A Bayesian approach to determining connectivity of the human brain - PubMed (original) (raw)

A Bayesian approach to determining connectivity of the human brain

Rajan S Patel et al. Hum Brain Mapp. 2006 Mar.

Abstract

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.

Copyright 2005 Wiley-Liss, Inc.

PubMed Disclaimer

Figures

Figure 1

Figure 1

Functional network consisting of functionally connected brain voxels, w, x, y, and z. Shading for a given voxel indicates elevated activity. w and z are ascendant to x and y, thus x and y can be thought of as satellite voxels to the central voxels, w and z. A, B, C, and D represent different time points in the voxel time series of w, x, y, and z.

Figure 2

Figure 2

Design matrix for Subject 1. The first 12 columns represent experimental design covariates. The column labeled linear represents a linear detrending covariate.

Figure 3

Figure 3

Three voxel pairs (a 1,b 1), (a 2,b 2), and (a 3,b 3) are illustrated, each with a different hierarchical relationship. As the slope of the line from the origin to (P(A a),P(A b)) gets further from 1, the degree of ascendancy between the voxel pair increases.

Figure 4

Figure 4

Functional connectivity from seed placed in the right amygdala (MNI: 26 −1 −12).

Figure 5

Figure 5

Functional connectivity from seed placed in the anteroventral striatum (MNI: 5 18 0).

Similar articles

Cited by

References

    1. Adolphs R (2003): Cognitive neuroscience of human social behaviour. Nat Rev Neurosci 4: 165–178. - PubMed
    1. Ashburner J, Friston KJ (1999): Nonlinear spatial normalization using basis functions. Hum Brain Mapp 7: 254–266. - PMC - PubMed
    1. Axelrod R, Hamilton WD (1981): The evolution of cooperation. Science 211: 1390–1396. - PubMed
    1. Boyd R (1998): Is the repeated Prisoner's Dilemma a good model of reciprocal altruism? Ethol Sociobiol 9: 211–212.
    1. Bullmore E, Horwitz B, Honey G, Brammer M, Williams S, Sharma T (2000): How good is good enough in path analysis of fMRI data? Neuroimage 11: 289–301. - PubMed

MeSH terms

LinkOut - more resources