Optimising rehabilitation and recovery after a stroke.

Stroke can cause significant disability and impact quality of life. Multidisciplinary neurorehabilitation that meets individual needs can help to optimise recovery. Rehabilitation is essential for best quality care but should start early, be ongoing and involve effective teamwork. We describe current stroke rehabilitation processes, from the hyperacute setting through to inpatient and community rehabilitation, to long-term care and report on which UK quality care standards are (or are not) being met. We also examine the gap between what stroke rehabilitation is recommended and what is being delivered, and suggest areas for further improvement.

EEG Fractal Analysis Reflects Brain Impairment after Stroke.

Stroke is the commonest cause of disability. Novel treatments require an improved understanding of the underlying mechanisms of recovery. Fractal approaches have demonstrated that a single metric can describe the complexity of seemingly random fluctuations of physiological signals. We hypothesize that fractal algorithms applied to electroencephalographic (EEG) signals may track brain impairment after stroke. Sixteen stroke survivors were studied in the hyperacute (<48 h) and in the acute phase (∼1 week after stroke), and 35 stroke survivors during the early subacute phase (from 8 days to 32 days and after ∼2 months after stroke): We compared resting-state EEG fractal changes using fractal measures (i.e., Higuchi Index, Tortuosity) with 11 healthy controls. Both Higuchi index and Tortuosity values were significantly lower after a stroke throughout the acute and early subacute stage compared to healthy subjects, reflecting a brain activity which is significantly less complex. These indices may be promising metrics to track behavioral changes in the very early stage after stroke. Our findings might contribute to the neurorehabilitation quest in identifying reliable biomarkers for a better tailoring of rehabilitation pathways.

Blowing up Neural Repair for Stroke Recovery: Preclinical and Clinical Trial Considerations.

The repair and recovery of the brain after stroke is a field that is emerging in its preclinical science and clinical trials. However, recent large, multicenter clinical trials have been negative, and conflicting results emerge on biological targets in preclinical studies. The coalescence of negative clinical translation and confusion in preclinical studies raises the suggestion that perhaps the field of stroke recovery faces a fate similar to stroke neuroprotection, with interesting science ultimately proving difficult to translate to the clinic. This review highlights improvements in 4 areas of the stroke neural repair field that should reorient the field toward successful clinical translation: improvements in rodent genetic models of stroke recovery, consideration of the biological target in stroke recovery, stratification in clinical trials, and the use of appropriate clinical trial end points.

Cortical beta oscillations are associated with motor performance following visuomotor learning.

People vary in their capacity to learn and retain new motor skills. Although the relationship between neuronal oscillations in the beta frequency range (15-30 Hz) and motor behaviour is well established, the electrophysiological mechanisms underlying individual differences in motor learning are incompletely understood. Here, we investigated the degree to which measures of resting and movement-related beta power from sensorimotor cortex account for inter-individual differences in motor learning behaviour in the young and elderly. Twenty young (18-30 years) and twenty elderly (62-77 years) healthy adults were trained on a novel wrist flexion/extension tracking task and subsequently retested at two different time points (45-60 min and 24 h after initial training). Scalp EEG was recorded during a separate simple motor task before each training and retest session. Although short-term motor learning was comparable between young and elderly individuals, there was considerable variability within groups with subsequent analysis aiming to find the predictors of this variability. As expected, performance during the training phase was the best predictor of performance at later time points. However, regression analysis revealed that movement-related beta activity significantly explained additional variance in individual performance levels 45-60 min, but not 24 h after initial training. In the context of disease, these findings suggest that measurements of beta-band activity may offer novel targets for therapeutic interventions designed to promote rehabilitative outcomes.

Intensive upper limb neurorehabilitation in chronic stroke: outcomes from the Queen Square programme.

OBJECTIVE: Persistent difficulty in using the upper limb remains a major contributor to physical disability post-stroke. There is a nihilistic view about what clinically relevant changes are possible after the early post-stroke phase. The Queen Square Upper Limb Neurorehabilitation programme delivers high-quality, high-dose, high-intensity upper limb neurorehabilitation during a 3-week (90 hours) programme. Here, we report clinical changes made by the chronic stroke patients treated on the programme, factors that might predict responsiveness to therapy and the relationship between changes in impairment and activity. METHODS: Upper limb impairment and activity were assessed on admission, discharge, 6 weeks and 6 months after treatment, with modified upper limb Fugl-Meyer (FM-UL, max-54), Action Research Arm Test (ARAT, max-57) and Chedoke Arm and Hand Activity Inventory (CAHAI, max-91). Patient-reported outcome measures were recorded with the Arm Activity Measure (ArmA) parts A (0-32) and B (0-52), where lower scores are better. RESULTS: 224 patients (median time post-stroke 18 months) completed the 6-month programme. Median scores on admission were as follows: FM-UL = 26 (IQR 16-37), ARAT=18 (IQR 7-33), CAHAI=40 (28-55), ArmA-A=8 (IQR 4.5-12) and ArmA-B=38 (IQR 24-46). The median scores 6 months after the programme were as follows: FM-UL=37 (IQR 24-48), ARAT=27 (IQR 12-45), CAHAI=52 (IQR 35-77), ArmA-A=3 (IQR 1-6.5) and ArmA-B=19 (IQR 8.5-32). We found no predictors of treatment response beyond admission scores. CONCLUSION: With intensive upper limb rehabilitation, chronic stroke patients can change by clinically important differences in measures of impairment and activity. Crucially, clinical gains continued during the 6-month follow-up period.

Interrogating cortical function with transcranial magnetic stimulation: insights from neurodegenerative disease and stroke.

Transcranial magnetic stimulation (TMS) is an accessible, non-invasive technique to study cortical function in vivo. TMS studies have provided important pathophysiological insights across a range of neurodegenerative disorders and enhanced our understanding of brain reorganisation after stroke. In neurodegenerative disease, TMS has provided novel insights into the function of cortical output cells and the related intracortical interneuronal networks. Characterisation of cortical hyperexcitability in amyotrophic lateral sclerosis and altered motor cortical function in frontotemporal dementia, demonstration of cholinergic deficits in Alzheimer's disease and Parkinson's disease are key examples where TMS has led to advances in understanding of disease pathophysiology and potential mechanisms of propagation, with the potential for diagnostic applications. In stroke, TMS methodology has facilitated the understanding of cortical reorganisation that underlie functional recovery. These insights are critical to the development of effective and targeted rehabilitation strategies in stroke. The present review will provide an overview of cortical function measures obtained using TMS and how such measures may provide insight into brain function. Through an improved understanding of cortical function across a range of neurodegenerative disorders, and identification of changes in neural structure and function associated with stroke that underlie clinical recovery, more targeted therapeutic approaches may now be developed in an evolving era of precision medicine.

Movement-related beta oscillations show high intra-individual reliability.

Oscillatory activity in the beta frequency range (15-30Hz) recorded from human sensorimotor cortex is of increasing interest as a putative biomarker of motor system function and dysfunction. Despite its increasing use in basic and clinical research, surprisingly little is known about the test-retest reliability of spectral power and peak frequency measures of beta oscillatory signals from sensorimotor cortex. Establishing that these beta measures are stable over time in healthy populations is a necessary precursor to their use in the clinic. Here, we used scalp electroencephalography (EEG) to evaluate intra-individual reliability of beta-band oscillations over six sessions, focusing on changes in beta activity during movement (Movement-Related Beta Desynchronization, MRBD) and after movement termination (Post-Movement Beta Rebound, PMBR). Subjects performed visually-cued unimanual wrist flexion and extension. We assessed Intraclass Correlation Coefficients (ICC) and between-session correlations for spectral power and peak frequency measures of movement-related and resting beta activity. Movement-related and resting beta power from both sensorimotor cortices was highly reliable across sessions. Resting beta power yielded highest reliability (average ICC=0.903), followed by MRBD (average ICC=0.886) and PMBR (average ICC=0.663). Notably, peak frequency measures yielded lower ICC values compared to the assessment of spectral power, particularly for movement-related beta activity (ICC=0.386-0.402). Our data highlight that power measures of movement-related beta oscillations are highly reliable, while corresponding peak frequency measures show greater intra-individual variability across sessions. Importantly, our finding that beta power estimates show high intra-individual reliability over time serves to validate the notion that these measures reflect meaningful individual differences that can be utilised in basic research and clinical studies.

Brain regions important for recovery after severe post-stroke upper limb paresis.

Background The ability to predict outcome after stroke is clinically important for planning treatment and for stratification in restorative clinical trials. In relation to the upper limbs, the main predictor of outcome is initial severity, with patients who present with mild to moderate impairment regaining about 70% of their initial impairment by 3 months post-stroke. However, in those with severe presentations, this proportional recovery applies in only about half, with the other half experiencing poor recovery. The reasons for this failure to recover are not established although the extent of corticospinal tract damage is suggested to be a contributory factor. In this study, we investigated 30 patients with chronic stroke who had presented with severe upper limb impairment and asked whether it was possible to differentiate those with a subsequent good or poor recovery of the upper limb based solely on a T1-weighted structural brain scan. Methods A support vector machine approach using voxel-wise lesion likelihood values was used to show that it was possible to classify patients as good or poor recoverers with variable accuracy depending on which brain regions were used to perform the classification. Results While considering damage within a corticospinal tract mask resulted in 73% classification accuracy, using other (non-corticospinal tract) motor areas provided 87% accuracy, and combining both resulted in 90% accuracy. Conclusion This proof of concept approach highlights the relative importance of different anatomical structures in supporting post-stroke upper limb motor recovery and points towards methodologies that might be used to stratify patients in future restorative clinical trials.

The Nottingham Fatigue after Stroke (NotFAST) study: factors associated with severity of fatigue in stroke patients without depression.

OBJECTIVE: To identify factors associated with post-stroke fatigue in a sample of stroke survivors without depression. DESIGN: Cross-sectional cohort study. SETTING: Recruitment was from four stroke units in the UK. SUBJECTS: Participants were assessed within four to six weeks of first stroke; those with high levels of depressive symptoms (score ⩾7 Brief Assessment Schedule Depression Cards) were excluded. MAIN MEASURES: Participants were assessed after stroke on the Fatigue Severity Scale of the Fatigue Assessment Inventory, the Rivermead Mobility Index, Nottingham Extended Activities of Daily Living scale, Beck Anxiety Index, Sleep Hygiene Index, 6m walk test, and measures of cognitive ability. RESULTS: Of the 371 participants recruited, 103 were excluded and 268 were assessed. Of the latter, the mean age was 67.7 years (SD 13.5) and 168 (63%) were men. The National Institutes of Health Stroke Scale mean score was 4.96 (SD 4.12). Post-stroke fatigue was reported by 115 (43%) of participants, with 71 (62%) reporting this to be a new symptom since their stroke. Multivariate analysis using the Fatigue Severity Scale as the outcome variable found pre-stroke fatigue, having a spouse/partner, lower Rivermead Mobility Index score, and higher scores on both the Brief Assessment Schedule Depression Cards and Beck Anxiety Index were independently associated with post-stroke fatigue, accounting for approximately 47% of the variance in Fatigue Severity Scale scores. CONCLUSIONS: Pre-stroke fatigue, lower mood, and poorer mobility were associated with post-stroke fatigue.

Computational modelling of movement-related beta-oscillatory dynamics in human motor cortex.

Oscillatory activity in the beta range, in human primary motor cortex (M1), shows interesting dynamics that are tied to behaviour and change systematically in disease. To investigate the pathophysiology underlying these changes, we must first understand how changes in beta activity are caused in healthy subjects. We therefore adapted a canonical (repeatable) microcircuit model used in dynamic causal modelling (DCM) previously used to model induced responses in visual cortex. We adapted this model to accommodate cytoarchitectural differences between visual and motor cortex. Using biologically plausible connections, we used Bayesian model selection to identify the best model of measured MEG data from 11 young healthy participants, performing a simple handgrip task. We found that the canonical M1 model had substantially more model evidence than the generic canonical microcircuit model when explaining measured MEG data. The canonical M1 model reproduced measured dynamics in humans at rest, in a manner consistent with equivalent studies performed in mice. Furthermore, the changes in excitability (self-inhibition) necessary to explain beta suppression during handgrip were consistent with the attenuation of sensory precision implied by predictive coding. These results establish the face validity of a model that can be used to explore the laminar interactions that underlie beta-oscillatory dynamics in humans in vivo. Our canonical M1 model may be useful for characterising the synaptic mechanisms that mediate pathophysiological beta dynamics associated with movement disorders, such as stroke or Parkinson's disease.

The contribution of lesion location to upper limb deficit after stroke.

BACKGROUND: Motor deficit after stroke is related to regional anatomical damage. OBJECTIVE: To examine the influence of lesion location on upper limb motor deficit in chronic patients with stroke. METHODS: Lesion likelihood maps were created from T1-weighted structural MRI in 33 chronic patients with stroke with either purely subcortical lesions (SC, n=19) or lesions extending to any of the cortical motor areas (CM, n=14). We estimated lesion likelihood maps over the whole brain and applied multivoxel pattern analysis to seek the contribution weight of lesion likelihood to upper limb motor deficit. Among 5 brain regions of interest, the brain region with the greatest contribution to motor deficit was determined for each subgroup. RESULTS: The corticospinal tract was most likely to be damaged in both subgroups. However, while damage in the corticospinal tract was the best indicator of motor deficit in the SC patients, motor deficit in the CM patients was best explained by damage in brain areas activated during handgrip. CONCLUSIONS: Quantification of structural damage can add to models explaining motor outcome after stroke, but assessment of corticospinal tract damage alone is unlikely to be sufficient when considering patients with stroke with a wide range of lesion topography.

Prior physical exertion modulates allocentric distance perception: a demonstration of task-irrelevant cross-modal transfer.

Physical exertion has been previously shown to influence distance perception in the egocentric framework. In this study, we show that physical exertion influences allocentric distance perception. Twenty healthy volunteers made allocentric line length estimates following varying levels of physical exertion. Each participant was presented with 30 different line lengths ranging from 1 to 12 cm, and each length was presented three times. Each line presentation was preceded by the participant exerting one of the following three levels of their maximal voluntary force (MVF): 20, 50, or 80 % MVF using their hand in the pinch force task. Psychometric curves were obtained for the lines perceived as 'long' following each of the three force levels. Lines that were perceived as 'short' following 20 and 50 % MVF were perceived as 'long' following 80 % MVF; that is, there was a significant leftward shift in the psychometric curve following 80 % MVF when compared to 20 and 50 % MVF. Here, we demonstrate that physical exertion influences perception of distances in the allocentric framework. We discuss our findings with respect to cross-modal interactions, fatigue physiology, peri- and extra-personal space interactions.

Post-stroke fatigue: a deficit in corticomotor excitability?

The pathophysiology of post-stroke fatigue is poorly understood although it is thought to be a consequence of central nervous system pathophysiology. In this study we investigate the relationship between corticomotor excitability and self-reported non-exercise related fatigue in chronic stroke population. Seventy first-time non-depressed stroke survivors (60.36 ± 12.4 years, 20 females, 56.81 ± 63 months post-stroke) with minimal motor and cognitive impairment were included in the cross-sectional observational study. Fatigue was measured using two validated questionnaires: Fatigue Severity Scale 7 and Neurological Fatigue Index - Stroke. Perception of effort was measured using a 0-10 numerical rating scale in an isometric biceps hold-task and was used as a secondary measure of fatigue. Neurophysiological measures of corticomotor excitability were performed using transcranial magnetic stimulation. Corticospinal excitability was quantified using resting and active motor thresholds and stimulus-response curves of the first dorsal interosseous muscle. Intracortical M1 excitability was measured using paired pulse paradigms: short and long interval intracortical inhibition in the same hand muscle as above. Excitability of cortical and subcortical inputs that drive M1 output was measured in the biceps muscle using a modified twitch interpolation technique to provide an index of central activation failure. Stepwise regression was performed to determine the explanatory variables that significantly accounted for variance in the fatigue and perception scores. Resting motor threshold (R = 0.384; 95% confidence interval = 0.071; P = 0.036) accounted for 14.7% (R(2)) of the variation in Fatigue Severity Scale 7. Central activation failure (R = 0.416; 95% confidence interval = -1.618; P = 0.003) accounted for 17.3% (R(2)) of the variation in perceived effort score. Thus chronic stroke survivors with high fatigue exhibit high motor thresholds and those who perceive high effort have low excitability of inputs that drive motor cortex output. We suggest that low excitability of both corticospinal output and its facilitatory synaptic inputs from cortical and sub-cortical sites contribute to high levels of fatigue after stroke.

The Neural Correlates of Long-Term Carryover following Functional Electrical Stimulation for Stroke.

Neurorehabilitation effective delivery for stroke is likely to be improved by establishing a mechanistic understanding of how to enhance adaptive plasticity. Functional electrical stimulation is effective at reducing poststroke foot drop; in some patients, the effect persists after therapy has finished with an unknown mechanism. We used fMRI to examine neural correlates of functional electrical stimulation key elements, volitional intent to move and concurrent stimulation, in a group of chronic stroke patients receiving functional electrical stimulation for foot-drop correction. Patients exhibited task-related activation in a complex network, sharing bilateral sensorimotor and supplementary motor activation with age-matched controls. We observed consistent separation of patients with and without carryover effect on the basis of brain responses. Patients who experienced the carryover effect had responses in supplementary motor area that correspond to healthy controls; the interaction between experimental factors in contralateral angular gyrus was seen only in those without carryover. We suggest that the functional electrical stimulation carryover mechanism of action is based on movement prediction and sense of agency/body ownership-the ability of a patient to plan the movement and to perceive the stimulation as a part of his/her own control loop is important for carryover effect to take place.

Computational neurorehabilitation: modeling plasticity and learning to predict recovery.

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling - regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity.

Does neuroimaging help to deliver better recovery of movement after stroke?

PURPOSE OF REVIEW: This review examines recent brain imaging studies that might contribute to delivering better recovery of motor function after stroke. RECENT FINDINGS: Most recent studies characterize differences in structural and functional organization of the poststroke brain in relation to impairment, or measure alterations in brain organization as the result of one form of therapy or another. These studies have not altered clinical practice. New approaches can test specific models of motor recovery after stroke. Firstly, anatomical assessment of key motor pathways, particularly corticospinal tract, may be useful in predicting long-term outcomes if used in combination with early clinical scores. Secondly, assessment of neuronal oscillations with electro or magneto-encephalography may provide a novel way of assessing the balance between excitatory and inhibitory cortical processes and thereby provide biomarkers of the potential for experience-dependent plasticity. SUMMARY: Most recent studies are observational and do not test a plausible model of motor recovery after stroke. Brain imaging studies of stroke recovery need to consider how to provide tools to aid prediction of long-term outcome or response to treatment, or describe potential therapeutic targets for novel recovery promoting interventions, if they are to be clinically useful.

An investigation of cortical neuroplasticity following stroke in adults: is there evidence for a critical window for rehabilitation?

BACKGROUND: Evidence in animal stroke models suggests that neuroplasticity takes place maximally in a specific time window after an ischaemic lesion, which may coincide with the optimal time to intervene with rehabilitation. The aim of this study is to investigate neurophysiological evidence for a "critical window" of enhanced neuroplasticity in patients following ischaemic stroke, and establish its duration. We will also investigate changes in cortical inhibition following stroke, and the influence this has on functional recovery. METHODS/DESIGN: We will recruit participants recently admitted to the Stroke Unit of major metropolitan hospitals who have had a stroke and can provide informed consent. Participants will be excluded if they have any contraindications to Transcranial Magnetic Stimulation. We will compare neurophysiological outcomes in an age-matched healthy control group. We conservatively hypothesise a 5% increase in neuroplasticity at the optimal timing following stroke, compared to control participants, and require 43 patients following stroke to detect a significant difference with 80% power. The primary outcome is the change in the motor evoked potential (MEP) amplitude in a hand muscle, after the administration of a plasticity-inducing paradigm to the affected hemisphere. Secondary outcomes include measures of cortical excitability, intracortical inhibition and arm function. DISCUSSION: The data from this trial will clarify whether there is a critical window for neuroplastic change in the brain following stroke. If so, intensive rehabilitation during this period could be more effective, reducing long-term disability and the cost burden of stroke.

Beta oscillations reflect changes in motor cortex inhibition in healthy ageing.

Beta oscillations are involved in movement and have previously been linked to levels of the inhibitory neurotransmitter GABA. We examined changes in beta oscillations during rest and movement in primary motor cortex (M1). Amplitude and frequency of beta power at rest and movement-related beta desynchronization (MRBD) were measured during a simple unimanual grip task and their relationship with age was explored in a group of healthy participants. We were able to show that at rest, increasing age was associated with greater baseline beta power in M1 contralateral to the active hand, with a similar (non-significant) trend in ipsilateral M1. During movement, increasing age was associated with increased MRBD amplitude in ipsilateral M1 and reduced frequency (in contralateral and ipsilateral M1). These findings would be consistent with greater GABAergic inhibitory activity within motor cortices of older subjects. These oscillatory parameters have the potential to reveal changes in the excitatory-inhibitory balance in M1 which in turn may be a useful marker of plasticity in the brain, both in healthy ageing and disease.

Re-thinking the role of motor cortex: context-sensitive motor outputs?

The standard account of motor control considers descending outputs from primary motor cortex (M1) as motor commands and efference copy. This account has been challenged recently by an alternative formulation in terms of active inference: M1 is considered as part of a sensorimotor hierarchy providing top-down proprioceptive predictions. The key difference between these accounts is that predictions are sensitive to the current proprioceptive context, whereas efference copy is not. Using functional electric stimulation to experimentally manipulate proprioception during voluntary movement in healthy human subjects, we assessed the evidence for context sensitive output from M1. Dynamic causal modeling of functional magnetic resonance imaging responses showed that FES altered proprioception increased the influence of M1 on primary somatosensory cortex (S1). These results disambiguate competing accounts of motor control, provide some insight into the synaptic mechanisms of sensory attenuation and may speak to potential mechanisms of action of FES in promoting motor learning in neurorehabilitation.