Combination of structural and functional connectivity explains unique variation in specific domains of cognitive function

Abstract

The relationship between structural and functional brain networks has been characterised as complex: the two networks mirror each other and show mutual influence but they also diverge in their organisation. This work explored whether a combination of structural and functional connectivity can improve models of cognitive performance, and whether this differs by cognitive domain. Principal Component Analysis (PCA) was applied to cognitive data from the Human Connectome Project. Four components were obtained, reflecting Retention and Retrieval, Processing Speed, Self-regulation, and Encoding. The PCA-Regression approach was applied to predict cognitive performance using structural, functional and joint structural-functional components. Model quality was evaluated using model evidence, model fit and generalisability. Functional connectivity components produced the most effective models of Retention and Retrieval and Encoding, whereas joint structural-functional components produced most effective models of Processing Speed, and Self-regulation. The present study demonstrates that multimodal data fusion using structural and functional connectivity can help predict cognitive performance, but that the additional explanatory value (relative to overfitting) may depend on the specific selection of cognitive domain. We discuss the implications of these results for studies of the brain basis of cognition in health and disease.

A spatially constrained independent component analysis jointly informed by structural and functional network connectivity

“…Our results represent multimodal ICNs that are more informative and enhanced by incorporating multi-modal information. They provide supporting evidence indicating the impact of the structural connectivity information on neural activities (Litwińczuk et al, 2022).…”

“…Alternatively, there is a hypothesis that structural connectivity may directly influence FNC, as higher structural connectivity leads to higher FNC (Litwińczuk et al, 2022;Zhao et al, 2023).…”

Using fingerprinting as a testbed for strategies to improve reproducibility of functional connectivity

“…One challenge to assessing functional connectivity metrics is the lack of a "ground truth" against which to evaluate their validity and which could therefore serve as a basis for comparing methods and pipelines for computing functional connectivity (Zuo et al, 2019). For example, while functional connectivity shows good correspondence with measures of anatomical or structural connectivity (e.g., as revealed by retrograde tracer studies in non-human animals, or diffusion-based connectivity studies in humans) (Litwińczuk et al, 2022;Margulies et al, 2009), it is clear that functional connectivity metrics reflect more than is captured by these anatomical measures, so correspondence with such measures alone cannot be used to evaluate functional connectivity metrics or methods for their computation. In the absence of a ground truth for evaluation of validity, many studies have focused on test-retest (TRT) reliability as an outcome measure; if a particular functional connectivity metric or analysis step or pipeline is advantageous, it should exhibit improved TRT reliability.…”