Dataset Information

The structural connectome and motor recovery after stroke: predicting natural recovery.

ABSTRACT: Stroke patients vary considerably in terms of outcomes: some patients present 'natural' recovery proportional to their initial impairment (fitters), while others do not (non-fitters). Thus, a key challenge in stroke rehabilitation is to identify individual recovery potential to make personalized decisions for neuro-rehabilitation, obviating the 'one-size-fits-all' approach. This goal requires (i) the prediction of individual courses of recovery in the acute stage; and (ii) an understanding of underlying neuronal network mechanisms. 'Natural' recovery is especially variable in severely impaired patients, underscoring the special clinical importance of prediction for this subgroup. Fractional anisotropy connectomes based on individual tractography of 92 patients were analysed 2 weeks after stroke (TA) and their changes to 3 months after stroke (TC - TA). Motor impairment was assessed using the Fugl-Meyer Upper Extremity (FMUE) scale. Support vector machine classifiers were trained to separate patients with natural recovery from patients without natural recovery based on their whole-brain structural connectomes and to define their respective underlying network patterns, focusing on severely impaired patients (FMUE < 20). Prediction accuracies were cross-validated internally, in one independent dataset and generalized in two independent datasets. The initial connectome 2 weeks after stroke was capable of segregating fitters from non-fitters, most importantly among severely impaired patients (TA: accuracy = 0.92, precision = 0.93). Secondary analyses studying recovery-relevant network characteristics based on the selected features revealed (i) relevant differences between networks contributing to recovery at 2 weeks and network changes over time (TC - TA); and (ii) network properties specific to severely impaired patients. Important features included the parietofrontal motor network including the intraparietal sulcus, premotor and primary motor cortices and beyond them also attentional, somatosensory or multimodal areas (e.g. the insula), strongly underscoring the importance of whole-brain connectome analyses for better predicting and understanding recovery from stroke. Computational approaches based on structural connectomes allowed the individual prediction of natural recovery 2 weeks after stroke onset, especially in the difficult to predict group of severely impaired patients, and identified the relevant underlying neuronal networks. This information will permit patients to be stratified into different recovery groups in clinical settings and will pave the way towards personalized precision neurorehabilitative treatment.

SUBMITTER: Koch PJ

PROVIDER: S-EPMC8370413 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background: Resting Motor threshold (rMT) is one of the measurement obtained by Transcranial Magnetic Stimulation (TMS) that reflects corticospinal excitability. As a functional marker of the corticospinal pathway, the question arises whether rMT is a suitable biomarker for predicting post-stroke upper limb function. To that aim, we conducted a systematic review of relevant studies that investigated the clinical significance of rMT in stroke survivors by using correlations between upper limb motor scores and rMT. Methods: Studies that reported correlations between upper limb motor function and rMT as a measure of corticospinal excitability in distal arm muscle were identified via a literature search in stroke patients. Two authors extracted the data using a home-made specific form. Subgroup analyses were carried out with patients classified with respect to time post-stroke onset (early vs. chronic stage) and stroke location (cortical, subcortical, or cortico-subcortical). Methodological quality of the study was also evaluated by a published checklist. Results: Eighteen studies with 22 groups (n = 508 stroke patients) were included in this systematic review. Mean methodological quality score was 14.75/24. rMT was often correlated with motor function or hand dexterity (n = 15/22, 68%), explaining on average 31% of the variance of the motor score. Moreover, the results did not seem impacted if patients were examined at the early or chronic stages of stroke. Two findings could not be properly interpreted: (i) the fact that the rMT is an independent predictor of motor function as several confounding factors are well-established, and, (ii) whether the stroke location impacts this prediction. Conclusion: Most of the studies found a correlation between rMT and upper limb motor function after stroke. However, it is still unclear if rMT is an independent predictor of upper limb motor function when taking into account for age, time post stroke onset and level of corticospinal tract damage as confounding factors. Clear-cut conclusions could not be drawn at that time but our results suggest that rMT could be a suitable candidate although future investigations are needed. Systematic Review Registration Number: (https://www.crd.york.ac.uk/prospero/): ID 114317.

Dataset Information

The structural connectome and motor recovery after stroke: predicting natural recovery.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets