Archive for category Neuroplasticity

[BOOK] Chapter 9: Neuroscience-Based Rehabilitation for Stroke Patients

The Book: Neuroscience-Based Rehabilitation for Stroke Patients | InTechOpen, Published on: 2017-05-10. Authors: Takayuki Kodama and Hideki Nakano

Chapter 9: Neuroscience-Based Rehabilitation for Stroke Patients

Abstract

Hitherto, physical therapy for rehabilitating patients with cerebral dysfunction has focused on acquiring and improving compensatory strategies by using the remaining functions; it has been presumed that once neural functions have been lost, they cannot be restored. However, neuroscience-based animal research and neuroimaging research since the 1980s have demonstrated that recovery arises from plastic changes in the central nervous system and reconstruction of neural networks; this research is ushering in a new age of neuroscience-based rehabilitation as a treatment for cerebral dysfunction (such as stroke). In this paper, in regard to mental practices using motor imagery and kinaesthetic illusion, we summarize basic discoveries and theories relating to motor function therapy based on neuroscientific theory; in particular, we outline a novel rehabilitation method using kinaesthetic illusion induced by vibrational stimulus, which the authors are currently attempting in stroke patients.

1. Introduction

Conventional physical therapy (PT) for the rehabilitation of patients with brain dysfunction focuses on the acquisition of function through alternative means by using and improving the patients’ existing functions, and it is based on the assumption that once a neutral function is lost, it can never be recovered [1]. However, animal neuroscience studies [24] that were conducted after the 1980s and neuroimaging studies [5, 6] have shown that recovery can occur as a result of plastic changes in the nervous system or reorganization of the neural network, and rehabilitation (neuroscience-based rehabilitation, NBR) after cerebral dysfunction (e.g. stroke) has reached a new era in treatment. These observations suggest that the plasticity that is observed in patients is related to the characteristic that the more the patient receives therapy in specific parts of their body, the more that the brain areas that control these parts will be functionally as well as anatomically extended.

Functional recovery originally referred to a patient’s recovery from limitations in their behavior, movements, and/or activity [7]. Therefore, the purpose of NBR is not only to induce the reorganization of brain functions through neural plasticity mechanisms but also recover comprehensive bodily motor functions and brain functions for autonomous and active social behavior. What type of treatment strategy is required so that patients feel positively engaged by it, gradually understand its effects, and work toward a goal? Previous studies have revealed important factors in the effects of NBR treatment, such as the amount of therapy [8, 9], rehabilitation implementation environment [10], and performance of neurocognitive rehabilitation [11] through mental practice techniques, such as motor imagery (MI) [12]. Among these factors, treatments involving MI are strongly recommended because MI contributes to the reorganization of neural functions. MI, which is an approach that is based on neuroscientific data and the motor learning theory, is defined as the capacity to internally mimic physical movements without any associated motor output [13]. The cognitive process that occurs during the imagination of movements involves various components, such as mutual understandings between oneself and others (environment), observations of movements, mental manipulations of objects, and psychological time and movement planning. Instead of repeating simple physical movements to receive feedback on outcome in the actual therapy, the practice of voluntary and skill-requiring movements that are geared toward task completion induces the functional recovery [14]. Thus, an important element of the patients’ engagement in the therapy is that it occurs in an active and top-down fashion through the use of MI. However, because MI has a task-specific nature, cognitive functions and memories of motor experiences that equip the patients to perform the task are required. Patients with neurofunctional states that make motor execution (ME) difficulty may suffer not only from impairments in motor-related brain areas but also from modifications in their intracerebral body representations (e.g. somatoparaphrenia) [15, 16]. In such cases, the exploitation of kinaesthetic illusions [1720], which can be induced in the brain by extraneous stimuli, such as vibratory stimulations, becomes important for inputting appropriate motor-sensory information into the brain in a passive and bottom-up fashion. Therefore, the implementation of a mental practice to determine the criteria for adequate treatment according to the states of the patient’s cognitive functions and motor functions is important in order to select and implement the best therapy. Thus, this paper summarizes the basic understanding and theories of mental practices that use MI or kinaesthetic illusion and discusses, in particular, research results concerning kinaesthetic illusions that are induced by vibratory stimulations, which we are currently attempting on stroke patients.

2. What is neuroscience-based rehabilitation?

NBR involves a series of processes that are selected for the intervention according to the current brain function theories that have been revealed by neuroscience and other similar studies and verification of its outcomes. For example, the selection of a NBR strategy for a stroke patient requires a combination of deep clinical reasoning, the experience of the therapist, and a vast understanding of the evidence obtained by studies from wide-ranging academic fields on the factors that support recovery mechanisms and produce particular outcomes. First, the neural basis of brain cell reorganization will be presented.

2.1. Neural basis of brain cell reorganization

The current understanding of neural reorganization after dysfunction is not that the neurons themselves recover after their axons are damaged but rather that damaged functional networks recover due to several processes that induce the recovery of motor and cognitive functions. Cajal [1], who was a proponent of neuron theory, stated that the central nervous system (brain and spinal cord) of adult mammals would not recover once it is damaged. However, studies that have been conducted since the 1980s and that have shown that alterations in the peripheral nervous system, such as denervation and amputation, change somatic sensations and the representations of body parts while they are in motion have revealed that the brain has plasticity. In 1998, Eriksson et al. [21] reported the new formation of neurons in the central nervous system of human beings. These findings raised the question of whether the plastic changes and functional reorganization that occur in subjects with cranial nerve disorders originate from an ischemic state, such as a cerebrovascular disturbance. The underlying mechanisms of the plasticity that occurs after a cortical deficit are thought to involve (i) the redundancy of neuronal connections in the central nervous system, (ii) morphological changes in the neurons, and (iii) changes in synaptic information transmission [22]. If neurons are damaged, astrocytes begin to divide due to the activity of microglia. These glial cells then reinforce the areas that have been damaged by brain lesions and release neurotrophic factors, such as nerve growth factor, to promote neuronal sprouting (it takes around two weeks for synapses to grow after nerve damage [23]). The sprouted neurons are then connected to an existing neural network, which forms a new network. In other words, if neurons are damaged, new neurons begin to reorganize themselves in order to compensate for it. Adequate NBR stimulates the neural network with the neurofunction that is most similar to the predamaged functional state of the neural network, even though the new network is not located in the damaged region. If strong inputs enter the network multiple times, the synaptic connections will be reinforced. However, plasticity will not be induced in synapses with little information (input specificity), and the synapses will be excluded from the network formation [24, 25].

These findings have been confirmed by several famous studies. Nudo et al. [8] caused artificial cerebral infarcts in monkeys in the region of the primary motor cortex (M1) that corresponds to fingers and then forced the monkeys to use fingers with motor deficits. Thus, they reported that the brain region that previously controlled the shoulders and elbows prior to the therapy then controlled the fingers and more distal body parts (Figure 1). Merzenich et al. [26] surgically sutured the fingers of monkeys and then compared the pre- and post-surgical somatotopies of Brodmann area (BA) 3b, which corresponds to the sensorimotor area (SMA). Microelectrodes were used to record the responses in BA3b to finger stimuli. The third and fourth fingers were then surgically sutured, and the responses were recorded again a month later. Thus, the boundary between the third and fourth fingers became unclear. In addition, the results of a study that was conducted in human beings suggested that the plasticity of brain cells depends on sensory input. The results of a magnetoencephalography study that compared the somatotopies of the first and fifth fingers of string players to normal controls showed that a broader cerebral cortical area was activated for string players compared to the controls [6].

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Figure 1. Representation of the distal forelimb in cortical area 4 derived from pre- and post-training mapping procedures [8].

These findings suggest that the size of the intracerebral somatotopic representation, which is vital to ME, is determined by the degree of use of the region. If you try to induce plasticity in specific parts of the bodies of stroke patients, as mentioned above, the induction of neural plasticity in a pathway that allows highly efficient information processing by repeating movements in a pattern like the normal pattern should be possible, provided the patient has retained their motor functions to a certain degree. However, if a patient has the functional level of almost not able to perform movement or is only able to perform the movement in an abnormal pattern, the stimulation of the plasticity for the formation of a neural network that is required to be able to regain normal motor function may not be possible. Ward et al. [27] chronologically examined the relationships between motor function recovery scores and task-related brain activities for approximately 12 months after the onset of stroke with functional magnetic resonance imaging. They found a negative correlation between motor function recovery scores and a decline in the hyperactivity of brain areas in the damaged and undamaged hemispheres (M1, premotor cortex; PMC, supplementary motor cortex; SMC, cerebellum). These findings suggest that a better recovery of motor function is associated with better connectivity between the functional systems of multiple brain regions and that a continuous and long-term approach is required to study the changes in the morphologies and networks of neurons. Thus, a qualitative and continuous approach [28] is required in studies of the recovery of the entire neural system (e.g. transcortical network, M1-PMC neural network [29]) in order to be able to perform movement rather than merely establishing quantitative interventions of movement. Thus, next, we will discuss the current understanding of what is required in interventions for stroke patients.[…]

Continue —> Neuroscience-Based Rehabilitation for Stroke Patients | InTechOpen

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[ARTICLE] Neural Plasticity in Moderate to Severe Chronic Stroke Following a Device-Assisted Task-Specific Arm/Hand Intervention – Full Text

Currently, hand rehabilitation following stroke tends to focus on mildly impaired individuals, partially due to the inability for severely impaired subjects to sufficiently use the paretic hand. Device-assisted interventions offer a means to include this more severe population and show promising behavioral results. However, the ability for this population to demonstrate neural plasticity, a crucial factor in functional recovery following effective post-stroke interventions, remains unclear. This study aimed to investigate neural changes related to hand function induced by a device-assisted task-specific intervention in individuals with moderate to severe chronic stroke (upper extremity Fugl-Meyer < 30). We examined functional cortical reorganization related to paretic hand opening and gray matter (GM) structural changes using a multimodal imaging approach. Individuals demonstrated a shift in cortical activity related to hand opening from the contralesional to the ipsilesional hemisphere following the intervention. This was driven by decreased activity in contralesional primary sensorimotor cortex and increased activity in ipsilesional secondary motor cortex. Additionally, subjects displayed increased GM density in ipsilesional primary sensorimotor cortex and decreased GM density in contralesional primary sensorimotor cortex. These findings suggest that despite moderate to severe chronic impairments, post-stroke participants maintain ability to show cortical reorganization and GM structural changes following a device-assisted task-specific arm/hand intervention. These changes are similar as those reported in post-stroke individuals with mild impairment, suggesting that residual neural plasticity in more severely impaired individuals may have the potential to support improved hand function.

Introduction

Nearly 800,000 people experience a new or recurrent stroke each year in the US (1). Popular therapies, such as constraint-induced movement therapy (CIMT), utilize intense task-specific practice of the affected limb to improve arm/hand function in acute and chronic stroke with mild impairments (2, 3). Neuroimaging results partially attribute the effectiveness of these arm/hand interventions to cortical reorganization in the ipsilesional hemisphere following training in acute and mild chronic stroke (4). Unfortunately, CIMT requires certain remaining functionality in the paretic hand to execute the tasks, and only about 10% of screened patients are eligible (5), thus disqualifying a large population of individuals with moderate to severe impairments. Recently, studies using device-assisted task-specific interventions specifically targeted toward moderate to severe chronic stroke reported positive clinical results (68). However, these studies primarily focus on clinical measures, but it is widely accepted that neural plasticity is a key factor for determining outcome (911). Consequently, it remains unclear whether moderate to severe chronic stroke [upper extremity Fugl-Meyer Assessment (UEFMA) < 30] maintains the ability to demonstrate neural changes following an arm/hand intervention.

Neural changes induced by task-specific training have been investigated widely using animal models (12). For instance, monkeys or rodents trained on a skilled reach-to-grasp task express enlarged representation of the digits of the hand or forelimb in primary motor cortex (M1) following training as measured by intracortical microstimulation (13, 14). Additionally, rapid local structural changes in the form of dendritic growth, axonal sprouting, myelination, and synaptogenesis occur (1518). Importantly, both cortical and structural reorganization corresponds to motor recovery following rehabilitative training in these animals (19, 20).

The functional neural mechanisms underlying effective task-specific arm/hand interventions in acute and chronic stroke subjects with mild impairments support those seen in the animal literature described above. Several variations of task-specific combined arm/hand interventions, including CIMT, bilateral task-specific training, and hand-specific robot-assisted practice, have shown cortical reorganization such as increased sensorimotor activity and enlarged motor maps in the ipsilesional hemisphere related to the paretic arm/hand (2124). These results suggest increased recruitment of residual resources from the ipsilesional hemisphere and/or decreased recruitment of contralesional resources following training. Although the evidence for a pattern of intervention-driven structural changes remains unclear in humans, several groups have shown increases in gray matter (GM) density in sensorimotor cortices (25), along with increases in fractional anisotropy in ipsilesional corticospinal tract (CST) (26) following task-specific training in acute and chronic stroke individuals with mild impairments.

The extensive nature of neural damage in moderate to severe chronic stroke may result in compensatory mechanisms, such as contralesional or secondary motor area recruitment (27). These individuals show increased contralesional activity when moving their paretic arm, which correlates with impairment (28, 29) and may be related to the extent of damage to the ipsilesional CST (30). This suggests that more impaired individuals may increasingly rely on contralesional corticobulbar tracts such as the corticoreticulospinal tract to activate the paretic limb (29). These tracts lack comparable resolution and innervation to the distal parts of the limb, thus sacrificing functionality at the paretic arm/hand (31). Since this population is largely ignored in current arm/hand interventions, it is unknown whether an arm/hand intervention for these more severely impaired post-stroke individuals will increase recruitment of residual ipsilesional corticospinal resources. These ipsilesional CSTs maintain the primary control of hand and finger extensor muscles (32) and are thus crucial for improved hand function. Task-specific training assisted by a device may reengage and strengthen residual ipsilesional corticospinal resources by training distal hand opening together with overall arm use.

The current study seeks to determine whether individuals with moderate to severe chronic stroke maintain the ability to show cortical reorganization and/or structural changes alongside behavioral improvement following a task-specific intervention. We hypothesize that following a device-assisted task-specific intervention, moderate to severe chronic stroke individuals will show similar functional and structural changes as observed in mildly impaired individuals, demonstrated by (i) a shift in cortical activity related to paretic hand opening from the contralesional hemisphere toward the ipsilesional hemisphere and (ii) an increase in GM density in sensorimotor cortices in the ipsilesional hemisphere.[…]

Continue —> Frontiers | Neural Plasticity in Moderate to Severe Chronic Stroke Following a Device-Assisted Task-Specific Arm/Hand Intervention | Neurology

Figure 5. Statistical maps of gray matter (GM) density changes across all patients. Significant increases (red/yellow) and decreases (Blue) in GM density are depicted on sagittal, coronal, and axial sections (left to right) on Montreal Neurological Institute T1 slices. Sections show the maximum effect on (A) ipsilesioned M1/S1, (B) contralesional M1/S1, and (C) ipsilesional thalamus. Les indicates the side of the lesioned hemisphere. Color maps indicate the t values at every voxel. A statistical threshold was set at p < 0.001 uncorrected.

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[ARTICLE] Diffusion MRI and the detection of alterations following traumatic brain injury – Full Text

Abstract

This article provides a review of brain tissue alterations that may be detectable using diffusion magnetic resonance imaging MRI (dMRI) approaches and an overview and perspective on the modern dMRI toolkits for characterizing alterations that follow traumatic brain injury (TBI). Noninvasive imaging is a cornerstone of clinical treatment of TBI and has become increasingly used for preclinical and basic research studies. In particular, quantitative MRI methods have the potential to distinguish and evaluate the complex collection of neurobiological responses to TBI arising from pathology, neuroprotection, and recovery. dMRI provides unique information about the physical environment in tissue and can be used to probe physiological, architectural, and microstructural features. Although well-established approaches such as diffusion tensor imaging are known to be highly sensitive to changes in the tissue environment, more advanced dMRI techniques have been developed that may offer increased specificity or new information for describing abnormalities. These tools are promising, but incompletely understood in the context of TBI. Furthermore, model dependencies and relative limitations may impact the implementation of these approaches and the interpretation of abnormalities in their metrics. The objective of this paper is to present a basic review and comparison across dMRI methods as they pertain to the detection of the most commonly observed tissue and cellular alterations following TBI.

1 INTRODUCTION

Despite the long history of traumatic brain injury (TBI) as a prevalent cause of death and disability in humans, defining the neurobiological underpinnings of damage and recovery following TBI remains a central challenge. The complex collection of physiological, cellular, and molecular changes that follow TBI can appear to be remarkably heterogeneous, but at the same time they are highly organized into coordinated responses such as neurodegeneration, inflammation, and regeneration. The corpus of histological studies spanning a variety of experimental animal models of TBI have provided crucial insights about the pathomechanisms and cellular alterations that accompany posttraumatic tissue change, but considerable work remains to determine the spatiotemporal evolution of abnormalities, interrelationships among different tissue responses, and their impact on health and behavioral outcomes. Noninvasive imaging in animal models has the potential to build on what is known from histology by providing longitudinal and whole-brain information, but for this approach to be successful it is essential to first improve the understanding of how imaging abnormalities correspond to tissue and cellular changes.

Diffusion magnetic resonance imaging (dMRI) methods are particularly promising for the development of imaging markers of TBI pathology because they are sensitive to microscale water displacement as a proxy for tissue environment geometry and provide a range of quantitative scalar metrics across the whole brain. Furthermore, dMRI may be combined with other conventional or advanced magnetic resonance imaging (MRI) methods such as arterial spin labeling, susceptibility-weighted imaging, or a variety of contrast agent MRI approaches to provide complementary and comprehensive outcome measures. Standard dMRI methods and especially diffusion tensor imaging (DTI) have already demonstrated sensitive detection of abnormalities in a number of experimental models of TBI. In the past decade, multiple advanced dMRI approaches have extended beyond the conventional models with the goals of improving the physical description of water diffusion (e.g., by modeling “non-Gaussian” diffusion) or parameterizing dMRI with respect to the expected biological environment (e.g., by modeling cellular compartments and/or fiber geometry). These new tools will be valuable if they are able to improve the sensitivity or specificity of dMRI following TBI; however, we lack a systematic understanding of how dMRI methods differ from one another for detecting and describing tissue alterations.

A number of excellent reviews exist to describe the current understanding of cellular mechanisms of TBI in general (Bramlett & Dietrich, 2015; Pekna & Pekny, 2012) and within particular areas of neurobiology including neurodegeneration (Johnson, Stewart, & Smith, 2013; Stoica & Faden, 2010), inflammation (Burda, Bernstein, & Sofroniew, 2016; Ziebell & Morganti-Kossmann, 2010), and myelin changes (Armstrong, Mierzwa, Marion, & Sullivan, 2016), among others. As well, several existing reviews have been published regarding MRI and DTI to study human TBI (Brody, Mac Donald, & Shimony, 2015; Duhaime et al., 2010; Hulkower, Poliak, Rosenbaum, Zimmerman, & Lipton, 2013), and recently a pertinent overview and summary of advanced dMRI tools and their relevance to clinical outcomes was published (Douglas et al., 2015). The focus of the present review is to combine what is known from work in experimental models of TBI about tissue and cellular alterations that may affect the physical tissue environment with a comparative description of the major methods for dMRI that may be differentially sensitive to TBI-related tissue change alongside several important caveats for their use and interpretation. The first section provides a categorical summary of cellular response to trauma, emphasizing alterations with microstructural, architectural, or neuroanatomical manifestations that may give rise to detectable dMRI abnormalities, including a review of the existing dMRI studies in experimental TBI models. The second section contains a comparative overview of presently available dMRI methods from standard approaches to advanced techniques. The objective of this article is to provide a reference for the current understanding of these topics as well as a perspective to help guide selection of dMRI tools based on particular aspects of TBI questions.

Continue —> Diffusion MRI and the detection of alterations following traumatic brain injury – Hutchinson – 2017 – Journal of Neuroscience Research – Wiley Online Library

Figure 2. Cross-model comparison of scalar maps in the injured brain. A range of tissue and injury-related contrasts may be visually observed in this collage of 16 representative metrics in the same slice from different dMRI models. This cross-model view of scalar maps demonstrates the potential for nonredundant information about regions of injury that may be gleaned from different models. DTI metrics of fractional anisotropy (FA), trace (TR), axial and radial diffusivity (Dax and Drad), directionally encoded color (DEC) map weighted by lattice index, DEC weighted by Westin linear anisotropy (WL) and DEC weighted by Westin planar anisotropy (WP), DKI metrics of mean kurtosis (MK), axial and radial kurtosis (AK and RK) and kurtosis FA (KFA), MAP-MRI metrics of return to the origin, axis, and plane probabilities (RTOP, RTAP, and RTPP), propagator anisotropy (PA) and non-Gaussianity (NG) and NODDI metrics of compartment volume fractions for isotropic free water (Viso), intracellular water (Vic) and intracellular restricted water (Vir), and orientation dispersion index (ODI). Insets of each map show tissue near the injury site where dMRI values are expected to be abnormal.

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[VIDEO] Plasticity Brain Centers: Neuroplasticity. What Does the Term Mean? – YouTube

What is Neuroplasticity? Dr. Matthew Antonucci from Plasticity Brain Centers of Orlando, Florida gives us a breakdown of what the term really means.

 

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[BLOG POST] 20 Must-Know Facts To Harness Neuroplasticity And Improve Brain Health

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June is Alzheimer’s & Brain Awareness Month, so let me share these 20 Must-Know Facts to Harness Neuroplasticity & Improve Brain Health that come from the hundreds of scientific and medical studies we analyzed to prepare the book The SharpBrains Guide to Brain Fitness: How to Improve Brain Health and Performance at Any Age:

  1. There is more than one “It” in “Use It or Lose It” — our performance depends on a variety of brain functions and cognitive skills, not just one (be it “attention” or “memory” or any other).
  2. Genes do not determine the fate of our brains. Thanks to lifelong neuroplasticity, our lifestyles are as important as our genes-if not more- in how our brains grow and our minds evolve.
  3. We need to pay more attention to Randomized Controlled Trials (RCTs) to verify whether any intervention causes an effect, and under what specific circumstances.
  4. The largest recent RCT (the ongoing FINGER study) and a 2010 systematic review of all relevant RCTs provide useful guidance: First, they report a protective effect of social and cognitive engagement, physical exercise, and the Mediterranean diet. Second, the average benefits at the population level appear quite limited, so we need to have realistic expectations.
  5. Physical exercise and increased fitness promote brain functioning through a variety of mechanisms, including increased brain volume, blood supply and growth hormone levels.
  6. Cardiovascular exercise that gets the heart beating – from walking to skiing, tennis and basketball – seems to bring the greatest brain benefits; thirty to sixty minutes per day, three days a week, seems to be the best regimen.
  7. Mental stimulation strengthens the connections between neurons (synapses), improving neuron survival and cognitive functioning. Mental stimulation also helps build cognitive reserve, helping the brain better cope with potential AD pathology.
  8. Routine activities do not challenge the brain. Keeping up the challenge requires going to the next level of difficulty, or trying something new.
  9. The only leisure activity that has been associated with reduced cognitive function is watching television.
  10. Brain training can work, putting the “cells that fire together wire together” to good use, but available RCTs suggest some key conditions must be met to transfer to real-life benefits.
  11. The brain needs a lot of energy: It extracts approximately 50% of the oxygen and 10% of the glucose from arterial blood.
  12. The Mediterranean Diet, supplemented with olive oil and nuts, is associated with decreased risk of cognitive decline.
  13. Moderate doses of caffeine increase alertness but there is no clear sustained lifetime health benefit (or harm).
  14. Light-to-moderate alcohol consumption seems to lower the risk of dementia.
  15. Taking “brain supplements” of any kind does not seem to boost cognitive function or reduce risks of cognitive decline or dementia, unless directed to address an identified deficiency.
  16. The larger and the more complex a person’s social network is, the bigger the amygdala (which plays a major role in our behavior and motivation). There is no clear evidence to date on whether “online” relationships are fundamentally different from “offline” ones in this regard.
  17. Chronic stress reduces and can even inhibit neurogenesis. Memory and general mental flexibility are impaired by chronic stress.
  18. There is increasing evidence that meditation and biofeedback can successfully teach users to self-regulate physiological stress responses.
  19. We will not have a Magic Pill or General Solution to solve all our cognitive challenges any time soon, so a holistic multi-pronged approach is recommended, centered around nutrition, stress management, and both physical and mental exercise.
  20. Having said that, no size fits all, so it’s critical to understand and address individual needs, priorities and starting points.

Now, remember that what counts in terms of brain health is not reading this article, or any other, but practicing some healthy behaviors every day until small steps become internalized habits.

Revisit the fact above that really grabbed your attention…and make a decision to try something new this summer.

Source: 20 Must-Know Facts To Harness Neuroplasticity And Improve Brain Health | HuffPost

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[WEB PAGE] Can A Single Exercise Session Benefit Your Brain? – Neuroscience News

Summary: Researchers document not only the behavioral and cognitive effects of a single exercise session, but also the neurochemical and neurophysiological changes that occur.

Source: IOS Press.

Even a single bout of physical activity can have significant positive effects on people’s mood and cognitive functions, according to a new study inBrain Plasticity.

In a new review of the effects of acute exercise published in Brain Plasticity, researchers not only summarize the behavioral and cognitive effects of a single bout of exercise, but also summarize data from a large number of neurophysiological and neurochemical studies in both humans and animals showing the wide range of brain changes that result from a single session of physical exercise (i.e., acute exercise).

There is currently enormous interest in the beneficial effects of aerobic exercise on a wide range of brain functions including mood, memory, attention, motor/reaction times, and even creativity. Understanding the immediate effects of a single bout of exercise is the first step to understanding how the positive effects of exercise may accrue over time to cause long-lasting changes in select brain circuits.

According to principal investigator Wendy A. Suzuki, PhD, Professor of Neural Science and Psychology in the Center for Neural Science, New York University, “Exercise interventions are currently being used to help address everything from cognitive impairments in normal aging, minimal cognitive impairment (MCI), and Alzheimer’s disease to motor deficits in Parkinson’s disease and mood states in depression. Our review highlights the neural mechanisms and pathways by which exercise might produce these clinically relevant effects.”

The investigators summarized a large and growing body of research examining the changes that occur at the cognitive/behavioral, neurophysiological, and neurochemical levels after a single bout of physical exercise in both humans and animals. They reviewed brain imaging and electrophysiological studies, including electroencephalography (EEG), functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), and transcranial magnetic stimulation (TMS). They then turned to neurochemical studies, including lactate, glutamate and glutamine metabolism, effects on the hypothalamic-pituitary-adrenal (HPA) axis through cortisol secretion, and neurotrophins such as brain-derived neurotrophic factor (BDNF), insulin-like growth factor 1 (IGF-1), and vascular endothelial growth factor (VEGF). Neurotransmitter studies of monoamines (dopamine, serotonin, epinephrine and norepinephrine), acetylcholine, glutamate and gamma-aminobutyric acid (GABA) were reviewed, as well as neuromodulators such as endogenous opioids and endocannabinoids.

Image shows a mouse on a wheel and a woman running a race.

What is the relationship between the central neurochemical changes following acute exercise that have mainly been described in rodents and the behavioral changes seen after acute exercise that have mainly been described in humans? NeuroscienceNews.com image is credited to Henriette van Praag and MarathonFoto.

This extensive review resulted in three main observations. First, the most consistent behavioral effects of acute exercise are improved executive function, enhanced mood, and decreased stress levels. Second, neurophysiological and neurochemical changes that have been reported after acute exercise show that widespread brain areas and brain systems are activated. Third, one of the biggest open questions in this area is the relationship between the central neurochemical changes following acute exercise, that have mainly been described in rodents, and the behavioral changes seen after acute exercise reported in humans. Bridging this gap will be an important area of future study.

Co-author Julia C. Basso, PhD, post-doctoral research fellow, Center for Neural Science at New York University, commented, “The studies presented in this review clearly demonstrate that acute exercise has profound effects on brain chemistry and physiology, which has important implications for cognitive enhancements in healthy populations and symptom remediation in clinical populations.”

ABOUT THIS NEUROSCIENCE RESEARCH ARTICLE

Source: Diana Murray – IOS Press
Image Source: NeuroscienceNews.com image is credited to Henriette van Praag and MarathonFoto.
Original Research: Full open access research for “The Effects of Acute Exercise on Mood, Cognition, Neurophysiology, and Neurochemical Pathways: A Review” by Basso, Julia C. and Suzuki, Wendy A. in Brain Plasticity. Published online March 28 2017 doi:10.3233/BPL-160040

Source: Can A Single Exercise Session Benefit Your Brain? – Neuroscience News

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[Abstract] Neural Plasticity in Moderate to Severe Chronic Stroke Following a Device-Assisted Task-Specific Arm/Hand Intervention

Currently, hand rehabilitation following stroke tends to focus on mildly impaired individuals, partially due to the inability for severely impaired subjects to sufficiently use the paretic hand. Device-assisted interventions offer a means to include this more severe population, and show promising behavioral results. However, the ability for this population to demonstrate neural plasticity, a crucial factor in functional recovery following effective post-stroke interventions, remains unclear. This study aimed to investigate neural changes related to hand function induced by a device-assisted task-specific intervention in individuals with moderate to severe chronic stroke (upper extremity Fugl Meyer < 30). We examined functional cortical reorganization related to paretic hand opening and gray matter structural changes using a multi-modal imaging approach. Individuals demonstrated a shift in cortical activity related to hand opening from the contralesional to the ipsilesional hemisphere following the intervention. This was driven by decreased activity in contralesional primary sensorimotor cortex and increased activity in ipsilesional secondary motor cortex. Additionally, subjects displayed increased gray matter density in ipsilesional primary sensorimotor cortex and decreased gray matter density in contralesional primary sensorimotor cortex. These findings suggest that despite moderate to severe chronic impairments, post-stroke participants maintain ability to show cortical reorganization and gray matter structural changes following a device-assisted task-specific arm/hand intervention. These changes are similar as those reported in post-stroke individuals with mild impairment, suggesting that residual neural plasticity in more severely impaired individuals may have the potential to support improved hand function.

Source: Neural Plasticity in Moderate to Severe Chronic Stroke Following a Device-Assisted Task-Specific Arm/Hand Intervention

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[ARTICLE] Interindividual differences in motor network connectivity and behavioral response to iTBS in stroke patients – Full Text

Highlights

Multimodal assessment of motor system integrity for predicting iTBS-aftereffects

Effective connectivity of M1 predicts behavioral iTBS-aftereffects

No association between iTBS-aftereffects and BOLD activity or RMT/AMT/SICI

Effects of brain stimulation strongly influenced by connectivity of stimulated region

Abstract

Cerebral plasticity-inducing approaches like repetitive transcranial magnetic stimulation (rTMS) are of high interest in situations where reorganization of neural networks can be observed, e.g., after stroke. However, an increasing number of studies suggest that improvements in motor performance of the stroke-affected hand following modulation of primary motor cortex (M1) excitability by rTMS shows a high interindividual variability. We here tested the hypothesis that in stroke patients the interindividual variability of behavioral response to excitatory rTMS is related to interindividual differences in network connectivity of the stimulated region. Chronic stroke patients (n = 14) and healthy controls (n = 12) were scanned with functional magnetic resonance imaging (fMRI) while performing a simple hand motor task. Dynamic causal modeling (DCM) was used to investigate effective connectivity of key motor regions. On two different days after the fMRI experiment, patients received either intermittent theta-burst stimulation (iTBS) over ipsilesional M1 or control stimulation over the parieto-occipital cortex. Motor performance and TMS parameters of cortical excitability were measured before and after iTBS. Our results revealed that patients with better motor performance of the affected hand showed stronger endogenous coupling from supplemental motor area (SMA) onto M1 before starting the iTBS intervention. Applying iTBS to ipsilesional M1 significantly increased ipsilesional M1 excitability and decreased contralesional M1 excitability as compared to control stimulation. Individual behavioral improvements following iTBS specifically correlated with neural coupling strengths in the stimulated hemisphere prior to stimulation, especially for connections targeting the stimulated M1. Combining endogenous connectivity and behavioral parameters explained 82% of the variance in hand motor performance observed after iTBS. In conclusion, the data suggest that the individual susceptibility to iTBS after stroke is influenced by interindividual differences in motor network connectivity of the lesioned hemisphere.

1. Introduction

Recovery of function after stroke is driven by reorganization of neural networks in both the lesioned and unaffected hemispheres (Cramer, 2008). However, spontaneous recovery after stroke often remains incomplete (Kolominsky-Rabas et al., 2006). One strategy to improve the functional outcome of patients suffering from brain lesions is to modulate cerebral plasticity by means of non-invasive brain stimulation such as, e.g., repetitive transcranial magnetic stimulation (rTMS) (Ridding and Rothwell, 2007). Although to date a direct proof is missing, increasing evidence exist that rTMS-effects are mediated by changes in synaptic transmission (Funke and Benali, 2011 ;  Hoogendam et al., 2010). One specific strategy to ameliorate motor impairments in stroke patients is to enhance cortical excitability of the motor cortex in the lesioned hemisphere (Khedr et al., 2005). An effective protocol of rTMS to induce such increase in excitability of the motor cortex following a relatively short (i.e., 3.5 min) stimulation period is intermittent theta-burst stimulation (iTBS) (Huang et al., 2005).

Consequently, proof-of-principle studies have been able to demonstrate that iTBS applied to ipsilesional M1 improve hand motor function in stroke patients (Ackerley et al., 2010Hsu et al., 2012 ;  Talelli et al., 2007b). A major issue, however, with rTMS (including iTBS) induced cerebral plasticity is high inter-individual variability of the effects induced in both healthy subjects (Daskalakis et al., 2006Hamada et al., 2013 ;  Muller-Dahlhaus et al., 2008) and stroke patients (Ameli et al., 2009 ;  Grefkes and Fink, 2012). For example, Hamada et al. (2013) demonstrated that application of iTBS in healthy subjects leads to an increase of motor-cortical excitability in only 52% subjects, while the other half responded in an opposite way with a decrease of excitability. Likewise, Ameli et al. (2009) reported that in patients suffering from cortical strokes, only half of them showed behavioral improvements after 10 Hz rTMS while the other half even deteriorated with their stroke affected hands. Such opposed stimulation after-effects are likely to contribute to absent overall effects across the entire group (Hamada et al., 2013).

Apart from known sources of response variability following iTBS like age (Freitas et al., 2011), genetic polymorphisms of the brain-derived neurotrophic factor (Cheeran et al., 2008 ;  Kleim et al., 2006) and technical aspects such as the direction of current flow, the intensity of stimulation and the number of pulses applied (Gamboa et al., 2010Gentner et al., 2008 ;  Talelli et al., 2007a), clinical factors like lesion location, degree of neurological impairment and time since stroke are also likely to impact on the response to rTMS (Grefkes and Fink, 2012). For example, several studies demonstrated that patients with subcortical lesions have a higher probability to improve after rTMS than patients with cortical lesions (Ameli et al., 2009 ;  Hsu et al., 2012). Moreover, the pathomechanisms underlying stroke-induced motor deficits do not only depend on direct tissue damage due to ischemia, but might also comprise network disturbances remote from the stroke lesion (Grefkes and Fink, 2011 ;  Grefkes and Fink, 2014). Thus, changes in network interactions are likely to constitute another important factor for the evolution of rTMS-aftereffects as TMS does not only interfere with neural tissue of the stimulated hemisphere but also with neural activity levels of regions that are interconnected with the stimulation site (Bestmann et al., 2005).

Hence, there is good reason to assume that specific inter-individual differences (or abnormalities post-stroke) in network connectivity might – at least in part – influence response to rTMS. Support for this hypothesis stems from studies with patients suffering from dystonia in which reduced functional connectivity between premotor cortex and M1 was indicative for responding to rTMS (Huang et al., 2010 ;  Quartarone et al., 2003). Furthermore, changes in motor-evoked potential (MEP) amplitudes following rTMS have been shown to be associated with higher effective connectivity between supplementary motor area (SMA), ventral premotor cortex (vPMC) and M1 of the stimulated hemisphere (Cardenas-Morales et al., 2014).

Therefore, in stroke patients, the variability of the individual response to plasticity-inducing intervention might depend on how the stimulation interacts with the pre-existing connectivity in a given functional network, e.g., the motor system. In order to identify factors that are associated with a positive behavioral effect in response to intermittent theta burst stimulation (here: iTBS) applied to ipsilesional M1, we used a multimodal approach consisting of clinical scales, electrophysiological parameters measured using single- and paired-pulse TMS, as well as functional magnetic resonance imaging (fMRI) and dynamic causal modeling (DCM) to assess effective connectivity of the cortical motor network. We reasoned that the systems level perspective offered by DCM might be useful for identifying predictors that indicate whether or not a patient will respond to non-invasive brain stimulation given that (i) focal brain stimulation also impacts on activity levels of areas connected to the stimulation site (Bestmann et al., 2003 ;  Grefkes et al., 2010) and (ii) recovery of motor function depends on changes in the entire motor network rather than changes in M1 only (Rehme et al., 2012 ;  Ward et al., 2003). Here, especially the coupling strengths between ipsilesional M1 and premotor areas might be indicative for the behavioral after-effect of iTBS given the role of these connections in motor performance in both healthy subjects and stroke (Pool et al., 2013Pool et al., 2014 ;  Rehme et al., 2011a). […]

Continue —>  Interindividual differences in motor network connectivity and behavioral response to iTBS in stroke patients

Fig. 4

Fig. 4. Neural activity when patients and controls moved the affected or unaffected hand. Fist closures were conducted at a fixed movement frequency of 0.8 Hz and at a frequency adjusted to individual performance levels. Compared to controls, patients featured enhanced activity in both hemispheres during movements of the affected hand. Movements of the unaffected hand yielded a similar activation pattern in patients and controls. T-values are represented by the color bar. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

 

 

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[WEB SITE] Neuroplasticity after Stroke

Neuroplasticity after Stroke

Neuroplasticity after stroke is the #1 thing that every stroke survivor should know about.

If you want to maximize your recovery, then understanding and applying the concept of neuroplasticity to your regimen will help you harness your brain’s full healing potential.

It’s an inspiring phenomenon, so let’s get started.

What Is Neuroplasticity?

The word neuroplasticity is the combination of 2 words: neuron and plasticity. Neurons are the nerve cells in your brain, and plasticity refers to something that is capable of being molded or reorganized.

Therefore, neuroplasticity refers to the process of reorganizing the neurons in your brain. It’s the mechanism that your brain uses to heal from damage and rewire itself.

Rewiring Your Brain after Stroke

After a stroke, certain parts of the brain can become damaged (depending on what type of stroke and where it occurred) and the functions that were once stored in those parts of the brain become impaired. For example, if the part of your brain responsible for motor control on the right side of your body becomes damaged, it will make it hard to move your right arm.

That’s when neuroplasticity comes into play.

Neuroplasticity allows your brain to rewire functions that were once held in damaged areas of the brain over to new, healthy parts of the brain. So with our right arm example, a different, healthy area of your brain is capable of picking up the slack and taking on the task of moving your right arm.

There’s one important requisite for neuroplasticity to occur, however, and it’s repetition.

You need to utilize a high number of repetitions during your rehab exercises, otherewise it won’t work that well.

How to Make Neuroplasticity Work for You

To rewire your brain after stroke, think of it as paving new roads.

If you only put a little effort in, then the new pathways won’t be that strong and they will fade with time. However, if you put a lot of effort in, you can pave a strong, durable road that will last for a long time.

The same goes with your rehab exercises.

The more you practice and repeat an exercise over and over, the stronger those new pathways in your brain become.

Neuroplasticity is nothing without good reinforcement and diligence.

One Last Bit

To really maximize your brain’s healing, you should be aware of all the other elements that go into stroke recovery.

This guide covers all the bases, we hope you find it useful.

Did you know that your brain was capable of such magic?

How will you apply this concept to your rehabilitation?

Leave us a comment below and share your thoughts with us!

Source: Neuroplasticity after Stroke – Flint Rehab

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[ARTICLE] Update on cell therapy for stroke – Full Text

Abstract

Ischaemic stroke remains a leading cause of death and disability. Current stroke treatment options aim to minimise the damage from a pending stroke during the acute stroke period using intravenous thrombolytics and endovascular thrombectomy; however, there are no currently approved treatment options for reversing neurological damage once a stroke is completed. Preclinical studies suggest that cell therapy may be safe and effective in improving functional outcomes. Several recent clinical trials have reported safety and some improvement in outcomes following cell therapy administration in ischaemic stroke, which are reviewed. Cell therapy may provide a promising new treatment for stroke reducing stroke-related disability. Further investigation is needed to determine specific effects of cell therapy and to optimise cell delivery methods, cell dosing, type of cells used, timing of delivery, infarct size and location of infarct that are likely to benefit from cell therapy.

Introduction

Until recently, intravenous recombinant tissue plasminogen activator was the only proven effective treatment for acute stroke. Endovascular thrombectomy has now been added to our arsenal for acute stroke treatment following the publication of five randomised trials demonstrating highly significant treatment effects favouring endovascular therapy.1–6 Outcome data support advancements in acute stroke care and neurorehabilitation with a significant increase in stroke survivors over time.7 However, despite these advancements, stroke remains a leading cause of long-term disability.8 For patients with residual deficits after stroke, we have no currently approved therapy for restoring function.

Cell therapy is one approach to enhancing recovery after stroke. In animal models, delivery of several different types of stem cells reduce infarct size and improve functional outcomes.9 Clinical trials of cell therapy completed in the 2000s mostly treating small cohorts of patients with chronic stroke demonstrated adequate safety and a suggestion of efficacy with the use of cell therapy. Kondziolka and colleagues used N-Tera 2 cells derived from a lung metastasis of a human testicular germ cell tumour that when treated with retinoic acid generate postmitotic neurons that maintain a fetal neuronal phenotype indefinitely in vitro (LBS neurons). LBS neurons were stereotactically implanted around the stroke bed of chronic subcortical ischaemic stroke. This study demonstrated safety and feasibility of stereotactic cell implantation, although there was no significant improvement in functional outcomes.10 11 Using a similar stereotactic approach implanting cells into the basal ganglia, Savitz and colleagues transplanted LGE cells (fetal porcine striatum-derived cells, Genvec) in five patients. Two patients showed improvements, but two patients experienced adverse effects including delayed worsening of neurological symptoms and seizure resulting in early termination of the study.12 Bang and colleagues reported the safety and feasibility of intravenous infusion of autologous mesenchymal stem cells (MSCs) with no reported adverse effects in five patients treated with intravenous MSCs. Although they reported some initial motor improvements, at 12 months, there was no significant difference in motor scores.13 These early clinical trials mostly focused on chronic subcortical strokes, but more recent trials are now investigating cell therapy for treatment of both cortical and subcortical infarcts. This review discusses the considerations for design of cell therapy trials and summarises the results of more recent studies.

Continue —> Update on cell therapy for stroke | Stroke and Vascular Neurology

Table 1

Summary of recent human cell therapy trials for stroke

Clinical trial/sponsor Age Time after stroke Additional selection criteria Cell type Route Stroke location Patients (n) Safety results Efficacy results
MASTERS/Athersys 18–83 24–48 hours NIHSS 8–20, infarct 5-100cc, premorbid mRS 0–1 Multistem adult-derived stem cell product Intravenous Cortical 129 Similar SAE at 1 year 22(34%) versus 24 (39%) placebo,
Lower mortality—5 deaths (8%) versus 9deaths (15%) in placebo19
No effect on 90-day Global Stroke Recovery Assessment (mRS 0–2, NIHSS increase by 75%, Barthel Index >95) but trend towards improved outcome with earlier delivery of cells19
InveST/Department of Biotechnology, India 18–75 7–29 days NIHSS >7, GCS >8, BI <50, paretic arm or leg stable >48 hours Autologous marrow-derived stem cells Intravenous 120
(58 cell therapy)
61 AE (33%) and eight deaths versus 60 AEs (36%) and five deaths placebo22 No effect on 180-day Barthel Index Score, mRS shift or score >3, NIHSS, change of infarct volume22
RECOVER-Stroke/Aldagen 30–75 13–19 days NIHSS 7–22, mRS >3 ALDHbrautologous marrow-derived stem cells Intracarotid infusion distal to ophthalmic Anterior circulation ± subcortical 29 IA, 19 sham 12 SAE IA, 11 SAE sham; 0 cell-related SAE23 No difference in mRS, Barthel, NIHSS at 90 days or 1 year
PISCES-II/ReNeuron 40–89 2–13 months Paretic arm with NIHSS motor arm score 2–3 CTX0E03 DP allogeneic human fetal neural stem cells Stereotaxic infusion into ipsilateral putamen 21 Pending Pending
Sanbio 18–75 6–60 months NIHSS>7, mRS 3–4, stable symptoms>3 weeks SB623 allogeneic marrow-derived stem cells transiently transfected with plasmid encoding Notch122 Stereotaxic infusion peri-infarct Subcortical ± cortical component24 18 28 SAE, 0 cell-related SAE25 Improved ESS at 6 months (p<0.01) and 12 months (p<0.001)
Improved NIHSS at 6 months (p<0.01) and 12 months(p<0.001)
Improved Fugl-Meyer at 6 months (p<0.001) and 12 months(p<0.001)25
PISCES/ReNeuron >60, male only 6–60 months Persistent hemiparesis, Stable NIHSS over 4 weeks (Pt 2 CTX0E03 DP allogeneic human neural stem cells Stereotaxic infusion into putamen Subcortical 11 16 SAE (in nine patients), 0 cell-related SAE28 Improved NIHSS at 2 years (p=0.002), No change, Barthel Index, MMSE, Ashworth, mRS28 29
  • AE, Adverse Event; ARAT, Action Research Arm Test; BI, Barthel Index; DP, drug product; ESS, European Stroke Scale; IA, intra-arterially; MASTERS, Multistem Administration for Stroke Treatment and Enhanced Recovery Study; MMSE, Mini-Mental Status Examination; mRS, modified Rankin Score; NIHSS, National Institutes of Health Stroke Scale; PISCES, Pilot Investigation of Stem Cells in Stroke; SAE, Serious aAverse Events.

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