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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Seraji, Masoud Mirjalili, Soroush Duarte, Audrey Calhoun, Vince D Georgia State University Georgia Institute of Technology Emory University Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Atlanta GA United States School of Psychology University of Texas at Austin Austin United States

ISBN: (纸本)9798331520526

Sleep is crucial for episodic memory consolidation, yet the impact of habitual sleep quality on memory performance remains underexplored. This study investigates the relationship between sleep quality and episodic memory retrieval using EEG-based representational similarity analysis (RSA). Thirty-six participants wore wrist accelerometers for one week to capture habitual sleep patterns, including total sleep time and restlessness. Memory performance was assessed through a paired associate learning task, with EEG data recorded during encoding and retrieval phases. RSA was applied to EEG oscillatory power across time-frequency windows to examine the neural similarity between encoding and retrieval. The results showed both positive and negative correlations between sleep metric and memory performance, with sleep restlessness being linked to both increases and decreases in neural similarity across specific clusters. These findings emphasize the important role of sleep quality in shaping the neural processes underlying episodic memory retrieval, indicating a strong connection between sleep patterns and memory function. © 2025 IEEE.

关键词： EEG encoding-retrieval similarity (ERS) memory natural sleep representational similarity analysis (RSA)

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Replication and Refinement of Brain Age Model for Adolescent Development

Replication and Refinement of Brain Age Model for Adolescent...

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IEEE International Symposium on Biomedical Imaging

作者： Bhaskar Ray Jiayu Chen Zening Fu Pranav Suresh Bishal Thapaliya Britny Farahdel Vince D. Calhoun Jingyu Liu Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Atlanta USA Department of Computer Science Georgia State University Atlanta USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

The discrepancy between chronological age and estimated brain age, known as the brain age gap, may serve as a biomarker to reveal brain development and neuropsychiatric problems. This has motivated many studies focusing on the accurate estimation of brain age using different features and models, of which the generalizability is yet to be tested. In the current study, we tested the replicability of various pre-trained brain age models on the Adolescent Brain Cognitive Development (ABCD) cohort and furthermore proposed a refined model to improve the robustness of prediction within a narrow age range. The direct replication tests for existing brain age models derived from the age range of 8-22 years onto the ABCD participants at baseline (9 to 10 years old) and year-two follow-up (11 to 12 years old) indicate that pre-trained models could capture the overall mean age but failed precisely estimating brain age variation. The refined model, which combined broad prediction of the pre-trained model and granular information of specific age range, achieved the best performance with a mean absolute error of 0.49 and 0.48 years on the baseline and year-two data, respectively. The brain age gap yielded by the refined model showed significant associations with the participants’ information processing speed and verbal comprehension ability on baseline data.

关键词： Biological system modeling Magnetic resonance imaging Focusing Estimation Information processing Predictive models Brain modeling

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Deep multimodal predictome for studying mental disorders (vol 44, pg 509, 2023)

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HUMAN BRAIN MAPPING 2023年第14期44卷 4967-4967页

作者： Rahaman, M. A. Chen, J. Fu, Z. Lewis, N. Iraji, A. van Erp, T. G. M. Calhoun, V. D. Department of Computational Science and Engineering Georgia Institute of Technology Atlanta Georgia USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta Georgia USA Clinical Translational Neuroscience Laboratory Department of Psychiatry and Human Behavior University of California Irvine Irvine California USA Center for the Neurobiology of Learning and Memory University of California Irvine Irvine California USA

Characterizing neuropsychiatric disorders is challenging due to heterogeneity in the population. We propose combining structural and functional neuroimaging and genomic data in a multimodal classification framework to leverage their complementary information. Our objectives are two-fold (i) to improve the classification of disorders and (ii) to introspect the concepts learned to explore underlying neural and biological mechanisms linked to mental disorders. Previous multimodal studies have focused on naïve neural networks, mostly perceptron, to learn modality-wise features and often assume equal contribution from each modality. Our focus is on the development of neural networks for feature learning and implementing an adaptive control unit for the fusion phase. Our mid fusion with attention model includes a multilayer feed-forward network, an autoencoder, a bi-directional long short-term memory unit with attention as the features extractor, and a linear attention module for controlling modality-specific influence. The proposed model acquired 92% (p < .0001) accuracy in schizophrenia prediction, outperforming several other state-of-the-art models applied to unimodal or multimodal data. Post hoc feature analyses uncovered critical neural features and genes/biological pathways associated with schizophrenia. The proposed model effectively combines multimodal neuroimaging and genomics data for predicting mental disorders. Interpreting salient features identified by the model may advance our understanding of their underlying etiological mechanisms.

关键词： functional network connectivity multimodal deep learning resting-state functional and structural MRI saliency schizophrenia classification single nucleotide polymorphism

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A New Noisy Label Learning Method for Accurate Brain Age Prediction 22

A New Noisy Label Learning Method for Accurate Brain Age Pre...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Du, Yuhui Li, Ruotong Wang, Zheng Calhoun, Vince D. Shanxi University School of Computer and Information Technology Taiyuan China 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

Brain age prediction using neuroimaging data is crucial for understanding the mechanisms of brain aging. However, many previous methods for predicting brain age have relied on chronological age as the guiding label for model training. The models produced may not accurately predict true brain age. To overcome this problem, we propose a new noisy label learning method to train a robust brain age prediction model by addressing the possible label bias in age. The method incorporates two deep neural networks and their corresponding mean-teacher networks, which effectively identify and leverage samples with clean and noisy labels that show consistency and inconsistency between brain age and chronological age, respectively. Experiment using simulated datasets including 10,000 samples demonstrates high predictive accuracy, with a correlation coefficient greater than 0.9 between the predicted brain age and the simulated brain age. Compared with traditional regression algorithms, our method obtains better performance for the simulated datasets. We applied our method to brain functional connectivity data of 10,000 healthy controls from the UK Biobank dataset to validate the capability of our method. Results show that the predicted brain ages have greater correlations with cognitive measures compared to the chronological age. In summary, our new noisy label learning method can effectively predict brain age using neuroimaging data by overcoming potential age bias. © 2025 IEEE.

关键词： brain age prediction deep learning multi-network noisy label learning UK Biobank

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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 ...

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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...

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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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The Dynamics of triple Interactions in Resting FMRI: Insights Into Psychotic Disorders 22

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

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Li, Qiang Calhoun, Vince D. Mirzaeian, Shiva Iraji, Armin Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State Georgia Tech Emory University Atlanta 30303 GA United States Georgia State University Department of Computer Science Atlanta 30303 GA United States

ISBN: (纸本)9798331520526

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. © 2025 IEEE.

关键词： Dynamic triple Interactions High-order Functional Connectivity Multiscale Network Psychotic Brain Disorders Resting-state fMRI

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Genetics Encoded Joint Embedding of Multimodal Connectomes with Explainable Graph Neural Network for Schizophrenia Classification 22

Genetics Encoded Joint Embedding of Multimodal Connectomes w...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Mazumder, Badhan Wu, Lei Calhoun, Vince D. Ye, Dong Hye Georgia State University Department of Computer Science Atlanta GA United States 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

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. © 2025 IEEE.

关键词： Brain Networks Explanation Genomics Graph Neural Network Schizophrenia

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

Dynamic Fusion: Merging Structural and Functional Connectivi...

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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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Classification of Schizophrenia using Intrinsic Connectivity Networks and Incremental Boosting Convolution Neural Networks

Classification of Schizophrenia using Intrinsic Connectivity...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Duc My Vo Sergey Plis Vince D. Calhourn Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State Georgia Tech Emory University Atlanta GA USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

One of the key challenges in the use of resting brain functional magnetic resonance imaging (fMRI) network analysis for predicting mental illnesses such as schizophrenia (SZ) is the high noise levels variability among individuals including age, sex, and different protocols used in labs. To deal with these challenging problems, we designed a recognition method for using brain functional networks to classify SZs and healthy controls (HCs). Our method includes two stages of training. In the first stage, we use a deep convolutional neural network (DCNN) to extract valuable deep features from functional network connectivity (FNC) images. In the next stage, these deep features are used as inputs to a gradient-boosting trees classifier. After the training process, the boosting trees classifier gains a remarkable performance compared to the DCNN classifier. We evaluate this approach using a large dataset of schizophrenia and healthy controls divided into separate validation and training sets. Experimental results showed that the recognition accuracy is over 98 %, compared to a support vector machine baseline of 77% demonstrating the ability of our system to distinguish differences between the two groups. We also estimate heatmaps for each FNC image, representing a 2D FNC matrix indicated which pairs of networks are most predictive of SZ. Our method thus provides both high accuracy, and provides insights into the relevant brain regions for SZ.

关键词： Training Support vector machines Accuracy Protocols Functional magnetic resonance imaging Boosting Feature extraction Convolutional neural networks Random forests Biological neural networks

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