Integrative Bayesian analysis of neuroimaging-genetic data with application to cocaine dependence
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Neuroimaging and genetic studies provide distinct and complementary information about the structural and biological aspects of a disease. Integrating the two sources of data facilitates the investigation of the links between genetic variability and brain mechanisms among different individuals for various medical disorders. This article presents a general statistical framework for integrative Bayesian analysis of neuroimaging-genetic (iBANG) data, which is motivated by a neuroimaging-genetic study in cocaine dependence. Statistical inference necessitated the integration of spatially dependent voxel-level measurements with various patient-level genetic and demographic characteristics under an appropriate probability model to account for the multiple inherent sources of variation. Our framework uses Bayesian model averaging to integrate genetic information into the analysis of voxel-wise neuroimaging data, accounting for spatial correlations in the voxels. Using multiplicity controls based on the false discovery rate, we delineate voxels associated with genetic and demographic features that may impact diffusion as measured by fractional anisotropy (FA) obtained from DTI images. We demonstrate the benefits of accounting for model uncertainties in both model fit and prediction. Our results suggest that cocaine consumption is associated with FA reduction in most white matter regions of interest in the brain. Additionally, gene polymorphisms associated with GABAergic, serotonergic and dopaminergic neurotransmitters and receptors were associated with FA.
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Cocaine users were reported to have lower white matter fractional anisotropy than controls in several DTI studies.
“Several recent DTI studies have found that cocaine users had lower white matter FA relative to controls (Azadeh et al., 2016; Romero, Asensio, Palau, Sanchez, & Romero, 2010).”
From Substance abuse and white matter: Findings, limitations, and future of diffusion tensor imaging research (2019) · cites this paper as Azadeh et al., 2016
Cocaine users have lower white matter FA than controls.
“Several recent DTI studies have found that cocaine users had lower white matter FA relative to controls (Azadeh et al., 2016; Romero, Asensio, Palau, Sanchez, & Romero, 2010).”
From Substance abuse and white matter: Findings, limitations, and future of diffusion tensor imaging research (2019) · cites this paper as Azadeh et al., 2016
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- Authors
- Azadeh, S., Hobbs, B. P., Ma, L., Nielsen, D. A., Gerard Moeller, F., & Baladandayuthapani, V.
- Affiliations
- The University of Texas MD Anderson Cancer Center (US)
- Virginia Commonwealth University (US)
- Michael E. DeBakey VA Medical Center (US)
- Corresponding author
- Baladandayuthapani, Veerabhadran
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2016-01
- Volume
- 125
- Pages
- 813-824
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC5042574
- OpenAlex
- W2134357550
- Cited by
- 12
- References
- 91
- Citations by year
- 2023: 1 · 2022: 2 · 2021: 2 · 2020: 4 · 2019: 2 · 2017: 1
- FWCI
- 0.27
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIDA (R25T)
- NIH (P30 CA016672, R01 CA160736)
- NSF DMS (1463233)
- NIH/NIDA (DA018197)
- University of Texas MD Anderson Cancer Center (DA026120)
- Toomim Family Fund
- National Institutes of Health (R25DA026120, P50DA009262)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- cocaine dependence, Bayesian model averaging, false discovery rate, fractional anisotropy, diffusion tensor imaging, white matter, genetic polymorphism, GABA receptors, serotonergic receptors, dopamine receptors, model uncertainty
- MeSH terms
- Adult, Bayes Theorem, Brain, Computer Simulation, Female, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Anisotropy, Cocaine-Related Disorders, Polymorphism, Single Nucleotide, Young Adult, Diffusion Tensor Imaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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