Corticospinal Fibers With Different Origins Impact Motor Outcome and Brain After Subcortical Stroke

Abstract

Background and Purpose: Motor deficit is the most common disability after stroke, and early prediction of motor outcome is critical for early interventions. Here, we constructed a fine map of the corticospinal tract (CST) for early prediction of motor outcome and for understanding the secondary brain changes after subcortical stroke. Methods: Diffusion spectrum imaging data from 50 healthy adults were used to reconstruct fine maps of CST with different origins, including primary motor area (M1), primary sensory area (S1), premotor cortex, and supplementary motor area (SMA). Their diffusion properties correlated with motor functions in healthy adults. The impacts of the impairments of different CST on motor outcomes and on structural and functional changes of brain were investigated in 136 patients with subcortical stroke by combining CST damage-symptom association study and voxel-based lesion-symptom mapping. Results: In healthy adults, the isotropy of M1 fiber correlated with walking endurance and that of SMA fiber with motor dexterity. In chronic stroke patients, the integrity of M1 and SMA fibers showed the most significant correlation with motor deficits. The percentage of early damage of M1 and SMA fibers correlated with that of chronic motor deficits. Voxel-based lesion-symptom mapping revealed that acute stroke lesions in the bilateral M1 and right SMA fibers were associated with chronic motor deficits. The early damage of M1 fiber negatively correlated with the integrity of M1-M1 fiber, and the early damage of SMA fiber negatively correlated with gray matter volume of the contralateral cerebellum in the chronic stage. Conclusions: The CST that originated from the M1 and SMA are closely associated with motor outcomes and brain structural changes, and the fine maps of CST from these 2 cortical areas are useful in assessing and predicting long-term motor outcome in patients with subcortical stroke.

Corticospinal recruitment of spinal motor neurons in human stroke survivors

“…Human studies have established that subcortical lesions within the posterior limb of the internal capsule typically result in poorer functional recovery relative to cortical strokes (Shelton & Reding, 2001;Wenzelburger et al 2005), presumably due to overlap with descending corticospinal fibres synapsing on spinal motor neurons. These trends are consistent with a growing number of studies demonstrating that the extent of corticospinal damage early after stroke tracks with functional capacity of the paretic limb (Carter et al 2012;Ingo et al 2020) and is a strong prognostic indicator of later recovery (Stinear et al 2007;Feng et al 2015;Liu et al 2020;Berndt et al 2021). However, post-mortem autopsies of humans with stroke hemiparesis and age-matched controls who died of natural causes revealed axonal loss in the lateral corticospinal tract despite lack of any evidence of α-motor neuronal degeneration (Terao et al 1997).…”

The disrupted topological properties of structural networks showed recovery in ischemic stroke patients: a longitudinal design study

“…Only detected in these regions might relate to their specific function in the brain. Liu et al [ 60 ] reported that the integrity of fibers connecting SMA showed the most significant correlation with motor deficits, and is thus useful for assessing and predicting long-term motor outcome in patients with subcortical stroke. A previous study using voxel-based morphometry on structural MRI found that gray matter volume in the contralesional SMA was positively correlated with the motor function in patients after local subcortical infarction [ 5 ].…”

A Comparative Study of Diffusion Fiber Reconstruction Models for Pyramidal Tract Branches

“…The number of fibers in the innervating upper limbs was larger than that in the lower limbs. More fibers indicate better information transfer, and the number of fibers was related to the difficulty of the action dominating the skeleton muscle ( Liu et al, 2020 ; Pannek et al, 2020 ). Moreover, we shared the groupwise probabilistic maps of the PT branches based on the GQI diffusion model 5 to provide more location information of each PT branch at the different sections for use in clinical practice and neuroscientific research and to supplement the gaps in research on the PT branch.…”