# Neuroimaging and kinematic biomarkers of post-stroke upper limb motor impairment

## TL;DR

This study identifies neuroimaging and movement-based biomarkers that explain nearly half of the variance in upper limb motor impairment after stroke.

## Contribution

The study introduces a kinematic biomarker of skilled reaching that adds significant explanatory power to traditional neuroimaging markers.

## Key findings

- Neuroimaging and kinematic biomarkers together explain 49% of the variance in motor impairment.
- The Trunk-based Index of Performance (IPt) explains 14% of the variance in motor impairment.
- Corticospinal tract involvement accounts for 27% of the variance in motor impairment.

## Abstract

•Biomarkers provide insight on how movements are affected in people living with stroke.•Neuroimaging and kinematic biomarkers explain 49% variance in motor impairment.•The amount of corticospinal tract affected by stroke accounts for 27% variance in motor impairment.•A kinematic biomarker of skilled reaching accounts for 14% variance in motor impairment.•The resting state motor connectivity accounts for 8% variance in motor impairment.

Biomarkers provide insight on how movements are affected in people living with stroke.

Neuroimaging and kinematic biomarkers explain 49% variance in motor impairment.

The amount of corticospinal tract affected by stroke accounts for 27% variance in motor impairment.

A kinematic biomarker of skilled reaching accounts for 14% variance in motor impairment.

The resting state motor connectivity accounts for 8% variance in motor impairment.

Structural and functional biomarkers derived from magnetic resonance imaging explain some variance in post-stroke motor impairment. The understanding of the nature of impairment and the discrimination between true behavioural motor recovery/restitution and motor compensation may be improved by the addition of kinematic information. The aim of the study was to determine the influence of neuroimaging combined with kinematic biomarkers in explaining the variance in motor impairment of the upper limb. People living with late sub-acute to chronic stroke (n = 25) underwent the Fugl Meyer Assessment – Upper Limb (FMA-UL), magnetic resonance imaging, and completed a reaching task where upper limb and trunk kinematics were recorded. Regression analyses were performed to determine the amount of variability in FMA-UL explained by the following biomarkers: the amount of corticospinal tract impacted by the stroke lesion (CST involvement), interhemispheric and ipsilesional resting state connectivity, and the Trunk-based Index of Performance (IPt) that measures skilled reaching ability while accounting for trunk compensation. CST involvement, interhemispheric connectivity, and the IPt, together explained ∼49 % of the variance in the FMA-UL (F(3,21) = 8.694, p = 0.001, R2adj = 0.49). The IPt explained an additional 14 % of the variance in the FMA-UL compared to CST involvement alone (p = 0.02). The IPt is a relevant kinematic biomarker of post-stroke upper limb motor impairment. Our findings suggest the importance of using multiple categories of biomarkers to better understand the level of post-stroke motor impairment.