A Bayesian approach to determining connectivity of the human brain
Open accessAbstract
Recent work regarding the analysis of brain imaging data has focused on examining functional and effective connectivity of the brain. We develop a novel descriptive and inferential method to analyze the connectivity of the human brain using functional MRI (fMRI). We assess the relationship between pairs of distinct brain regions by comparing expected joint and marginal probabilities of elevated activity of voxel pairs through a Bayesian paradigm, which allows for the incorporation of previously known anatomical and functional information. We define the relationship between two distinct brain regions by measures of functional connectivity and ascendancy. After assessing the relationship between all pairs of brain voxels, we are able to construct hierarchical functional networks from any given brain region and assess significant functional connectivity and ascendancy in these networks. We illustrate the use of our connectivity analysis using data from an fMRI study of social cooperation among women who played an iterated "Prisoner's Dilemma" game. Our analysis reveals a functional network that includes the amygdala, anterior insula cortex, and anterior cingulate cortex, and another network that includes the ventral striatum, orbitofrontal cortex, and anterior insula. Our method can be used to develop causal brain networks for use with structural equation modeling and dynamic causal models.
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Non-Gaussianities in the timeseries provide a distinct, potentially valuable source of causality information.
“Non-Gaussianities in the timeseries [60, 61] provide a distinct, potentially valuable source of causality information.”
From Functional connectomics from resting-state fMRI (2013) · cites this paper as [60]
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- Authors
- Patel, R. S., Bowman, F. D., & Rilling, J. K.
- Affiliations
- Emory University (US)
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2005-08-09
- Volume
- 27
- Issue
- 3
- Pages
- 267-276
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- PMC
- PMC6871439
- OpenAlex
- W2006240253
- Cited by
- 127
- References
- 38
- Citations by year
- 2026: 3 · 2024: 8 · 2023: 3 · 2022: 7 · 2021: 9 · 2020: 12 · 2019: 9 · 2018: 8 · 2017: 9 · 2016: 7 · 2015: 6 · 2014: 6 · 2013: 9 · 2012: 4
- FWCI
- 3.14
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Human Brain Mapping (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- functional connectivity, effective connectivity, Bayesian inference, Ascendancy, social cooperation, iterated prisoner's dilemma, amygdala, anterior cingulate cortex, ventral striatum, orbitofrontal cortex
- MeSH terms
- Adult, Basal Ganglia, Bayes Theorem, Brain, Brain Mapping, Cerebral Cortex, Cognition, Female, Gyrus Cinguli, Humans, Limbic System, Magnetic Resonance Imaging, Nerve Net, Neural Pathways, Neuropsychological Tests, Models, Statistical, Prefrontal Cortex
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