Inter-individual differences in human brain structure and morphology link to variation in demographics and behavior.

Peter Mulders,Thomas Wolfers,Alberto Llera,Christian F Beckmann

doi:10.7554/elife.44443

Abstract

We perform a comprehensive integrative analysis of multiple structural MR-based brain features and find for the first-time strong evidence relating inter-individual brain structural variations to a wide range of demographic and behavioral variates across a large cohort of young healthy human volunteers. Our analyses reveal that a robust 'positive-negative' spectrum of behavioral and demographic variates, recently associated to covariation in brain function, can already be identified using only structural features, highlighting the importance of careful integration of structural features in any analysis of inter-individual differences in functional connectivity and downstream associations with behavioral/demographic variates.

Highlights

Understanding individual human behavior has attracted the attention of scientists and philosophers since antiquity
Intellectual and clinical advances in the last two centuries allow us to accurately quantify brain structure and function (Lerch et al, 2017; Huettel et al, 2004; Friston et al, 2002; Woolrich et al, 2004; Rorden et al, 2007), and to summarize certain ‘aspects’ of human behavior by means of standardized tests. Such advances facilitate exploratory statistical learning analyses to uncover previously hidden relationships between brain features and human behavior, demographics or pathologies (Poldrack and Farah, 2015). These developments are expected to be pushed even further with the emergence of the big data magnetic resonance imaging (MRI) epidemiology phenomenon (Van Essen et al, 2013; Collins, 2012), and some examples of such expectations have already reported associations with blood-oxygen-level dependent (BOLD) brain function (Finn et al, 2015; Smith et al, 2015); for example, functional connectivity patterns can be used to identify individuals (Finn et al, 2015), predict fluid intelligence (Finn et al, 2015), or describe a mode of functional connectivity variation that relates to lifestyle, happiness and well-being (Smith et al, 2015)
The multi-modal structural brain data analyses (Figure 1, operations A and B) resulted in a total of 100 collections of component maps, each of which can be represented by a collection of 7 spatial maps covering the gray-matter space (voxel-based morphometry feature (VBM)), diffusion skeleton space (Fractional Anisotropy (FA), Mean Diffusivity (MD) and Anisotropy Mode (MO) features), cortical vertex space (cortical thickness (CT) and pial area (PA) features) and a voxel-wise map of the Jacobian deformation (JD)

Summary

Introduction

Understanding individual human behavior has attracted the attention of scientists and philosophers since antiquity. Intellectual and clinical advances in the last two centuries allow us to accurately quantify brain structure and function (Lerch et al, 2017; Huettel et al, 2004; Friston et al, 2002; Woolrich et al, 2004; Rorden et al, 2007), and to summarize certain ‘aspects’ of human behavior by means of standardized tests. Such advances facilitate exploratory statistical learning analyses to uncover previously hidden relationships between brain features and human behavior, demographics or pathologies (Poldrack and Farah, 2015). Given the long-term character of some demographic variables

Methods

Results

Conclusion