Prognosis of stroke upper limb recovery with physiological variables using regression tree ensembles

A comprehensive guide to BCI-based stroke neurorehabilitation interventions

Brain-Computer Interfaces (BCIs) offer the potential to facilitate neurorehabilitation in stroke patients by decoding user intentions from the central nervous system, thereby enabling control over external devices. Despite their promise, the diverse range of intervention parameters and technical challenges in clinical settings have hindered the accumulation of substantial evidence supporting the efficacy and effectiveness of BCIs in stroke rehabilitation. This article introduces a practical guide designed to navigate through these challenges in conducting BCI interventions for stroke rehabilitation. Applicable regardless of infrastructure and study design limitations, this guide acts as a comprehensive reference for executing BCI-based stroke interventions. Furthermore, it encapsulates insights gleaned from administering hundreds of BCI rehabilitation sessions to stroke patients.•Presents a comprehensive methodology for implementing BCI-based upper extremity therapy in stroke patients.•Provides detailed guidance on the number of sessions, trials, as well as the necessary hardware and software for effective intervention.

PSIs of EEG With Refined Frequency Decomposition Could Prognose Motor Recovery Before Rehabilitation Intervention

Stroke often leads to permanent impairment in motor function. Accurate and quantitative prognosis of potential motor recovery before rehabilitation intervention can help healthcare centers improve resources organization and enable individualized intervention. The context of this paper investigated the potential of using electroencephalography (EEG) functional connectivity (FC) measures as biomarkers for assessing and prognosing improvement of Fugl-Meyer Assessment in upper extremity motor function ( ∆FMU) among participants with chronic stroke. EEG data from resting and motor imagery task were recorded from 13 participants with chronic stroke. Three functional connectivity methods, which were Pearson correlation measure (PCM), weighted Phase Lag Index (wPLI) and phase synchronization index (PSI), were investigated, under three regions of interest (inter-hemispheric, intra-hemispheric, and whole-brain), in two statues (resting and motor imagery), with 15 refined center frequencies. We applied correlation analysis to identify the optimal center frequencies and pairs of synchronized channels that were consistently associated with ∆FMU . Predictive models were generated using regression analysis algorithms based on optimized center frequencies and channel pairs identified from the proposed analysis method, with leave-one-out cross-validation. We found that PSI in the Alpha band (with center frequency of 9Hz) was the most sensitive FC measures for prognosing motor recovery. Strong and significant correlations were identified between the predictions and actual ∆FMU scores both in the resting state ( [Formula: see text], [Formula: see text], N=13) and motor imagery ( [Formula: see text], [Formula: see text], N=13). Our results suggested that EEG connectivity measured with PSI in resting state could be a promising biomarker for quantifying motor recovery before motor rehabilitation intervention.

The Prognostic Utility of Electroencephalography in Stroke Recovery: A Systematic Review and Meta-Analysis

BACKGROUND

Improved ability to predict patient recovery would guide post-stroke care by helping clinicians personalize treatment and maximize outcomes. Electroencephalography (EEG) provides a direct measure of the functional neuroelectric activity in the brain that forms the basis for neuroplasticity and recovery, and thus may increase prognostic ability.

OBJECTIVE

To examine evidence for the prognostic utility of EEG in stroke recovery via systematic review/meta-analysis.

METHODS

Peer-reviewed journal articles that examined the relationship between EEG and subsequent clinical outcome(s) in stroke were searched using electronic databases. Two independent researchers extracted data for synthesis. Linear meta-regressions were performed across subsets of papers with common outcome measures to quantify the association between EEG and outcome.

RESULTS

75 papers were included. Association between EEG and clinical outcomes was seen not only early post-stroke, but more than 6 months post-stroke. The most studied prognostic potential of EEG was in predicting independence and stroke severity in the standard acute stroke care setting. The meta-analysis showed that EEG was associated with subsequent clinical outcomes measured by the Modified Rankin Scale, National Institutes of Health Stroke Scale, and Fugl-Meyer Upper Extremity Assessment (r = .72, .70, and .53 from 8, 13, and 12 papers, respectively). EEG improved prognostic abilities beyond prediction afforded by standard clinical assessments. However, the EEG variables examined were highly variable across studies and did not converge.

CONCLUSIONS

EEG shows potential to predict post-stroke recovery outcomes. However, evidence is largely explorative, primarily due to the lack of a definitive set of EEG measures to be used for prognosis.

Priming Intermittent Theta Burst Stimulation for Hemiparetic Upper Limb After Stroke: A Randomized Controlled Trial

BACKGROUND

Intermittent theta burst stimulation (iTBS) creates a state with increased excitability that permits treatment modalities to induce neuroplasticity and motor learning. Continuous theta burst stimulation before iTBS may induce metaplasticity and boost the facilitatory effect of iTBS. This study investigated the effects of priming iTBS (ie, applying continuous theta burst stimulation before iTBS) on poststroke hemiparetic upper limb recovery.

METHODS

In this randomized controlled trial, 42 patients with chronic stroke were recruited and randomly allocated to 10 sessions of either priming iTBS, nonpriming iTBS, or sham stimulation to the ipsilesional motor cortex, immediately before robot-assisted training. Outcomes included Fugl-Meyer Assessment-Upper Extremity, Action Research Arm Test and mean movement velocity during each robot-assisted training session. Twenty-one patients were enrolled for measuring the sensorimotor beta event-related desynchronization induced by either mirror visual feedback or movement.

RESULTS

The Fugl-Meyer Assessment-Upper Extremity scores revealed a significant time-by-group interaction (P=0.011). Priming and nonpriming iTBS were both superior to sham stimulation in post hoc comparisons; however, the superiority was diminished at follow-up. Among patients with a higher functioning upper limb, priming iTBS yielded a significantly greater improvement in Fugl-Meyer Assessment-Upper Extremity scores than nonpriming iTBS (P=0.025) and sham stimulation (P=0.029) did. No significant interaction was found when analyzing the Action Research Arm Test and mean movement velocity. Priming iTBS enhanced the patients' mirror visual feedback-induced high beta sensorimotor event-related desynchronization over their ipsilesional hemisphere.

CONCLUSIONS

Priming and nonpriming iTBS are both superior to sham stimulation in enhancing treatment gains from robot-assisted training, and patients with a higher functioning upper limb may experience more benefits from priming iTBS. Priming iTBS may facilitate poststroke motor learning by enhancing the permissiveness of the ipsilesional sensorimotor area to therapeutic sensory modalities, such as the mirror visual feedback.

REGISTRATION

URL: https://www.

CLINICALTRIALS

gov; Unique identifier: NCT04034069.