYue Han, Qiu-Hua Lin, Li-Dan Kuang, Ying-Guang Hao, Wei-Xing Li, Xiao-Feng Gong, Vince D. Calhoun. iconip 2024: 518-527 [doi]

The Nonlinear Brain: Towards Uncovering Hidden Brain Networks Using Explicitly Nonlinear Functional InteractionArmin Iraji, Katarzyna Kazimierczak, Jiayu Chen 0003, Sara Motlaghian, Karsten Specht, Tülay Adali, Vince D. Calhoun. isbi 2023: 1-4 [doi]

Capturing Spatial Dynamics Using Time-Resolved Referenced-Informed Network Estimation TechniquesArmin Iraji, Jiayu Chen 0003, Ashkan Faghiri, Zening Fu, Jingyu Liu 0001, Juan R. Bustillo, Tülay Adali, Mukesh Dhamala, Vince D. Calhoun. isbi 2023: 1-4 [doi]

Flexible Multisubject Multiset FMRI Data Analysis Using Robust Discriminative Dictionary LearningRui Jin, Shuai Xu, Seung-Jun Kim 0002, Vince D. Calhoun. acssc 2023: 1458-1462 [doi]

Multi-Modal Deep Learning on Imaging Genetics for Schizophrenia ClassificationAyush Kanyal, Srinivas Kandula, Vince D. Calhoun, Dong Hye Ye. icassp 2023: 1-5 [doi]

Reproducibility in Joint Blind Source Separation: Application to fMRI AnalysisFrancisco Laport, Trung Vu, Hanlu Yang 0001, Vince D. Calhoun, Tülay Adali. acssc 2023: 1448-1452 [doi]

Topological Correction of Subject-Level Intrinsic Connectivity NetworksNoah Lewis, Armin Iraji, Robyn L. Miller, Vince D. Calhoun. isbi 2023: 1-4 [doi]

Evaluating Trade-Offs in IVA of Multimodal Neuroimaging using Cross-Platform Multidataset Independent Subspace AnalysisXinhui Li, Daniel Khosravinezhad, Vince D. Calhoun, Rogers F. Silva. isbi 2023: 1-5 [doi]

ICA-based Individualized Differential Structure Similarity Networks for Predicting Symptom Scores in Adolescents with Major Depressive DisorderXiang Li, Ming Xu, Rongtao Jiang, Xuemei Li, Vince D. Calhoun, Xinyu Zhou, Jing Sui. embc 2023: 1-5 [doi]

Higher-Order Organization in the Human Brain From Matrix-Based Rényi's EntropyQiang Li, Shujian Yu, Kristoffer H. Madsen, Vince D. Calhoun, Armin Iraji. icassp 2023: 1-5 [doi]

Multimodal Subspace Independent Vector Analysis Better Captures Hidden Relationships in Multimodal Neuroimaging DataXinhui Li, Tülay Adali, Rogers F. Silva, Vince D. Calhoun. isbi 2023: 1-5 [doi]

The Individualized Prediction of Neurocognitive Function in People Living With HIV Based on Clinical and Multimodal Connectome DataXiang Li, Sheri L. Towe, Ryan P. Bell, Rongtao Jiang, Shana A. Hall, Vince D. Calhoun, Christina S. Meade, Jing Sui. titb, 27(4):2094-2104, April 2023. [doi]

Fusion of Multi-Modal Neuroimaging Data and Association With Cognitive DataMark D. LoPresto, Mohammad A. B. S. Akhonda, Vince D. Calhoun, Tülay Adali. icassp 2023: 1-5 [doi]

Glacier: Glass-Box Transformer for Interpretable Dynamic NeuroimagingUsman Mahmood, Zening Fu, Vince D. Calhoun, Sergey M. Plis. icassp 2023: 1-5 [doi]

Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibilityDylan Martin, Sunitha Basodi, Sandeep R. Panta, Kelly Rootes-Murdy, Paul Prae, Anand D. Sarwate, Ross Kelly, Javier Romero, Bradley T. Baker, Harshvardhan Gazula, Henry Jeremy Bockholt, Jessica A. Turner, Nathalia Bianchini Esper, Alexandre R. Franco, Sergey M. Plis, Vince D. Calhoun. fini, 17, March 2023. [doi]

Hyperlocal Spatial Flows in BOLD fMRI Expose Novel Brain-Based Correlates of SchizophreniaRobyn L. Miller, Victor M. Vergara, Vince D. Calhoun. embc 2023: 1-4 [doi]

Local Spatial Flow Strengths in Bold FMRI are Strongly Impacted by SchizophreniaRobyn L. Miller, Victor M. Vergara, Helen Petropoulos, Vince D. Calhoun. icassp 2023: 1-4 [doi]

Latent Similarity Identifies Important Functional Connections for Phenotype PredictionAnton Orlichenko, Gang Qu 0002, Gemeng Zhang, Binish Patel, Tony W. Wilson, Julia M. Stephen, Vince D. Calhoun, Yu-Ping Wang 0002. tbe, 70(6):1979-1989, June 2023. [doi]

Decentralized Parallel Independent Component Analysis for Multimodal, Multisite DataChan Aek Panichvatana, Jiayu Chen 0003, Bradley T. Baker, Bishal Thapaliya, Vince D. Calhoun, Jingyu Liu 0001. embc 2023: 1-4 [doi]

Deep learning with explainability for characterizing age-related intrinsic differences in dynamic brain functional connectivityChen Qiao, Bin Gao, Yuechen Liu, Xin-Yu Hu, Wenxing Hu, Vince D. Calhoun, Yu-Ping Wang 0002. mia, 90:102941, December 2023. [doi]

A Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging DataHooman Rokham, Haleh Falakshahi, Vince D. Calhoun. embc 2023: 1-4 [doi]

Multimodal Fusion of Functional and Structural Data to Recognize Longitudinal Change Patterns in the Adolescent BrainRekha Saha, Debbrata K. Saha, Zening Fu, Rogers F. Silva, Vince D. Calhoun. bhi 2023: 1-5 [doi]

Functional and Structural Longitudinal Change Patterns in Adolescent BrainRekha Saha, Debbrata K. Saha, Zening Fu, Rogers F. Silva, Vince D. Calhoun. embc 2023: 1-4 [doi]

A Multivariate Method for Estimating and comparing whole brain functional connectomes from fMRI and PET dataDebbrata Kumar Saha, Anastasia Bohsali, Rekha Saha, Ihab Hajjar, Vince D. Calhoun. embc 2023: 1-4 [doi]

Topological Characteristics of 5d Spatially Dynamic Brain Networks in SchizophreniaMustafa S. Salman, Armin Iraji, Noah Lewis, Vince D. Calhoun. isbi 2023: 1-5 [doi]

An Explainable and Robust Deep Learning Approach for Automated Electroencephalography-Based Schizophrenia DiagnosisAbhinav Sattiraju, Charles A. Ellis, Robyn L. Miller, Vince D. Calhoun. bibe 2023: 255-259 [doi]

Disrupted Dynamic Functional Network Connectivity Among Cognitive Control Networks in the Progression of Alzheimer's DiseaseMohammad S. Eslampanah Sendi, Elaheh Zendehrouh, Zening Fu, Jingyu Liu 0001, Yuhui Du, Elizabeth Mormino, David H. Salat, Vince D. Calhoun, Robyn L. Miller. brain, 13(6):334-343, August 2023. [doi]

A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI AnalysisMingyu Sun, Ben Gabrielson, Mohammad Abu Baker Siddique Akhonda, Hanlu Yang 0001, Francisco Laport, Vince D. Calhoun, Tülay Adali. sensors, 23(11):5333, 2023. [doi]

Effective Training Strategy for NN Models of Working Memory Classification with Limited SamplesPranav Suresh, Bhaskar Ray, Bishal Thapaliya, Britny Farahdel, Behnam Kazemivash, Jiayu Chen 0003, Kuaikuai Duan, Vince D. Calhoun, Jingyu Liu 0001. isbi 2023: 1-4 [doi]

Highlight results, don't hide them: Enhance interpretation, reduce biases and improve reproducibilityPaul A. Taylor, Richard C. Reynolds, Vince D. Calhoun, Javier Gonzalez-Castillo, Daniel A. Handwerker, Peter A. Bandettini, Amanda F. Mejia, Gang Chen 0008. neuroimage, 274:120138, July 2023. [doi]

BrainForge: An online data analysis platform for integrative neuroimaging acquisition, analysis, and sharingEric Verner, Helen Petropoulos, Bradley T. Baker, Henry Jeremy Bockholt, Jill Fries, Anastasia Bohsali, Rajikha Raja, Duc Hoai Trinh, Vince D. Calhoun. concurrency, 35(18), 2023. [doi]

Phase and amplitude, two sides of functional connectivitySir-Lord Wiafe, Zening Fu, Vince D. Calhoun, Ashkan Faghiri. embc 2023: 1-4 [doi]

A Novel Neighborhood Rough Set-Based Feature Selection Method and Its Application to Biomarker Identification of SchizophreniaYing Xing, Peter V. Kochunov, Theo G. M. van Erp, Tianzhou Ma, Vince D. Calhoun, Yuhui Du. titb, 27(1):215-226, 2023. [doi]

An explainable autoencoder with multi-paradigm fMRI fusion for identifying differences in dynamic functional connectivity during brain developmentFaming Xu, Chen Qiao, Huiyu Zhou, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson, Yu-Ping Wang 0002. NN, 159:185-197, February 2023. [doi]

Explainable Multimodal Deep Dictionary Learning to Capture Developmental Differences From Three fMRI ParadigmsLan Yang, Chen Qiao, Huiyu Zhou 0001, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson, Yu-Ping Wang 0002. tbe, 70(8):2404-2415, August 2023. [doi]

Identification of Homogeneous Subgroups from Resting-State fMRI DataHanLu Yang, Trung Vu, Qunfang Long, Vince D. Calhoun, Tülay Adali. sensors, 23(6):3264, March 2023. [doi]

Towards a multimodal neuroimaging-based risk score for Alzheimer's disease by combining clinical and large N>37000 population dataElaheh Zendehrouh, Mohammad S. Eslampanah Sendi, Vince D. Calhoun. embc 2023: 1-4 [doi]

2022

Probing the link between the APOE-ε4 allele and whole-brain gray matter using deep learningAnees Abrol, Ihab Hajjar, Vince D. Calhoun. embc 2022: 3506-3509 [doi]

Multimodal fusion of brain imaging data with joint non-linear independent component analysisOktay Agcaoglu, Rogers F. Silva, Vince D. Calhoun. ivmsp 2022: 1-5 [doi]

Learning Active Multimodal Subspaces in the BrainIshaan Batta, Anees Abrol, Zening Fu, Vince D. Calhoun. embc 2022: 3822-3825 [doi]

An ICA-based framework for joint analysis of cognitive scores and MEG event-related fieldsPoomipat Boonyakitanont, Ben Gabrielson, Irina Belyaeva, Parthan Olikkal, Jitkomut Songsiri, Yu-Ping Wang, Tony W. Wilson, Vince D. Calhoun, Julia M. Stephen, Tülay Adali. embc 2022: 3594-3598 [doi]

Multi-Site Mild Traumatic Brain Injury Classification with Machine Learning and HarmonizationBiozid Bostami, Flor A. Espinoza, Harm J. van der Horn, Joukje van der Naalt, Vince D. Calhoun, Victor M. Vergara. embc 2022: 537-540 [doi]

Multimodal Analysis Uncovers Links between Grey Matter Volume and both Low-and High-frequency Dynamic Connectivity States in SchizophreniaMarlena Duda, Ashkan Faghiri, Vince D. Calhoun. bibm 2022: 1521-1525 [doi]

Spatially Constrained ICA Enables Robust Detection of Schizophrenia from Very Short Resting-state fMRIMarlena Duda, Armin Iraji, Vince D. Calhoun. embc 2022: 1867-1870 [doi]

Examining Reproducibility of EEG Schizophrenia Biomarkers Across Explainable Machine Learning ModelsCharles A. Ellis, Abhinav Sattiraju, Robyn L. Miller, Vince D. Calhoun. bibe 2022: 305-308 [doi]

Examining Effects of Schizophrenia on EEG with Explainable Deep Learning ModelsCharles A. Ellis, Abhinav Sattiraju, Robyn L. Miller, Vince D. Calhoun. bibe 2022: 301-304 [doi]

An Unsupervised Feature Learning Approach for Elucidating Hidden Dynamics in rs-fMRI Functional Network ConnectivityCharles A. Ellis, Mohammad S. Eslampanah Sendi, Robyn L. Miller, Vince D. Calhoun. embc 2022: 4449-4452 [doi]

An Approach for Estimating Explanation Uncertainty in fMRI dFNC ClassificationCharles A. Ellis, Robyn L. Miller, Vince D. Calhoun. bibe 2022: 297-300 [doi]

A Model Visualization-based Approach for Insight into Waveforms and Spectra Learned by CNNsCharles A. Ellis, Robyn L. Miller, Vince D. Calhoun. embc 2022: 1643-1646 [doi]

Exploring Relationships between Functional Network Connectivity and Cognition with an Explainable Clustering ApproachCharles A. Ellis, Martina Lapera Sancho, Mohammad S. Eslampanah Sendi, Robyn L. Miller, Vince D. Calhoun. bibe 2022: 293-296 [doi]

Going from lines to triangles: A formulation for time-frequency moments of time-series with application to study fMRIAshkan Faghiri, Armin Iraji, Noah Lewis, Kun Yang, Koko Ishizuka, Akira Sawa, Tülay Adali, Vince D. Calhoun. ivmsp 2022: 1-5 [doi]

A Unified Framework for Modularizing and Comparing Time-Resolved Functional Connectivity MethodsAshkan Faghiri, Armin Iraji, Marlena Duda, Tülay Adali, Vince D. Calhoun. embc 2022: 4631-4634 [doi]

Transient Intervals of Significantly Different Whole Brain Connectivity Predict Recovery vs. Progression from Mild Cognitive Impairment: New Insights from Interpretable LSTM ClassifiersYutong Gao, Vince D. Calhoun, Robyn L. Miller. embc 2022: 4645-4648 [doi]

A Supervised Contrastive Learning-based Analysis of rs-tMRI Data Captures Gender Differences in Nonlinear Functional Network CouplingReihaneh Hassanzadeh, Vince D. Calhoun. embc 2022: 4641-4644 [doi]

Data-driven spatio-temporal dynamic brain connectivity analysis using fALFF: Application to sensorimotor task dataKhondoker Murad Hossain, Suchita Bhinge, Qunfang Long, Vince D. Calhoun, Tülay Adali. ciss 2022: 200-205 [doi]

An Accelerated Rank-(L, L, 1, 1) Block Term Decomposition Of Multi-Subject Fmri Data Under Spatial Orthonormality ConstraintLi-Dan Kuang, Biao Wang, Qiu-Hua Lin, Hao-Peng Zhang, Jianming Zhang, Wenjun Li, Feng Li, Vince D. Calhoun. icassp 2022: 3933-3937 [doi]

Mind the gap: functional network connectivity interpolation between schizophrenia patients and controls using a variational autoencoderXinhui Li, Eloy Geenjaar, Zening Fu, Sergey M. Plis, Vince D. Calhoun. embc 2022: 1477-1480 [doi]

Deep Dynamic Effective Connectivity Estimation from Multivariate Time SeriesUsman Mahmood, Zening Fu, Vince D. Calhoun, Sergey M. Plis. ijcnn 2022: 1-10 [doi]

Two-Dimensional Attentive Fusion for Multi-Modal Learning of Neuroimaging and Genomics DataMd Abdur Rahaman, Yash Garg, Armin Iraji, Zening Fu, Jiayu Chen 0003, Vince D. Calhoun. mlsp 2022: 1-6 [doi]

Decentralized Spatially Constrained Source-Based MorphometryDebbrata K. Saha, Rogers F. Silva, Bradley T. Baker, Vince D. Calhoun. isbi 2022: 1-5 [doi]

Longitudinal Whole-Brain Functional Network Change Patterns Over A Two-Year Period In The ABCD DataRekha Saha, Debbrata K. Saha, Md Abdur Rahaman, Zening Fu, Vince D. Calhoun. isbi 2022: 1-4 [doi]

Default mode network dynamic functional network connectivity predicts psychotic symptom severityMohammad S. Eslampanah Sendi, Hossein Dini, Luis Emilio Bruni, Vince D. Calhoun. embc 2022: 247-250 [doi]

Comparison of Energy Signals from the 4D DWT of Resting State FMRI Data Obtained from a Study on SchizophreniaMichael Weeks, Vince D. Calhoun, Robyn L. Miller. embc 2022: 4635-4640 [doi]

Explainable AI (XAI) In Biomedical Signal and Image Processing: Promises and ChallengesGuang Yang, Arvind Rao, Christine Fernandez-Maloigne, Vince D. Calhoun, Gloria Menegaz. icip 2022: 1531-1535 [doi]

Deep Learning From Imaging Genetics for Schizophrenia ClassificationHongkun Yu, Thomas Florian, Vince D. Calhoun, Dong Hye Ye. icip 2022: 3291-3295 [doi]

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