Publication
Predicting brain activity using a Bayesian spatial model
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- Persistent URL
- Last modified
- 05/15/2025
- Type of Material
- Authors
-
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Gordana Derado, Emory UniversityF Dubois Bowman, Emory UniversityLijun Zhang, Emory University
- Language
- English
- Date
- 2013-08-01
- Publisher
- SAGE Publications (UK and US)
- Publication Version
- Copyright Statement
- © The Author(s) 2012 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.
- Final Published Version (URL)
- Title of Journal or Parent Work
- ISSN
- 0962-2802
- Volume
- 22
- Issue
- 4
- Start Page
- 382
- End Page
- 397
- Grant/Funding Information
- This research was supported by NIH grants R01-MH079251 (Bowman) and NIH predoctoral training grant T32 GM074909-01 (Derado).
- Supplemental Material (URL)
- Abstract
- Increasing the clinical applicability of functional neuroimaging technology is an emerging objective, e.g. for diagnostic and treatment purposes. We propose a novel Bayesian spatial hierarchical framework for predicting follow-up neural activity based on an individual's baseline functional neuroimaging data. Our approach attempts to overcome some shortcomings of the modeling methods used in other neuroimaging settings, by borrowing strength from the spatial correlations present in the data. Our proposed methodology is applicable to data from various imaging modalities including functional magnetic resonance imaging and positron emission tomography, and we provide an illustration here using positron emission tomography data from a study of Alzheimer's disease to predict disease progression.
- Author Notes
- Keywords
- Research Categories
- Psychology, Cognitive
- Biology, Neuroscience
- Biology, Biostatistics
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