Creating probabilistic maps of the face network in the adolescent brain: A multicentre functional MRI study

Abstract

Large-scale magnetic resonance (MR) studies of the human brain offer unique opportunities for identifying genetic and environmental factors shaping the human brain. Here, we describe a dataset collected in the context of a multi-centre study of the adolescent brain, namely the IMAGEN Study. We focus on one of the functional paradigms included in the project to probe the brain network underlying processing of ambiguous and angry faces. Using functional MR (fMRI) data collected in 1,110 adolescents, we constructed probabilistic maps of the neural network engaged consistently while viewing the ambiguous or angry faces; 21 brain regions responding to faces with high probability were identified. We were also able to address several methodological issues, including the minimal sample size yielding a stable location of a test region, namely the fusiform face area (FFA), as well as the effect of acquisition site (eight sites) and scanner (four manufacturers) on the location and magnitude of the fMRI response to faces in the FFA. Finally, we provided a comparison between male and female adolescents in terms of the effect sizes of sex differences in brain response to the ambiguous and angry faces in the 21 regions of interest. Overall, we found a stronger neural response to the ambiguous faces in several cortical regions, including the fusiform face area, in female (vs. male) adolescents, and a slightly stronger response to the angry faces in the amygdala of male (vs. female) adolescents.

Robust regression for large-scale neuroimaging studies

“…It includes four fMRI tasks and a resulting neuroimaging database of 99 different contrast images in more than 2000 subjects, who gave informed signed assent (parents gave informed consent). In the following examples, the faces task (Grosbras and Paus, 2006;Tahmasebi et al, 2012) was used, with the [angry faces -control] contrast, i.e. the difference between watching angry faces and nonbiological stimuli (concentric circles).…”

Quantifying interindividual variability and asymmetry of face-selective regions: A probabilistic functional atlas

“…Several groups have recently begun developing probabilistic atlases (or maps) to characterize face-selective activation (Engell and McCarthy, 2013;Julian et al, 2012;Tahmasebi et al, 2012). Our PFA significantly extended these atlases.…”

“…The interindividual variability of the FSRs is rarely investigated, although the variability has commonly been observed in previous studies (Fox et al, 2009;Goffaux et al, 2011;Kanwisher et al, 1997;Pinsk et al, 2009). Only recently have four groups begun to develop probabilistic atlases (or maps) to characterize the variability of face-selective activation (Engell and McCarthy, 2013;Frost and Goebel, 2012;Julian et al, 2012;Tahmasebi et al, 2012). Three of them (Engell and McCarthy, 2013;Julian et al, 2012;Tahmasebi et al, 2012) constructed the atlases at the voxel level, not at the FSR level; therefore, they didn't characterize the interindividual variability of the FSRs, which is critical information for understanding the structural-functional correspondence in the brain.…”

“…Only recently have four groups begun to develop probabilistic atlases (or maps) to characterize the variability of face-selective activation (Engell and McCarthy, 2013;Frost and Goebel, 2012;Julian et al, 2012;Tahmasebi et al, 2012). Three of them (Engell and McCarthy, 2013;Julian et al, 2012;Tahmasebi et al, 2012) constructed the atlases at the voxel level, not at the FSR level; therefore, they didn't characterize the interindividual variability of the FSRs, which is critical information for understanding the structural-functional correspondence in the brain. One study indeed created the atlas at the FSR level (Frost and Goebel, 2012), but it is only based on 10 subjects and one FSR (i.e., the FFA).…”

fMRI Analysis with Sparse Weisfeiler-Lehman Graph Statistics

“…Numerous approaches have been proposed for analyzing fMRI data. Most of them select features either by a predefined set of regions of interest (ROIs) using prior knowledge [1,2], statistical methods [3,4] or thresholding [5]. Recently, there has been an exploration of sparsity regularization methods in fMRI analysis [6,7].…”