Posts Tagged recovery

[WEB SITE] Helping you find the right app after a Stroke or Brain Injury

Find the right apps to aid rehabilitation and recovery. Our NHS specialists have trialed thousands of apps and selected the best.

3 in 1

Honest feedback and ratings provided helping you make the choice that is right for you.

“Apps tell you how you’ve done …. you want to do better. Not scary.” (Stroke Patient)

“Excellent, user-friendly website ….reliable assessment, description and app reviews… would recommend” (Charles Brain Injury Therapist)

Top Rated Apps

SitFit

Free

iOS

Primary Use:

Being Active

Free Flow

Free

iOS Android

Primary Use:

Thinking

Peak

Free

iOS Android

Primary Use:

Thinking

Change4Life Be Food Smart

Free

iOS Android

Primary Use:

Eating and Drinking

Bla Bla Bla

Free

iOS

Primary Use:

Communication

Balloon Frenzy!

Free

iOS Android

Primary Use:

Arms and Fingers

Couch to 5K

Free

iOS Android

Primary Use:

Being Active

Lumosity

Free

iOS Android

Primary Use:

Thinking

Sudoku

Free

iOS Android

Primary Use:

Thinking

Color me

Free

iOS

Primary Use:

Relaxing

Language Therapy

Free

iOS Android

Primary Use:

Communication

Headspace

Free

iOS Android

Primary Use:

My Mood

What’s New

Jointly – for carers

£2.99

iOS

Primary Use:

Got questions

Alpha Topics AAC

£4.99

iOS

Primary Use:

Communication

Advanced Comprehension

£23.99

iOS

Primary Use:

Communication

Advanced Naming

£23.99

iOS

Primary Use:

Communication

Apraxia

£23.99

iOS

Primary Use:

Communication

OT Magazine

Free

iOS

Primary Use:

Got questions

Source: Helping you find the right app after a Stroke or Brain Injury – MyTherappy

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[WEB SITE] How Would One Define Recovery? Ask the Patient – Rehab Managment

 

A study borne from an international research partnership between the University of Pennsylvania School of Nursing (Penn Nursing) and Griffith University School of Nursing and Midwifery in Australia looks at injury recovery from the patient’s point of view.

“While it is recognized that focusing on what patients envision to be good outcomes is an important part of patient-centered care, asking trauma patients and their families what they consider to be the priorities of care and recovery has been neglected,” says Penn Nursing’s Therese S. Richmond, PhD, FAAN, CRNP, the Andrea B. Laporte Professor of Nursing and Associate Dean for Research & Innovation.

She, and study’s lead author Leanne M. Aitken, PhD, RN, Professor of Nursing, now at the City, University of London, conceived the study—published recently in the journal Injury—while Aitken was undertaking a Fulbright Senior Scholarship at the University of Pennsylvania, according to a media release from University of Pennsylvania School of Nursing.

Their study, which included 33 trauma patients, 22 family members, and 40 clinicians from trauma departments in two Australian teaching hospitals, focused on two areas: learning what patients, family members, and clinicians considered to be the indicators of successful recovery from an acute hospitalization after traumatic injury; and understanding if these indicators differed between these groups of stakeholders or changed over time, from during hospitalization to 3 months after discharge.

Five specific indicators of recovery included returning to work, resuming family roles, achieving independence, recapturing normality, and achieving comfort.

In some participants, their perceptions of indicators of injury recovery changed over the 3 months post-discharge. The changes fell into three broad groups: increasing recognition that activities of daily living were important; increasing realization of the impact of the injury; and unfolding appreciation that life could not be taken for granted, the release continues.

While in the hospital, trauma patients often noted their desire to care for themselves. However, the implications of their physical limitations did not fully reveal themselves until after discharge and became increasingly apparent within the first month of being at home.

“Changes in expectations and priorities over time have implications for how we provide education and support that should be tailored to different phases in the recovery trajectory,” Richmond notes in the release. “As patients and family members change their expectations over time, appropriate care needs to be provided across the care continuum.”

The study’s findings indicate a further need to explore recovery priorities using quantitative techniques to determine relevance to a broad cross-section of trauma patients and to develop an appropriate set of outcome measures that patients consider to be important. Although some differences between stakeholder groups were identified, similarities and differences should be tested further in larger groups, the release explains.

“It is expected that by understanding what matters to patients and family members will help us empower patients to be active participants in the healthcare process and will underpin development of patient-reported outcomes that should be used in practice and research in trauma care,” Aitken comments. “This information will also inform future trauma outcome research to ensure these priority areas are appropriate for a broader range of participants.”

[Source(s): University of Pennsylvania School of Nursing, Newswise]

Source: How Would One Define Recovery? Ask the Patient – Rehab Managment

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[ARTICLE] Prediction of Walking and Arm Recovery after Stroke: A Critical Review – Full Text HTML

Abstract

Clinicians often base their predictions of walking and arm recovery on multiple predictors. Multivariate prediction models may assist clinicians to make accurate predictions. Several reviews have been published on the prediction of motor recovery after stroke, but none have critically appraised development and validation studies of models for predicting walking and arm recovery. In this review, we highlight some common methodological limitations of models that have been developed and validated. Notable models include the proportional recovery model and the PREP algorithm. We also identify five other models based on clinical predictors that might be ready for further validation. It has been suggested that neurophysiological and neuroimaging data may be used to predict arm recovery. Current evidence suggests, but does not show conclusively, that the addition of neurophysiological and neuroimaging data to models containing clinical predictors yields clinically important increases in predictive accuracy.

1. Introduction

It would be useful to be able to predict recovery of walking and arm after stroke. Accurate predictions are needed so that clinicians can provide patients with prognoses, set goals, select therapies and plan discharge [1,2,3,4]. For example, if it was possible to predict with some certainty that a particular patient would be unable to walk independently at six months, the clinicians providing that patient with acute and subacute care might work toward a discharge goal of safe transfers. Therapy might involve carer training and equipment prescription rather than intensive gait training. The ability to make accurate predictions could reduce the length of stay in hospitals and enable efficient utilization of stroke care resources [4,5].
Several systematic reviews have identified strong predictors of walking and arm recovery after stroke [2,3,6]. In one systematic review of prognostic studies on walking, clinical variables such as age, severity of paresis and leg power were found to be strong predictors of walking after stroke (based on five studies, each of between 197 and 804 patients) [2]. In another systematic review of prognostic studies on arm recovery, clinical, neurophysiological and neuroimaging data were found to be strong predictors of arm recovery after stroke (based on 58 studies of 9–1197 patients) [3]. These clinical, neurophysiological and neuroimaging data included measures of upper limb impairment, upper limb function, lower limb impairment, motor and somatosensory evoked potentials, and measures obtained with diffusion tensor imaging [3].
In practice, clinicians base their predictions about clinical outcomes on multiple variables [7,8,9]. If multiple predictors are to be used to make prognoses, there needs to be a proper accounting of the independent (incremental) predictive value of each predictor variable. Therefore the most useful information about prognosis is likely to come from multivariate prediction models [7,8,9].
The research which underpins establishment of clinically useful multivariate prediction models involves several steps. First ‘development studies’ are conducted to build the multivariate prediction models [7]. Subsequently the predictive accuracy of the models is tested on new cohorts [7,10]. These studies are known as ‘validation studies’ [7]. It is recommended that prediction models should not be used in clinical practice until both development and validation studies have been conducted [7,10]. Once development and validation studies have been conducted, impact studies may be conducted, although the reality is that few reports of impact studies are published. Impact studies resemble clinical trials; they test the efficacy of use of prediction models on patient outcomes, clinician behaviour and cost-effectiveness of care [7,11]. Recent narrative reviews have provided updates on the prediction of motor recovery after stroke [5,12] but these reviews have not focused on development and validation studies of models for predicting walking and arm recovery.
This review provides a critical review of prediction models of walking and arm recovery after stroke. Studies were identified using the search strategy and inclusion criteria in the Appendix. The review begins in the second section with the definitions and measurements of walking and arm recovery. The third section provides a detailed description of the recommended process for developing and validating a prediction model because this process provides a benchmark against which prediction modelling studies of walking and arm recovery can be evaluated. The fourth section critically appraises development and validation studies of walking and arm recovery with the aim of identifying multivariate models that could potentially be implemented in clinical practice. Much has been written about the role of neurophysiological and neuroimaging data in predicting arm recovery. The fifth section considers whether neurophysiological and neuroimaging data provide additional predictive value over clinical data alone in predicting arm recovery. We conclude with a summary and recommendations for future prediction modelling studies.

Continue —> Brain Sciences | Free Full-Text | Prediction of Walking and Arm Recovery after Stroke: A Critical Review | HTML

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[ARTICLE] Prediction of Walking and Arm Recovery after Stroke: A Critical Review – Full Text HTML

Abstract

Clinicians often base their predictions of walking and arm recovery on multiple predictors. Multivariate prediction models may assist clinicians to make accurate predictions. Several reviews have been published on the prediction of motor recovery after stroke, but none have critically appraised development and validation studies of models for predicting walking and arm recovery.
In this review, we highlight some common methodological limitations of models that have been developed and validated. Notable models include the proportional recovery model and the PREP algorithm. We also identify five other models based on clinical predictors that might be ready for further validation. It has been suggested that neurophysiological and neuroimaging data may be used to predict arm recovery. Current evidence suggests, but does not show conclusively, that the addition of neurophysiological and neuroimaging data to models containing clinical predictors yields clinically important increases in predictive accuracy.

1. Introduction

It would be useful to be able to predict recovery of walking and arm after stroke. Accurate predictions are needed so that clinicians can provide patients with prognoses, set goals, select therapies and plan discharge [1,2,3,4]. For example, if it was possible to predict with some certainty that a particular patient would be unable to walk independently at six months, the clinicians providing that patient with acute and subacute care might work toward a discharge goal of safe transfers. Therapy might involve carer training and equipment prescription rather than intensive gait training. The ability to make accurate predictions could reduce the length of stay in hospitals and enable efficient utilization of stroke care resources [4,5].
Several systematic reviews have identified strong predictors of walking and arm recovery after stroke [2,3,6]. In one systematic review of prognostic studies on walking, clinical variables such as age, severity of paresis and leg power were found to be strong predictors of walking after stroke (based on five studies, each of between 197 and 804 patients) [2]. In another systematic review of prognostic studies on arm recovery, clinical, neurophysiological and neuroimaging data were found to be strong predictors of arm recovery after stroke (based on 58 studies of 9–1197 patients) [3]. These clinical, neurophysiological and neuroimaging data included measures of upper limb impairment, upper limb function, lower limb impairment, motor and somatosensory evoked potentials, and measures obtained with diffusion tensor imaging [3].
In practice, clinicians base their predictions about clinical outcomes on multiple variables [7,8,9]. If multiple predictors are to be used to make prognoses, there needs to be a proper accounting of the independent (incremental) predictive value of each predictor variable. Therefore the most useful information about prognosis is likely to come from multivariate prediction models [7,8,9].
The research which underpins establishment of clinically useful multivariate prediction models involves several steps. First ‘development studies’ are conducted to build the multivariate prediction models [7]. Subsequently the predictive accuracy of the models is tested on new cohorts [7,10]. These studies are known as ‘validation studies’ [7]. It is recommended that prediction models should not be used in clinical practice until both development and validation studies have been conducted [7,10]. Once development and validation studies have been conducted, impact studies may be conducted, although the reality is that few reports of impact studies are published. Impact studies resemble clinical trials; they test the efficacy of use of prediction models on patient outcomes, clinician behaviour and cost-effectiveness of care [7,11]. Recent narrative reviews have provided updates on the prediction of motor recovery after stroke [5,12] but these reviews have not focused on development and validation studies of models for predicting walking and arm recovery.
This review provides a critical review of prediction models of walking and arm recovery after stroke. Studies were identified using the search strategy and inclusion criteria in the Appendix. The review begins in the second section with the definitions and measurements of walking and arm recovery. The third section provides a detailed description of the recommended process for developing and validating a prediction model because this process provides a benchmark against which prediction modelling studies of walking and arm recovery can be evaluated. The fourth section critically appraises development and validation studies of walking and arm recovery with the aim of identifying multivariate models that could potentially be implemented in clinical practice. Much has been written about the role of neurophysiological and neuroimaging data in predicting arm recovery. The fifth section considers whether neurophysiological and neuroimaging data provide additional predictive value over clinical data alone in predicting arm recovery. We conclude with a summary and recommendations for future prediction modelling studies.

Continue —> Brain Sciences | Free Full-Text | Prediction of Walking and Arm Recovery after Stroke: A Critical Review | HTML

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[WEB SITE] 5 steps to speed recovery from concussions and traumatic brain injury

Concussions and traumatic brain injury, or TBIs, affect over a million Americans every year. The vast majority are relatively mild, not requiring hospitalization. However, even in these mild concussions, over 75% will develop chronic pain, problems with memory and attention, irritability, and other neurocognitive issues that interfere with school, work, and family life. When people are discharged from the emergency room after a TBI, they usually receive little guidance on what they can do to speed their recovery and greatly reduce the risk of long-term problems with pain or chronic mental health issues that can become severe.

I am a clinical professor of medicine and work with a team in a traumatic brain injury clinic that treats patients with mild to severe injuries. I also do clinical research on diet and lifestyle interventions to improve neurocognitive (thinking) ability and mood of people with traumatic brain injury and multiple sclerosis.

Science has demonstrated that the axons, or wiring, between brain cells are damaged in a concussion: The more severe the concussion, the greater the damage. In addition, brain injury leads to inflammation in the brain, which further slows down the healing process.

We used to think that the adult brain lacked the ability to repair itself, but now we know the opposite is true: the adult brain is capable of building new synapses (connections) between brain cells and even growing more brain cells given the right environment. We have also observed that stem cells, which orchestrate these changes, are present even in the adult brain, and can begin the repair process.

Your brain needs the right tools to repair itself. Here are the top 5 things you can do to speed recovery following a concussion or traumatic brain injury.

1) Strength train at least 4 times a week. Exercise, particularly strength training, stimulates the production of nerve growth factors that encourage stem cell activity and help build more synapses between brain cells.

2) Stop the sugar and artificial sweeteners. Sugar increases insulin levels in the brain. Higher insulin levels are associated with more rapid loss of synapses and accelerated shrinkage of the brain and spinal cord. Artificial sweeteners are excitotoxins, which induce excessive production of glutamate in the brain, again leading to accelerated shrinkage.

3) Replace flour-based food (bread, pasta, rice, cereal) with vegetables. Get your carbohydrates from eating 6 to 9 cups of vegetables each day, which will dramatically increase your intake of vitamins and antioxidants. Eating more vegetables and berries has been shown to improve cognition and mood markedly.

4) Increase omega-3 fatty acid intake. Omega-3 fatty acids reduce the severity of injury and speed recovery. Eat more wild fish and grass-fed meat; you may also take a fish oil supplement.

5) Eat sufficient protein every day. The brain uses amino acids from protein to make neurotransmitters. For most, eating 6 to 12 ounces of protein (depending on your size and gender) will provide sufficient protein. If you are vegetarian, pay attention to protein intake and also take vitamin B12–many vegetarians are B12 deficient, which can also lead to cognitive and mood problems.

This is not theoretical. I have seen it over and over in my traumatic brain injury clinics: when my patients drop the sugar and white flour and instead eat six to nine cups of vegetables a day, their thinking ability improves, mood improves, pain diminishes, fatigue fades and they are steadily happier. They begin thriving again. In short, when people adopt a diet and lifestyle designed specifically for optimal function of their brain cells, their brain and overall health steadily improves. If you want to learn more about the dietary programs that we use in our clinics, visit www.terrywahls.com and pick up my book, The Wahls Protocol: A Radical New Way to Treat All Chronic Autoimmune Conditions Using Paleo Principles, which details the protocols we use in our clinics and in our clinical trials to restore health and vitality.

Source: 5 steps to speed recovery from concussions and traumatic brain injury

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[WEB SITE] Stroke Recovery

Learning to live a normal life after stroke is possible. Learn to take an active approach, adapting new limitations, and finding support for a life after stroke.

Source: Stroke Recovery

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[Slide Show] Why do only some arms recover? OR How good are we at predicting upper limb recovery? – UCL

Predicting UL recovery after stroke

How do we treat people after stroke?

How good are we?

Watch the Slide Show

 

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[Abstract] Upper-Limb Recovery After Stroke. A Randomized Controlled Trial Comparing EMG-Triggered, Cyclic, and Sensory Electrical Stimulation – 

Abstract

Background and purpose. This study compared the effect of cyclic neuromuscular electrical stimulation (NMES), electromyographically (EMG)-triggered NMES, and sensory stimulation on motor impairment and activity limitations in patients with upper-limb hemiplegia.

Methods. This was a multicenter, single-blind, multiarm parallel-group study of nonhospitalized hemiplegic stroke survivors within 6 months of stroke. A total of 122 individuals were randomized to receive either cyclic NMES, EMG-triggered NMES, or sensory stimulation twice every weekday in 40-minute sessions, over an 8 week-period. Patients were followed for 6 months after treatment concluded.

Results. There were significant increases in the Fugl-Meyer Assessment [F(1, 111) = 92.6, P < .001], FMA Wrist and Hand [F(1, 111) = 66.7, P < .001], and modified Arm Motor Ability Test [mAMAT; time effect: F(1, 111) = 91.0, P < .001] for all 3 groups. There was no significant difference in the improvement among groups in the FMA [F(2, 384) = 0.2, P = .83], FMA Wrist and Hand [F(2, 384) = 0.4, P = .70], or the mAMAT [F(2, 379) = 1.2, P = .31].

Conclusions. All groups exhibited significant improvement of impairment and functional limitation with electrical stimulation therapy applied within 6 months of stroke. Improvements were likely a result of spontaneous recovery. There was no difference based on the type of electrical stimulation that was administered.

 

Source: Upper-Limb Recovery After Stroke

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[Abstract] GABAergic drug use and global, cognitive, and motor functional outcomes after stroke.

Abstract

Background

In animal models and healthy volunteers, the use of GABA A receptor agonists (GABA-AGs) seem deleterious for functional recovery. The agents are widely used for subacute stroke, but their effect on functional recovery remains unclear.

Objectives

We aimed to evaluate the association between GABA-AG use and functional recovery after stroke.

Methods

We retrospectively recruited 434 survivors of subacute stroke admitted for inpatient rehabilitation between 2000 and 2013 in our institution (107 with and 327 without GABA-AG use). We used multivariate regression to assess the association of GABA-AG use and successful functional recovery, defined as reaching, between admission and discharge, the minimal clinically important difference (MCID) of 22 points on the global Functional Independence Measure (FIM). Secondary analyses were the associations of GABA-AG with cognitive and motor FIM MCID and constant GABA-AG exposure (24 h/24 GABA-AG) with global, cognitive and motor FIM MCID. A new estimation of the MCID was performed with the standard error of measurement.

Results

Reaching the global FIM MCID was associated with GABA-AG use (adjusted odds ratio [aOR] 0.54 [95% CI 0.31–0.91], P = 0.02) as well as 24 h/24 GABA-AG use (aOR 0.25 [0.08–0.83]; P = 0.02). Furthermore, GABA-AG and 24 h/24 GABA-AG use was inversely but not always significantly associated with reaching the cognitive FIM MCID (aOR 0.56,P = 0.07; aOR 0.26, P = 0.06, respectively) and motor FIM MCID (aOR 0.51, P = 0.07; aOR 0.13, P = 0.01, respectively). The estimated MCID was 19 for global FIM, 4 for cognitive FIM, and 16 for motor FIM.

Conclusions

GABA-AG use is associated with not reaching successful functional recovery during stroke rehabilitation. Randomised trials are needed to formally establish the potential deleterious effect of GABA-AG use on functional recovery.

 

Source: GABAergic drug use and global, cognitive, and motor functional outcomes after stroke

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[ARTICLE] Brain activation is related to smoothness of upper limb movements after stroke – Full Text 

Abstract

It is unclear whether additionally recruited sensorimotor areas in the ipsilesional and contralesional hemisphere and the cerebellum can compensate for lost neuronal functions after stroke. The objective of this study was to investigate how increased recruitment of secondary sensorimotor areas is associated with quality of motor control after stroke. In seventeen patients (three females, fourteen males; age: 59.9 ± 12.6 years), cortical activation levels were determined with functional magnetic resonance imaging (fMRI) in 12 regions of interest during a finger flexion–extension task in weeks 6 and 29 after stroke. At the same time points and by using 3D kinematics, the quality of motor control was assessed by smoothness of the grasp aperture during a reach-to-grasp task, quantified by normalized jerk. Ipsilesional premotor cortex, insula and cerebellum, as well as the contralesional supplementary motor area, insula and cerebellum, correlated significantly and positively with the normalized jerk of grasp aperture at week 6 after stroke. A positive trend towards this correlation was observed in week 29. This study suggests that recruitment of secondary motor areas at 6 weeks after stroke is highly associated with increased jerk during reaching and grasping. As jerk represents the change in acceleration, the recruitment of additional sensorimotor areas seems to reflect a type of control in which deviations from an optimal movement pattern are continuously corrected. This relationship suggests that additional recruitment of sensorimotor areas after stroke may not correspond to restitution of motor function, but more likely to adaptive motor learning strategies to compensate for motor impairments.

Introduction

Outcomes of neurorehabilitation after stroke are variable and depend largely on the intensity and task specificity of the intervention applied as well as the severity of initial impairment at stroke onset (Langhorne et al. 2011). For the paretic upper limb in particular, treatment effects are mainly restricted to patients with some voluntary control of finger extension after stroke (Kwakkel and Kollen 2013; Langhorne et al. 2011). These findings suggest that there is a need for a better understanding of the neuronal mechanisms underlying functional recovery after stroke.

Task-related recruitment of secondary sensorimotor areas in the affected and non-affected hemisphere has been associated with poor motor recovery in terms of body functions and activities (Buma et al. 2010; Ward et al. 2004). It is therefore unlikely that secondary sensorimotor areas are able to take over the functions of the primary injured motor areas (Buma et al. 2010; Ward et al. 2004). Recruitment of these additional areas may rather reflect support in the execution of compensatory motor control while performing a motor task with the paretic upper limb.

However, it is still unclear how brain activation patterns are associated with quality of upper limb control after stroke (Buma et al. 2013). Most traditional clinical assessment scales are not suitable for capturing howpatients perform functional tasks. By contrast, 3D kinematics can assess intra-limb coordination and smoothness of movement patterns, which are important characteristics of quality of motor control.

A recent study with intensive repeated 3D kinematic measurements in the first 6 months after stroke suggested that basic synergistic couplings between the shoulder and elbow during a functional reaching task diminished as a function of time after stroke (van Kordelaar et al. 2013). This suggests that the ability to plan movements in advance (i.e. feedforward motor control) may improve, thereby decreasing the continuous online corrections based on proprioceptive feedback (van Kordelaar et al. 2014; Meulenbroek et al. 2001). Such corrections based on afferent information have been shown to negatively affect the smoothness of hand and finger movements (Merdler et al. 2013).

An important measure to quantify smoothness is normalized jerk. Jerk is the third time derivative of the position of a particular body part. Normalized jerk is obtained by correcting for differences in movement duration and movement distance (Caimmi et al. 2008). As high smoothness is reflected by minimal changes in position, smoothness is inversely related to normalized jerk. We have recently shown that this jerk measure decreases (i.e. smoothness increases) substantially in the first 8 weeks after stroke (van Kordelaar et al. 2014) and levels off up to 26 weeks after stroke, suggesting that jerkiness is a sensitive measure to investigate time-dependent changes in quality of motor control, particularly early after stroke. However, due to a lack of studies combining imaging techniques with kinematic analyses, the neurological mechanisms underlying the recovery of smoothness of upper limb movements are still largely unknown.

We hypothesized that elevated recruitment of secondary sensorimotor areas would be associated with jerky movements. This hypothesis was tested by investigating the association between smoothness of finger movements during a reach-to-grasp task, measured with 3D kinematics, and activation levels in sensorimotor networks of the brain during a finger flexion–extension task, measured with functional MRI (fMRI) (Buma et al. 2010). There are strong indications that the potential for neural adaptation is mainly limited to a time window of 10 weeks after stroke in which most spontaneous neurological recovery occurs (Murphy and Corbett 2009; Langhorne et al. 2011). We tested the association between brain activation and smoothness of finger movements at 6 and 29 weeks after stroke, to assess whether this association changes with time after stroke (Buma et al. 2010; van Kordelaar et al. 2014).

Continue —> Brain activation is related to smoothness of upper limb movements after stroke – Springer

https://tbirehabilitation.files.wordpress.com/2016/04/221_2015_4538_fig1_html.gif?w=709&h=473

Fig. 1 a Example of definition of cortical ROIs for one patient. b Mean results for task-related activity for the affected hand at weeks 6 and 29 after stroke. Mean beta values (±1 SE) in the cerebellum, premotor area (PM), supplementary motor area (SMA), postcentral gyrus, precentral gyrus and insula for the left (affected) and right (unaffected) hemispheres (LH and RH, respectively). Patients’ T-maps were flipped so the affected hand corresponded to the right hand

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