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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Ballem, Adithya Ram Bajracharya, Prerana Andrés-Camazón, Pablo Chen, Jiayu Calhoun, Vince D. Iraji, Armin Georgia State University Atlanta United States Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University Atlanta United States Institute of Psychiatry and Mental Health Hospital General Universitario Gregorio Marañón IiSGM Cibersam Isciii School of Medicine Universidad Complutense Madrid Spain

ISBN: (纸本)9798331520526

Psychosis is a functionally disruptive condition, however little is known about the varying connectivity between different sexes and the extent of its variability. Resting-state fMRI (rsfMRI) data from the BSNIP consortium involving 626 controls (CON) and 1127 psychosis (PSY) participants (873 Females and 719 Males) were analyzed. Multiscale functional network connectivity (msFNC) for each subject was extracted by applying multivariate objective optimization ICA with Neuromark 2.2 data-driven reference template. The principal component space of PSY msFNC was then identified, within which sex specificity was uncovered. Our analysis revealed 4 statistically significant sex biotypes, 2 female dominant and 2 male dominant ones with unique connectivity characteristics. Psychosis dysconnectivity along cerebellar-subcortical regions was more prominent in males than females. These biotype findings highlight the manifestations of psychosis across different sexes with varying connectivity deficits relative to CON. © 2025 IEEE.

关键词： msFNC sex specificity Multiscale Functional Network Connectivity Psychosis connectivity subspace Psychosis sex biotypes

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Ultra-High Order Independent Component Analysis for Intrinsic Connectivity Networks in Resting-State Functional Magnetic Resonance Imaging data

Ultra-High Order Independent Component Analysis for Intrinsi...

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IEEE International Symposium on Biomedical Imaging

作者： Shiva Mirzaeian Kyle M. Jensen Adithya Ram Ballem Vince D. Calhoun Armin Iraji Tri-institutional Center for Translational Research in Neuroimaging and Data Science(TReNDS) Atlanta GA Department of Mathematics and Statistics Georgia State University Atlanta GA Department of Computer Science Georgia State University Atlanta GA Department of Psychology Georgia State University Atlanta GA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Spatial group independent component analysis (sgr-ICA) has become a crucial method to understand brain function in functional magnetic resonance imaging (fMRI) research, especially in resting-state fMRI (rs-fMRI) studies. Early studies using low-order sgr-ICA identified large-scale brain networks, but higher-order sgr-ICA (75–150 components) revealed more refined intrinsic connectivity networks (ICNs). While previous studies employing high-order sgr-ICA models (300 or even 1000 components) were often limited by small datasets, we applied an ultra-high-order ICA model with 500 components to a large rs-fMRI dataset of over 100,000 individuals' selecting approximately 58,000 after quality control. This comprehensive dataset enabled us to delineate the brain into fine-grained intrinsic connectivity networks (ICNs), offering a robust and detailed representation of these networks. We also evaluate functional connectivity differences between healthy people and individuals with schizophrenia. Our results demonstrate that ultra-high-order sgr-ICA provides reliable, precise estimations of ICNs and captures significant group differences between healthy people and schizophrenia patients. These findings highlight the potential of ultra-high-order sgr-ICA for improving rs-fMRI's clinical applications and single-subject analyses.

关键词： Stability criteria Independent component analysis Quality control Organizations Functional magnetic resonance imaging Schizophrenia Brain modeling Reliability Indexes Biomedical imaging

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A Multivariate Method for Estimating and comparing whole brain functional connectomes from fMRI and PET data

A Multivariate Method for Estimating and comparing whole bra...

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

作者： D. K. Saha Anastasia Bohsali Rekha Saha Ihab Hajjar 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 University of Texas Southwestern Dallas TX

Positron emission tomography (PET) and magnetic resonance imaging (MRI) are two commonly used imaging techniques to visualize brain function. The use of inter-network covariation (a functional connectome) is a widely used approach to infer links among different brain networks. While whole brain resting fMRI connectomes are widely used, PET data has mostly been analyzed using a few regions of interest. There has been much less work estimating PET spatial networks and almost no work on their connectivity (covariation) in the context of a whole brain data-driven connectome, nor have there been direct comparisons between whole brain PET and fMRI connectomes. Here we present an approach to leverage spatially constrained ICA to compute an estimate of the PET connectome. Results reveal highly modularized connectome patterns that are complementary to that identified from resting fMRI. Similarly, we were able to identify comparable resting networks from a PiB PET scan that can be directly compared to networks in rest fMRI data and results reveal similar, but not identical, network spatial patterns, with the PET networks being slightly smoother and, in some cases, showing variations in subnodes. The resulting networks, decomposed into spatial maps and subject expressions (loading parameters) linked to resting fMRI provide a new way to evaluate the complementary information in PET and fMRI and open up new possibilities for biomarker *** Relevance—This study analyzes the whole-brain PET and fMRI connectomes, capturing the complementary information from both imaging modalities, thereby introducing a new scope for biomarker development.

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yixin Ji Vince D. Calhoun Rongtao Jiang Daoqiang Zhang Shile Qi Department of Artificial Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Atlanta USA Department of Radiology and Biomedical Imaging Yale School of Medicine New Haven USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Dynamic functional connectivity (DFC) can capture the neural activity changes over time in the brain. Most existing DFC constructions rely on sliding windows, which can be highly impacted by window type and width. In addition, previous methods fail to fully optimize for discriminative spatial-temporal (ST) information and can suffer from inter-site heterogeneity, resulting in suboptimal sensitivity to brain disorders. Here, we propose a novel DFC model by combining ST attention-based bidirectional long short-term memory (BiLSTM) and multi-source domain adaptation (DA) to extract inherent ST information and reduce inter-site heterogeneity. An adaptive similarity sparse representation (SR)-based Kalman filter is proposed to obtain DFC with accurate connectivity strength at each time point. ST attention modules are integrated into BiLSTM to capture discriminative ST features with a maximum mean discrepancy (MMD)-constrained module for multi-source DA. Experimental results show that our method achieves high accuracy (90.67%±2.43%) in discriminating schizophrenia (SZ) from controls, outperforming 7 DA, 6 ST, and 5 DFC models. These results demonstrate the effectiveness of the proposed DFC model, which can be used to investigate multi-site fMRI DFC for the diagnosis of brain disorders.

关键词： Adaptation models Accuracy Sparse approximation Bidirectional long short term memory Estimation Schizophrenia Signal processing Brain modeling Kalman filters Speech processing

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Predictive Modeling of Mood Using Functional Network Connectivity: Differential Impacts in Exercise + Smart and Control Groups

Predictive Modeling of Mood Using Functional Network Connect...

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IEEE International Symposium on Biomedical Imaging

作者： Meenu Ajith Dawn M. Aycock Erin B. Tone Jingyu Liu Maria B. Misiura Rebecca Ellis Sergey M. Plis Tricia Z. King Vonetta M. Dotson 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 USA Georgia State University Atlanta GA USA Emory University Atlanta GA USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Accurate mood prediction is essential for understanding brain health dynamics and developing interventions, particularly among young adult populations such as undergraduate students. This study explores the use of static functional network connectivity (sFNC) measures from resting-state fMRI (rs-fMRI) data to predict a composite mood index reflecting anxiety, depression, and stress. An autoencoder was employed to capture nonlinear relationships among mood variables, followed by Gaussian process regression (GPR) to predict the composite index at baseline and post-intervention, as well as changes over time. The model demonstrated strong predictive performance, with $R^{2}$ values of 0.60 (baseline), 0.81 (post-intervention), and 0.64 (change). K-means clustering identified three baseline mood groups (low, medium, high) and two post-intervention groups, linking higher mood index values with increased stress, anxiety, and depression. Notably, the Exercise+Smart group showed greater baseline variability and improved mood post-intervention, shifting to a lower composite mood index cluster.

关键词： Mood Anxiety disorders Autoencoders Gaussian processes Predictive models Depression Particle measurements Indexes Reliability Stress measurement

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Ai-Driven Automated Tool for Abdominal CT Body Composition Analysis in Gastrointestinal Cancer Management 22

Ai-Driven Automated Tool for Abdominal CT Body Composition A...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Nan, Xinyu He, Meng Chen, Zifan Dong, Bin Tang, Lei Zhang, Li Peking University Center for Data Science China Peking University Cancer Hospital and Institute Key Laboratory of Carcinogenesis and Translational Research Ministry of Education Department of Radiology Beijing China Beijing China Peking University Center for Machine Learning Research Beijing China Peking University National Biomedical Imaging Center Beijing China

ISBN: (纸本)9798331520526

The incidence of gastrointestinal cancers remains significantly high, particularly in China, emphasizing the importance of accurate prognostic assessments and effective treatment strategies. research shows a strong correlation between abdominal muscle and fat tissue composition and patient outcomes. However, existing manual methods for analyzing abdominal tissue composition are time-consuming and costly, limiting clinical research scalability. To address these challenges, we developed an AI-driven tool for automated analysis of abdominal CT scans to effectively identify and segment muscle, subcutaneous fat, and visceral fat. Our tool integrates a multi-view localization model and a high-precision 2D nnUNet-based segmentation model, demonstrating a localization accuracy of 90% and a Dice Score Coefficient of 0.967 for segmentation. Furthermore, it features an interactive interface that allows clinicians to refine the segmentation results, ensuring high-quality outcomes effectively. Our tool offers a standardized method for effectively extracting critical abdominal tissues, potentially enhancing the management and treatment for gastrointestinal cancers. The code is available at https://***/NanXinyu/***. © 2025 IEEE.

关键词： Clinical research

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Improving the second-tier classification of methylmalonic acidemia patients using a machine learning ensemble method

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World Journal of Pediatrics 2024年第10期20卷 1090-1101页

作者： Zhi-Xing Zhu Georgi ZGenchev Yan-Min Wang Wei Ji Yong-Yong Ren Guo-Li Tian Sira Sriswasdi Hui Lu Shanghai Engineering Research Center for Big Datain Pediatric Precision Medicine Center for BiomedicalInformaticsShanghai Children’s HospitalSchoolof MedicineShanghai Jiao Tong UniversityShanghaiChina Center of Excellence in Computational Molecular Biology Faculty of MedicineChulalongkorn UniversityBangkokThailand Newborn Screening Center Shanghai Children’s HospitalSchool of MedicineShanghai Jiao Tong UniversityShanghaiChina SJTU-Yale Joint Center for Biostatistics and Data Science National Center for Translational MedicineShanghai JiaoTong UniversityShanghaiChina Center for Artifcial Intelligence in Medicine ResearchAfairsFaculty of MedicineChulalongkorn UniversityBangkokThailand State Key Laboratory of Microbial Metabolism JointInternational Research Laboratory of Metabolic&Developmental SciencesDepartment of Bioinformaticsand BiostatisticsSchool of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghaiChina

Introduction Methylmalonic acidemia(MMA)is a disorder of autosomal recessive inheritance,with an estimated prevalence of 1:50,***-tier clinical diagnostic tests often return many false positives[fve false positive(FP):one true positive(TP)].In this work,our goal was to refne a classifcation model that can minimize the number of false positives,currently an unmet need in the upstream diagnostics of *** We developed machine learning multivariable screening models for MMA with utility as a secondary-tier tool for false positives *** utilized mass spectrometry-based features consisting of 11 amino acids and 31 carnitines derived from dried blood samples of neonatal patients,followed by additional ratio feature *** selection strategies(selection by flter,recursive feature elimination,and learned vector quantization)were used to determine the input set for evaluating the performance of 14 classifcation models to identify a candidate model set for an ensemble model *** Our work identifed computational models that explore metabolic analytes to reduce the number of false positives without compromising *** best results[area under the receiver operating characteristic curve(AUROC)of 97%,sensitivity of 92%,and specifcity of 95%]were obtained utilizing an ensemble of the algorithms random forest,C5.0,sparse linear discriminant analysis,and autoencoder deep neural network stacked with the algorithm stochastic gradient boosting as the *** model achieved a good performance trade-of for a screening application with 6%false-positive rate(FPR)at 95%sensitivity,35%FPR at 99%sensitivity,and 39%FPR at 100%*** The classifcation results and approach of this research can be utilized by clinicians globally,to improve the overall discovery of MMA in pediatric *** improved method,when adjusted to 100%precision,can be used to further inform the diagnostic process journey of MMA and help r

关键词： Machine learning Methylmalonic acidemia Second-tier test Stacking

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

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

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

作者： Bishal Thapaliya Zundong Wu Ram Sapkota Bhaskar Ray Pranav Suresh Santosh Ghimire 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 Department of Applied Sciences and Chemical Engineering Tribhuvan University Nepal

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

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.

关键词： Deep learning neuroimaging Analytical models Biological system modeling Predictive models Functional magnetic resonance imaging Transformers Brain modeling Alzheimer's disease Engineering in medicine and biology

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Fusion of Novel FMRI Features Using Independent Vector Analysis for a Multifaceted Characterization of Schizophrenia

Fusion of Novel FMRI Features Using Independent Vector Analy...

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European Signal Processing Conference (EUSIPCO)

作者： Chunying Jia Mohammad Abu Baker Siddique Akhonda Hanlu Yang Vince D. Calhoun Tülay Adali Dept. of CSEE University of Maryland Baltimore County Baltimore MD USA 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)9789464593617

ISBN: (纸本)9798331519773

The fractional amplitude of low-frequency fluctuation (fALFF) is a widely used feature for resting-state functional magnetic resonance (fMRI) analysis but captures limited information. Here, we propose two novel features, max-TP (maximum amplitude across time points) and max-RSN (maximum values across resting-state networks), that capture temporal peaks and salient spatial components, respectively. Using fMRI data from the Bipolar and Schizophrenia Network for the Intermediate Phenotypes project, we constructed a dataset by combining fALFF with the proposed features. Subsequently, we applied a data fusion framework by utilizing independent vector analysis on this dataset, leveraging both second and higher-order statistical information. Our analysis revealed significant group differences between schizophrenia patients and healthy controls in various brain regions. Notably, differences in the visual cortex were detected across all three feature datasets, suggesting its potential as a schizophrenia biomarker across different measures. Thus, by incorporating new features and a multi-feature data fusion approach, this study provides insights into the multifaceted nature of brain alterations in schizophrenia, emphasizing the importance of conducting neuroimaging analyses with complementary features to explore brain activity changes in psychiatric conditions.

关键词： neuroimaging Brain Visualization Phenotypes Fluctuations data integration Magnetic resonance Functional magnetic resonance imaging Signal processing Vectors

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Deep P-Spline: Theory, Fast Tuning, and Application

arXiv

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arXiv 2025年

作者： Hung, Noah Yi-Ting Lin, Li-Hsiang Calhoun, Vince D. Department of Mathematics and Statistics Georgia State University AtlantaGA30303 United States Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University AtlantaGA30303 United States

Deep neural networks (DNNs) have been widely applied to solve real-world regression problems. However, selecting optimal network structures remains a significant challenge. This study addresses this issue by linking neuron selection in DNNs to knot placement in basis expansion techniques. We introduce a difference penalty that automates knot selection, thereby simplifying the complexities of neuron selection. We name this method Deep P-Spline (DPS). This approach extends the class of models considered in conventional DNN modeling and forms the basis for a latent variable modeling framework using the Expectation-Conditional Maximization (ECM) algorithm for efficient network structure tuning with theoretical guarantees. From a nonparametric regression perspective, DPS is proven to overcome the curse of dimensionality, enabling the effective handling of datasets with a large number of input variables-a scenario where conventional nonparametric regression methods typically underperform. This capability motivates the application of the proposed methodology to computer experiments and image data analyses, where the associated regression problems involving numerous inputs are common. Numerical results validate the effectiveness of the model, underscoring its potential for advanced nonlinear regression tasks. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep neural networks

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