White matter substrates of functional connectivity dynamics in the human brain

NeuroImage

Rosella Ciurleo¹  Salvatore Bertino²  Gianpaolo Antonio Basile³  Victor Nozais³  Alberto Cacciola³  Giuseppe Pio Anastasi⁴  Alessia Bramanti⁵  Demetrio Milardi⁶ 

[1] Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France;Co-Corresponding author. Gianpaolo Antonio Basile, MD, Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy, Phone: [phone].;Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy;Department of Medicine, Surgery and Dentistry ''Medical School of Salerno''- University of Salerno, Italy;Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France;IRCCS Centro Neurolesi “Bonino Pulejo”, Messina, Italy;

关键词: MRI;    ICA;    Networks;    Neuroanatomy;    Resting state;    Tractography;   

DOI  :  

来源: DOAJ

【 摘 要 】

The contribution of structural connectivity to functional connectivity dynamics is still far from being elucidated. Herein, we applied track-weighted dynamic functional connectivity (tw-dFC), a model integrating structural, functional, and dynamic connectivity, on high quality diffusion weighted imaging and resting-state fMRI data from two independent repositories. The tw-dFC maps were analyzed using independent component analysis, aiming at identifying spatially independent white matter components which support dynamic changes in functional connectivity. Each component consisted of a spatial map of white matter bundles that show consistent fluctuations in functional connectivity at their endpoints, and a time course representative of such functional activity. These components show high intra-subject, inter-subject, and inter-cohort reproducibility. We provided also converging evidence that functional information about white matter activity derived by this method can capture biologically meaningful features of brain connectivity organization, as well as predict higher-order cognitive performance.

【 授权许可】

Unknown