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

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.

Keywords: MRI; human; neuroscience; structure-behavior relationships; structure-function integration.

Figure 1.. Data processing pipeline and main results.

(A) Structural and diffusion-weighted MRI data are used to extract relevant features, that is, Voxel-Based Morphometry (VBM), Fractional Anisotropy (FA), Mean Diffusivity (MD), Anisotropy Mode (MO), Cortical Thickness (CT), Pial Area (PA) and Jacobian Determinants (JD). (B) These features are used as input to the Linked ICA algorithm. (C) Subject loadings of each independent component are fed together with the behavioral/demographic measures into a correlation analysis. The bottom left panel presents demographic and behavioral measures grouped by categories (y-axis), and a representative set of components reflecting significant correlation with at least one behavioral measure (x-axis). The color-scale encodes the Pearson correlation coefficient and only significant correlations are color-coded. In the bottom right panel, we present a summary of component number six significant correlations to behavioral and demographic variates where the behavioral measures are grouped and ordered according to a decreasing correlation value. These results resemble a mode of structural variation that links to and extends the ‘positive-negative’ behavioral spectrum previously attributed to functional connectivity variations (Smith et al., 2015).

Figure 2.. Component number six feature sources of variation.

From top to bottom we visualize the VBM (Voxel Based Morphometry), JD (Jacobian Determinants), FA (Fractional Anisotropy), MD (Mean Diffusivity), MO (Mode of Anisotropy), PA (Pial Area), and CT (Cortical Thickness) spatial maps. For improved visualization, each modality has been thresholded at a z-value of 2. This mode of structural variation, component 6, that strongly reflects a ‘positive-negative’ behavioral spectrum, links to a wide range of brain regions across structural modalities and might reflect the structural multi-modal foundation of a functional brain network linked to these variations that has been earlier identified.

Figure 3.. Summary of relevant modalities spatial maps associated with the components indexed in the most left column.

For component one we show spatial maps for VBM and PA, and for components number 2 and 89 just VBM. For numbers 7 and 24 we present VBM, FA and CT. For number 20 we show VBM and JD and finally for numbers 25, 29 and 55 we present VBM and FA.

Appendix 1—figure 1.. Relative contributions of each feature modality to the most relevant components.

Appendix 1—figure 2.. Top: Significant correlations between the reported (100 dimensional) factorization and a 90 dimensional (left panel) and 110 dimensional (right panel).

Bottom: sorted absolute correlations for each of the components of the reported factorization with the other model orders components. The black discontinuous line represents component number 6.