A Bayesian Regularized and Annotation-Informed Integrative Analysis of Cognition (BRAINIAC)
We present the novel Bayesian Regularized and Annotation-Informed Integrative Analysis of Cognition (BRAINIAC) model. BRAINIAC allows for estimation of total variance explained by all features for a given cognitive phenotype, as well as a principled assessment of the impact of annotations on relativ...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Developmental Cognitive Neuroscience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1878929325000647 |
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| author | Rong W. Zablocki Bohan Xu Chun-Chieh Fan Wesley K. Thompson |
| author_facet | Rong W. Zablocki Bohan Xu Chun-Chieh Fan Wesley K. Thompson |
| author_sort | Rong W. Zablocki |
| collection | DOAJ |
| description | We present the novel Bayesian Regularized and Annotation-Informed Integrative Analysis of Cognition (BRAINIAC) model. BRAINIAC allows for estimation of total variance explained by all features for a given cognitive phenotype, as well as a principled assessment of the impact of annotations on relative enrichment of predictive features compared to others in terms of variance explained, without relying on a potentially unrealistic assumption of sparsity of brain–behavior associations. We validate BRAINIAC in Monte Carlo simulation studies. In real data analyses, we train the BRAINIAC model on resting state functional magnetic resonance imaging (rsMRI) and neuropsychiatric data from the Adolescent Brain Cognitive Development (ABCD) Study and use the trained model in an out-of-study application to harmonized resting-state data from the Human Connectome Project Development (HCP-D), demonstrating a substantial improvement in out-of-study predictive power by incorporating relevant annotations into the BRAINIAC model. |
| format | Article |
| id | doaj-art-b35a797a594a4d22aac33ecfadadb8f0 |
| institution | Kabale University |
| issn | 1878-9293 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Developmental Cognitive Neuroscience |
| spelling | doaj-art-b35a797a594a4d22aac33ecfadadb8f02025-08-20T03:29:10ZengElsevierDevelopmental Cognitive Neuroscience1878-92932025-08-017410156910.1016/j.dcn.2025.101569A Bayesian Regularized and Annotation-Informed Integrative Analysis of Cognition (BRAINIAC)Rong W. Zablocki0Bohan Xu1Chun-Chieh Fan2Wesley K. Thompson3Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USAPopulation Neuroscience and Genetics (PoNG) Center, Laureate Institute for Brain Research, Tulsa, OK, USAPopulation Neuroscience and Genetics (PoNG) Center, Laureate Institute for Brain Research, Tulsa, OK, USAPopulation Neuroscience and Genetics (PoNG) Center, Laureate Institute for Brain Research, Tulsa, OK, USA; Corresponding author.We present the novel Bayesian Regularized and Annotation-Informed Integrative Analysis of Cognition (BRAINIAC) model. BRAINIAC allows for estimation of total variance explained by all features for a given cognitive phenotype, as well as a principled assessment of the impact of annotations on relative enrichment of predictive features compared to others in terms of variance explained, without relying on a potentially unrealistic assumption of sparsity of brain–behavior associations. We validate BRAINIAC in Monte Carlo simulation studies. In real data analyses, we train the BRAINIAC model on resting state functional magnetic resonance imaging (rsMRI) and neuropsychiatric data from the Adolescent Brain Cognitive Development (ABCD) Study and use the trained model in an out-of-study application to harmonized resting-state data from the Human Connectome Project Development (HCP-D), demonstrating a substantial improvement in out-of-study predictive power by incorporating relevant annotations into the BRAINIAC model.http://www.sciencedirect.com/science/article/pii/S1878929325000647Bayesian modelingVariance componentsAnnotationsABCD StudyWhole-brain analyses |
| spellingShingle | Rong W. Zablocki Bohan Xu Chun-Chieh Fan Wesley K. Thompson A Bayesian Regularized and Annotation-Informed Integrative Analysis of Cognition (BRAINIAC) Developmental Cognitive Neuroscience Bayesian modeling Variance components Annotations ABCD Study Whole-brain analyses |
| title | A Bayesian Regularized and Annotation-Informed Integrative Analysis of Cognition (BRAINIAC) |
| title_full | A Bayesian Regularized and Annotation-Informed Integrative Analysis of Cognition (BRAINIAC) |
| title_fullStr | A Bayesian Regularized and Annotation-Informed Integrative Analysis of Cognition (BRAINIAC) |
| title_full_unstemmed | A Bayesian Regularized and Annotation-Informed Integrative Analysis of Cognition (BRAINIAC) |
| title_short | A Bayesian Regularized and Annotation-Informed Integrative Analysis of Cognition (BRAINIAC) |
| title_sort | bayesian regularized and annotation informed integrative analysis of cognition brainiac |
| topic | Bayesian modeling Variance components Annotations ABCD Study Whole-brain analyses |
| url | http://www.sciencedirect.com/science/article/pii/S1878929325000647 |
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