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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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GUIDELINES FOR THE CHOICE OF THE BASELINE IN XAI ATtriBUTION METHODS

arXiv

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arXiv 2025年

作者： Morasso, Cristian Dolci, Giorgio Galazzo, Ilaria Boscolo Plis, Sergey M. Menegaz, Gloria Department of Engineering for Innovation Medicine University of Verona Verona Italy Department of Computer Science University of Verona Verona Italy Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University United States

Given the broad adoption of artificial intelligence, it is essential to provide evidence that AI models are reliable, trustable, and fair. To this end, the emerging field of eXplainable AI develops techniques to probe such requirements, counterbalancing the hype pushing the pervasiveness of this technology. Among the many facets of this issue, this paper focuses on baseline attribution methods, aiming at deriving a feature attribution map at the network input relying on a "neutral" stimulus usually called "baseline". The choice of the baseline is crucial as it determines the explanation of the network behavior. In this framework, this paper has the twofold goal of shedding light on the implications of the choice of the baseline and providing a simple yet effective method for identifying the best baseline for the task. To achieve this, we propose a decision boundary sampling method, since the baseline, by definition, lies on the decision boundary, which naturally becomes the search domain. Experiments are performed on synthetic examples and validated relying on state-of-the-art methods. Despite being limited to the experimental scope, this contribution is relevant as it offers clear guidelines and a simple proxy for baseline selection, reducing ambiguity and enhancing deep models’ reliability and trust. © 2025, CC BY.

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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...

引用

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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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...

引用

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

arXiv

引用

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

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

引用

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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The Nonlinear Brain: Towards Uncovering Hidden Brain Networks Using Explicitly Nonlinear Functional Interaction

The Nonlinear Brain: Towards Uncovering Hidden Brain Network...

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IEEE International Symposium on Biomedical Imaging

作者： Armin Iraji Katarzyna Kazimierczak Jiayu Chen Sara Motlaghian Karsten Specht Tulay Adali Vince D. Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Department of Biological and Medical Psychology University of Bergen Bergen Norway Department of CSEE University of Maryland Baltimore County Baltimore MD USA

Estimating brain functional networks has been commonly accomplished by applying independent component analysis (ICA) on activity time series collected by resting state fMRI data. Our earlier provided a showcase for the benefit of applying ICA on voxel-wise functional connectivity (FC) commonly measured by second-order statistics such as Pearson correlation instead of activity time series. However, in both cases, i.e., applying ICA on activity time series or FC maps, previous methods are designed to identify brain networks that reflect linear FC. Here, we propose an approach to capture, for the first time, brain networks that are estimated from explicitly nonlinear FC. Results show that networks (e.g., the default mode network) calculated from the linear FC have a strong footprint in explicitly nonlinear FC, and the spatial distributions of the linear and nonlinear interactions differ. In addition, nonlinear information allows us to identify new networks hidden from linear FC, highlighting the possibility of discovering additional information by incorporating nonlinear patterns.

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Reproducibility and Replicability in neuroimaging: Constrained IVA as an Effective Assessment Tool

Reproducibility and Replicability in Neuroimaging: Constrain...

引用

European Signal Processing Conference (EUSIPCO)

作者： Francisco Laport Adriana Dapena Trung Vu Hanlu Yang Vince Calhoun Tülay Adali Department of Computer Science and Electrical Engineering University of Maryland Baltimore County MD USA CITIC Research Center University of A Coruña A Coruña Spain 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)9789464593617

ISBN: (纸本)9798331519773

Matrix decomposition techniques have been successfully applied in the analysis of multi-subject functional magnetic resonance imaging (fMRI) data. These data-driven approaches that assume the linear blind source separation (BSS) problem can yield an unsupervised and fully interpretable solution when there is a good model match. However, selecting a suitable model order that provides an accurate model match is an important challenge. Replicability and computational reproducibility are two key aspects that are also intimately related to interpretability. Despite clear evidence that solutions with poor reproducibility can lead to suboptimal results, the evaluation of reproducibility in matrix decomposition techniques remains limited in the existing literature. We propose the use of constrained independent vector analysis (cIVA), a state-of-the-art joint BSS technique, to assess the influence of model order selection for replicability and reproducibility. We demonstrate the attractiveness of clVA for replicability by alleviating permutation ambiguity as well as enabling additional quantification opportunities. Our results show that highly reproducible model orders achieve a good model match with highly interpretable and replicable solutions when clVA is applied to four different restina-state fMRI datasets.

关键词： Analytical models Computational modeling Europe Functional magnetic resonance imaging Signal processing Reproducibility of results Vectors Blind source separation Matrix decomposition Replicability

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Cross-Modality Translation with Generative Adversarial Networks to Unveil Alzheimer’s Disease Biomarkers

Cross-Modality Translation with Generative Adversarial Netwo...

引用

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

作者： Reihaneh Hassanzadeh Anees Abrol Hamid Reza Hassanzadeh Vince D. Calhoun School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Atlanta GA USA Courtesy Faculty Appointment College of Pharmacy University of Florida

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Generative approaches for cross-modality transformation have recently gained significant attention in neuroimaging. While most previous work has focused on case-control data, the application of generative models to disorder-specific datasets and their ability to preserve diagnostic patterns remain relatively unexplored. Hence, in this study, we investigated the use of a generative adversarial network (GAN) in the context of Alzheimer’s disease (AD) to generate functional network connectivity (FNC) and T1-weighted structural magnetic resonance imaging data from each other. We employed a cycle-GAN to synthesize data in an unpaired data transition and enhanced the transition by integrating weak supervision in cases where paired data were available. Our findings revealed that our model could offer remarkable capability, achieving a structural similarity index measure (SSIM) of 0.89 ± 0.003 for T1s and a correlation of 0.71 ± 0.004 for FNCs. Moreover, our qualitative analysis revealed similar patterns between generated and actual data when comparing AD to cognitively normal (CN) individuals. In particular, we observed significantly increased functional connectivity in cerebellar-sensory motor and cerebellar-visual networks and reduced connectivity in cerebellar-subcortical, auditory-sensory motor, sensory motor-visual, and cerebellar-cognitive control networks. Additionally, the T1 images generated by our model showed a similar pattern of atrophy in the hippocampal and other temporal regions of Alzheimer’s patients.

关键词： neuroimaging Biological system modeling Magnetic resonance imaging Generative adversarial networks Motors Particle measurements data models Indexes Alzheimer's disease Engineering in medicine and biology

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Linking Multi-Scale Brain Connectivity with Vigilance, Working Memory, and Behavior in Adolescents

Linking Multi-Scale Brain Connectivity with Vigilance, Worki...

引用

IEEE International Symposium on Biomedical Imaging

作者： Prerana Bajracharya Ram Ballem Jiayu Chen Pablo Andrés-Camazón Nigar Khasayeva Vince Calhoun Jingyu Liu Armin Iraji Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Atlanta USA Department of Computer Science Georgia State University Atlanta USA Institute of Psychiatry and Mental Health Hospital General Universitario Gregorio Marañón IiSGM CIBERSAM ISCIII School of Medicine Universidad Complutense Madrid Spain

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

This study examines how multi-scale intrinsic connectivity networks (ICNs) relate to cognitive and behavioral functions in adolescents, focusing on attention/vigilance, working memory, and behavioral regulation. Leveraging the NeuroMark 2.2 multi-scale ICN template obtained from over 100,000 subjects, we obtained multi-scale ICNs from baseline resting-state fMRI data from the ABCD Study. For this study, we are interested in “the fronto-thalamo-cerebellar (FTC) circuitry” and choose the subdomains of Neuromark 2.2 that cover it: Cerebellar (CB), Subcortical - Extended Thalamic (SC-ET), Higher Cognition - Insular Temporal (HC-IT), and Higher Cognition - Frontal (HC-FR), previously identified as relevant to cognitive and behavioral functions. Employing a multivariate approach combining principal component analysis (PCA) and canonical correlation analysis (CCA), we examined associations between these multi-scale ICNs and cognitive-behavioral outcomes. Our findings revealed significant associations, particularly for one of the estimated canonical components, linking multi-scale ICNs to cognitive and behavioral measures across both discovery and replication sets. This connectivity pattern may serve as a potential marker for attention, working memory, and behavioral regulation, offering new insights into a wide spectrum of neurodevelopmental disorders including Attention-Deficit/Hyperactivity Disorder (ADHD).

关键词： Correlation Focusing Functional magnetic resonance imaging Biomedical measurement Regulation Cognition Stability analysis Circuit stability Principal component analysis Biomedical imaging

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