Underlying sources of cognitive-anatomical variation in multi-modal neuroimaging and cognitive testing

Abstract

Healthy adults have robust individual differences in neuroanatomy and cognitive ability not captured by demographics or gross morphology (Luders, Narr, Thompson, & Toga, 2009). We used a hierarchical independent component analysis (hICA) to create novel characterizations of individual differences in our participants (N=190). These components fused data across multiple cognitive tests and neuroanatomical variables. The first level contained four independent, underlying sources of phenotypic variance that predominately modeled broad relationships within types of data (e.g., "white matter," or "subcortical gray matter"), but were not reflective of traditional individual difference measures such as sex, age, or intracranial volume. After accounting for the novel individual difference measures, a second level analysis identified two underlying sources of phenotypic variation. One of these made strong, joint contributions to both the anatomical structures associated with the core fronto-parietal "rich club" network (van den Heuvel & Sporns, 2011), and to cognitive factors. These findings suggest that a hierarchical, data-driven approach is able to identify underlying sources of individual difference that contribute to cognitive-anatomical variation in healthy young adults.

Published by Elsevier Inc.

Figures

Fig. 1

Intercorrelations in multimodal imaging and cognitive data. Global correlation matrix, range of uncorrected non-significant correlations are indicated. In the global correlation matrix, interactions within similar measures (e.g., cortical thickness) exceed interactions between data types (e.g., cortical thickness and cortical volume), suggesting that these different data types are driven by a small number of underlying sources of variance. Independent component analysis (right column) decomposes the global correlations into four underlying sources of global variation.

Fig. 2

First-level ICA brain maps. Projecting these four independent components (ICs) onto a representative brain in standardized MNI space shows underlying sources of variation associated with different brain regions. IC1: predominately fronto-parietal, with moderate positively loadings on cognitive factors. IC2: predominantly fronto-cortical, with slight negative loadings on all cognitive factors. IC3: posterior- and sub-cortical located with negative loadings on cognitive factors. IC4: white matter and ventricles with negative loadings on cognitive factors.

Fig. 3

Residual variation unaccounted for by first-level sources. After regressing out the effects of the first four ICs, considerable cross-domain residual correlations remain. These are decomposed into two second-level residual ICs.

Fig. 4

Second level ICA brain maps. Projecting the two residual ICs onto a representative brain in standard space. RIC1) Has strong cognitive loadings, and a spatial profile highly similar to the “rich club” fronto-parietal control network, with especially strong weightings on white matter connectivity within this network. RIC2) no supra-threshold cognitive loadings, but some strong loadings on white matter and ventricles.