Neuroimaging contrast across the cortical hierarchy is the feature maximally linked to behavior and demographics

Abstract

An essential task of neuroscience is to elucidate the relationship between brain activity, brain structure, and human behavior. This study aims to understand this 3-way relationship by studying the population covariance of resting-state functional connectivity, cortical thickness, and behavioral/demographic measures in a large cohort of individuals. Using a data-driven canonical correlation analysis, we found that maximal pairwise correlations between the three modalities are approximately along the same direction across subjects, which is characterized by the change of the overall positive-negative trait of human behavior. More importantly, this behavioral change is associated with a divergent modulation of both resting-state connectivity and cortical thickness across cortical hierarchies between the higher-order cognitive networks and lower-order sensory/motor regions. The findings suggest that the cross-hierarchy contrast of structural and functional brain measures is tightly linked to the overall positive-negative trait of human behavior/demographics.

On stability of Canonical Correlation Analysis and Partial Least Squares with application to brain-behavior associations

“…the number of principal components retained for analysis: whereas in Fig. 4 100 principal components were used for both datasets, in agreement with previous studies of HCP data [3,[20][21][22][23][24], here we chose the number of principal component with the "max-min detector" from [25]. As the algorithm provided multiple values for the optimal number of components p X (neuroimaging data) and p Y (behavioral and demographic data), we selected here the pair that minimized p X + p Y .…”

“…Brain-behavior associations. CCA and PLS have become popular methods to reveal associations between neuroimaging and behavioral measures [2,3,9,13,[21][22][23][24]28]. The main interest in these applications lies in interpreting weights or loadings to understand the profiles of neural and behavioral features carrying the brain-behavior relationship.…”

“…We focused on two state-of-the-art population neuroimaging datasets: Human Connectome Project (HCP) [1] and UK Biobank (UKB) [2]. Both datasets provide multi-modal neuroimaging data along with a wide range of behavioral and demographic measures, and both have been used in prior studies using CCA to map brain-behavior relationships [2,3,[20][21][22][23][24]. HCP, comprising around 1200 subjects, is one of the lager neuroimaging datasets available and is of exceptional quality.…”

“…After modality-specific preprocessing (see Methods), both datasets in each of the three analyses were deconfounded and reduced to 100 principal components (see Methods and Fig. S1K), in agreement with prior CCA studies of HCP data [3,[20][21][22][23][24]. Functional connectivity features were extracted from fMRI data and structural connectivity features were extracted from dMRI.…”

Gender‐Related and Hemispheric Effects in Cortical Thickness‐Based Hemispheric Brain Morphological Network

“…On the other hand, diffusion tensor imaging (DTI) and functional magnetic resonance imaging MRI (fMRI) have been commonly used for constructing the individual brain networks connected by way of the axonal pathways (in DTI) or coupled changes of brain functional activations (in fMRI), respectively [ 3 , 4 ]. The brain structural covariance network partly reflects the patterns of brain white matter-based physical connectivity [ 5 – 7 ], coordinated oscillations in BOLD signal changes across the whole brain during the resting status (so-called resting state functional connectivity) [ 8 , 9 ], and coordinated brain development and maturation [ 1 , 10 , 11 ]. For the construction of brain network, the sMRI could be preferred to fMRI or DTI because of its advantages in terms of easy access, high signal-to-noise ratio, and relative insensitivity to artifacts.…”