Summary

The fact that people think or behave differently from one another is rooted in individual differences in brain anatomy and connectivity. Here, we used repeated-measurement resting-state functional MRI to explore intersubject variability in connectivity. Individual differences in functional connectivity were heterogeneous across the cortex, with significantly higher variability in heteromodal association cortex and lower variability in unimodal cortices. Intersubject variability in connectivity was significantly correlated with the degree of evolutionary cortical expansion, suggesting a potential evolutionary root of functional variability. The connectivity variability was also related to variability in sulcal depth but not cortical thickness, positively correlated with the degree of long-range connectivity but negatively correlated with local connectivity. A meta-analysis further revealed that regions predicting individual differences in cognitive domains are predominantly located in regions of high connectivity variability. Our findings have potential implications for understanding brain evolution and development, guiding intervention, and interpreting statistical maps in neuroimaging.

Highlights

Functional connectivity is most variable in association cortex
Connectivity variability is rooted in evolutionary cortical expansion
Variability is associated with cortical folding and long-range connection
Brain regions of high connectivity variability predict behavioral differences

Introduction

The human brain is characterized by striking interindividual variability in neuroanatomy and function (Frost and Goebel, 2012; Rademacher et al., 2001; Sugiura et al., 2007; van Essen and Dierker, 2007) that is reflected in great individual differences in human cognition and behavior. Such variability is a joint output of genetic and environmental influences that may differentially impact on different brain systems (Glendenning and Masterton, 1998). For example, structural variability of association cortex is less influenced by genetic factors during development (Brun et al., 2009), allowing more variable impact of postnatal environmental factors that lead to the diversity of neural connections beyond their genetic determination (Petanjek et al., 2011). A plethora of evidences suggest that neural systems subserving higher-order association and integration processes are more variable than those implicated in unimodal processing. Language areas for example exhibit overproportionally high variability in cytoarchitectonically defined volume (Amunts et al., 1999), as well as in fMRI-derived localization (Frost and Goebel, 2012). At a macroscopic scale, structural variability in cortical folding is higher in association areas than in the motor cortex (Hill et al., 2010a). In addition, long association white matter fiber tracts are more variable than the optic radiation and the corticospinal tract (Bürgel et al., 2006). In contrast to the large amount of work assessing structural variability across brain areas, individual variability in functional connectivity has not been systematically investigated and quantified.

An individual brain might be best characterized by its connectome (Seung, 2012). One powerful technique for assessing connectivity utilizes fMRI data obtained under resting conditions, often referred to as intrinsic functional connectivity (Fox and Raichle, 2007). Individual differences in intrinsic functional connectivity can predict individual performance variability in several cognitive domains in the healthy (Andrews-Hanna et al., 2007; Seeley et al., 2007; van den Heuvel et al., 2009) and symptom severity in neuropsychiatric disorders (Fox and Greicius, 2010; Greicius, 2008). Quantifying the spatial distribution of intersubject variability in connectivity could therefore provide new insights into the neural underpinnings of individual differences in human functions. This distribution could also have practical implications in guiding surgical mapping, interpreting imaging results (if results are averaged across subjects, it is less likely to obtain a significant effect in highly variable regions) and understanding which areas are the most likely to relate to variability in behavior.

In the present article, we collected intrinsic functional connectivity MRI data on 23 healthy subjects each scanned five times over 6 months. This unique data set allows us to assess the spatial distribution of intersubject variability while controlling for measurement instability based on intrasubject variance. This map of intersubject variability was then directly compared to maps of evolutionary cortical expansion, anatomical variability, and long-range integration and regional segregation (Sepulcre et al., 2010). Finally we performed a meta-analysis to explore how functional connectivity variability may relate to previously observed individual differences in cognition and behavior.

Results

Intersubject Connectivity Variability Is Nonuniformly Distributed across Brain Networks
Intersubject variability in intrinsic functional connectivity was quantified at each vertex of the brain surface after correction for nuisance variance (see Figures S1A and S1B, available online, and Experimental Procedures for the details). Intersubject variability demonstrated a nonuniform distribution across brain regions (Figure 1). Individual differences were largest in heteromodal association cortex including the lateral prefrontal lobe and the temporal-parietal junction and minimal in unimodal sensory and motor cortices. Functional variability was also assessed within 7 specific brain networks (Yeo et al., 2011; Figure 2, top row). Intersubject variability within the boundary of each network was averaged and compared (Figure 2). We found that frontoparietal control and attentional networks demonstrated a high level of functional variability, whereas sensory-motor and visual systems were least variable. The default network demonstrated a moderate level of variability, which is lower than that of frontoparietal and attentional networks, but higher than the variability of sensorimotor and visual networks.

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