Bayesian multisource data integration for explainable brain-behavior analysis
Different data sources can provide complementary information. Moving from a simple approach based on using one data source at a time to a systems approach that integrates multiple data sources provides an opportunity to understand complex brain disorders or cognitive processes. We propose a data fus...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.1044680/full |
| _version_ | 1811336017523769344 |
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| author | Rong Chen |
| author_facet | Rong Chen |
| author_sort | Rong Chen |
| collection | DOAJ |
| description | Different data sources can provide complementary information. Moving from a simple approach based on using one data source at a time to a systems approach that integrates multiple data sources provides an opportunity to understand complex brain disorders or cognitive processes. We propose a data fusion method, called Bayesian Multisource Data Integration, to model the interactions among data sources and behavioral variables. The proposed method generates representations from data sources and uses Bayesian network modeling to associate representations with behavioral variables. The generated Bayesian network is transparent and easy to understand. Bayesian inference is used to understand how the perturbation of representation is related to behavioral changes. The proposed method was assessed on the simulated data and data from the Adolescent Brain Cognitive Development study. For the Adolescent Brain Cognitive Development study, we found diffusion tensor imaging and resting-state functional magnetic resonance imaging were synergistic in understanding the fluid intelligence composite and the total score composite in healthy youth (9–11 years of age). |
| first_indexed | 2024-04-13T17:33:14Z |
| format | Article |
| id | doaj.art-4dc7efcaff88421cabec5146c3a0f384 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-13T17:33:14Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-4dc7efcaff88421cabec5146c3a0f3842022-12-22T02:37:29ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-10-011610.3389/fnins.2022.10446801044680Bayesian multisource data integration for explainable brain-behavior analysisRong ChenDifferent data sources can provide complementary information. Moving from a simple approach based on using one data source at a time to a systems approach that integrates multiple data sources provides an opportunity to understand complex brain disorders or cognitive processes. We propose a data fusion method, called Bayesian Multisource Data Integration, to model the interactions among data sources and behavioral variables. The proposed method generates representations from data sources and uses Bayesian network modeling to associate representations with behavioral variables. The generated Bayesian network is transparent and easy to understand. Bayesian inference is used to understand how the perturbation of representation is related to behavioral changes. The proposed method was assessed on the simulated data and data from the Adolescent Brain Cognitive Development study. For the Adolescent Brain Cognitive Development study, we found diffusion tensor imaging and resting-state functional magnetic resonance imaging were synergistic in understanding the fluid intelligence composite and the total score composite in healthy youth (9–11 years of age).https://www.frontiersin.org/articles/10.3389/fnins.2022.1044680/fullBayesian networkbrain-behavior analysisexplainable AIBayesian inferencedata fusion |
| spellingShingle | Rong Chen Bayesian multisource data integration for explainable brain-behavior analysis Frontiers in Neuroscience Bayesian network brain-behavior analysis explainable AI Bayesian inference data fusion |
| title | Bayesian multisource data integration for explainable brain-behavior analysis |
| title_full | Bayesian multisource data integration for explainable brain-behavior analysis |
| title_fullStr | Bayesian multisource data integration for explainable brain-behavior analysis |
| title_full_unstemmed | Bayesian multisource data integration for explainable brain-behavior analysis |
| title_short | Bayesian multisource data integration for explainable brain-behavior analysis |
| title_sort | bayesian multisource data integration for explainable brain behavior analysis |
| topic | Bayesian network brain-behavior analysis explainable AI Bayesian inference data fusion |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.1044680/full |
| work_keys_str_mv | AT rongchen bayesianmultisourcedataintegrationforexplainablebrainbehavioranalysis |