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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Khasayeva, Nigar Jensen, Kyle M. Eierud, Cyrus Petropoulos, Helen Calhoun, Vince D. Iraji, Armin Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA United States

ISBN: (纸本)9798331520526

Alzheimer's disease (AD) is widely recognized for hallmark gray matter (GM) changes, including extracellular amyloid plaques and neurofibrillary tangles. However, recent neuroimaging research suggests that white matter (WM) aberrations may also play a significant role in AD risk and development. This study investigates AD dementia by examining regional covariance patterns of WM changes, expanding beyond the conventional focus on GM. Using [18F]Florbetapir ([18F]FBP) Positron Emission Tomography (PET) data from 716 participants in the Alzheimer's Disease neuroimaging Initiative (ADNI), we analyzed inter-subject covarying patterns to reveal latent WM sources associated with AD dementia. Employing a model order of 100 and independent component analysis (ICA), we identified 13 highly reliable and distinct WM components, along with 80 GM components, enabling a comparative analysis of group effects across the AD continuum and highlighting the differential significance of WM integrity. We assessed the discrimination ability of WM components between cognitively normal (CN), mild cognitive impairment (MCI), and AD dementia diagnostic groups. Statistical analysis on 13 WM component weights highlighted significant diagnostic contrasts, with 11 components differentiating AD dementia from CN, 10 distinguishing AD dementia from MCI, and 8 distinguishing MCI from CN. Notably, results were replicable, reinforcing the robustness of latent WM sources. Our findings underscore the importance of WM integrity in AD pathology, suggesting WM degeneration as a potentially integral feature of the AD continuum. This work demonstrates the potential of combining ICA with [18F]FBP PET to illuminate nuanced neurobiological mechanisms underlying AD-related WM degeneration, offering valuable insights that may inform future diagnostic approaches across the AD continuum. © 2025 IEEE.

关键词： Alzheimer's disease Dementia Florbetapir Independent Component Analysis Positron Emission Tomography White Matter

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Dynamic Convergence of Multiple Overlapping Brain States as a Biomarker for Mental Disorders 22

Dynamic Convergence of Multiple Overlapping Brain States as ...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Soleimani, Najme Calhoun, Vince D. Georgia State University Georgia Institute of Technology Emory University Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Atlanta GA United States

ISBN: (纸本)9798331520526

Exploring the biomarkers linked to various psychiatric disorders is essential for improving diagnostic accuracy and monitoring disease progression. Recent developments in neuroimaging techniques have shifted the emphasis from conventional univariate approaches, which evaluate isolated brain areas, to sophisticated multivariate models that examine intricate interactions across multiple brain networks. Dynamic functional network connectivity (dFNC), the coupling among independent component fluctuations, has become an important method in this landscape, providing a sophisticated understanding of how connectivity fluctuates over time between brain regions. In this research, we aim to uncover unique connectivity profiles and their transitions that are characteristic of disorders such as schizophrenia (SCZ), autism spectrum disorder(ASD), bipolar disorder (BPD) and major depressive disorder (MDD) by leveraging FNC-based biomarkers. The results reveal distinct patterns of convergence and divergence in functional connectivity across clinical groups, highlighting increased connectivity variability in ASD and SCZ, stable but rigid patterns in MDD, and moderate divergence in BPD, which together provide insights into disorder-specific neurobiological signatures. © 2025 IEEE.

关键词： biomarkers Dynamic functional network connectivity (dFNC) Independent component analysis (ICA) Mental disorders

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Dynamic Fusion: Merging Structural and Functional Connectivity Dynamics Via Joint CMICA 22

Dynamic Fusion: Merging Structural and Functional Connectivi...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Wu, Lei Duda, Marlena Iraji, Armin Calhoun, Vince D. Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta 30303 GA United States

ISBN: (纸本)9798331520526

This study introduces 'dynamic fusion', a novel framework that bridges structural and functional connectivity in the context of dynamic reconfiguration, to illustrate how brain networks adapt over time. By combining insights from functional MRI data on functional networks with diffusion MRI measurements of anatomical connections, dynamic fusion employs a joint analysis method, 'joint cmICA', to analyze their relationship over time. Our approach gauges the interplay between these joint components across various temporal states, aiming to discover both static and transient features of brain connectivity. Applied to empirical MRI data, our approach reveals how intricately structural connectivity patterns adapt to support functional dynamics and demands. Overall, dynamic fusion is an innovative tool for integrating connectivity data, advancing our understanding into fundamental principles of brain organization and operation. © 2025 IEEE.

关键词： data Fusion Functional Connectivity Magnetic Resonance Imaging Structural Connectivity

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Advanced machine learning in neuroimaging studies via federated learning

Advanced machine learning in neuroimaging studies via federa...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Basodi, Sunitha Romero, Javier Panta, Sandeep Martin, Dylan Plis, Sergey Sarwate, Anand D. Calhoun, Vince D. Georgia State University Georgia Institute of Technology Emory University Tri-Institutional Center for Translational Research in Neuroimaging and Data Science AtlantaGA United States Rutgers University Department of Electrical & Computer Engineering NJ United States

ISBN: (纸本)9798350362480

Federated analysis can help perform large-scale analyses using neuroimaging datasets across various research groups overcoming the limitations of institutional data-sharing policies, privacy or regulatory concerns as it requires no data sharing. In this work, we employ a federated neuromark algorithm to generate independent component analysis (ICA) time courses data from functional magnetic resonance imaging (fMRI) data and feed it to a federated deep neural network (DNN) model to perform classification of schizophrenia patients versus controls. © 2024 IEEE.

关键词： Federated learning

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Improved Dictionary Learning for FMRI data Analysis Capturing Common and Individual Activation Maps 58

Improved Dictionary Learning for FMRI Data Analysis Capturin...

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58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024

作者： Xu, Shuai Jin, Rui Kim, Seung-Jun Adali, Tülay Calhoun, Vince D. University of Maryland Baltimore County Department of Computer Science and Electrical Engineering United States Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology United States Emory University United States

ISBN: (纸本)9798350354058

A dictionary learning (DL)-based method for mul-tisubject functional magnetic resonance imaging (fMRI) data analysis is proposed. The method can incorporate group attributes to find the neural activation maps that are common across groups as well as those that are explanatory of the group differences. The key contribution is to significantly improve the map estimation performance by mitigating undesirable local optima obtained from a supervised DL formulation for fMRI data analysis. We introduce two novel ideas, namely, developing a variant of Fisher's discriminant cost, which is imposed on the non-discriminative features, and optimally permuting the dictionary and the sparse factor such that the solution is not trapped to unwanted local optima. Preliminary tests on synthetic and real data sets verify the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Functional neuroimaging

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Sex Differences in Coupled Dynamic Functional Connectivity and Structural Brain Morphology: Insights from the ABCD Study 22

Sex Differences in Coupled Dynamic Functional Connectivity a...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Kotoski, Aline Wiafe, Sir-Lord Calhoun, Vince Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Atlanta United States Neuroscience Institute Georgia State University Atlanta United States Georgia State University Department of Computer Science Atlanta United States

ISBN: (纸本)9798331520526

This study investigates sex-based differences in brain structure-function coupling using a novel dynamic intermodality source coupling (dIMSC) method, which we use to integrate dynamic functional network connectivity (dFNC) and structural brain morphometry (SBM) data from the ABCD study. This study aims to examine the coupling between dFNC and sMRI gray matter volume, comparing the coupling in relation to sex differences across brain regions. By examining the coupling between dFNC and SBM matrices, we identified distinct functional-structural coupling patterns across sexes in various brain regions. Females showed stronger correlations in areas previously associated with social and language processing, such as the inferior frontal gyrus and superior parietal lobule, while males exhibited stronger correlations in regions that have previously been linked to spatial processing, including the superior temporal gyrus, lingual gyrus, and cerebellum. These findings underscore nuanced sex-specific variations in brain structure-function relationships, offering insights into distinct patterns of the brain. © 2025 IEEE.

关键词： Dynamic structure-function coupling Magnetic resonance imaging Sex differences

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Cross-Sampling Rate Transfer Learning for Enhanced Raw EEG Deep Learning Classifier Performance in Major Depressive Disorder Diagnosis

Cross-Sampling Rate Transfer Learning for Enhanced Raw EEG D...

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IEEE International Symposium on Biomedical Imaging

作者： Charles A. Ellis Robyn L. Miller Vince D. Calhoun Tri-Institutional Center for Translational Research in Neuroimaging Data Science at Georgia State University Emory University and Georgia Institute of Technology

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Transfer learning offers a route for developing robust deep learning models on small raw electroencephalography (EEG) datasets. Nevertheless, the utility of transferring representations from large datasets with lower sampling rates to smaller datasets with higher sampling rates is relatively unexplored. In this study, we transfer representations learned by a convolutional neural network on a large, publicly available sleep dataset (100 Hertz sampling rate) to a major depressive disorder (MDD) diagnosis task (200 Hertz sampling rate) and examine their effects upon the MDD models via spectral and spatial explainability analyses. Importantly, we find that the early convolutional layers have representations that are generalizable across tasks. Moreover, our approach significantly increases mean model accuracy from 82.33% to 86.99%, increases use of lower frequencies, (θ-band), and increases robustness to channel loss. We expect this analysis to provide useful guidance and enable more widespread use of transfer learning in EEG deep learning studies.

关键词： Deep learning Sleep Biological system modeling Transfer learning Brain modeling Depression Electroencephalography

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SELF-Clustering Graph Transformer Approach to Model Resting State Functional Brain Activity 22

SELF-Clustering Graph Transformer Approach to Model Resting ...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Thapaliya, Bishal Akbas, Esra Sapkota, Ram Ray, Bhaskar Calhoun, Vince Liu, Jingyu Georgia State University Department of Computer Science Atlanta United States Tri-Institutional Center for Translational Research in Neuroimaging and Data Science United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9798331520526

Resting-state functional magnetic resonance imaging (rs-fMRI) offers valuable insights into the human brain's functional organization and is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this study, we introduce a novel attention mechanism for graphs with subnetworks, named Self Clustering Graph Transformer (SCGT), designed to handle the issue of uniform node updates in graph transformers. By using static functional connectivity (FC) correlation features as input to the transformer model, SCGT effectively captures the sub-network structure of the brain by performing cluster-specific updates to the nodes unlike uniform node updates like vanilla graph transformers, further allowing us to learn and interpret the sub clusters. We validate our approach on the Adolescent Brain Cognitive Development (ABCD) dataset, comprising 7,957 participants, for the prediction of total cognitive score and gender classification. Our results demonstrate that SCGT outperforms the vanilla graph transformer method, and other recent models, offering a promising tool for modeling brain functional connectivity and interpreting the underlying subnetwork structures. © 2025 IEEE.

关键词： Brain Networks Cognitive Score Prediction Functional Connectivity Gender Classification Graph Transformers

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Cross-Modal Synthesis of Structural MRI and Functional Connectivity Networks via Conditional ViT-GANs

arXiv

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arXiv 2023年

作者： Bi, Yuda Abrol, Anees Sui, Jing Calhoun, Vince Tri-institutional Center for Translational Research in Neuroimaging and Data Science GSU GATech Emory United States

The cross-modal synthesis between structural magnetic resonance imaging (sMRI) and functional network connectivity (FNC) is a relatively unexplored area in medical imaging, especially with respect to schizophrenia. This study employs conditional Vision Transformer Generative Adversarial Networks (cViT-GANs) to generate FNC data based on sMRI inputs. After training on a comprehensive dataset that included both individuals with schizophrenia and healthy control subjects, our cViT-GAN model effectively synthesized the FNC matrix for each subject, and then formed a group difference FNC matrix, obtaining a Pearson correlation of 0.73 with the actual FNC matrix. In addition, our FNC visualization results demonstrate significant correlations in particular subcortical brain regions, highlighting the model’s capability of capturing detailed structural-functional associations. This performance distinguishes our model from conditional CNN-based GAN alternatives such as Pix2Pix. Our research is one of the first attempts to link sMRI and FNC synthesis, setting it apart from other cross-modal studies that concentrate on T1- and T2-weighted MR images or the fusion of MRI and CT scans. © 2023, CC BY.

关键词： Magnetic resonance imaging

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Graph-based deep learning models in the prediction of early-stage Alzheimers 46

Graph-based deep learning models in the prediction of early-...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Thapaliya, Bishal Wu, Zundong Sapkota, Ram Ray, Bhaskar Suresh, Pranav Ghimire, Santosh Calhoun, Vince Liu, Jingyu Georgia State University Department of Computer Science Atlanta United States Tri-Institutional Center for Translational Research in Neuroimaging and Data Science United States Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta United States Tribhuvan University Department of Applied Sciences and Chemical Engineering Nepal

ISBN: (纸本)9798350371499

Alzheimer's disease is the most common age-related problem and progresses in different stages, from cognitively normal to early mild cognitive impairment, and severe dementia. This study investigates the predictive potential of resting-state functional magnetic resonance imaging (rs-fMRI) and its derived functional connectivity (FC) in understanding Alzheimer's progression. Leveraging deep learning and graph-based models, we introduce two key contributions: 1) a comparative analysis of rs-fMRI time points and FC for Alzheimer's prediction. 2) an innovative graph transformer variant incorporating self-clustering for enhanced prediction accuracy. Experiments on the Alzheimer's Disease neuroimaging Initiative dataset with 830 subjects reveal two notable conclusions. Firstly, rs-fMRI time points offer limited utility compared to functional network connectivity for transformer-based models, even when considering temporal information. Secondly, a clustering-based attention module proves effective for classifying brain networks in predicting Alzheimer's disease progression, providing valuable insights for future research and clinical applications. © 2024 IEEE.

关键词： Prediction models

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