Posts Tagged neuroimaging

[ARTICLE] Increased gait variability during robot-assisted walking is accompanied by increased sensorimotor brain activity in healthy people – Full Text

Abstract

Background

Gait disorders are major symptoms of neurological diseases affecting the quality of life. Interventions that restore walking and allow patients to maintain safe and independent mobility are essential. Robot-assisted gait training (RAGT) proved to be a promising treatment for restoring and improving the ability to walk. Due to heterogenuous study designs and fragmentary knowlegde about the neural correlates associated with RAGT and the relation to motor recovery, guidelines for an individually optimized therapy can hardly be derived. To optimize robotic rehabilitation, it is crucial to understand how robotic assistance affect locomotor control and its underlying brain activity. Thus, this study aimed to investigate the effects of robotic assistance (RA) during treadmill walking (TW) on cortical activity and the relationship between RA-related changes of cortical activity and biomechanical gait characteristics.

Methods

Twelve healthy, right-handed volunteers (9 females; M = 25 ± 4 years) performed unassisted walking (UAW) and robot-assisted walking (RAW) trials on a treadmill, at 2.8 km/h, in a randomized, within-subject design. Ground reaction forces (GRFs) provided information regarding the individual gait patterns, while brain activity was examined by measuring cerebral hemodynamic changes in brain regions associated with the cortical locomotor network, including the sensorimotor cortex (SMC), premotor cortex (PMC) and supplementary motor area (SMA), using functional near-infrared spectroscopy (fNIRS).

Results

A statistically significant increase in brain activity was observed in the SMC compared with the PMC and SMA (p < 0.05), and a classical double bump in the vertical GRF was observed during both UAW and RAW throughout the stance phase. However, intraindividual gait variability increased significantly with RA and was correlated with increased brain activity in the SMC (p = 0.05; r = 0.57).

Conclusions

On the one hand, robotic guidance could generate sensory feedback that promotes active participation, leading to increased gait variability and somatosensory brain activity. On the other hand, changes in brain activity and biomechanical gait characteristics may also be due to the sensory feedback of the robot, which disrupts the cortical network of automated walking in healthy individuals. More comprehensive neurophysiological studies both in laboratory and in clinical settings are necessary to investigate the entire brain network associated with RAW.

Background

Safe and independent locomotion represents a fundamental motor function for humans that is essential for self-contained living and good quality of life [1,2,3,4,5]. Locomotion requires the ability to coordinate a number of different muscles acting on different joints [6,7,8], which are guided by cortical and subcortical brain structures within the locomotor network [9]. Structural and functional changes within the locomotor network are often accompanied by gait and balance impairments which are frequently considered to be the most significant concerns in individuals suffering from brain injuries or neurological diseases [51011]. Reduced walking speeds and step lengths [12] as well as non-optimal amount of gait variability [13,14,15] are common symptoms associated with gait impairments that increase the risk of falling [16].

In addition to manual-assisted therapy, robotic neurorehabilitation has often been applied in recent years [1718] because it provides early, intensive, task-specific and multi-sensory training which is thought to be effective for balance and gait recovery [171920]. Depending on the severity of the disease, movements can be completely guided or assisted, tailored to individual needs [17], using either stationary robotic systems or wearable powered exoskeletons.

Previous studies investigated the effectiveness of robot-assisted gait training (RAGT) in patients suffering from stroke [2122], multiple sclerosis [23,24,25,26], Parkinson’s disease [2728], traumatic brain injury [29] or spinal cord injury [30,31,32]. Positive effects of RAGT on walking speed [3334], leg muscle force [23] step length, and gait symmetry [2935] were reported. However, the results of different studies are difficult to summarize due to the lack of consistency in protocols and settings of robotic-assisted treatments (e.g., amount and frequency of training sessions, amount and type of provided robotic support) as well as fragmentary knowledge of the effects on functional brain reorganization, motor recovery and their relation [3637]. Therefore, it is currently a huge challenge to draw guidelines for robotic rehabilitation protocols [2236,37,38]. To design prologned personalized training protocols in robotic rehabilitation to maximize individual treatment effects [37], it is crucial to increase the understanding of changes in locomotor patterns [39] and brain signals [40] underlying RAGT and how they are related [3641].

A series of studies investigated the effects of robotic assistance (RA) on biomechanical gait patterns in healthy people [3942,43,44]. On one side, altered gait patterns were reported during robot-assisted walking (RAW) compared to unassisted walking (UAW), in particular, substantially higher muscle activity in the quadriceps, gluteus and adductor longus leg muscles and lower muscle activity in the gastrocnemius and tibialis anterior ankle muscles [3942] as well as reduced  lower-body joint angles due to the little medial-lateral hip movements [45,46,47]. On the other side, similar muscle activation patterns were observed during RAW compared to UAW [444849], indicating that robotic devices allow physiological muscle activation patterns during gait [48]. However, it is hypothesized that the ability to execute a physiological gait pattern depends on how the training parameters such as body weight support (BWS), guidance force (GF) or kinematic restrictions in the robotic devices are set [444850]. For example, Aurich-Schuler et al. [48] reported that the movements of the trunk and pelvis are more similar to UAW on a treadmill when the pelvis is not fixed during RAW, indicating that differences in musle activity and kinematic gait characteristics between RAW and UAW are due to the reduction in degrees of freedom that user’s experience while walking in the robotic device [45]. In line with this, a clinical concern that is often raised with respect to RAW is the lack of gait variability [454850]. It is assumed that since the robotic systems are often operated with 100% GF, which means that the devices attempt to force a particular gait pattern regardless of the user’s intentions, the user lacks the ability to vary and adapt his gait patterns [45]. Contrary to this, Hidler et al. [45] observed differences in kinematic gait patterns between subsequent steps during RAW, as demonstrated by variability in relative knee and hip movements. Nevertheless, Gizzi et al. [49] showed that the muscular activity during RAW was clearly more stereotyped and similar among individuals compared to UAW. They concluded that RAW provides a therapeutic approach to restore and improve walking that is more repeatable and standardized than approaches based on exercising during UAW [49].

In addition to biomechanical gait changes, insights into brain activity and intervention-related changes in brain activity that relate to gait responses, will contribute to the optimization of therapy interventions [4151]. Whereas the application of functional magnetic resonance imaging (fMRI), considered as gold standard for the assessment of activity in cortical and subcortical structures, is restricted due to the vulnerability for movement artifacts and the range of motion in the scanner [52], functional near infrared spectroscopy (fNIRS) is affordable and easily implementable in a portable system, less susceptible to motion artifacts, thus facilitation a wider range of application with special cohorts (e.g., children, patients) and in everyday environments (e.g., during a therapeutic session of RAW or UAW) [5354]. Although with lower resolution compared to fMRI [55], fNIRS also relies on the principle of neurovascular coupling and allows the indirect evaluation of cortical activation [5657] based on hemodynamic changes which are analogous to the blood-oxygenation-level-dependent responses measured by fMRI [56]. Despite limited depth sensitivity, which restricts the measurement of brain activity to cortical layers, it is a promising tool to investigate the contribution of cortical areas to the neuromotor control of gross motor skills, such as walking [53]. Regarding the cortical correlates of walking, numerous studies identified either increaesed oxygenated hemoglobin (Hboxy) concentration changes in the sensorimotor cortex (SMC) by using fNIRS [5357,58,59] or suppressed alpha and beta power in sensorimotor areas by using electroencephalography (EEG) [60,61,62] demonstrating that motor cortex and corticospinal tract contribute directly to the muscle activity of locomotion [63]. However, brain activity during RAW [366164,65,66,67,68], especially in patients [6970] or by using fNIRS [6869], is rarely studied [71].

Analyzing the effects of RA on brain activity in healthy volunteers, Knaepen et al. [36] reported significantly suppressed alpha and beta rhythms in the right sensory cortex during UAW compared to RAW with 100% GF and 0% BWS. Thus, significantly larger involvement of the SMC during UAW compared to RAW were concluded [36]. In contrast, increases of Hboxy were observed in motor areas during RAW compared UAW, leading to the conclusion that RA facilitated increased cortical activation within locomotor control systems [68]. Furthermore, Simis et al. [69] demonstrated the feasibility of fNIRS to evaluate the real-time activation of the primary motor cortex (M1) in both hemispheres during RAW in patients suffering from spinal cord injury. Two out of three patients exhibited enhanced M1 activation during RAW compared with standing which indicate the enhanced involvement of motor cortical areas in walking with RA [69].

To summarize, previous studies mostly focused the effects of RA on either gait characteristics or brain activity. Combined measurements investigating the effects of RA on both biomechanical and hemodynamic patterns might help for a better understanding of the neurophysiological mechanisms underlying gait and gait disorders as well as the effectiveness of robotic rehabilitation on motor recovery [3771]. Up to now, no consensus exists regarding how robotic devices should be designed, controlled or adjusted (i.e., device settings, such as the level of support) for synergistic interactions with the human body to achieve optimal neurorehabilitation [3772]. Therefore, further research concerning behavioral and neurophysiological mechanisms underlying RAW as well as the modulatory effect of RAGT on neuroplasticy and gait recovery are required giving the fact that such knowledge is of clinical relevance for the development of gait rehabilitation strategies.

Consequently, the central purpose of this study was to investigate both gait characteristics and hemodynamic activity during RAW to identify RAW-related changes in brain activity and their relationship to gait responses. Assuming that sensorimotor areas play a pivotal role within the cortical network of automatic gait [953] and that RA affects gait and brain patterns in young, healthy volunteers [39424568], we hypothesized that RA result in both altered gait and brain activity patterns. Based on previous studies, more stereotypical gait characteristics with less inter- and intraindividual variability are expected during RAW due to 100% GF and the fixed pelvis compared to UAW [4548], wheares brain activity in SMC can be either decreased [36] or increased [68].

Methods

This study was performed in accordance with the Declaration of Helsinki. Experimental procedures were performed in accordance with the recommendations of the Deutsche Gesellschaft für Psychologie and were approved by the ethical committee of the Medical Association Hessen in Frankfurt (Germany). The participants were informed about all relevant study-related contents and gave their written consent prior to the initiation of the experiment.

Participants

Twelve healthy subjects (9 female, 3 male; aged 25 ± 4 years), without any gait pathologies and free of extremity injuries, were recruited to participate in this study. All participants were right-handed, according to the Edinburg handedness-scale [73], without any neurological or psychological disorders and with normal or corrected-to-normal vision. All participants were requested to disclose pre-existing neurological and psychological conditions, medical conditions, drug intake, and alcohol or caffeine intake during the preceding week.

Experimental equipment

The Lokomat (Hocoma AG, Volketswil, Switzerland) is a robotic gait-orthosis, consisting of a motorized treadmill and a BWS system. Two robotic actuators can guide the knee and hip joints of participants to match pre-programmed gait patterns, which were derived from average joint trajectories of healthy walkers, using a GF ranging from 0 to 100% [7475] (Fig. 1a). Kinematic trajectories can be adjusted to each individual’s size and step preferences [45]. The BWS was adjusted to 30% body weight for each participant, and the control mode was set to provide 100% guidance [64].

figure1

Montage and Setup. a Participant during robot-assisted walking (RAW), with functional near-infrared spectroscopy (fNIRS) montage. b fNIRS montage; S = Sources; D = Detectors c Classification of regions of interest (ROI): supplementary motor area/premotor cortex  (SMA/PMC) and sensorimotor cortex (SMC) 

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[ARTICLE] Effects of virtual reality therapy on upper limb function after stroke and the role of neuroimaging as a predictor of a better response – Full Text

ABSTRACT

Background:

Virtual reality therapy (VRT) is an interactive intervention that induces neuroplasticity. The aim was to evaluate the effects of VRT associated with conventional rehabilitation for an upper limb after stroke, and the neuroimaging predictors of a better response to VRT.

Methods:

Patients with stroke were selected, and clinical neurological, upper limb function, and quality of life were evaluated. Statistical analysis was performed using a linear model comparing pre- and post-VRT. Lesions were segmented in the post-stroke computed tomography. A voxel-based lesion-symptom mapping approach was used to investigate the relationship between the lesion and upper limb function.

Results:

Eighteen patients were studied (55.5 ± 13.9 years of age). Quality of life, functional independence, and dexterity of the upper limb showed improvement after VRT (p < 0.001). Neuroimaging analysis showed negative correlations between the internal capsule lesion and functional recovery.

Conclusion:

VRT showed benefits for patients with stroke, but when there was an internal capsule lesion, a worse response was observed.

 

Stroke can be defined as a neurological deficit resulting from focal and acute central nervous system injury. It is considered a major cause of mortality and disability worldwide1. Stroke is the second leading cause of death in Brazil and the leading cause of chronic disability in adults, resulting in socioeconomic consequences and reduced quality of life. Therefore, it is an important public health problem, particularly because of long-term dependence on public health services2,3.

Cerebrovascular injury may damage the cells of the cortex and emerging axons, generating dysfunction of the upper motor neurons. Motor function can be impaired, reducing functional capacity, particularly that of the upper limbs4. Approximately 85% of individuals experience hemiparesis immediately after the stroke, particularly in an upper limb, and 55%–75% of these individuals have persistence of motor deficits, making it difficult to return to work and leisure, consequently worsening their quality of life5.

Epidemiological clinical studies have suggested that 33%–66% of stroke patients had no motor recovery after six months. Several techniques aiming to improve upper limb function are still being developed. However, the implementation of these techniques requires great team work, a high degree of specialization, and requires more time5,6. Currently, there are new approaches to rehabilitation, and virtual reality is still developing, with the objective of restoring the functional capacity of individuals after stroke as an easy, interactive, and low-cost intervention7,8.

The objective of stroke rehabilitation is to provide maximal physical, functional, and psychosocial recovery for the patients9. Comprehensive rehabilitation initiated early after stroke (within the first 24 hours) is generally accepted as being associated with better motor outcomes for these patients9. Strength training is an important part of the therapeutic process for upper-limb motor impairment after stroke10.

Virtual reality is defined as any hardware or software system that provides a simulated environment with real or imagined conditions that allow participating individuals to interact with the environment. The interaction is made by body movements using motion capture technology or by manipulating a device11. This interaction generates information necessary for proper understanding of the movement with particular emphasis on the upper limbs12. The technique consists of an avatar (graphic representation of the person) generated by the video game, where the individual manages a wireless control, directing the movement during the practice of different activities5. This is a good option for rehabilitation for individuals with stroke due to the variety of nonimmersive video game systems developed by the entertainment industry for home use.

This wide availability makes virtual reality an accessible and inexpensive rehabilitation method for rehabilitation centers13. Despite the ease of application, virtual reality therapy (VRT), added to conventional physical therapy has not been associated with a better outcome than recreational activity13. Adverse events usually are mild and the main effects described are transient dizziness and headache, pain and numbness13,14. Time since the onset of stroke, severity of impairment, and the type of device (commercial or customized) usually do not influence the outcome14. However, the variable methodology is an important bias for these investigations14. Therefore, virtual reality is still a promising tool. Some authors have reported that VRT can be combined with conventional rehabilitation to improve upper limb function after stroke15,16. The clinical situations wherein VRT may best be used have not yet been established in the literature. Also, the exact mechanism of action of this treatment modality is not yet fully understood.

The objectives of this study were to evaluate the effects of VRT combined with conventional rehabilitation for upper limb function in the recovery of individuals after stroke. Neuroimaging characteristics that could be used as predictors of a better response to VRT were also investigated.[…]

Continue —>  Effects of virtual reality therapy on upper limb function after stroke and the role of neuroimaging as a predictor of a better response

Figure Brain areas affected in nine patients with stroke and upper limb impairment underwent virtual reality therapy (A). Findings are overlaid in a template of axial magnetic resonance image slices and in a three-dimensional model of the brain (B). The color code bar in the inferior portion of the figure indicates the number of patients with the area injured. 

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[WEB SITE] New method based on artificial intelligence may help predict epilepsy outcomes

 

Medical University of South Carolina (MUSC) neurologists have developed a new method based on artificial intelligence that may eventually help both patients and doctors weigh the pros and cons of using brain surgery to treat debilitating seizures caused by epilepsy. This study, which focused on mesial temporal lobe epilepsy (TLE), was published in the September 2018 issue of Epilepsia. Beyond the clinical implications of incorporating this analytical method into clinicians’ decision making processes, this work also highlights how artificial intelligence is driving change in the medical field.

Despite the increase in the number of epilepsy medications available, as many as one-third of patients are refractory, or non-responders, to the medication. Uncontrolled epilepsy has many dangers associated with seizures, including injury from falls, breathing problems, and even sudden death. Debilitating seizures from epilepsy also greatly reduce quality of life, as normal activities are impaired.

Epilepsy surgery is often recommended to patients who do not respond to medications. Many patients are hesitant to undergo brain surgery, in part, due to fear of operative risks and the fact that only about two-thirds of patients are seizure-free one year after surgery. To tackle this critical gap in the treatment of this epilepsy population, Dr. Leonardo Bonilha and his team in the Department of Neurology at MUSC looked to predict which patients are likely to have success in being seizure free after the surgery.

Neurology Department Chief Resident Dr. Gleichgerrcht explains that they tried “to incorporate advanced neuroimaging and computational techniques to anticipate surgical outcomes in treating seizures that occur with loss of consciousness in order to eventually enhance quality of life”. In order to do this, the team turned to a computational technique, called deep learning, due to the massive amount of data analysis required for this project.

The whole-brain connectome, the key component of this study, is a map of all physical connections in a person’s brain. The brain map is created by in-depth analysis of diffusion magnetic resonance imaging (dMRI), which patients receive as standard-of-care in the clinic. The brains of epilepsy patients were imaged by dMRI prior to having surgery.

Deep learning is a statistical computational approach, within the realm of artificial intelligence, where patterns in data are automatically learned. The physical connections in the brain are very individualized and thus it is challenging to find patterns across multiple patients. Fortunately, the deep learning method is able to isolate the patterns in a more statistically reliable method in order to provide a highly accurate prediction.

Currently, the decision to perform brain surgery on a refractory epilepsy patient is made based on a set of clinical variables including visual interpretation of radiologic studies. Unfortunately, the current classification model is 50 to 70 percent accurate in predicting patient outcomes post-surgery. The deep learning method that the MUSC neurologists developed was 79 to 88 percent accurate. This gives the doctors a more reliable tool for deciding whether the benefits of surgery outweigh the risks for the patient.

A further benefit of this new technique is that no extra diagnostic tests are required for the patients, since dMRIs are routinely performed with epilepsy patients at most centers.

This first study was retrospective in nature, meaning that the clinicians looked at past data. The researchers propose that an ideal next step would include a multi-site prospective study. In a prospective study, they would analyze the dMRI scans of patients prior to surgery and follow-up with the patients for at least one year after surgery. The MUSC neurologists also believe that integrating the brain’s functional connectome, which is a map of simultaneously occurring neural activity across different brain regions, could enhance the prediction of outcomes.

Dr. Gleichgerrcht says that the novelty in the development of this study lies in the fact that this “is not a question of human versus machine, as is often the fear when we hear about artificial intelligence. In this case, we are using artificial intelligence as an extra tool to eventually make better informed decisions regarding a surgical intervention that holds the hope for a cure of epilepsy in a large number of patients.”

 

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[WEB SITE] Largest-ever study to examine anatomical alterations in the brains of epilepsy patients

Largest-ever study to examine anatomical alterations in the brains of epilepsy patients 

An international research consortium used neuroimaging techniques to analyze the brains of more than 3,800 volunteers in different countries. The largest study of its kind ever conducted set out to investigate anatomical similarities and differences in the brains of individuals with different types of epilepsy and to seek markers that could help with prognosis and treatment.

Epilepsy’s seizure frequency and severity, as well as the patient’s response to drug therapy, vary with the part of the brain affected and other poorly understood factors. Data from the scientific literature suggests that roughly one-third of patients do not respond well to anti-epileptic drugs. Research has shown that these individuals are more likely to develop cognitive and behavioral impairments over the years.

The new study was conducted by a specific working group within an international consortium called ENIGMA, short for Enhancing NeuroImaging Genetics through Meta-Analysis, established to investigate several neurological and psychiatric diseases. Twenty-four cross-sectional samples from 14 countries were included in the epilepsy study.

Altogether, the study included data for 2,149 people with epilepsy and 1,727 healthy control subjects (with no neurological or psychiatric disorders). The Brazilian Research Institute for Neuroscience and Neurotechnology (BRAINN), which participated in the multicenter study, was the center with the largest sample, comprising 291 patients and 398 controls. Hosted in Brazil, at the State University of Campinas (UNICAMP), BRAINN is a Research, Innovation and Dissemination Center (RIDC http://cepid.fapesp.br/en/home/) supported by the Sao Paulo Research Foundation – FAPESP.

“Each center was responsible for collecting and analyzing data on its own patients. All the material was then sent to the University of Southern California’s Imaging Genetics Center in the US, which consolidated the results and performed a meta-analysis,” said Fernando Cendes, a professor at UNICAMP and coordinator of BRAINN.

A differential study

All volunteers were subjected to MRI scans. According to Cendes, a specific protocol was used to acquire three-dimensional images. “This permitted image post-processing with the aid of computer software, which segmented the images into thousands of anatomical points for individual assessment and comparison,” he said.

According to the researcher, advances in neuroimaging techniques have enabled the detection of structural alterations in the brains of people with epilepsy that hadn’t been noticed previously.

Cendes also highlighted that this is the first epilepsy study built on a really large number of patients, which allowed researchers to obtain more robust data. “There were many discrepancies in earlier studies, which comprised a few dozen or hundred volunteers.”

The patients included in the study were divided into four subgroups: mesial temporal lobe epilepsy (MTLE) with left hippocampal sclerosis, MTLE with right hippocampal sclerosis, idiopathic (genetic) generalized epilepsy, and a fourth group comprising various less common subtypes of the disease.

The analysis covered both patients who had had epilepsy for years and patients who had been diagnosed recently. According to Cendes, the analysis – whose results were published in the international journal Brain – aimed at the identification of atrophied brain regions in which the cortical thickness was smaller than in the control group.

First analysis

The researchers first analyzed data from the four patient subgroups as a whole and compared them with the controls to determine whether there were anatomical alterations common to all forms of epilepsy. “We found that all four subgroups displayed atrophy in areas of the sensitive-motor cortex and also in some parts of the frontal lobe,” Cendes said.

“Ordinary MRI scans don’t show anatomical alterations in cases of genetic generalized epilepsy,” Cendes said. “One of the goals of this study was to confirm whether areas of atrophy also occur in these patients. We found that they do.”

This finding, he added, shows that in the case of MTLE, there are alterations in regions other than those in which seizures are produced (the hippocampus, parahippocampus, and amygdala). Brain impairment is, therefore, more extensive than previously thought.

Cendes also noted that a larger proportion of the brain was compromised in patients who had had the disease for longer. “This reinforces the hypothesis that more brain regions atrophy and more cognitive impairment occurs as the disease progresses.”

The next step was a separate analysis of each patient subgroup in search of alterations that characterize each form of the disease. The findings confirmed, for example, that MTLE with left hippocampal sclerosis is associated with alterations in different neuronal circuits from those associated with MTLE with right hippocampal sclerosis.

“Temporal lobe epilepsy occurs in a specific brain region and is therefore termed a focal form of the disease. It’s also the most common treatment-refractory subtype of epilepsy in adults,” Cendes said. “We know it has different and more severe effects when it involves the left hemisphere than the right. They’re different diseases.”

“These two forms of the disease are not mere mirror-images of each other,” he said. “When the left hemisphere is involved, the seizures are more intense and diffuse. It used to be thought that this happened because the left hemisphere is dominant for language, but this doesn’t appear to be the only reason. Somehow, it’s more vulnerable than the right hemisphere.”

In the GGE group, the researchers observed atrophy in the thalamus, a central deep-lying brain region above the hypothalamus, and in the motor cortex. “These are subtle alterations but were observed in patients with epilepsy and not in the controls,” Cendes said.

Genetic generalized epilepsies (GGEs) may involve all brain regions but can usually be controlled by drugs and are less damaging to patients.

Future developments

From the vantage point of the coordinator for the FAPESP-funded center, the findings published in the article will benefit research in the area and will also have future implications for the diagnosis of the disease. In parallel with their anatomical analysis, the group is also evaluating genetic alterations that may explain certain hereditary patterns in brain atrophy. The results of this genetic analysis will be published soon.

“If we know there are more or less specific signatures of the different epileptic subtypes, instead of looking for alterations everywhere in the brain, we can focus on suspect regions, reducing cost, saving time and bolstering the statistical power of the analysis. Next, we’ll be able to correlate these alterations with cognitive and behavioral dysfunction,” Cendes said.

 

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[WEB PAGE] Excitatory magnetic brain stimulation reduces emotional arousal to fearful faces, study shows

February 6, 2018

A new study in Biological Psychiatry: Cognitive Neuroscience and Neuroimaging looks at the modulation of emotion in the brain

A new study published in Biological Psychiatry: Cognitive Neuroscience and Neuroimaging reports that processing of negative emotion can be strengthened or weakened by tuning the excitability of the right frontal part of the brain.

Using magnetic stimulation outside the brain, a technique called repetitive transcranial magnetic stimulation (rTMS), researchers at University of Münster, Germany, show that, despite the use of inhibitory stimulation currently used to treat depression, excitatory stimulation better reduced a person’s response to fearful images.

The findings provide the first support for an idea that clinicians use to guide treatment in depression, but has never been verified in a lab. “This study confirms that modulating the frontal region of the brain, in the right hemisphere, directly effects the regulation of processing of emotional information in the brain in a ‘top-down’ manner,” said Cameron Carter, M.D., Editor of Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, referring to the function of this region as a control center for the emotion-generating structures of the brain. “These results highlight and expand the scope of the potential therapeutic applications of rTMS,” said Dr. Carter.

In depression, processing of emotion is disrupted in the frontal region of both the left and right brain hemispheres (known as the dorsolateral prefrontal cortices, dlPFC). The disruptions are thought to be at the root of increased negative emotion and diminished positive emotion in the disorder. Reducing excitability of the right dlPFC using inhibitory magnetic stimulation has been shown to have antidepressant effects, even though it’s based on an idea-that this might reduce processing of negative emotion in depression-that has yet to be fully tested in humans.

Co-first authors Swantje Notzon, M.D., and Christian Steinberg, Ph.D, and colleagues divided 41 healthy participants into two groups to compare the effects of a single-session of excitatory or inhibitory magnetic stimulation of the right dlPFC. They performed rTMS while the participants viewed images of fearful faces to evoke negative emotion, or neutral faces for a comparison.

Excitatory and inhibitory rTMS had opposite effects-excitatory reduced visual sensory processing of fearful faces, whereas inhibitory increased visual sensory processing. Similarly, excitatory rTMS reduced participants’ reaction times to respond to fearful faces and reduced feelings of emotional arousal to fearful faces, which were both increased by inhibitory rTMS.

Although the study was limited to healthy participants, senior author Markus Junghöfer, Ph.D., notes that “…these results should encourage more research on the mechanisms of excitatory and inhibitory magnetic stimulation of the right dlPFC in the treatment of depression.”

 

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[WEB SITE] New method uses advanced noninvasive neuroimaging to localize and identify epileptic lesions

Epilepsy affects more than 65 million people worldwide. One-third of these patients have seizures that are not controlled by medications. In addition, one-third have brain lesions, the hallmark of the disease, which cannot be located by conventional imaging methods. Researchers at the Perelman School of Medicine at the University of Pennsylvania have piloted a new method using advanced noninvasive neuroimaging to recognize the neurotransmitter glutamate, thought to be the culprit in the most common form of medication-resistant epilepsy. Their work is published today in Science Translational Medicine.

Glutamate is an amino acid which transmits signals from neuron to neuron, telling them when to fire. Glutamate normally docks with the neuron, gives it the signal to fire and is swiftly cleared. In patients with epilepsy, stroke and possibly ALS, the glutamate is not cleared, leaving the neuron overwhelmed with messages and in a toxic state of prolonged excitation.

In localization-related epilepsy, the most common form of medication-resistant epilepsy, seizures are generated in a focused section of the brain; in 65 percent of patients, this occurs in the temporal lobe. Removal of the seizure-generating region of the temporal lobe, guided by preoperative MRI, can offer a cure. However, a third of these patients have no identified abnormality on conventional imaging studies and, therefore, more limited surgical options.

“Identification of the brain region generating seizures in location-related epilepsy is associated with significantly increased chance of seizure freedom after surgery,” said the new study’s lead author, Kathryn Davis, MD, MSTR, an assistant professor of Neurology at Penn. “The aim of the study was to investigate whether a novel imaging method, developed at Penn, could use glutamate to localize and identify the epileptic lesions and map epileptic networks in these most challenging patients.”

“We theorized that if we could develop a technique which allows us to track the path of and make noninvasive measurements of glutamate in the brain, we would be able to better identify the brain lesions and epileptic foci that current methods miss,” said senior author Ravinder Reddy, PhD, a professor of Radiology and director of Penn’s Center for Magnetic Resonance and Optical Imaging.

Reddy’s lab developed the glutamate chemical exchange saturation transfer (GluCEST) imaging method, a very high resolution magnetic resonance imaging contrast method not available before now, to measure how much glutamate was in different regions of the brain including the hippocampi, two structures within the left and right temporal lobes responsible for short- and long-term memory and spatial navigation and the most frequent seizure onset region in adult epilepsy patients.

The study tested four patients with medication-resistant epilepsy and 11 controls. In all four patients, concentrations of glutamate were found to be higher in one of the hippocampi, and confirmatory methods (electroencephalography and magnetic resonance spectra) verified independently that the hippocampus with the elevated glutamate was located in the same hemisphere as the epileptic focus/lesion. Consistent lateralization to one side was not seen in the control group.

While preliminary, this work indicates the ability of GluCEST to detect asymmetrical hippocampal glutamate levels in patients thought to have nonlesional temporal lobe epilepsy. The authors say this approach could reduce the need for invasive intracranial monitoring, which is often associated with complications, morbidity risk, and added expense.

“This demonstration that GluCEST can localize small brain hot spots of high glutamate levels is a promising first step in our research,” Davis said. “By finding the epileptic foci in more patients, this approach could guide clinicians toward the best therapy for these patients, which could translate to a higher rate of successful surgeries and improved outcomes from surgery or other therapies in this difficult disease.”

Source: Penn Medicine

Source: New method uses advanced noninvasive neuroimaging to localize and identify epileptic lesions

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[ARTICLE] Traumatic Brain Injury Severity, Neuropathophysiology, and Clinical Outcome: Insights from Multimodal Neuroimaging – Full Text

Background: The relationship between the acute clinical presentation of patients with traumatic brain injury (TBI), long-term changes in brain structure prompted by injury and chronic functional outcome is insufficiently understood. In this preliminary study, we investigate how acute Glasgow coma score (GCS) and epileptic seizure occurrence after TBIs are statistically related to functional outcome (as quantified using the Glasgow Outcome Score) and to the extent of cortical thinning observed 6 months after the traumatic event.

Methods: Using multivariate linear regression, the extent to which the acute GCS and epileptic seizure occurrence (predictor variables) correlate with structural brain changes (relative cortical atrophy) was examined in a group of 33 TBI patients. The statistical significance of the correlation between relative cortical atrophy and the Glasgow Outcome Score was also investigated.

Results: A statistically significant correlative relationship between cortical thinning and the predictor variables (acute GCS and seizure occurrence) was identified in the study sample. Regions where the statistical model was found to have highest statistical reliability in predicting both gray matter atrophy and neurological outcome include the frontopolar, middle frontal, postcentral, paracentral, middle temporal, angular, and lingual gyri. In addition, relative atrophy and GOS were also found to be significantly correlated over large portions of the cortex.

Conclusion: This study contributes to our understanding of the relationship between clinical descriptors of acute TBI, the extent of injury-related chronic brain changes and neurological outcome. This is partly because the brain areas where cortical thinning was found to be correlated with GCS and with seizure occurrence are implicated in executive control, sensory function, motor acuity, memory, and language, all of which may be affected by TBI. Thus, our quantification suggests the existence of a statistical relationship between acute clinical presentation, on the one hand, and structural/functional brain features which are particularly susceptible to post-injury degradation, on the other hand.

Introduction

Long-term clinical outcome after traumatic brain injury (TBI) is predicated upon a large variety of often poorly understood factors which substantially complicate the task of identifying the relationship between acute clinical variables and chronic functional deficits. Nevertheless, understanding how post-TBI cortical atrophy patterns reflect acute-stage patient presentation may help to identify cortical areas that are likely to undergo substantial atrophy, and implicitly to isolate aspects of cognitive, affective and neural function which are at highest risk for long-term degradation.

Attempts to relate TBI-related changes in brain structure to clinical variables often involve structural brain variables provided by neuroimaging methodologies, such as magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) (13). In previous studies, quantitative metrics provided by acute neuroimaging of TBI patients have been used to describe the relationship between acute injury profiles and chronic dysfunction (47). By contrast, hardly any non-neuroimaging clinical variables have been identified which can be used to elucidate the pattern of structural brain changes after TBI. Nevertheless, the ability to incorporate such non-neuroimaging clinical descriptors into outcome forecasting models is important because many such descriptors—including the Glasgow Coma Score (GCS)—are recorded routinely by clinicians and relied upon during the treatment decision-making process.

In this study, we illustrate how two important TBI severity indicators that are routinely assessed by clinicians in the acute care setting and without the use of neuroimaging can be used to relate patient presentation in the acute stage of TBI to the pattern and extent of post-TBI cortical atrophy as well as to neurological outcome. These two indicators—the GCS and the occurrence of epileptic seizures during the acute stage of TBI—can likely assist in predicting cortical atrophy patterns and in evaluating the risk for poor neurological outcome. This study additionally identifies cortical regions whose susceptibility to post-traumatic atrophy is correlated significantly and reliably—in a statistical sense—with functional outcome and with clinical descriptors of TBI severity. […]

Continue —>  Frontiers | Traumatic Brain Injury Severity, Neuropathophysiology, and Clinical Outcome: Insights from Multimodal Neuroimaging | Neurology

Figure 1. (A) Quantification of the linear model’s ability to predict cortical atrophy extent at 6 months after injury. For each gyrus and sulcus, the null hypothesis that there is no statistically significant correlation between the predictor variables and the response variable (cortical thinning, in millimeters) was tested. Values of the F2,30 statistic for each statistical test are encoded on the cortical surface, subject to the false discovery rate correction for multiple comparisons. Darker red hues indicate higher significance of the statistical test and, consequently, stronger ability to predict cortical thinning for the areas in question. Regions where the null hypothesis was not tested because less than 90% of cortical thickness data were available (see text) are drawn in black. Regions where the test statistic was lower than the threshold F statistic of the reliability analysis permutation test are drawn in white. (B) Statistical significance of the correlation between relative cortical atrophy and the GOS-E. Values of the t31 statistic for each statistical test are encoded on the cortical surface, as in panel (A). Note that all values of this statistic are negative, which confirms that greater regional atrophy is associated with lower GOS-E values (i.e., poorer functional outcome), as expected. The values of F and t statistics in (A) and (B), respectively, are associated with different statistical tests and different degrees of freedom and, therefore, they should not be compared to one another.

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[ARTICLE] Biomarkers of stroke recovery: Consensus-based core recommendations from the Stroke Recovery and Rehabilitation Roundtable – Full Text

In practical terms, biomarkers should improve our ability to predict long-term outcomes after stroke across multiple domains. This is beneficial for: (a) patients, caregivers and clinicians; (b) planning subsequent clinical pathways and goal setting; and (c) identifying whom and when to target, and in some instances at which dose, with interventions for promoting stroke recovery.2 This last point is particularly important as methods for accurate prediction of long-term outcome would allow clinical trials of restorative and rehabilitation interventions to be stratified based on the potential for neurobiological recovery in a way that is currently not possible when trials are performed in the absence of valid biomarkers. Unpredictable outcomes after stroke, particularly in those who present with the most severe impairment3 mean that clinical trials of rehabilitation interventions need hundreds of patients to be appropriately powered. Use of biomarkers would allow incorporation of accurate information about the underlying impairment, and thus the size of these intervention trials could be considerably reduced,4 with obvious benefits. These principles are no different in the context of stroke recovery as compared to general medical research.5

Interventions fall into two broad mechanistic categories: (1) behavioural interventions that take advantage of experience and learning-dependent plasticity (e.g. motor, sensory, cognitive, and speech and language therapy), and (2) treatments that enhance the potential for experience and learning-dependent plasticity to maximise the effects of behavioural interventions (e.g. pharmacotherapy or non-invasive brain stimulation).6 To identify in whom and when to intervene, we need biomarkers that reflect the underlying biological mechanisms being targeted therapeutically.

Our goal is to provide a consensus statement regarding the evidence for SRBs that are helpful in outcome prediction and therefore identifying subgroups for stratification to be used in trials.7 We focused on SRBs that can investigate the structure or function of the brain (Table 1). Four functional domains (motor, somatosensation, cognition, and language (Table 2)) were considered according to recovery phase post stroke (hyperacute: <24 h; acute: 1 to 7 days; early subacute: 1 week to 3 months; late subacute: 3 months to 6 months; chronic: > 6 months8). For each functional domain, we provide recommendations for biomarkers that either are: (1) ready to guide stratification of subgroups of patients for clinical trials and/or to predict outcome, or (2) are a developmental priority (Table 3). Finally, we provide an example of how inclusion of a clinical trial-ready biomarker might have benefitted a recent phase III trial. As there is generally limited evidence at this time for blood or genetic biomarkers, we do not discuss these, but recommend they are a developmental priority.912 We also recognize that many other functional domains exist, but focus here on the four that have the most developed science. […]

Continue —> Biomarkers of stroke recovery: Consensus-based core recommendations from the Stroke Recovery and Rehabilitation RoundtableInternational Journal of Stroke – Lara A Boyd, Kathryn S Hayward, Nick S Ward, Cathy M Stinear, Charlotte Rosso, Rebecca J Fisher, Alexandre R Carter, Alex P Leff, David A Copland, Leeanne M Carey, Leonardo G Cohen, D Michele Basso, Jane M Maguire, Steven C Cramer, 2017

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[WEB SITE] Research provides insights for why some epilepsy patients continue to experience postoperative seizures

New research from the University of Liverpool, published in the journal Brain, has highlighted the potential reasons why many patients with severe epilepsy still continue to experience seizures even after surgery.

Epilepsy continues to be a serious health problem and is the most common serious neurological disorder. Medically intractable temporal lobe epilepsy (TLE) remains the most frequent neurosurgically treated epilepsy disorder.

Many people with this condition will undergo a temporal lobe resection which is a surgery performed on the brain to control seizures. In this procedure, brain tissue in the temporal lobe is resected, or cut away, to remove the seizure focus.

Unfortunately, approximately one in every two patients with TLE will not be rendered completely seizure free after temporal lobe surgery, and the reasons underlying persistent postoperative seizures have not been resolved.

Reliable biomarkers

Understanding the reasons why so many patients continue to experience postoperative seizures, and identifying reliable biomarkers to predict who will continue to experience seizures, are crucial clinical and scientific research endeavours.

Researchers from the University’s Institute of Translational Medicine, led by Neuroimaging Lead Dr Simon Keller and collaborating with Medical University Bonn (Germany), Medical University of South Carolina (USA) and King’s College London, performed a comprehensive diffusion tensor imaging (DTI) study in patients with TLE who were scanned preoperatively, postoperatively and assessed for postoperative seizure outcome.

Diffusion tensor imaging (DTI) is a MRI-based neuroimaging technique that provides insights into brain network connectivity.

The results of these scans allowed the researchers to examine regional tissue characteristics along the length of temporal lobe white matter tract bundles. White matter is mainly composed of axons of nerve cells, which form connections between various grey matter areas of the brain, and carry nerve impulses between neurons allowing communication between different brain regions.

Through their analysis the researchers could determine how abnormal the white matter tracts were before surgery and how the extent of resection had affected each tract from the postoperative MRI scans.

Surgery outcomes

The researchers identified preoperative abnormalities of two temporal lobe white matter tracts that are not included in standardised temporal lobe surgery in patients who had postoperative seizures but not in patients with no seizures after surgery.

The two tracts were in the ‘fornix’ area on the same side as surgery, and in the white matter of the ‘parahippocampal’ region on the opposite side of the brain.

The tissue characteristics of these white matter tracts enabled researchers to correctly identify those likely to have further seizures in 84% of cases (sensitivity) and those unlikely to have further seizures in 89% of cases (specificity). This is significantly greater than current estimates.

The researchers also found that a particular temporal lobe white matter tract called the ‘uncinate fasciculus’ was abnormal – and potentially involved in the generation of seizures – in patients with excellent and suboptimal postoperative outcomes.

However, it was found that significantly more of this tract was surgically resected/removed in the patients with an excellent outcome.

New insights

Dr Simon Keller, said: “There is scarce information on the prediction of postoperative seizure outcome using preoperative imaging technology, and this study is the first to rigorously investigate the tissue characteristics of temporal lobe white matter tracts with respect to future seizure classifications.

“Although there is some way to go before this kind of data can influence routine clinical practice, these results may have the potential to be developed into imaging prognostic markers of postoperative outcome and provide new insights for why some patients with temporal lobe epilepsy continue to experience postoperative seizures.”

Source: Research provides insights for why some epilepsy patients continue to experience postoperative seizures

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[WEB SITE] Brain surgery helps remove scar tissue causing seizures in epilepsy patients

By the time epilepsy patient Erika Fleck came to Loyola Medicine for a second opinion, she was having three or four seizures a week and hadn’t been able to drive her two young children for five years.

“It was no way to live,” she said.

Loyola epileptologist Jorge Asconapé, MD, recommended surgery to remove scar tissue in her brain that was triggering the seizures. Neurosurgeon Douglas Anderson, MD, performed the surgery, called an amygdalohippocampectomy. Ms. Fleck hasn’t had a single seizure in the more than three years since her surgery.

“I’ve got my life back,” she said. “I left my seizures at Loyola.”

Surgery can be an option for a minority of patients who do not respond to medications or other treatments and have epileptic scar tissue that can be removed safely. In 60 to 70 percent of surgery patients, seizures are completely eliminated, and the success rate likely will improve as imaging and surgical techniques improve, Dr. Anderson said.

Traditionally, patients would have to try several medications with poor results for years or decades before being considered for surgery, according to the Epilepsy Foundation. “More recently, surgery is being considered sooner,” the foundation said. “Studies have shown that the earlier surgery is performed, the better the outcome.” (Ms. Fleck is a service coordinator for the Epilepsy Foundation North/Central Illinois Iowa and Nebraska.)

Dr. Asconapé said Ms. Fleck was a perfect candidate for surgery because the scar tissue causing her seizures was located in an area of the brain that could be removed without damaging critical structures.

Ms. Fleck experienced complex partial seizures, characterized by a deep stare, unresponsiveness and loss of control for a minute or two. An MRI found the cause: A small area of scar tissue in a structure of the brain called the hippocampus. The subtle lesion had been overlooked at another center.

Epilepsy surgery takes about three hours, and patients typically are in the hospital for two or three days. Like all surgery, epilepsy surgery entails risks, including infection, hemorrhage, injury to other parts of the brain and slight personality changes. But such complications are rare, and they pose less risk to patients than the risk of being injured during seizures, Dr. Asconapé said.

Loyola has been designated a Level Four Epilepsy Center by the National Association of Epilepsy Centers. Level Four is the highest level of specialized epilepsy care available. Level Four centers have the professional expertise and facilities to provide the highest level of medical and surgical evaluation and treatment for patients with complex epilepsy.

Loyola’s comprehensive, multidisciplinary Epilepsy Center offers a comprehensive multidisciplinary approach to epilepsy and seizure disorders for adults and children as young as two years old. Pediatric and adult epileptologist consultation and state-of-the-art neuroimaging and electrodiagnostic technology are used to identify and assess complex seizure disorders by short- and long-term monitoring.

Source: Loyola University Health System

Source: Brain surgery helps remove scar tissue causing seizures in epilepsy patients

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