Posts Tagged recovery

[Abstract] Motor Recovery Beginning 23 Years After Ischemic Stroke

Abstract

It is widely believed that most stroke recovery occurs within 6 months, with little benefit of physiotherapy or other modalities beyond a year. We report a remarkable case of stroke recovery beginning 23 years after a severe stroke due to embolization from the innominate artery and subclavian artery, resulting from compression of the right subclavian artery by a cervical rib. The patient had a large right fronto-parietal infarction with severe left hemiparesis, and a totally non-functional spastic left hand. He experienced some recovery of hand function that began 23 years after the stroke, a year after he took up regular swimming. As a result, intensive physiotherapy was initiated, with repetetive large muscle movement and a spring-loaded mechanical orthosis that provides resistance to finger flexors and supports finger extensors. Within two years he could pick up coins with the previously useless left hand. Functional MRI studies document widespread distribution of the recovery in both hemispheres. This case provides impetus not only to more intensive and prolonged physiotherapy, but also to treatment with emerging modalities such as stem cell therapy, exosome and micro-RNA therapies.

Source: ARTICLES | Journal of Neurophysiology

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[ARTICLE] Standardized measurement of sensorimotor recovery in stroke trials: Consensus-based core recommendations from the Stroke Recovery and Rehabilitation Roundtable – Full Text 

Finding, testing and demonstrating efficacy of new treatments for stroke recovery is a multifaceted challenge. We believe that to advance the field, neurorehabilitation trials need a conceptually rigorous starting framework. An essential first step is to agree on definitions of sensorimotor recovery and on measures consistent with these definitions. Such standardization would allow pooling of participant data across studies and institutions aiding meta-analyses of completed trials, more detailed exploration of recovery profiles of our patients and the generation of new hypotheses. Here, we present the results of a consensus meeting about measurement standards and patient characteristics that we suggest should be collected in all future stroke recovery trials. Recommendations are made considering time post stroke and are aligned with the international classification of functioning and disability. A strong case is made for addition of kinematic and kinetic movement quantification. Further work is being undertaken by our group to form consensus on clinical predictors and pre-stroke clinical data that should be collected, as well as recommendations for additional outcome measurement tools. To improve stroke recovery trials, we urge the research community to consider adopting our recommendations in their trial design.

Lack of a standardized approach to measurement in stroke recovery research hampers our ability to advance understanding of recovery mechanisms, devise better treatments and consolidate knowledge from a body of research using meta-analyses.1 As examples, examination of a recent Cochrane Overview of interventions to improve upper limb function after stroke identified 208 unique assessment tools from 243 trials2; another review found more than 100 measures of activities of daily living (ADLs).3 Furthermore, in most motor rehabilitation trials, measures are taken at arbitrary time points relative to stroke onset, e.g. time of admission to, or discharge from, rehabilitation rather than at standard time points aligned with underlying recovery processes.4

We must challenge the common assumption that most sensorimotor therapies are universally applicable and will achieve the same benefit for all people with stroke. The magnitude of change and likelihood of achieving clinically meaningful improvement in response to specific therapies will depend on age, stroke severity, and other factors including pre-existing comorbid conditions (e.g. diabetes, cognitive impairment, depression)5 and pre-stroke lifestyle factors (e.g. social engagement, exercise).6 The respective contributions of these factors have yet to be fully understood. Going forward, we need to identify the determinants that may help predict responders and non-responders to interventions.

The measurement working group of the Stroke Recovery and Rehabilitation Roundtable (SRRR)7 was established to develop recommendations for standardized assessment time points and measures to be included in all adult trials of sensorimotor recovery after stroke. Given the current lack of standards for data collection and heterogeneous reports in stroke recovery trials, our expert group also considered pre-stroke clinical, demographic and stroke-related data that should be collected to improve clinical prediction of recovery and characterization of patient cohorts.

The decision to focus on sensorimotor recovery reflects the volume of existing trials in this area, the range of outcomes currently in use across these trials, and the gap in current research that known international initiatives has not addressed (e.g. Core Outcome Measures in Effectiveness Trials Initiative (COMET), National Institute of Neurological Disorders and Stroke Common Data Elements (NINDS CDE), The International Consortium for Health Outcomes Measurement (ICHOM),8 Improving Research Outcome Measurement in Aphasia (ROMA)9 and Standardization of Measures in Arm Rehabilitation Trials after Stroke (SMART), Supplementary Table 1). Acknowledging that clinical measures cannot distinguish between true neurological repair (behavioral restitution) and use of compensatory strategies,10 a second objective was to consider whether we could recommend specific kinetic and/or kinematic outcomes that reflect quality of motor performance in order to better understand the neurophysiological changes that occur when patients improve.11,12 Our overall objective of the roundtable was to provide recommendations that, if applied, could improve the methodology of rehabilitation and recovery trials, help build our understanding of the trajectory of stroke recovery and aid discovery of new and more targeted treatments.

Continue —>  Standardized measurement of sensorimotor recovery in stroke trials: Consensus-based core recommendations from the Stroke Recovery and Rehabilitation RoundtableInternational Journal of Stroke – Gert Kwakkel, Natasha A Lannin, Karen Borschmann, Coralie English, Myzoon Ali, Leonid Churilov, Gustavo Saposnik, Carolee Winstein, Erwin EH van Wegen, Steven L Wolf, John W Krakauer, Julie Bernhardt, 2017

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[Abstract] Motor Recovery Beginning 23 Years After Ischemic Stroke – Journal of Neurophysiology

Abstract

It is widely believed that most stroke recovery occurs within 6 months, with little benefit of physiotherapy or other modalities beyond a year. We report a remarkable case of stroke recovery beginning 23 years after a severe stroke due to embolization from the innominate artery and subclavian artery, resulting from compression of the right subclavian artery by a cervical rib. The patient had a large right fronto-parietal infarction with severe left hemiparesis, and a totally non-functional spastic left hand. He experienced some recovery of hand function that began 23 years after the stroke, a year after he took up regular swimming. As a result, intensive physiotherapy was initiated, with repetetive large muscle movement and a spring-loaded mechanical orthosis that provides resistance to finger flexors and supports finger extensors. Within two years he could pick up coins with the previously useless left hand. Functional MRI studies document widespread distribution of the recovery in both hemispheres. This case provides impetus not only to more intensive and prolonged physiotherapy, but also to treatment with emerging modalities such as stem cell therapy, exosome and micro-RNA therapies.

 

Source: ARTICLES | Journal of Neurophysiology

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[ARTICLE] Using Brain Oscillations and Corticospinal Excitability to Understand and Predict Post-Stroke Motor Function – Full Text

What determines motor recovery in stroke is still unknown and finding markers that could predict and improve stroke recovery is a challenge. In this study, we aimed at understanding the neural mechanisms of motor function recovery after stroke using neurophysiological markers by means of cortical excitability (Transcranial Magnetic Stimulation – TMS) and brain oscillations (electroencephalography – EEG). In this cross-sectional study, fifty-five subjects with chronic stroke (62±14 yo, 17 women, 32±42 months post-stroke) were recruited in two sites. We analyzed TMS measures (i.e. motor threshold – MT – of the affected and unaffected sides) and EEG variables (i.e. power spectrum in different frequency bands and different brain regions of the affected and unaffected hemispheres) and their correlation with motor impairment as measured by Fugl-Meyer. Multiple univariate and multivariate linear regression analyses were performed to identify the predictors of good motor function. A significant interaction effect of MT in the affected hemisphere and power in beta bandwidth over the central region for both affected and unaffected hemispheres was found. We identified that motor function positively correlates with beta rhythm over the central region of the unaffected hemisphere, while it negatively correlates with beta rhythm in the affected hemisphere. Our results suggest that cortical activity in the affected and unaffected hemisphere measured by EEG provides new insights on the association between high frequency rhythms and motor impairment, highlighting the role of excess of beta in the affected central cortical region in poor motor function in stroke recovery.

Introduction

Stroke is a leading cause of morbidity, mortality, and disability worldwide (12). Among the sequels of stroke, motor impairment is one of the most relevant, since it conditions the quality of life of patients, it reduces their capability to perform their daily activities and it impairs their autonomy (3). Despite the advancements of the acute stroke therapy, patients require an intensive rehabilitation program that will partially determine the extent of their recovery (4). These rehabilitation programs aim at stimulating cortical plasticity to improve motor performance and functional recovery (5). However, what determines motor improvement is still unknown. Indeed, finding markers that could predict and enhance stroke recovery is still a challenge (6). Different types of biomarkers exist: diagnostic, prognostic, surrogate outcome, and predictive biomarkers (7). The identification of these biomarkers is critical in the management of stroke patients. In the field of stroke research, great attention has been put to biomarkers found in the serum, especially in acute care. However, research on biomarkers of stroke recovery is still limited, especially using neurophysiological tools.

A critical research area in stroke is to understand the neural mechanisms underlying motor recovery. In this context, neurophysiological techniques such as transcranial magnetic stimulation (TMS) and electroencephalography (EEG) are useful tools that could be used to identify potential biomarkers of stroke recovery. However, there is still limited data to draw further conclusions on neural reorganization in human trials using these techniques. A few studies have shown that, in acute and sub-acute stage, stroke patients present increased power in low frequency bands (i.e., delta and theta bandwidths) in both affected and unaffected sides, as well as increased delta/alpha ratio in the affected brain area; these patterns being also correlated to functional outcome (811). Recently, we have identified that, besides TMS-indexed motor threshold (MT), an increased excitability in the unaffected hemisphere, coupled with a decreased excitability in the affected hemisphere, was associated with poor motor function (12), as measured by Fugl-Meyer (FM) [assessing symptoms severity and motor recovery in post-stroke patients with hemiplegia—Fugl-Meyer et al. (13); Gladstone et al. (14)]. However, MT measurement is associated with a poor resolution as it indexes global corticospinal excitability. Therefore, combining this information with direct cortical measures such as cortical oscillations, as measured by EEG, can help us to understand further neural mechanisms of stroke recovery.

To date, there are very few studies looking into EEG and motor recovery. For that reason, we aimed, in the present study, to investigate the relationship between motor impairment, EEG, and TMS variables. To do so, we conducted a prospective multicenter study of patients who had suffered from a stroke, in which we measured functional outcome using FM and performed TMS and EEG recordings. Based on our preliminary work, we expected to identify changes in interhemispheric imbalances on EEG power, especially in frequency bands associated with learning, such as alpha and beta bandwidths. […]

Continue —> Frontiers | Using Brain Oscillations and Corticospinal Excitability to Understand and Predict Post-Stroke Motor Function | Neurology

Figure 1. Topoplots showing the topographic distribution of high-beta bandwidth (25 Hz) for every individual. Red areas represent higher high-beta activity, while blue areas represent lower high-beta activity. Central region (C3 or C4) in red stands for the affected side. For patients with poor motor function, a higher beta activity of the affected central region as compared to the affected side is observed in 16 out of 28 individuals. For patients with good motor function, a similar activity over central regions bilaterally, or higher activity over the unaffected central area can be identified in 21 out of 27 individuals. FM = Fugl-Meyer.

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[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

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Alpha Topics AAC

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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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