Development of a Bayesian multimodal model to detect biomarkers in neuroimaging studies
In this article, we developed a Bayesian multimodal model to detect biomarkers (or neuromarkers) using resting-state functional and structural data while comparing a late-life depression group with a healthy control group. Biomarker detection helps determine a target for treatment intervention to ge...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Neuroimaging |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnimg.2023.1147508/full |
| _version_ | 1797821463587192832 |
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| author | Dulal K. Bhaumik Dulal K. Bhaumik Yue Wang Pei-Shan Yen Olusola A. Ajilore |
| author_facet | Dulal K. Bhaumik Dulal K. Bhaumik Yue Wang Pei-Shan Yen Olusola A. Ajilore |
| author_sort | Dulal K. Bhaumik |
| collection | DOAJ |
| description | In this article, we developed a Bayesian multimodal model to detect biomarkers (or neuromarkers) using resting-state functional and structural data while comparing a late-life depression group with a healthy control group. Biomarker detection helps determine a target for treatment intervention to get the optimal therapeutic benefit for treatment-resistant patients. The borrowing strength of the structural connectivity has been quantified for functional activity while detecting the biomarker. In the biomarker searching process, thousands of hypotheses are generated and tested simultaneously using our novel method to control the false discovery rate for small samples. Several existing statistical approaches, frequently used in analyzing neuroimaging data have been investigated and compared via simulation with the proposed approach to show its excellent performance. Results are illustrated with a live data set generated in a late-life depression study. The role of detected biomarkers in terms of cognitive function has been explored. |
| first_indexed | 2024-03-13T09:53:04Z |
| format | Article |
| id | doaj.art-e92232ae245242fa8ec6b62c166c4d72 |
| institution | Directory Open Access Journal |
| issn | 2813-1193 |
| language | English |
| last_indexed | 2024-03-13T09:53:04Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroimaging |
| spelling | doaj.art-e92232ae245242fa8ec6b62c166c4d722023-05-24T06:09:10ZengFrontiers Media S.A.Frontiers in Neuroimaging2813-11932023-05-01210.3389/fnimg.2023.11475081147508Development of a Bayesian multimodal model to detect biomarkers in neuroimaging studiesDulal K. Bhaumik0Dulal K. Bhaumik1Yue Wang2Pei-Shan Yen3Olusola A. Ajilore4Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, IL, United StatesDepartment of Psychiatry, University of Illinois at Chicago, Chicago, IL, United StatesDivision of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, IL, United StatesDivision of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, IL, United StatesDepartment of Psychiatry, University of Illinois at Chicago, Chicago, IL, United StatesIn this article, we developed a Bayesian multimodal model to detect biomarkers (or neuromarkers) using resting-state functional and structural data while comparing a late-life depression group with a healthy control group. Biomarker detection helps determine a target for treatment intervention to get the optimal therapeutic benefit for treatment-resistant patients. The borrowing strength of the structural connectivity has been quantified for functional activity while detecting the biomarker. In the biomarker searching process, thousands of hypotheses are generated and tested simultaneously using our novel method to control the false discovery rate for small samples. Several existing statistical approaches, frequently used in analyzing neuroimaging data have been investigated and compared via simulation with the proposed approach to show its excellent performance. Results are illustrated with a live data set generated in a late-life depression study. The role of detected biomarkers in terms of cognitive function has been explored.https://www.frontiersin.org/articles/10.3389/fnimg.2023.1147508/fullmultiple testinglocal false discovery ratemultimodalfunctional connectivitymixed-effects model |
| spellingShingle | Dulal K. Bhaumik Dulal K. Bhaumik Yue Wang Pei-Shan Yen Olusola A. Ajilore Development of a Bayesian multimodal model to detect biomarkers in neuroimaging studies Frontiers in Neuroimaging multiple testing local false discovery rate multimodal functional connectivity mixed-effects model |
| title | Development of a Bayesian multimodal model to detect biomarkers in neuroimaging studies |
| title_full | Development of a Bayesian multimodal model to detect biomarkers in neuroimaging studies |
| title_fullStr | Development of a Bayesian multimodal model to detect biomarkers in neuroimaging studies |
| title_full_unstemmed | Development of a Bayesian multimodal model to detect biomarkers in neuroimaging studies |
| title_short | Development of a Bayesian multimodal model to detect biomarkers in neuroimaging studies |
| title_sort | development of a bayesian multimodal model to detect biomarkers in neuroimaging studies |
| topic | multiple testing local false discovery rate multimodal functional connectivity mixed-effects model |
| url | https://www.frontiersin.org/articles/10.3389/fnimg.2023.1147508/full |
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