Search for patterns of functional specificity in the brain: A nonparametric hierarchical Bayesian model for group fMRI data

By Danial Lashkari, Ramesh Sridharan, E. Vul, Po-Jang Hsieh, Nancy Kanwisher, Polina Golland. 2011

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Functional MRI studies have uncovered a number of brain areas that demonstrate highly specific functional patterns. In the case of visual object recognition, small, focal regions have been characterized with selectivity for visual categories such as human faces. In this paper, we develop an algorithm that automatically learns patterns of functional specificity from fMRI data in a group of subjects. The method does not require spatial alignment of functional images from different subjects. The algorithm is based on a generative model that comprises two main layers. At the lower level, we express the functional brain response to each stimulus as a binary activation variable. At the next level, we define a prior over sets of activation variables in all subjects. We use a Hierarchical Dirichlet Process as the prior in order to learn the patterns of functional specificity shared across the group, which we call functional systems, and estimate the number of these systems. Inference based on our model enables automatic discovery and characterization of dominant and consistent functional systems. We apply the method to data from a visual fMRI study comprised of 69 distinct stimulus images. The discovered system activation profiles correspond to selectivity for a number of image categories such as faces, bodies, and scenes. Among systems found by our method, we identify new areas that are deactivated by face stimuli. In empirical comparisons with previously proposed exploratory methods, our results appear superior in capturing the structure in the space of visual categories of stimuli.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Geometry and Topology

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Citations in the corpus

Variational Inference: A Review for Statisticians (2016, background): “…of multiple subjects (Wool-rich et al. 2004), spatial models (Sato et al. 2004; Zumer et al. 2007; Kiebel et al. 2008; Wipf and Nagarajan 2009; Lashkari et al. 2012; Nathoo et al. 2014), brain-computer interfaces (Sykacek, Roberts, and Stokes 2004), and factor models (Manning et al. 2014;…”
Bayes procedures for adaptive inference in inverse problems for the white noise model (2012, methodology): “Some examples of papers using Bayesian methods in nonparametric inverse problems in various applied settings include [3,16,24,27,28].”
Which fMRI clustering gives good brain parcellations? (2014, methodology): “…ontology of brain structures –known anatomical names and concepts–, but they may much better represent the measurements or features of interest, i.e. they provide a better model of the signal ( Flandin et al. , 2002; Simon et al. , 2004; Thirion et al. , 2006; Lashkari et al. , 2010, 2012).”