Recent works showed the reproducibility and clinical potential of some neuroimaging biomarkers derived from diffusion-weighted Magnetic Resonance Imaging (dMRI) in predicting the clinical outcome of stroke patients after six months from the injury. In these works, the descriptors were extensively characterized in contralateral white matter (WM) and more precisely along single connections and motor networks through tract-based analyses followed by statistical evaluation. Conversely to WM, dMRI modelling in gray matter (GM) for characterizing stroke patients based on the contralateral hemisphere is still lacking of literature. Aiming to overcome this issue and to provide a complement to previous studies, we assess Diffusion Tensor Imaging (DTI) derived indices along with descriptors calculated by the more complex three-dimensional Simple Harmonics Reconstruction and Estimation (3D-SHORE) model. Ten subjects with ischemic stroke disease underwent Diffusion Spectrum (DSI) and T1-weighted imaging at three time points. For all subjects and acquisitions, Fractional Anisotropy (FA), Mean Diffusivity (MD), Generalized Fractional Anisotropy (GFA), Propagator Anisotropy (PA), Return To Axis Probability (RTAP), Return To Plane Probability (RTPP) and Mean Square Displacement (MSD) were calculated. The region-based analysis in GM were successively performed. The Spearman’s coefficient revealed the correlation of all the indices values and the clinical motor score depending on the region. ANOVA analysis showed the sensitivity of the DTI and 3D-SHORE derived indices to longitudinal changes highlighting significances in some motor regions as well as in others devoted to cognitive functions such as the frontal pole. The predictive model emphasized PA and FA as potential predictors for clinical motor outcome after six months from stroke (adjusted R2 = 0.848). This study opens the way to a widened investigation of the GM.

Diffusion MRI sensitivity to contralateral GM modulations after stroke

Lorenza Brusini
;
Ilaria Boscolo Galazzo
;
M. Zucchelli
;
G. Menegaz
2018-01-01

Abstract

Recent works showed the reproducibility and clinical potential of some neuroimaging biomarkers derived from diffusion-weighted Magnetic Resonance Imaging (dMRI) in predicting the clinical outcome of stroke patients after six months from the injury. In these works, the descriptors were extensively characterized in contralateral white matter (WM) and more precisely along single connections and motor networks through tract-based analyses followed by statistical evaluation. Conversely to WM, dMRI modelling in gray matter (GM) for characterizing stroke patients based on the contralateral hemisphere is still lacking of literature. Aiming to overcome this issue and to provide a complement to previous studies, we assess Diffusion Tensor Imaging (DTI) derived indices along with descriptors calculated by the more complex three-dimensional Simple Harmonics Reconstruction and Estimation (3D-SHORE) model. Ten subjects with ischemic stroke disease underwent Diffusion Spectrum (DSI) and T1-weighted imaging at three time points. For all subjects and acquisitions, Fractional Anisotropy (FA), Mean Diffusivity (MD), Generalized Fractional Anisotropy (GFA), Propagator Anisotropy (PA), Return To Axis Probability (RTAP), Return To Plane Probability (RTPP) and Mean Square Displacement (MSD) were calculated. The region-based analysis in GM were successively performed. The Spearman’s coefficient revealed the correlation of all the indices values and the clinical motor score depending on the region. ANOVA analysis showed the sensitivity of the DTI and 3D-SHORE derived indices to longitudinal changes highlighting significances in some motor regions as well as in others devoted to cognitive functions such as the frontal pole. The predictive model emphasized PA and FA as potential predictors for clinical motor outcome after six months from stroke (adjusted R2 = 0.848). This study opens the way to a widened investigation of the GM.
2018
DTI, 3D-SHORE, ANOVA, remodelling
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/981437
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