The Relation Between White Matter Microstructure and Network Complexity: Implications for Processing Efficiency

Abstract

Brain structure has been proposed to facilitate as well as constrain functional interactions within brain networks. Simulation models suggest that integrity of white matter (WM) microstructure should be positively related to the complexity of BOLD signal - a measure of network interactions. Using 121 young adults from the Human Connectome Project, we empirically tested whether greater WM integrity would be associated with greater complexity of the BOLD signal during rest via multiscale entropy. Multiscale entropy measures the lack of predictability within a given time series across varying time scales, thus being able to estimate fluctuating signal dynamics within brain networks. Using multivariate analysis techniques (Partial Least Squares), we found that greater WM integrity was associated with greater network complexity at fast time scales, but less network complexity at slower time scales. These findings implicate two separate pathways through which WM integrity affects brain function in the prefrontal cortex an executive-prefrontal pathway and a perceptuo-occipital pathway. In two additional samples, the main patterns of WM and network complexity were replicated. These findings support simulation models of WM integrity and network complexity and provide new insights into brain structure-function relationships.

Description
Keywords
diffusion tensor imaging, Human Connectome Project, fMRI, multiscale entropy analysis, resting state networks, white matter microstructure, HUMAN CONNECTOME PROJECT, RESTING-STATE FMRI, SUPERIOR LONGITUDINAL FASCICULUS, MULTISCALE ENTROPY ANALYSIS, BRAIN SIGNAL VARIABILITY, AGE-RELATED DIFFERENCES, TIME-SERIES ANALYSIS, FUNCTIONAL CONNECTIVITY, WORKING-MEMORY, RADIAL DIFFUSIVITY, Behavioral Sciences, Neurosciences, Neurosciences & Neurology
Citation
McDonough, I., Siegel, J. (2018): The Relation between White Matter Microstructure and Network Complexity: Implications for Processing Efficiency. Frontiers in Integrative Neuroscience, vol. 12. DOI: 10.3389/fnint.2018.00043