Neural mechanisms underlying improved new-word learning with high-density transcranial direct current stimulation.

Neurobehavioral studies have provided evidence for the effectiveness of anodal tDCS on language production, by stimulation of the left Inferior Frontal Gyrus (IFG) or of left Temporo-Parietal Junction (TPJ). However, tDCS is currently not used in clinical practice outside of trials, because behavioral effects have been inconsistent and underlying neural effects unclear. Here, we propose to elucidate the neural correlates of verb and noun learning and to determine if they can be modulated with anodal high-definition (HD) tDCS stimulation. Thirty-six neurotypical participants were randomly allocated to anodal HD-tDCS over either the left IFG, the left TPJ, or sham stimulation. On day one, participants performed a naming task (pre-test). On day two, participants underwent a new-word learning task with rare nouns and verbs concurrently to HD-tDCS for 20 min. The third day consisted of a post-test of naming performance. EEG was recorded at rest and during naming on each day. Verb learning was significantly facilitated by left IFG stimulation. HD-tDCS over the left IFG enhanced functional connectivity between the left IFG and TPJ and this correlated with improved learning. HD-tDCS over the left TPJ enabled stronger local activation of the stimulated area (as indexed by greater alpha and beta-band power decrease) during naming, but this did not translate into better learning. Thus, tDCS can induce local activation or modulation of network interactions. Only the enhancement of network interactions, but not the increase in local activation, leads to robust improvement of word learning. This emphasizes the need to develop new neuromodulation methods influencing network interactions. Our study suggests that this may be achieved through behavioral activation of one area and concomitant activation of another area with HD-tDCS.

Post-stroke cognitive impairment remains highly prevalent and disabling despite state-of-the-art stroke treatment.

BACKGROUND: State-of-the-art stroke treatment significantly reduces lesion size and stroke severity, but it remains unclear whether these therapeutic advances have diminished the burden of post-stroke cognitive impairment (PSCI). AIMS: In a cohort of patients receiving modern state-of-the-art stroke care including endovascular therapy, we assessed the frequency of PSCI and the pattern of domain-specific cognitive deficits, identified risk factors for PSCI, and determined the impact of acute PSCI on stroke outcome. METHODS: In this prospective monocentric cohort study, we examined patients with first-ever anterior circulation ischemic stroke without pre-stroke cognitive decline, using a comprehensive neuropsychological assessment ⩽10 days after symptom onset. Normative data were stratified by demographic variables. We defined PSCI as at least moderate (<1.5 standard deviation) deficits in ⩾2 cognitive domains. Multivariable regression analysis was applied to define risk factors for PSCI. RESULTS: We analyzed 329 non-aphasic patients admitted from December 2020 to July 2023 (67.2 ± 14.4 years old, 41.3% female, 13.1 ± 2.7 years of education). Although most patients had mild stroke (median National Institutes of Health Stroke Scale (NIHSS) 24 h = 1.00 (0.00; 3.00); 87.5% with NIHSS ⩽ 5), 69.3% of them presented with PSCI 2.7 ± 2.0 days post-stroke. The most severely and often affected cognitive domains were verbal learning, episodic memory, executive functions, selective attention, and constructive abilities (39.1%-51.2% of patients), whereas spatial neglect was less frequent (18.5%). The risk of PSCI was reduced with more years of education (odds ratio (OR) = 0.47, 95% confidence interval (CI) = 0.23-0.99) and right hemisphere lesions (OR = 0.47, 95% CI = 0.26-0.84), and increased with stroke severity (NIHSS 24 h, OR = 4.19, 95% CI = 2.72-6.45), presence of hyperlipidemia (OR = 1.93, 95% CI = 1.01-3.68), but was not influenced by age. After adjusting for stroke severity and depressive symptoms, acute PSCI was associated with poor functional outcome (modified Rankin Scale > 2, F = 13.695, p < 0.001) and worse global cognition (Montreal Cognitive Assessment (MoCA) score, F = 20.069, p < 0.001) at 3 months post-stroke. CONCLUSION: Despite modern stroke therapy and many strokes having mild severity, PSCI in the acute stroke phase remains frequent and associated with worse outcome. The most prevalent were learning and memory deficits. Cognitive reserve operationalized as years of education independently protects post-stroke cognition.

Novel personalized treatment strategy for patients with chronic stroke with severe upper-extremity impairment: The first patient of the AVANCER trial.

BACKGROUND: Around 25% of patients who have had a stroke suffer from severe upper-limb impairment and lack effective rehabilitation strategies. The AVANCER proof-of-concept clinical trial (NCT04448483) tackles this issue through an intensive and personalized-dosage cumulative intervention that combines multiple non-invasive neurotechnologies. METHODS: The therapy consists of two sequential interventions, lasting until the patient shows no further motor improvement, for a minimum of 11 sessions each. The first phase involves a brain-computer interface governing an exoskeleton and multi-channel functional electrical stimulation enabling full upper-limb movements. The second phase adds anodal transcranial direct current stimulation of the motor cortex of the lesioned hemisphere. Clinical, electrophysiological, and neuroimaging examinations are performed before, between, and after the two interventions (T0, T1, and T2). This case report presents the results from the first patient of the study. FINDINGS: The primary outcome (i.e., 4-point improvement in the Fugl-Meyer assessment of the upper extremity) was met in the first patient, with an increase from 6 to 11 points between T0 and T2. This improvement was paralleled by changes in motor-network structure and function. Resting-state and transcranial magnetic stimulation-evoked electroencephalography revealed brain functional changes, and magnetic resonance imaging (MRI) measures detected structural and task-related functional changes. CONCLUSIONS: These first results are promising, pointing to feasibility, safety, and potential efficacy of this personalized approach acting synergistically on the nervous and musculoskeletal systems. Integrating multi-modal data may provide valuable insights into underlying mechanisms driving the improvements and providing predictive information regarding treatment response and outcomes. FUNDING: This work was funded by the Wyss-Center for Bio and Neuro Engineering (WCP-030), the Defitech Foundation, PHRT-#2017-205, ERA-NET-NEURON (Discover), and SNSF (320030L_197899, NiBS-iCog).

Prediction of post-stroke motor recovery benefits from measures of sub-acute widespread network damages.

Following a stroke in regions of the brain responsible for motor activity, patients can lose their ability to control parts of their body. Over time, some patients recover almost completely, while others barely recover at all. It is known that lesion volume, initial motor impairment and cortico-spinal tract asymmetry significantly impact motor changes over time. Recent work suggested that disabilities arise not only from focal structural changes but also from widespread alterations in inter-regional connectivity. Models that consider damage to the entire network instead of only local structural alterations lead to a more accurate prediction of patients' recovery. However, assessing white matter connections in stroke patients is challenging and time-consuming. Here, we evaluated in a data set of 37 patients whether we could predict upper extremity motor recovery from brain connectivity measures obtained by using the patient's lesion mask to introduce virtual lesions in 60 healthy streamline tractography connectomes. This indirect estimation of the stroke impact on the whole brain connectome is more readily available than direct measures of structural connectivity obtained with magnetic resonance imaging. We added these measures to benchmark structural features, and we used a ridge regression regularization to predict motor recovery at 3 months post-injury. As hypothesized, accuracy in prediction significantly increased (R 2 = 0.68) as compared to benchmark features (R 2 = 0.38). This improved prediction of recovery could be beneficial to clinical care and might allow for a better choice of intervention.

EEG Beta functional connectivity decrease in the left amygdala correlates with the affective pain in fibromyalgia: A pilot study.

Fibromyalgia (FM) is a major chronic pain disease with prominent affective disturbances, and pain-associated changes in neurotransmitters activity and in brain connectivity. However, correlates of affective pain dimension lack. The primary goal of this correlational cross-sectional case-control pilot study was to find electrophysiological correlates of the affective pain component in FM. We examined the resting-state EEG spectral power and imaginary coherence in the beta (ß) band (supposedly indexing the GABAergic neurotransmission) in 16 female patients with FM and 11 age-adjusted female controls. FM patients displayed lower functional connectivity in the High ß (Hß, 20-30 Hz) sub-band than controls (p = 0.039) in the left basolateral complex of the amygdala (p = 0.039) within the left mesiotemporal area, in particular, in correlation with a higher affective pain component level (r = 0.50, p = 0.049). Patients showed higher Low ß (Lß, 13-20 Hz) relative power than controls in the left prefrontal cortex (p = 0.001), correlated with ongoing pain intensity (r = 0.54, p = 0.032). For the first time, GABA-related connectivity changes correlated with the affective pain component are shown in the amygdala, a region highly involved in the affective regulation of pain. The ß power increase in the prefrontal cortex could be compensatory to pain-related GABAergic dysfunction.

Intensive Multi-Disciplinary Outpatient Rehabilitation for Facilitating Return-to-Work after Acquired Brain Injury: A Case-Control Study.

OBJECTIVE: Return-to-work is often the most important objective of working-age patients with acquired brain injury, but is often difficult to achieve. There is a lack of evidence for effective treatment. This study aimed to assess the benefit of a multidisciplinary neurorehabilitation in a daytime hospital on return-to-work after an acquired brain injury. DESIGN: Retrospective case-control study. PATIENTS: Acquired brain injury patients between 18 and 65 years of age. METHODS: Two periods, before (n = 82 patients) and after (n = 89 patients) the implementation of a daytime hospital in our neuro-rehabilitation unit were compared. Patients followed in the daytime hospital received intensive, interdisciplinary, coordinated, individual and group-level physical, cognitive, and vocational rehabilitation. During the control period, patients received outpatient neurorehabilitation with less intensive treatment without interdisciplinary coordination. The main outcome was the proportion of patients returning to > 50% of their premorbid work activity. RESULTS: Fifty-five percent of patients were able to resume more than 50% of their premorbid work level in the daytime hospital period vs 41% in the control period (p = 0.076). CONCLUSION: Intensive and coordinated outpatient neurorehabilitation may facilitate return-to-work after an acquired brain injury.

Recovery after stroke: the severely impaired are a distinct group.

INTRODUCTION: Stroke causes different levels of impairment and the degree of recovery varies greatly between patients. The majority of recovery studies are biased towards patients with mild-to-moderate impairments, challenging a unified recovery process framework. Our aim was to develop a statistical framework to analyse recovery patterns in patients with severe and non-severe initial impairment and concurrently investigate whether they recovered differently. METHODS: We designed a Bayesian hierarchical model to estimate 3-6 months upper limb Fugl-Meyer (FM) scores after stroke. When focusing on the explanation of recovery patterns, we addressed confounds affecting previous recovery studies and considered patients with FM-initial scores <45 only. We systematically explored different FM-breakpoints between severe/non-severe patients (FM-initial=5-30). In model comparisons, we evaluated whether impairment-level-specific recovery patterns indeed existed. Finally, we estimated the out-of-sample prediction performance for patients across the entire initial impairment range. RESULTS: Recovery data was assembled from eight patient cohorts (n=489). Data were best modelled by incorporating two subgroups (breakpoint: FM-initial=10). Both subgroups recovered a comparable constant amount, but with different proportional components: severely affected patients recovered more the smaller their impairment, while non-severely affected patients recovered more the larger their initial impairment. Prediction of 3-6 months outcomes could be done with an R2=63.5% (95% CI=51.4% to 75.5%). CONCLUSIONS: Our work highlights the benefit of simultaneously modelling recovery of severely-to-non-severely impaired patients and demonstrates both shared and distinct recovery patterns. Our findings provide evidence that the severe/non-severe subdivision in recovery modelling is not an artefact of previous confounds. The presented out-of-sample prediction performance may serve as benchmark to evaluate promising biomarkers of stroke recovery.

Feasibility of Reconstructing Source Functional Connectivity with Low-Density EEG.

OBJECTIVES: Functional connectivity (FC) is increasingly used as target for neuromodulation and enhancement of performance. A reliable assessment of FC with electroencephalography (EEG) currently requires a laboratory environment with high-density montages and a long preparation time. This study investigated the feasibility of reconstructing source FC with a low-density EEG montage towards a usage in real life applications. METHODS: Source FC was reconstructed with inverse solutions and quantified as node degree of absolute imaginary coherence in alpha frequencies. We used simulated coherent point sources as well as two real datasets to investigate the impact of electrode density (19 vs. 128 electrodes) and usage of template vs. individual MRI-based head models on localization accuracy. In addition, we checked whether low-density EEG is able to capture inter-individual variations in coherence strength. RESULTS: In numerical simulations as well as real data, a reduction of the number of electrodes led to less reliable reconstructions of coherent sources and of coupling strength. Yet, when comparing different approaches to reconstructing FC from 19 electrodes, source FC obtained with beamformers outperformed sensor FC, FC computed after independent component analysis, and source FC obtained with sLORETA. In particular, only source FC based on beamformers was able to capture neural correlates of motor behavior. CONCLUSION: Reconstructions of FC from low-density EEG is challenging, but may be feasible when using source reconstructions with beamformers.

Disrupted resting-state EEG alpha-band interactions as a novel marker for the severity of visual field deficits after brain lesion.

OBJECTIVE: Homonymous visual field deficits (HFVDs) are frequent following brain lesions. Current restoration treatments aim at activating areas of residual vision through numerous stimuli, but show limited effect. Recent findings suggest that spontaneous neural α-band coupling is more efficient for enabling visual perception in healthy humans than task-induced activations. Here, we evaluated whether it is also associated with the severity of HFVD. METHODS: Ten patients with HFVDs after brain damage in the subacute to chronic stage and ten matched healthy controls underwent visual stimulation with alternating checkerboards and electroencephalography recordings of stimulation-induced power changes and of spontaneous neural interactions during rest. RESULTS: Visual areas of the affected hemisphere showed reduced event-related power decrease in α and ß frequency bands, but also reduced spontaneous α-band interactions during rest, as compared to contralesional areas and healthy controls. A multivariate stepwise regression retained the degree of disruption of spontaneous interactions, but not the reduced task-induced power changes as predictor for the severity of the visual deficit. CONCLUSIONS: Spontaneous α-band interactions of visual areas appear as a better marker for the severity of HFVDs than task-induced activations. SIGNIFICANCE: Treatment attempts of HFVDs should try to enhance spontaneous α-band coupling of structurally intact ipsilesional areas.

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

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.

Disconnectomics of the Rich Club Impacts Motor Recovery After Stroke.

BACKGROUND AND PURPOSE: Structural brain networks possess a few hubs, which are not only highly connected to the rest of the brain but are also highly connected to each other. These hubs, which form a rich-club, play a central role in global brain organization. To investigate whether the concept of rich-club sheds new light on poststroke recovery, we applied a novel network-theoretical quantification of lesions to patients with stroke and compared the outcomes with what lesion size alone would indicate. METHODS: Whole-brain structural networks of 73 patients with ischemic stroke were reconstructed using diffusion-weighted imaging data. Disconnectomes, a new type of network analyses, were constructed using only those fibers that pass through the lesion. Fugl-Meyer upper extremity scores and their changes were used to determine whether the patients show natural recovery or not. RESULTS: Cluster analysis revealed 3 patient clusters: small-lesion-good-recovery, midsized-lesion-poor-recovery (MLPR), and large-lesion-poor-recovery (LLPR). The small-lesion-good-recovery consisted of subjects whose lesions were small, and whose prospects for recovery were relatively good. To explain the nondifference in recovery between the MLPR and LLPR clusters despite the difference (LLPR>MLPR) in lesion volume, we defined the [Formula: see text] metric to be the sum of the entries in the disconnectome and, more importantly, the [Formula: see text] to be the sum of all entries in the disconnectome corresponding to edges with at least one node in the rich-club. Unlike lesion volume and corticospinal tract damage (MLPRLLPR) or showed no difference for [Formula: see text]. CONCLUSIONS: Smaller lesions that focus on the rich-club can be just as devastating as much larger lesions that do not focus on the rich-club, pointing to the role of the rich-club as a backbone for functional communication within brain networks and for recovery from stroke.

Spontaneous Network Coupling Enables Efficient Task Performance without Local Task-Induced Activations.

Neurobehavioral studies in humans have long concentrated on changes in local activity levels during repetitive executions of a task. Spontaneous neural coupling within extended networks has latterly been found to also influence performance. Here, we intend to uncover the underlying mechanisms, the relative importance, and the interaction between spontaneous coupling and task-induced activations. To do so, we recorded two groups of healthy participants (male and female) during rest and while they performed either a visual perception or a motor sequence task. We demonstrate that, for both tasks, stronger activations during the task as well as greater network coupling through spontaneous α rhythms at rest predict performance. However, high performers present an absence of classical task-induced activations and, instead, stronger spontaneous network coupling. Activations were thus a compensation mechanism needed only in subjects with lower spontaneous network interactions. This challenges classical models of neural processing and calls for new strategies in attempts to train and enhance performance.SIGNIFICANCE STATEMENT Our findings challenge the widely accepted notion that task-induced activations are of paramount importance for behavior. This will have an important impact on interpretations of human neurobehavioral research. They further link the widely used techniques of quantifying network communication in the brain with classical neuroscience methods and demonstrate possible ways of how network communication influences human behavior. Traditional training methods attempt to enhance neural activations through task repetitions. Our findings suggest a more efficient neural target for learning: enhancing spontaneous neural interactions. This will be of major interest for a large variety of scientific fields with very broad applications in schools, work, and others.

The "implicit" serial reaction time task induces rapid and temporary adaptation rather than implicit motor learning.

The serial reaction time task (SRTT) has been widely used to induce learning of a repeated motor sequence without the participants' awareness. The task has also been of major influence for defining current concepts of offline consolidation after motor learning. The present study intended to replicate previous findings in a larger population of 53 healthy individuals. We were unable to reproduce previous results of online and offline implicit motor learning with the SRTT. Trials with a repeated sequence rapidly induced shorter reaction times compared to random trials, but this improvement was lost in a post-test obtained a few minutes after the training block. Furthermore, no offline consolidation was observed as there was no change in sequence specific reaction time gain between the post-test immediately after training and a re-test obtained 8 h after training. Online or offline learning remained absent when we modulated the number of sequence repetitions, the error levels, and the structure of random sequences. We conclude that the SRTT induces a rapid and temporary adaptation to the sequence rather than learning, since the repeated motor sequence does not seem to be encoded in memory.

Bringing proportional recovery into proportion: Bayesian modelling of post-stroke motor impairment.

Accurate predictions of motor impairment after stroke are of cardinal importance for the patient, clinician, and healthcare system. More than 10 years ago, the proportional recovery rule was introduced by promising that high-fidelity predictions of recovery following stroke were based only on the initially lost motor function, at least for a specific fraction of patients. However, emerging evidence suggests that this recovery rule is subject to various confounds and may apply less universally than previously assumed. Here, we systematically revisited stroke outcome predictions by applying strategies to avoid confounds and fitting hierarchical Bayesian models. We jointly analysed 385 post-stroke trajectories from six separate studies-one of the largest overall datasets of upper limb motor recovery. We addressed confounding ceiling effects by introducing a subset approach and ensured correct model estimation through synthetic data simulations. Subsequently, we used model comparisons to assess the underlying nature of recovery within our empirical recovery data. The first model comparison, relying on the conventional fraction of patients called 'fitters', pointed to a combination of proportional to lost function and constant recovery. 'Proportional to lost' here describes the original notion of proportionality, indicating greater recovery in case of a more severe initial impairment. This combination explained only 32% of the variance in recovery, which is in stark contrast to previous reports of >80%. When instead analysing the complete spectrum of subjects, 'fitters' and 'non-fitters', a combination of proportional to spared function and constant recovery was favoured, implying a more significant improvement in case of more preserved function. Explained variance was at 53%. Therefore, our quantitative findings suggest that motor recovery post-stroke may exhibit some characteristics of proportionality. However, the variance explained was substantially reduced compared to what has previously been reported. This finding motivates future research moving beyond solely behaviour scores to explain stroke recovery and establish robust and discriminating single-subject predictions.

A multimodal approach to capture post-stroke temporal dynamics of recovery.

OBJECTIVE: Several training programs have been developed in the past to restore motor functions after stroke. Their efficacy strongly relies on the possibility to assess individual levels of impairment and recovery rate. However, commonly used clinical scales rely mainly on subjective functional assessments and are not able to provide a complete description of patients' neuro-biomechanical status. Therefore, current clinical tests should be integrated with specific physiological measurements, i.e. kinematic, muscular, and brain activities, to obtain a deep understanding of patients' condition and of its evolution through time and rehabilitative intervention. APPROACH: We proposed a multivariate approach for motor control assessment that simultaneously measures kinematic, muscle and brain activity and combines the main physiological variables extracted from these signals using principal component analysis (PCA). We tested it in a group of six sub-acute stroke subjects evaluated extensively before and after a four-week training, using an upper-limb exoskeleton while performing a reaching task, along with brain and muscle measurements. MAIN RESULTS: After training, all subjects exhibited clinical improvements correlating with changes in kinematics, muscle synergies, and spinal maps. Movements were smoother and faster, while muscle synergies increased in numbers and became more similar to those of the healthy controls. These findings were coupled with changes in cortical oscillations depicted by EEG-topographies. When combining these physiological variables using PCA, we found that (i) patients' kinematic and spinal maps parameters improved continuously during the four assessments; (ii) muscle coordination augmented mainly during treatment, and (iii) brain oscillations recovered mostly pre-treatment as a consequence of short-term subacute changes. SIGNIFICANCE: Although these are preliminary results, the proposed approach has the potential of identifying significant biomarkers for patient stratification as well as for the design of more effective rehabilitation protocols.

[Fatigue after acquired brain injury and its impact on socio-professional reintegration]. / La fatigue chez le patient cérébrolésé et son impact sur la réinsertion socioprofessionnelle.

Patients with acquired brain injury often suffer from pathological fatigue that differs from "normal" fatigue in that it appears more quickly and during non-demanding tasks, and recovery is not complete despite rest. It limits physical and cognitive activities, interferes with rehabilitation and return to work. The underlying mechanisms are poorly understood but appear to involve dysfunction of brain interactions. Current management combining physical reconditioning, cognitive compensatory strategies, and treatment of associated factors often leads to significant clinical improvement and promotes socio-professional reintegration. However, the effect remains insufficient in some patients, which underlines the importance of developing new therapeutic approaches based on a better understanding of the underlying neuronal deficits.

Les patients cérébrolésés présentent souvent une fatigue pathologique qui diffère de la fatigue « normale ¼, car elle apparaît plus rapidement, lors de tâches peu exigeantes, et ne disparaît pas complètement avec le repos. Elle limite les activités physiques et cognitives,et interfère avec la réadaptation et la reprise du travail. Les mécanismes sous-jacents, peu connus, semblent impliquer une altération des interactions cérébrales. La prise en charge actuelle combine reconditionnement physique, apprentissage de stratégies de compensation et traitement des facteurs associés ; cela favorise souvent l'amélioration clinique et la réinsertion socioprofessionnelle. L'effet reste cependant insuffisant chez certains patients, d'où l'importance de développer des thérapies se basant sur une meilleure compréhension des déficits neuronaux sous-jacents.

Motor improvement estimation and task adaptation for personalized robot-aided therapy: a feasibility study.

BACKGROUND: In the past years, robotic systems have become increasingly popular in upper limb rehabilitation. Nevertheless, clinical studies have so far not been able to confirm superior efficacy of robotic therapy over conventional methods. The personalization of robot-aided therapy according to the patients' individual motor deficits has been suggested as a pivotal step to improve the clinical outcome of such approaches. METHODS: Here, we present a model-based approach to personalize robot-aided rehabilitation therapy within training sessions. The proposed method combines the information from different motor performance measures recorded from the robot to continuously estimate patients' motor improvement for a series of point-to-point reaching movements in different directions. Additionally, it comprises a personalization routine to automatically adapt the rehabilitation training. We engineered our approach using an upper-limb exoskeleton. The implementation was tested with 17 healthy subjects, who underwent a motor-adaptation paradigm, and two subacute stroke patients, exhibiting different degrees of motor impairment, who participated in a pilot test undergoing rehabilitative motor training. RESULTS: The results of the exploratory study with healthy subjects showed that the participants divided into fast and slow adapters. The model was able to correctly estimate distinct motor improvement progressions between the two groups of participants while proposing individual training protocols. For the two pilot patients, an analysis of the selected motor performance measures showed that both patients were able to retain the improvements gained during training when reaching movements were reintroduced at a later stage. These results suggest that the automated training adaptation was appropriately timed and specifically tailored to the abilities of each individual. CONCLUSIONS: The results of our exploratory study demonstrated the feasibility of the proposed model-based approach for the personalization of robot-aided rehabilitation therapy. The pilot test with two subacute stroke patients further supported our approach, while providing encouraging results for the applicability in clinical settings. Trial registration This study is registered in ClinicalTrials.gov (NCT02770300, registered 30 March 2016, https://clinicaltrials.gov/ct2/show/NCT02770300).

Neurotechnology-aided interventions for upper limb motor rehabilitation in severe chronic stroke.

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.

Brain networks and their relevance for stroke rehabilitation.

Stroke has long been regarded as focal disease with circumscribed damage leading to neurological deficits. However, advances in methods for assessing the human brain and in statistics have enabled new tools for the examination of the consequences of stroke on brain structure and function. Thereby, it has become evident that stroke has impact on the entire brain and its network properties and can therefore be considered as a network disease. The present review first gives an overview of current methodological opportunities and pitfalls for assessing stroke-induced changes and reorganization in the human brain. We then summarize principles of plasticity after stroke that have emerged from the assessment of networks. Thereby, it is shown that neurological deficits do not only arise from focal tissue damage but also from local and remote changes in white-matter tracts and in neural interactions among wide-spread networks. Similarly, plasticity and clinical improvements are associated with specific compensatory structural and functional patterns of neural network interactions. Innovative treatment approaches have started to target such network patterns to enhance recovery. Network assessments to predict treatment response and to individualize rehabilitation is a promising way to enhance specific treatment effects and overall outcome after stroke.