Abstract
Variation in cognitive ability arises from subtle differences in underlying neural architecture. Understanding and predicting individual variability in cognition from the differences in brain networks requires harnessing the unique variance captured by different neuroimaging modalities. Here we adopted a multi-level machine learning approach that combines diffusion, functional, and structural MRI data from the Human Connectome Project (N = 1050) to provide unitary prediction models of various cognitive abilities: global cognitive function, fluid intelligence, crystallized intelligence, impulsivity, spatial orientation, verbal episodic memory and sustained attention. Out-of-sample predictions of each cognitive score were first generated using a sparsity-constrained principal component regression on individual neuroimaging modalities. These individual predictions were then aggregated and submitted to a LASSO estimator that removed redundant variability across channels. This stacked prediction led to a significant improvement in accuracy, relative to the best single modality predictions (approximately 1% to more than 3% boost in variance explained), across a majority of the cognitive abilities tested. Further analysis found that diffusion and brain surface properties contribute the most to the predictive power. Our findings establish a lower bound to predict individual differences in cognition using multiple neuroimaging measures of brain architecture, both structural and functional, quantify the relative predictive power of the different imaging modalities, and reveal how each modality provides unique and complementary information about individual differences in cognitive function.
Highlights
Cognitive abilities are not modularly localized to individual brain areas, but rely on complex operations that are distributed across disparate brain systems (e.g., [1])
In order to unravel this question, studies usually look at one neuroimaging modality and associate the observed brain properties with individual differences in cognitive performance
The weight maps for the local connectome, cortical surface area and thickness, and sub-cortical volumetric features have been uploaded as NIFTI files to https://identifiers.org/ neurovault.collection
Summary
Cognitive abilities are not modularly localized to individual brain areas, but rely on complex operations that are distributed across disparate brain systems (e.g., [1]). Prior work on the association between macroscopic brain systems and individual differences in cognitive ability has, by and large, relied on correlational analyses that usually assess linear changes in a particular cognitive task or measure (e.g., general intelligence quotient) that coincide with specific brain properties such as region size [2, 3], gray matter [4] and white matter [5] volume, cortical thickness [6] and surface area [7], resting-state functional connectivity [8], task-related activity [9], global functional network properties [10], white matter connectivity [11], and other unimodal measures. The lack of out-of-sample validation tests leads to over-optimistic results (i.e., potential overfitting), lowering their generalizability across studies and applicability in clinical routines
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