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

Multi-Modal Imaging Genomics Transformer: Attentive Integrat...

引用

IEEE International Symposium on Biomedical Imaging

作者： Nagur Shareef Shaik Teja Krishna Cherukuri Vince Calhoun Dong Hye Ye Department of Computer Science Georgia State University Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State Georgia Tech Emory

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Schizophrenia (SZ) is a severe brain disorder marked by diverse cognitive impairments, abnormalities in brain structure, function, and genetic factors. Its complex symptoms and overlap with other psychiatric conditions complicate diagnosis, necessitating advanced systems. Existing research studies have mostly focused on imaging data, such as structural and functional MRI, for SZ diagnosis. There has been less focus on the integration of genomic features despite their potential in identifying heritable SZ traits. This study introduces a Multi-modal Imaging Genomics Transformer (MIGTrans) that integrates genomics with structural and functional imaging to identify SZ-related abnormalities. MIGTrans improved SZ classification accuracy to 86.05%, offering clear interpretations and identifying key genomic and brain morphological/connectivity patterns linked to SZ.

关键词： Brain Accuracy Magnetic resonance imaging Decision making Genomics Imaging Schizophrenia Transformers Bioinformatics Biomedical imaging

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

Dynamic Fusion: Merging Structural and Functional Connectivi...

引用

IEEE International Symposium on Biomedical Imaging

作者： Lei Wu Marlena Duda Armin Iraji Vince D. Calhoun Tri-institutional center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta Georgia

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

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.

关键词： Bridges Magnetic resonance imaging Merging data integration Organizations Biomedical measurement Time measurement Transient analysis Biomedical imaging

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

Dynamic Convergence of Multiple Overlapping Brain States as ...

引用

IEEE International Symposium on Biomedical Imaging

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

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

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.

关键词： neuroimaging Couplings Fluctuations Mental disorders Independent component analysis Biomarkers Schizophrenia Depression Monitoring Convergence

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

Correlation of Correlation Networks: High-Order Interactions...

引用

IEEE International Symposium on Biomedical Imaging

作者： Qiang Li Jingyu Liu Vince D. Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State Georgia Tech and Emory University Atlanta GA United States

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

To understand collective network behavior in the complex human brain, pairwise correlation networks alone are insufficient for capturing the high-order interactions that extend beyond pairwise interactions and play a crucial role in brain network dynamics. These interactions often reveal intricate relationships among multiple brain networks, significantly influencing cognitive processes. In this study, we explored the correlation of correlation networks and topological network analysis with resting-state fMRI to gain deeper insights into these higher-order interactions and their impact on the topology of brain networks, ultimately enhancing our under-standing of brain function. We observed that the correlation of correlation networks highlighted network connections while preserving the topological structure of correlation networks. Our findings suggested that the correlation of correlation networks surpassed traditional correlation networks, showcasing considerable potential for applications in various areas of network science. 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. These high-order hubs played a crucial role in information integration within the human brain.

关键词： Visualization Correlation Pairwise error probability Network topology Cognitive processes Network analyzers Functional magnetic resonance imaging Topology Behavioral sciences Biomedical imaging

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[18F]Florbetapir Pet Uptake Differences in White Matter Across the Alzheimer's Disease Continuum: From Cognitively Normal to MCI and Dementia

[18F]Florbetapir Pet Uptake Differences in White Matter Acro...

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IEEE International Symposium on Biomedical Imaging

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

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

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.

关键词： neuroimaging Pathology Statistical analysis Grey matter Independent component analysis Robustness Positron emission tomography Alzheimer's disease White matter Extracellular

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

Sex Differences in Coupled Dynamic Functional Connectivity a...

引用

IEEE International Symposium on Biomedical Imaging

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

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

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.

关键词： Couplings Cerebellum Correlation Limiting Head Magnetic resonance imaging Morphology Grey matter Magnetic heads Hippocampus

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Genetics Encoded Joint Embedding of Multimodal Connectomes with Explainable Graph Neural Network for Schizophrenia Classification

Genetics Encoded Joint Embedding of Multimodal Connectomes w...

引用

IEEE International Symposium on Biomedical Imaging

作者： Badhan Mazumder Lei Wu Vince D. Calhoun Dong Hye Ye Department of Computer Science Georgia State University Atlanta GA USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emory University Atlanta GA USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Investigating structural and functional brain connectivity is crucial for understanding neuropsychiatric disorders like schizophrenia (SZ), where genetic markers such as SNPs (single nucleotide polymorphisms) also play a significant role. Graph Neural Networks (GNNs) offer powerful tools for learning complex patterns from connectome data, yet their use across multimodal connectomes remains under-explored. In this study, we developed an explainable multiview GNN framework for SZ classification, integrating genetic markers with connectome features by leveraging message-passing scheme of GNN with Deep Canonical Correlation Analysis (DCCA) for multimodal fusion. Experiments on clinical dataset confirmed the robustness of the framework, highlighting potential clinical biomarkers.

关键词： Correlation Transfer learning Genomics Schizophrenia Biomarkers Graph neural networks Robustness Bioinformatics Biomedical imaging

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The Dynamics of Triple Interactions in Resting FMRI: Insights Into Psychotic Disorders

The Dynamics of Triple Interactions in Resting FMRI: Insight...

引用

IEEE International Symposium on Biomedical Imaging

作者： Qiang Li Vince D. Calhoun Shiva Mirzaeian Armin Iraji Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State Georgia Tech and Emory University Atlanta GA United States Department of Computer Science Georgia State University Atlanta GA United States

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The human brain dynamically integrated and configured information to adapt to the environment. To capture these changes over time, dynamic second-order functional connectivity was typically used to capture transient brain patterns. However, dynamic second-order functional connectivity typically ignored interactions beyond pairwise relationships. To address this limitation, we utilized dynamic triple interactions to investigate multiscale network interactions in the brain. In this study, we evaluated a resting-state fMRI dataset that included individuals with psychotic disorders (PD). We first estimated dynamic triple interactions using resting-state fMRI. After clustering, we estimated cohort-specific and cohort-common states for controls (CN), schizophrenia (SZ), and schizoaffective disorder (SAD). From the cohort-specific states, we observed significant triple interactions, particularly among visual, subcortical, and somatomotor networks, as well as temporal and higher cognitive networks in SZ. In SAD, key interactions involved temporal networks in the initial state and somatomotor networks in subsequent states. From the cohort-common states, we observed that high-cognitive networks were primarily involved in SZ and SAD compared to CN. Furthermore, the most significant differences between SZ and SAD also existed in high-cognitive networks. In summary, we studied PD using dynamic triple interaction, the first time such an approach has been used to study PD. Our findings highlighted the significant potential of dynamic high-order functional connectivity, paving the way for new avenues in the study of the healthy and disordered human brain.

关键词： Visualization Functional magnetic resonance imaging Schizophrenia Transient analysis Biomedical imaging

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

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

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IEEE International Symposium on Biomedical Imaging

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

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

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.

关键词： Biometrics Correlation Cognitive processes Biological system modeling Organizations Predictive models Functional magnetic resonance imaging Transformers Brain modeling Biomedical imaging

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Adaptive Constrained ICA with Mixing Matrix Column Constraints: Application to fMRI data

Adaptive Constrained ICA with Mixing Matrix Column Constrain...

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Annual Conference on Information sciences and Systems (CISS)

作者： Chunying Jia Weixin Wang Trung Vu Hanlu Yang Ben Gabrielson Vince D. Calhoun Tülay Adali University of Maryland Baltimore County Baltimore MD Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

Independent Component Analysis (ICA) is a powerful data-driven method that has been widely applied in functional magnetic resonance imaging (fMRI) data analysis to uncover underlying sources. An attractive way to boost ICA performance is via constraints to guide ICA factors to be similar to user-supplied "references", allowing incorporation of prior-knowledge into the factorization. However, most of existing constrained ICA methods typically only impose source constraints and are unable to impose constraints on the mixing matrix. With multi-subject medical imaging datasets, constraining the mixing matrix with subjects’ symptom-related measurements, such as clinical scores or cognitive variables, enhances the algorithm’s ability to identify brain activities associated with these symptoms. This offers a novel perspective for understanding the pathologies underlying various psychiatric disorders. Therefore, to overcome the limitations of existing constrained ICA algorithms, we introduce a new constrained ICA algorithm: adaptive-reverse constrained matrix entropy bound minimization (arc-M-EBM), which imposes constraints on the mixing matrix and uses adaptive-reverse thresholding to avoid overfitting or underfitting. This approach ensures flexibility and leads to more accurate and interpretable source separation. Simulations demonstrate that arc-M-EBM outperforms traditional ICA methods. Application to resting-state fMRI data from 176 subjects from healthy controls and patients reveals significant relationships between constrained components and clinical measures, enhancing our understanding of brain-behavior relationships.

关键词： Adaptation models Thresholding (Imaging) Source separation Correlation Loading Independent component analysis Functional magnetic resonance imaging Brain modeling data models Context modeling

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