A fusion analytic framework for investigating functional brain connectivity differences using resting-state fMRI
IntroductionFunctional magnetic resonance imaging (fMRI) data is highly complex and high-dimensional, capturing signals from regions of interest (ROIs) with intricate correlations. Analyzing such data is particularly challenging, especially in resting-state fMRI, where patterns are less identifiable...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2024.1402657/full |
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| author | Yeseul Jeon Jeong-Jae Kim SuMin Yu Junggu Choi Sanghoon Han Sanghoon Han |
| author_facet | Yeseul Jeon Jeong-Jae Kim SuMin Yu Junggu Choi Sanghoon Han Sanghoon Han |
| author_sort | Yeseul Jeon |
| collection | DOAJ |
| description | IntroductionFunctional magnetic resonance imaging (fMRI) data is highly complex and high-dimensional, capturing signals from regions of interest (ROIs) with intricate correlations. Analyzing such data is particularly challenging, especially in resting-state fMRI, where patterns are less identifiable without task-specific contexts. Nonetheless, interconnections among ROIs provide essential insights into brain activity and exhibit unique characteristics across groups.MethodsTo address these challenges, we propose an interpretable fusion analytic framework to identify and understand ROI connectivity differences between two groups, revealing their distinctive features. The framework involves three steps: first, constructing ROI-based Functional Connectivity Networks (FCNs) to manage resting-state fMRI data; second, employing a Self-Attention Deep Learning Model (Self-Attn) for binary classification to generate attention distributions encoding group-level differences; and third, utilizing a Latent Space Item-Response Model (LSIRM) to extract group-representative ROI features, visualized on group summary FCNs.ResultsWe applied our framework to analyze four types of cognitive impairments, demonstrating their effectiveness in identifying significant ROIs that contribute to the differences between the two disease groups. The results reveal distinct connectivity patterns and unique ROI features, which differentiate cognitive impairments. Specifically, our framework highlighted group-specific differences in functional connectivity, validating its capability to capture meaningful insights from high-dimensional fMRI data.DiscussionOur novel interpretable fusion analytic framework addresses the challenges of analyzing high-dimensional, resting-state fMRI data. By integrating FCNs, a Self-Attention Deep Learning Model, and LSIRM, the framework provides an innovative approach to discovering ROI connectivity disparities between groups. The attention distribution and group-representative ROI features offer interpretable insights into brain activity patterns and their variations among cognitive impairment groups. This methodology has significant potential to enhance our understanding of cognitive impairments, paving the way for more targeted therapeutic interventions. |
| format | Article |
| id | doaj-art-16efa7c0c3184ad9bd0337f82fbcaa55 |
| institution | Kabale University |
| issn | 1662-453X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj-art-16efa7c0c3184ad9bd0337f82fbcaa552024-12-11T06:45:00ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2024-12-011810.3389/fnins.2024.14026571402657A fusion analytic framework for investigating functional brain connectivity differences using resting-state fMRIYeseul Jeon0Jeong-Jae Kim1SuMin Yu2Junggu Choi3Sanghoon Han4Sanghoon Han5Department of Statistics, Texas A&M University, College Station, TX, United StatesGraduate Program in Cognitive Science, Yonsei University, Seoul, Republic of KoreaDepartment of Psychology and Neuroscience, Duke University, Durham, NC, United StatesCancer Biology, Cleveland Clinic, Lerner Research Institute, Cleveland, OH, United StatesGraduate Program in Cognitive Science, Yonsei University, Seoul, Republic of KoreaDepartment of Psychology, Yonsei University, Seoul, Republic of KoreaIntroductionFunctional magnetic resonance imaging (fMRI) data is highly complex and high-dimensional, capturing signals from regions of interest (ROIs) with intricate correlations. Analyzing such data is particularly challenging, especially in resting-state fMRI, where patterns are less identifiable without task-specific contexts. Nonetheless, interconnections among ROIs provide essential insights into brain activity and exhibit unique characteristics across groups.MethodsTo address these challenges, we propose an interpretable fusion analytic framework to identify and understand ROI connectivity differences between two groups, revealing their distinctive features. The framework involves three steps: first, constructing ROI-based Functional Connectivity Networks (FCNs) to manage resting-state fMRI data; second, employing a Self-Attention Deep Learning Model (Self-Attn) for binary classification to generate attention distributions encoding group-level differences; and third, utilizing a Latent Space Item-Response Model (LSIRM) to extract group-representative ROI features, visualized on group summary FCNs.ResultsWe applied our framework to analyze four types of cognitive impairments, demonstrating their effectiveness in identifying significant ROIs that contribute to the differences between the two disease groups. The results reveal distinct connectivity patterns and unique ROI features, which differentiate cognitive impairments. Specifically, our framework highlighted group-specific differences in functional connectivity, validating its capability to capture meaningful insights from high-dimensional fMRI data.DiscussionOur novel interpretable fusion analytic framework addresses the challenges of analyzing high-dimensional, resting-state fMRI data. By integrating FCNs, a Self-Attention Deep Learning Model, and LSIRM, the framework provides an innovative approach to discovering ROI connectivity disparities between groups. The attention distribution and group-representative ROI features offer interpretable insights into brain activity patterns and their variations among cognitive impairment groups. This methodology has significant potential to enhance our understanding of cognitive impairments, paving the way for more targeted therapeutic interventions.https://www.frontiersin.org/articles/10.3389/fnins.2024.1402657/fullfMRIADNIfunctional connectivity networkdeep learningLatent Space Item-Response Model |
| spellingShingle | Yeseul Jeon Jeong-Jae Kim SuMin Yu Junggu Choi Sanghoon Han Sanghoon Han A fusion analytic framework for investigating functional brain connectivity differences using resting-state fMRI Frontiers in Neuroscience fMRI ADNI functional connectivity network deep learning Latent Space Item-Response Model |
| title | A fusion analytic framework for investigating functional brain connectivity differences using resting-state fMRI |
| title_full | A fusion analytic framework for investigating functional brain connectivity differences using resting-state fMRI |
| title_fullStr | A fusion analytic framework for investigating functional brain connectivity differences using resting-state fMRI |
| title_full_unstemmed | A fusion analytic framework for investigating functional brain connectivity differences using resting-state fMRI |
| title_short | A fusion analytic framework for investigating functional brain connectivity differences using resting-state fMRI |
| title_sort | fusion analytic framework for investigating functional brain connectivity differences using resting state fmri |
| topic | fMRI ADNI functional connectivity network deep learning Latent Space Item-Response Model |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2024.1402657/full |
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