Advanced Diffusion MRI for prediction of Stroke Recovery

Abstract

There is an urgent need for ways to improve our understanding of poststroke recovery to inform the development of novel rehabilitative interventions, and improve the clinical management of stroke patients. Supported by the notion that predictive information on poststroke recovery is embedded not only in the individual brain regions, but also the connections throughout the brain, majority of previous investigations have focused on the relationship between brain functional connections and post‐stroke deficit and recovery. However, considering the fact that it is the static anatomical brain connections that constrain and facilitate the dynamic functional brain connections, the microstructures and structural connections of the brain may potentially be better alternatives to the functional MRI‐based biomarkers of stroke recovery. This review, therefore, seeks to provide an overview of the basic concept and applications of two recently proposed advanced diffusion MRI techniques, namely lesion network mapping and fixel‐based morphometry, that may be useful for the investigation of stroke recovery at the local and global levels of the brain. This review will also highlight the application of some of other emerging advanced diffusion MRI techniques that warrant further investigation in the context of stroke recovery research.

Quantitative MRI in leukodystrophies

“…Several recent papers have reviewed quantitative MRI techniques that extract microstructural information ( Alexander et al, 2019 , Lee et al, 2021 , Piredda et al, 2020 , van der Weijden et al, 2022 ). Some of these techniques have been utilized to investigate normal white matter development ( Dean et al, 2014 , Deoni et al, 2012 , Horska et al, 2002 , Jelescu et al, 2015 , Lebel and Deoni, 2018 , Snook et al, 2005 , van Buchem et al, 2001 ), while others have been applied in a wide range of brain diseases, such as stroke ( Hui, 2022 ), neoplasms ( Masjoodi et al, 2018 , Nilsson et al, 2018 ), Alzheimer disease ( Fu et al, 2020 , Graff et al, 2022 ), and multiple sclerosis (MS) ( Alotaibi et al, 2021 , Filippi et al, 2017 , Granziera, C., Wuerfel, J., Barkhof, F., Calabrese, M., De Stefano, N., Enzinger, C., Evangelou, N., Filippi, M., Geurts, J.J.G., Reich, D.S., Rocca, M.A., Ropele, S., Rovira, A., Sati, P., Toosy, A.T., Vrenken, H., Gandini Wheeler-Kingshott, C.A.M., Kappos, L., Group, M.S., 2021 , Hagiwara et al, 2019 ).…”