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arXiv 2025年

作者： Thapaliya, Bishal Akbas, Esra Sapkota, Ram Ray, Bhaskar Calhoun, Vince Liu, Jingyu Department of Computer Science Georgia State University 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

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 subclusters. 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, CC BY.

关键词： Distribution transformers

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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...

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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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Longitudinal Patterns of Functional Network Changes Over Four-Year Period Associated with Sex in the Developing Brain

Longitudinal Patterns of Functional Network Changes Over Fou...

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IEEE International Symposium on Biomedical Imaging

作者： Rekha Saha Debbrata K. Saha Zening Fu Tulay Adali 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 Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Maryland USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Functional network connectivity (FNC) is a valuable measure for assessing the temporal interdependence and intrinsic functional relationships among brain networks. While longitudinal research on intrinsic functional connectivity has been useful for characterizing age-related changes across whole-brain networks, there has been limited focus on capturing multivariate patterns of FNC changes during brain development. In this study, we employed a previously proposed method that utilizes FNC matrices to estimate multiple overlapping brain functional change patterns (FCPs). This method was applied to the extensive Adolescent Brain and Cognitive Development (ABCD) dataset. Our findings reveal several well-structured FCPs indicating significant changes over a four-year period, including brain functional connectivity between visual network (VSN) and sensorimotor network (SMN) domains. This FNC expression pattern strengthens with age. Additionally, we observed a distinct pattern of changes between male and female individuals for two components including males show stronger increasing coupling between the default mode network (DMN) and VSN compared to females.

关键词： Couplings Visualization Functional magnetic resonance imaging Biomedical measurement Biomedical imaging

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Multiset FMRI data Analysis with Subject Group Information using Structured Dictionary Learning

Multiset FMRI Data Analysis with Subject Group Information u...

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IEEE International Symposium on Biomedical Imaging

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

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

A dictionary learning (DL)-based method for analyzing multiple functional magnetic resonance imaging (fMRI) data sets is developed. The algorithm can incorporate subject group information to extract neural activation maps indicative of the group differences and the maps shared across groups. Furthermore, multiple data sets are jointly analyzed to obtain the maps common across data sets and those unique to individual data sets. For this, a novel structured supervised DL problem is formulated. Numerical tests on synthetic and real fMRI data sets verify the effectiveness of the proposed method

关键词： data analysis Accuracy Image color analysis Estimation Machine learning Functional magnetic resonance imaging data mining Biomedical imaging

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A New Noisy Label Learning Method for Accurate Brain Age Prediction

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

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IEEE International Symposium on Biomedical Imaging

作者： Yuhui Du Ruotong Li Zheng Wang Vince D Calhoun School of Computer and Information Technology Shanxi University Taiyuan China 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)9798331520526

ISBN: (纸本)9798331520533

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.

关键词： Learning systems neuroimaging Training Accuracy Predictive models Brain modeling Prediction algorithms Time measurement Noise measurement Biomedical imaging

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Low-Rank Tucker Decomposition of Multi-Subject Complex-Valued fMRI data

Low-Rank Tucker Decomposition of Multi-Subject Complex-Value...

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

作者： Bin-Hua Zhao Qiu-Hua Lin Yue Han Jia-Yang Song Yan-Wei Niu Vince D. Calhoun School of Information and Communication Engineering Dalian University of Technology Dalian China 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)9798350368741

ISBN: (纸本)9798350368758

Tucker decomposition has shown advantages in simultaneously extracting group shared and individual features for studying brain function from multi-subject fMRI data. However, Tucker decomposition of complex-valued fMRI data is challenging, since the data are highly noisy, and imposing sparsity constraints on spatial maps, previously used for denoising magnitude-only fMRI data, may remove signal voxels with low amplitudes. Here we propose a new complex-valued low-rank Tucker decomposition (clrTKD) method to extract principal group and individual components from multi-subject fMRI data, and to denoise spatial components based on the small phase change property at a post-processing stage. We derive the update rules using the alternating direction method of multipliers. Simulated and experimental complex-valued fMRI data are used to evaluate the proposed clrTKD method. Results show that the proposed method can extract more contiguous and vital brain activations such as the anterior cingulate cortex region, compared to Tucker decomposition for magnitude-only fMRI data.

关键词： Mental disorders Noise reduction Signal processing algorithms Functional magnetic resonance imaging Signal processing Feature extraction data models data mining Noise measurement Speech processing

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Grassmannian Kernels for Efficient and Effective Detection of Group Differences in fMRI data

Grassmannian Kernels for Efficient and Effective Detection o...

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Annual Conference on Information sciences and Systems (CISS)

作者： Ashfia Binte Habib Ben Gabrielson Hanlu Yang Vince D. Calhoun Tülay Adali Dept. of CSEE 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

Understanding group differences in multi-subject fMRI data is essential for advancing clinical and research applications. Factorizations such as independent component analysis (ICA) and independent vector analysis (IVA) are useful for extracting components revealing group differences among subject datasets; however, the complexity of factorizations can be intractable over many datasets. To efficiently quantify group differences among datasets, we propose a simple and effective measure of dataset similarity by measuring similarity between datasets’ linear subspaces – the set of all possible components within datasets – via the Grassmannian kernel (GK) between two datasets. By comparing component subspaces and not components themselves, the GK provides a useful summary of the overall group differences existing between two datasets’ components without requiring estimation of the components, resulting in a much more computationally friendly analysis useful as a foundational step for deeper analysis (e.g., assessing whether component-level group differences exist before estimating the components in a finer-grained analysis). We validate the GK’s abilities by showing it successfully identifies meaningful group differences in several functional magnetic resonance imaging (fMRI) datasets, quantified via statistical tests on distribution differences. We further observe that the degree of group separability identified by the GK aligns closely with a detailed analysis, functional network connectivity (FNC) differences derived following application of subject-wise ICA to the datasets. We further demonstrate that in addition to detecting group differences prior to an analysis like ICA or IVA, GK can be also used to guide order selection in fMRI data.

关键词： Estimation Independent component analysis Functional magnetic resonance imaging Vectors Complexity theory Kernel Principal component analysis

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Fusion of Multitask fMRI data with Constrained Independent Vector Analysis

Fusion of Multitask fMRI Data with Constrained Independent V...

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Annual Conference on Information sciences and Systems (CISS)

作者： Emin Erdem Kumbasar Hanlu Yang Trung Vu Vince D. Calhoun Tülay Adalı Dept. of CSEE 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

Functional magnetic resonance imaging (fMRI) is a widely used neuroimaging tool for investigating brain function. In multitask fMRI analysis, data fusion methods enable the integration of information across tasks to provide a comprehensive understanding of brain activity. Independent vector analysis (IVA) provides an attractive framework for data fusion as it enables datasets to fully interact with each other by maximizing statistical dependence across the datasets. IVA with multivariate Laplacian distribution IVA-L provides a good model match for fMRI analysis as fMRI signals often exhibit multivariate heavy-tailed distributions. However, IVA can benefit from incorporating prior information when available. This paper proposes a novel way for multitask fMRI data fusion by integrating prior information into an optimized IVA-L framework using a constrained cost function. The proposed method is applied to a multitask fMRI dataset comprising 271 subjects, successfully identifying task-related group differences between healthy controls and schizophrenia patients. Identified important functional areas include the caudate and thalamus during the sensory-motor task (SM), as well as the inferior parietal lobule, superior medial frontal gyrus, and inferior frontal gyrus during the auditory oddball (AOD) task. Additionally, this work highlights the importance of selecting a higher model order and allowing some components to remain unconstrained for the constrained IVA-L framework. These choices enhance the estimation performance and allow the algorithm to capture important information not included in the prior information.

关键词： Visualization Tensors Noise data integration Functional magnetic resonance imaging Schizophrenia Feature extraction Thalamus Vectors Robustness

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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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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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