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EEG-based motor network biomarkers for identifying target patients with stroke for upper limb rehabilitation and its construct validity.

ABSTRACT: Rehabilitation is the main therapeutic approach for reducing poststroke functional deficits in the affected upper limb; however, significant between-patient variability in rehabilitation efficacy indicates the need to target patients who are likely to have clinically significant improvement after treatment. Many studies have determined robust predictors of recovery and treatment gains and yielded many great results using linear approachs. Evidence has emerged that the nonlinearity is a crucial aspect to study the inter-areal communication in human brains and abnormality of oscillatory activities in the motor system is linked to the pathological states. In this study, we hypothesized that combinations of linear and nonlinear (cross-frequency) network connectivity parameters are favourable biomarkers for stratifying patients for upper limb rehabilitation with increased accuracy. We identified the biomarkers by using 37 prerehabilitation electroencephalogram (EEG) datasets during a movement task through effective connectivity and logistic regression analyses. The predictive power of these biomarkers was then tested by using 16 independent datasets (i.e. construct validation). In addition, 14 right handed healthy subjects were also enrolled for comparisons. The result shows that the beta plus gamma or theta network features provided the best classification accuracy of 92%. The predictive value and the sensitivity of these biomarkers were 81.3% and 90.9%, respectively. Subcortical lesion, the time poststroke and initial Wolf Motor Function Test (WMFT) score were identified as the most significant clinical variables affecting the classification accuracy of this predictive model. Moreover, 12 of 14 normal controls were classified as having favourable recovery. In conclusion, EEG-based linear and nonlinear motor network biomarkers are robust and can help clinical decision making.

SUBMITTER: Chen CC

PROVIDER: S-EPMC5470671 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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EEG-based motor network biomarkers for identifying target patients with stroke for upper limb rehabilitation and its construct validity.

Chen Chun-Chuan CC Lee Si-Huei SH Wang Wei-Jen WJ Lin Yu-Chen YC Su Mu-Chun MC

PloS one 20170614 6

Rehabilitation is the main therapeutic approach for reducing poststroke functional deficits in the affected upper limb; however, significant between-patient variability in rehabilitation efficacy indicates the need to target patients who are likely to have clinically significant improvement after treatment. Many studies have determined robust predictors of recovery and treatment gains and yielded many great results using linear approachs. Evidence has emerged that the nonlinearity is a crucial a ...[more]

PMID: 28614395

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Project description:Upper limb motor deficits in severe stroke survivors often remain unresolved over extended time periods. Novel neurotechnologies have the potential to significantly support upper limb motor restoration in severely impaired stroke individuals. Here, we review recent controlled clinical studies and reviews focusing on the mechanisms of action and effectiveness of single and combined technology-aided interventions for upper limb motor rehabilitation after stroke, including robotics, muscular electrical stimulation, brain stimulation and brain computer/machine interfaces. We aim at identifying possible guidance for the optimal use of these new technologies to enhance upper limb motor recovery especially in severe chronic stroke patients. We found that the current literature does not provide enough evidence to support strict guidelines, because of the variability of the procedures for each intervention and of the heterogeneity of the stroke population. The present results confirm that neurotechnology-aided upper limb rehabilitation is promising for severe chronic stroke patients, but the combination of interventions often lacks understanding of single intervention mechanisms of action, which may not reflect the summation of single intervention's effectiveness. Stroke rehabilitation is a long and complex process, and one single intervention administrated in a short time interval cannot have a large impact for motor recovery, especially in severely impaired patients. To design personalized interventions combining or proposing different interventions in sequence, it is necessary to have an excellent understanding of the mechanisms determining the effectiveness of a single treatment in this heterogeneous population of stroke patients. We encourage the identification of objective biomarkers for stroke recovery for patients' stratification and to tailor treatments. Furthermore, the advantage of longitudinal personalized trial designs compared to classical double-blind placebo-controlled clinical trials as the basis for precise personalized stroke rehabilitation medicine is discussed. Finally, we also promote the necessary conceptual change from 'one-suits-all' treatments within in-patient clinical rehabilitation set-ups towards personalized home-based treatment strategies, by adopting novel technologies merging rehabilitation and motor assistance, including implantable ones.

Project description:BackgroundAction observation (AO) is a physical rehabilitation approach that facilitates the occurrence of neural plasticity through the activation of the mirror-neural system, promoting motor recovery in people with stroke.ObjectivesTo assess whether AO enhances upper limb motor function in people with stroke.Search methodsWe searched the Cochrane Stroke Group Trials Register (last searched 18 May 2021), the Cochrane Central Register of Controlled Trials (18 May 2021), MEDLINE (1946 to 18 May 2021), Embase (1974 to 18 May 2021), and five additional databases. We also searched trial registries and reference lists.Selection criteriaRandomized controlled trials (RCTs) of AO alone or associated with physical practice in adults after stroke. The primary outcome was upper limb (arm and hand) motor function. Secondary outcomes included dependence on activities of daily living (ADL), motor performance, cortical activation, quality of life, and adverse effects.Data collection and analysisTwo review authors independently selected trials according to the predefined inclusion criteria, extracted data, assessed risk of bias using RoB 1, and applied the GRADE approach to assess the certainty of the evidence. The reviews authors contacted trial authors for clarification and missing information.Main resultsWe included 16 trials involving 574 individuals. Most trials provided AO followed by the practice of motor actions. Training varied between 1 day and 8 weeks of therapy, 10 to 90 minutes per session. The time of AO ranged from 1 minute to 10 minutes for each motor action, task or movement observed. The total number of motor actions ranged from 1 to 3. Control comparisons included sham observation, physical therapy, and functional activity practice.Primary outcomesAO improved arm function (standardized mean difference (SMD) 0.39, 95% confidence interval (CI) 0.17 to 0.61; 11 trials, 373 participants; low-certainty evidence); and improved hand function (mean difference (MD) 2.76, 95% CI 1.04 to 4.49; 5 trials, 178 participants; low-certainty evidence).Secondary outcomesAO did not improve ADL performance (SMD 0.37, 95% CI -0.34 to 1.08; 7 trials, 302 participants; very low-certainty evidence), or quality of life (MD 5.52, 95% CI -30.74 to 41.78; 2 trials, 30 participants; very low-certainty evidence). We were unable to pool the other secondary outcomes (motor performance and cortical activation). Only two trials reported adverse events without significant adverse effects.Authors' conclusionsThe effects of AO are small for arm function compared to any control group; for hand function the effects are large, but not clinically significant. For both, the certainty of evidence is low. There is no evidence of benefit or detriment from AO on ADL and quality of life of people with stroke; however, the certainty of evidence is very low. As such, our confidence in the effect estimate is limited because it will likely change with future research.

Project description:Introduction: Stroke rehabilitation should start early in order to optimize patients' outcomes, but most trials include subacute or chronic patients. Although suggested that early stroke rehabilitation trials face difficulties regarding patient recruitment with corresponding low recruitment rates, no systematically collected information regarding screening and associated costs has been published. Such knowledge is essential for optimizing enrollment. Therefore, this study evaluated screening procedures for an early upper limb rehabilitation study including first-ever ischemic stroke patients <48 h after onset. Methods: Screening data for a monocentric longitudinal observational cohort study was prospectively collected. Researchers screened health-care records, during the morning round and face-to-face at the stroke-unit on working days. Outcomes were the recruitment rate, reasons for non-enrollment, and screening costs. Results: Over 15 months, 27 out of 845 screened ischemic stroke patients were enrolled, equaling a recruitment rate of 1.8/month. Main reasons for non-enrollment were no upper limb paresis (N = 456), >48 h post-stroke (N = 257), general comorbidity (N = 150), unable to follow commands (N = 148), and recurrent stroke (N = 146). Four patients were missed due to time constraints of the personnel or patient. The recruitment rate would have been 1.2 higher if also patients with recurrent strokes but without residual motor deficits or pre-stroke mRS ≥2 were considered eligible. Screening costed € 7.48 per patient. Discussion: Screening at working days is sufficient to enroll patients in early stroke rehabilitation trials. Inclusion criteria regarding recurrent strokes should be less stringent to boost recruitment rates without increasing bias. Multicenter collaborations are needed to finish well-powered early stroke rehabilitation studies within a reasonable time. Ethics and Study Registration: Authorization from the local ethical committee was not required, as this study does not fall within the scope of the Human Research Act (BASEC Identifier: Req-2017-00844). The project was registered at http://www.clinicaltrials.gov (Identifier: NCT03633422).

Project description:IntroductionLongitudinal observations of upper limb motor recovery after stroke have suggested that certain subgroups may exhibit distinct recovery patterns. Here we sought to examine whether the predictive ability for post-stroke upper limb motor outcomes could be enhanced by applying conventional stratification strategies.MethodFor 60 individuals who suffered the first stroke, upper limb motor impairment was assessed with the upper extremity Fugl-Meyer assessment (UE-FMA) at 2 weeks as a baseline and then 3 months post-stroke. Brain structural damage at baseline was assessed by MRI data-derived markers ranging from traditional lesion size to the lesion load and to the disconnectome. Linear regression models for predicting upper limb motor outcomes (UE-FMA score at 3 months post-stroke) based on baseline upper limb motor impairment (UE-FMA score at 2 weeks post-stroke), brain structural damage, and their combinations were generated, and those with the best predictive performance were determined for individual subgroups stratified according to initial impairment (severe and non-severe), lesion location (cortical and non-cortical), and neurophysiological status (motor evoked potential-positive and motor evoked potential-negative).ResultsThe best predictions were made by baseline upper limb motor impairment alone for subgroups with less functional impairment (non-severe) or less structural involvement (non-cortical), but by the combination of baseline upper limb motor impairment and brain structural damage for the other subgroups. The predictive models tailored for subgroups determined according to initial impairment and neurophysiological status yielded a smaller overall error than that for the whole group in upper limb motor outcome predictions.DiscussionThe predictive ability for upper limb motor outcomes could be enhanced beyond the one-size-fits-all model for all individuals with stroke by applying specific stratification strategies, with stratification according to initial impairment being the most promising. We expect that predictive models tailored for individual subgroups could lead closer to the personalized prognosis of upper limb motor outcomes after stroke.

Project description:Stroke is the primary cause of adult disability. To support this large population in recovery, robotic technologies are being developed to assist in the delivery of rehabilitation. This paper presents an automated system for a rehabilitation robotic device that guides stroke patients through an upper-limb reaching task. The system uses a decision theoretic model (a partially observable Markov decision process, or POMDP) as its primary engine for decision making. The POMDP allows the system to automatically modify exercise parameters to account for the specific needs and abilities of different individuals, and to use these parameters to take appropriate decisions about stroke rehabilitation exercises.The performance of the system was evaluated by comparing the decisions made by the system with those of a human therapist. A single patient participant was paired up with a therapist participant for the duration of the study, for a total of six sessions. Each session was an hour long and occurred three times a week for two weeks. During each session, three steps were followed: (A) after the system made a decision, the therapist either agreed or disagreed with the decision made; (B) the researcher had the device execute the decision made by the therapist; (C) the patient then performed the reaching exercise. These parts were repeated in the order of A-B-C until the end of the session. Qualitative and quantitative question were asked at the end of each session and at the completion of the study for both participants.Overall, the therapist agreed with the system decisions approximately 65% of the time. In general, the therapist thought the system decisions were believable and could envision this system being used in both a clinical and home setting. The patient was satisfied with the system and would use this system as his/her primary method of rehabilitation.The data collected in this study can only be used to provide insight into the performance of the system since the sample size was limited. The next stage for this project is to test the system with a larger sample size to obtain significant results.

Dataset Information

EEG-based motor network biomarkers for identifying target patients with stroke for upper limb rehabilitation and its construct validity.

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EEG-based motor network biomarkers for identifying target patients with stroke for upper limb rehabilitation and its construct validity.

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