A Hierarchical Graph Learning Model for Brain Network Regression Analysis
Brain networks have attracted increasing attention due to the potential to better characterize brain dynamics and abnormalities in neurological and psychiatric conditions. Recent years have witnessed enormous successes in deep learning. Many AI algorithms, especially graph learning methods, have bee...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.963082/full |
| _version_ | 1811292580646748160 |
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| author | Haoteng Tang Lei Guo Xiyao Fu Benjamin Qu Olusola Ajilore Yalin Wang Paul M. Thompson Heng Huang Alex D. Leow Liang Zhan |
| author_facet | Haoteng Tang Lei Guo Xiyao Fu Benjamin Qu Olusola Ajilore Yalin Wang Paul M. Thompson Heng Huang Alex D. Leow Liang Zhan |
| author_sort | Haoteng Tang |
| collection | DOAJ |
| description | Brain networks have attracted increasing attention due to the potential to better characterize brain dynamics and abnormalities in neurological and psychiatric conditions. Recent years have witnessed enormous successes in deep learning. Many AI algorithms, especially graph learning methods, have been proposed to analyze brain networks. An important issue for existing graph learning methods is that those models are not typically easy to interpret. In this study, we proposed an interpretable graph learning model for brain network regression analysis. We applied this new framework on the subjects from Human Connectome Project (HCP) for predicting multiple Adult Self-Report (ASR) scores. We also use one of the ASR scores as the example to demonstrate how to identify sex differences in the regression process using our model. In comparison with other state-of-the-art methods, our results clearly demonstrate the superiority of our new model in effectiveness, fairness, and transparency. |
| first_indexed | 2024-04-13T04:47:31Z |
| format | Article |
| id | doaj.art-03dff03476db4f028ff41b5ef08909da |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-13T04:47:31Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-03dff03476db4f028ff41b5ef08909da2022-12-22T03:01:47ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-07-011610.3389/fnins.2022.963082963082A Hierarchical Graph Learning Model for Brain Network Regression AnalysisHaoteng Tang0Lei Guo1Xiyao Fu2Benjamin Qu3Olusola Ajilore4Yalin Wang5Paul M. Thompson6Heng Huang7Alex D. Leow8Liang Zhan9Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United StatesDepartment of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United StatesDepartment of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United StatesMission San Jose High School, Fremont, CA, United StatesDepartment of Psychiatry, University of Illinois Chicago, Chicago, IL, United StatesDepartment of Computer Science and Engineering, Arizona State University, Tempe, AZ, United StatesImaging Genetics Center, University of Southern California, Los Angeles, CA, United StatesDepartment of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United StatesDepartment of Psychiatry, University of Illinois Chicago, Chicago, IL, United StatesDepartment of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United StatesBrain networks have attracted increasing attention due to the potential to better characterize brain dynamics and abnormalities in neurological and psychiatric conditions. Recent years have witnessed enormous successes in deep learning. Many AI algorithms, especially graph learning methods, have been proposed to analyze brain networks. An important issue for existing graph learning methods is that those models are not typically easy to interpret. In this study, we proposed an interpretable graph learning model for brain network regression analysis. We applied this new framework on the subjects from Human Connectome Project (HCP) for predicting multiple Adult Self-Report (ASR) scores. We also use one of the ASR scores as the example to demonstrate how to identify sex differences in the regression process using our model. In comparison with other state-of-the-art methods, our results clearly demonstrate the superiority of our new model in effectiveness, fairness, and transparency.https://www.frontiersin.org/articles/10.3389/fnins.2022.963082/fullmultimodal brain networkshuman connectome projectgraph learninginterpretable AIadult self-report score |
| spellingShingle | Haoteng Tang Lei Guo Xiyao Fu Benjamin Qu Olusola Ajilore Yalin Wang Paul M. Thompson Heng Huang Alex D. Leow Liang Zhan A Hierarchical Graph Learning Model for Brain Network Regression Analysis Frontiers in Neuroscience multimodal brain networks human connectome project graph learning interpretable AI adult self-report score |
| title | A Hierarchical Graph Learning Model for Brain Network Regression Analysis |
| title_full | A Hierarchical Graph Learning Model for Brain Network Regression Analysis |
| title_fullStr | A Hierarchical Graph Learning Model for Brain Network Regression Analysis |
| title_full_unstemmed | A Hierarchical Graph Learning Model for Brain Network Regression Analysis |
| title_short | A Hierarchical Graph Learning Model for Brain Network Regression Analysis |
| title_sort | hierarchical graph learning model for brain network regression analysis |
| topic | multimodal brain networks human connectome project graph learning interpretable AI adult self-report score |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.963082/full |
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