Posts Tagged Magnetic resonance imaging

[ARTICLE] Transcranial direct current stimulation (tDCS) for upper limb rehabilitation after stroke: future directions. – Full Text

Abstract

Transcranial Direct Current Stimulation (tDCS) is a potentially useful tool to improve upper limb rehabilitation outcomes after stroke, although its effects in this regard have shown to be limited so far. Additional increases in effectiveness of tDCS in upper limb rehabilitation after stroke may for example be achieved by

(1) applying a more focal stimulation approach like high definition tDCS (HD-tDCS),

(2) involving functional imaging techniques during stimulation to identify target areas more exactly,

(3) applying tDCS during Electroencephalography (EEG) (EEG-tDCS),

(4) focusing on an effective upper limb rehabilitation strategy as an effective base treatment after stroke.

Perhaps going even beyond the application of tDCS and applying alternative stimulation techniques such as transcranial Alternating Current Stimulation (tACS) or transcranial Random Noise Stimulation (tRNS) will further increase effectiveness of upper limb rehabilitation after stroke.

Background

Impaired arm function after stroke is both frequent and a considerable burden for people with stroke and their caregivers. An emerging approach for enhancing neural plasticity after acute and chronic brain damage, thus enhancing rehabilitation outcomes in the upper limb rehabilitation after stroke, is non-invasive brain stimulation (NIBS), for example delivered by transcranial direct current stimulation (tDCS) [1]. tDCS is a potentially useful tool for facilitating neural plasticity, because it is relatively inexpensive, easy to administer and safe.

Many small trials regarding the effects of tDCS on arm motor function poststroke were undertaken in the past with partly promising but not conclusive results [23]. Based on these trials a lot of research interest increased in the last 10 to 15 years which still persists. This considerable research interest is a bit surprising first, given the fact that this type of therapy is not used across the board in clinical routine and second, the largest multicenter randomized clinical trial with appropriate methodology including 96 patients did not find clear results in favor of this type of stimulation [4]. A recent network meta-analysis of randomised controlled trials about the effectiveness of tDCS suggested only limited evidence for effectiveness of tDCS after stroke for arm rehabilitation [3]. The optimal stimulation paradigm regarding polarisation, electrode location, amount of direct current applied and stimulation duration still has to be established in order to maximize clinical effectiveness of tDCS [5]. Additionally, doubts emerged that the underlying rationale, the interhemispheric competition model, may be oversimplified or even incorrect [6]. The interhemispheric competition model postulates that a stroke leads to an inhibition of the ipsilateral and to an (over-) excitation of the contralateral brain hemisphere. Hence its clinical implications are to inhibit the contralateral hemisphere and to excited ipsilateral hemisphere. Moreover, electrode positioning and the resulting direction of electric fields as well as variation in head anatomy also modulate stimulation effects [78]. Hence, further approaches may be warranted beyond the approach of neuronavigation prior to stimulation: Additional increases in effectiveness of tDCS in upper limb rehabilitation after stroke may for example be achieved by (1) applying a more focal stimulation approach like high definition tDCS (HD-tDCS), (2) involving functional imaging techniques during stimulation to identify target areas more exactly, (3) applying tDCS during EEG (EEG-tDCS), (4) focusing on an effective upper limb rehabilitation strategy as an effective base treatment after stroke. Perhaps going even beyond the application of tDCS and applying alternative stimulation techniques such as transcranial Alternating Current Stimulation (tACS) [9] or transcranial Random Noise Stimulation (tRNS) [10] will further increase effectiveness of upper limb rehabilitation after stroke.[…]

 

Continue —>  Transcranial direct current stimulation (tDCS) for upper limb rehabilitation after stroke: future directions. | Journal of NeuroEngineering and Rehabilitation | Full Text

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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] Head MRI: Uses, results, and what to expect – Educational

What to know about head and brain MRI scans

Last reviewed

Doctors use MRI scans to diagnose and monitor head injuries and to check for abnormalities in the head or brain.

Magnetic resonance imaging (MRI) scans provide 3-D images of specific body parts. The scan produces highly detailed images from every angle. Depending on the purpose of the scan, a doctor may recommend contrast, which is a substance that a person takes beforehand. It helps the images to be more clearly defined.

An MRI scan is painless and noninvasive. The length of the procedure varies, depending on the situation.

In this article, we take a close look at head MRI scans in adults and children. We discuss their uses, what to expect during a scan, and how a person receives the results.

Purpose and uses of head MRI scans

Man having head and brain MRI

An MRI scan can provide detailed imagery of soft tissue.

MRI scans allow doctors to see what is happening inside the body. These scans do not produce radiation, unlike CT scans and X-rays.

MRI scans use strong magnetic forces and radio waves to create images. They can scan bone, organs, and tissue, which makes them ideal for a complex body part like the head.

MRI scans show a higher level of detail than other imaging techniques, especially in soft tissue. This is important when examining the brain or brain stem for damage or disease.

A doctor may recommend an MRI head scan if they suspect that a person has:

Procedure and what to expect during a head MRI

A head MRI is noninvasive. When a person arrives at the clinic, a doctor or technician will talk them through the process and tell them what to expect.

Preparation

First, a healthcare professional will ask a series of questions about a person’s medical history.

Radiographers also need to know if a woman is pregnant. Doctors tend not to recommend MRI scans during pregnancy, because it is unclear whether the magnetic force can affect fetal development.

They will also ask if a person has any metallic objects, such as piercings, metal plates, watches, or jewelry. These can interfere with the scan, and a person must remove them before entering the scanner.

Other metallic objects that can interfere with a scan include:

  • brain aneurysm clips
  • cochlear implants
  • dental fillings and bridges
  • eye implants
  • metallic fragments in the eyes or blood vessels
  • metal plates, wires, screws, or rods
  • surgical clips or staples

A healthcare team member will usually ask a person to put on a hospital gown. They will store a person’s clothes and any jewelry in a safe locker until the scan is finished.

During the scan

The technician will bring the person into the room that contains the MRI scanner. The person will lie on a sliding trolley, and the technician may cover them with a sheet.

The technician will then position the trolley so that the person’s head and neck are inside the MRI scanner. They will leave the room and speak to the person through a radio.

People should be aware of the following:

  • Pillows or foam blocks on the trolley will keep the head in the right position.
  • MRI machines make a lot of noise, so expect to hear loud hums, knocking sounds, and general electronic noise. Technicians will usually provide headphones or earplugs.
  • People must stay very still inside the scanner to ensure clear, accurate images. If a person moves, they may have to repeat the scan. If someone, such as a person with Parkinson’s, has trouble lying still, a technician may offer restraints to help.
  • Every MRI machine has a call button. If a person feels anxious or wants to stop the procedure, they can press the call button and talk to the medical staff.
  • Most tattoos are safe in an MRI. However, some inks contain traces of metal, which can cause heat or discomfort during a scan. If a person feels any discomfort, they should tell the radiographer.

The medical team may offer anesthetics or sedatives to people who have extreme claustrophobia.

If a person has taken a sedative, they should avoid driving themselves home. Also, a person needs time to recover from an anesthetic at the medical center. In the event of an allergic reaction, the healthcare team will keep the person under observation.

Types of MRI scanner

MRI scanner machine

MRI machines come in a range of sizes.

Several types of scanners can provide a head MRI. The size of the machine will depend on the purpose of the scan and whether the person has claustrophobia.

Types of scanner include:

  • Closed bore. These look like enormous tubes, which a person enters by lying on a sliding bench.
  • Short bore. In this type of machine, the tubular part is shorter, making it less likely to trigger claustrophobia.
  • Wide bore. The opening of the tubular area can be around 70 centimeters in these machines.
  • Open MRI. These come in a variety of shapes. They can have an open side or top.

The narrower the bore, the more detailed the image will be.

Head MRI scans with contrast vs. no contrast

Contrast is a magnetic substance. If a person drinks or receives an injection of contrast before a scan, it can help to improve the image. The majority of MRI scans do not require contrast.

The doctor and radiologist will decide if contrast is necessary, and a person takes it orally or by injection.

Contrast travels to organs and tissue through the bloodstream. The MRI procedure is the same, whether or not it requires contrast.

Contrast makes tissues and organs stand out on the MRI image. This can illuminate early abnormal tissue growth, including tumors. Receiving an early diagnosis can help improve a person’s outlook.

Scans related to the following issues can require contrast:

There is a small chance that a person may have an allergic reaction to contrast materials. Before administering the contrast, a doctor will ask about:

  • allergies
  • current medications
  • medical history
  • recent illnesses or operations

After taking the contrast, a person should check for any side effects. Report any adverse effects to a healthcare provider.

Results

The radiographer will review and interpret the scans. They will then contact the doctor with the results. This can take several days unless it was an emergency scan.

A person can request to see their scans by asking their doctor. The doctor may need a follow-up scan, and they will explain why.

Costs

The costs of an MRI procedure, and how much insurance will cover, varies.

There may also be associated costs, for contrast, anesthesia, and additional procedures.

Speak to the healthcare provider for an accurate estimate.

Head MRI scans in children

Doctor showing child MRI results

A doctor can explain the MRI process to children before undergoing the procedure.

Medical procedures can be scary. It is important for a caregiver to find out the details and explain them to the child beforehand, to reduce any anxiety. Some hospitals have leaflets that help to explain certain procedures.

Head MRI scans for children are almost identical to those for adults. The main difference is the use of a coil.

An MRI coil fits around the child’s head as they lie or sit in the machine because their heads are smaller.

Young children and babies find it hard to stay still for long, and the healthcare provider may recommend an intravenous sedative. The medical team will monitor them throughout the procedure.

Usually, a caregiver stays with the child during the scan. If this is not possible, the caregiver can often wait in the radiographer’s station.

Summary

Head MRI scans are an important tool for diagnosing and monitoring. They can indicate changes in tissue, which is vital in assessing many conditions, particularly those affecting the brain.

Unlike X-rays and CT scans, MRI scans do not involve radiation. They present no risk, apart from triggering certain anxieties or claustrophobia. There are ways to prevent this from happening.

MRI scanners are being improved all the time. With the new generation of scanners, the aim is to cut down scan times and enhance accuracy.

 

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[WEB SITE] Can MRI Brain Scans Help Us Understand Epilepsy?

epilepsy

A massive meta-analysis of global MRI imaging data on epilepsy patients seeks to clarify a complicated and mysterious neurological disorder.

Epilepsy is a neurological disorder characterized by seizures, which can vary from mild and almost undetectable to severe, featuring vigorous shaking. Almost 40 million people worldwide are affected by epilepsy. Epileptic seizures are caused by an abnormally high level of activity in nerve cells in the brain. A small number of cases have been tied to a genetic defect, and major trauma to the brain (such as an injury or stroke) can also induce seizures. However, for the majority of cases, the underlying cause of epilepsy is not known. In many instances, epilepsy can be treated with the use of anti-convulsant medication. Some people will experience an improvement in their symptoms to the point of no longer requiring medication, while others will not respond to medication at all. The variability of the disease with regards to physiology and progression makes it difficult to accurately diagnose.

How Does Epilepsy Affect the Brain?

There are multiple types of epilepsies, some more common than others, which affect different parts of the brain cortex. The disorder has been studied by using techniques such as magnetic resonance imaging (MRI), and analyses of brain tissue. The latter requires post-mortem collection of tissue, as biopsies are not routinely performed on living patients’ brains. A brain scan via MRI imaging can provide detail about pathological markers of epilepsy, but the massive amount of data collected worldwide by imaging has not yet been consolidated and analyzed in a robust manner. Gaining an understanding of distinct or shared disease markers for different forms of epilepsy could help clinicians identify targets for therapy and increase the personalization of treatment.

The ENIGMA Study

A recent study published in the journal BRAIN represents the largest neuroimaging analysis of epilepsy conducted to date.This study, called ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis)summarizes contributions from 24 research centers across 14 countries in Europe, North and South America, Asia, and Australia. Similar wide-ranging studies have revealed structural brain abnormalities in other neurological conditions such as schizophrenia, depression, and obsessive-compulsive disorder. The researchers had several goals in putting this meta-analysis together:

  1. To look at distinct types of epilepsy to see whether they share similar structural abnormalities of the brain.
  2. To analyze a well-known specific type of epilepsy, mesial temporal lobe epilepsy (MTLE) for differences between people afflicted with this disorder on different sides of the brain.
  3. To analyze idiopathic generalized epilepsies (IGE), which are thought to have a genetic component to their cause and aren’t often detectable via MRI.

The researchers compiled imaging data from 2,149 people with epilepsy and 1,727 healthy control subjects. The large sample size allowed them to perform high-powered statistical analysis of the data.

For analysis (1), the results showed that a diverse array of epilepsies showed common structural anomalies across several different regions of the brain. This suggested that distinct disease types share a common neuroanatomical signature.

For analysis (2), they found that people with mesial temporal lobe epilepsy on the right side of the hippocampus did not experience damage to the left side, and vice-versa. However, somewhat unexpectedly, they saw that damage extended to areas outside the hippocampus, suggesting that even a region-specific disorder like mesial temporal lobe epilepsy may be a network disease.

In analysis (3), the researchers found that contrary to many reports of a “normal” MRI for patients with idiopathic generalized epilepsy, several structural irregularities were observable over a large number of samples. These included reduced brain volume and thickness in several regions.

One Step Closer to Understanding Epilepsy

The authors noted some limitations to their study, such as the fact that all results were derived from cross-sectional data, meaning that it was not possible to determine whether certain features were the cause of severe brain damage at one point in time, or whether they were the product of progressive trauma. In addition, this study could not account for the possible contribution of other factors, such as medications, seizure type and frequency, and disease severity. However, this wide-scale meta-analysis represents an important step towards understanding how different types of epilepsies affect the brain, and hopefully can lead to more personalized and effective medical interventions.

Written by Adriano Vissa, PhD

Reference: Whelan CD, et al. Structural brain abnormalities in the common epilepsies assessed in a worldwide ENIGMA study. Brain. 2018; 141(2):391-408

 

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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] Repetitive reaching training combined with transcranial Random Noise Stimulation in stroke survivors with chronic and severe arm paresis is feasible: a pilot, triple-blind, randomised case series – Full Text

Abstract

Background

Therapy that combines repetitive training with non-invasive brain stimulation is a potential avenue to enhance upper limb recovery after stroke. This study aimed to investigate the feasibility of transcranial Random Noise Stimulation (tRNS), timed to coincide with the generation of voluntary motor commands, during reaching training.

Methods

A triple-blind pilot RCT was completed. Four stroke survivors with chronic (6-months to 5-years) and severe arm paresis, not taking any medications that had the potential to alter cortical excitability, and no contraindications to tRNS or MRI were recruited. Participants were randomly allocated to 12 sessions of reaching training over 4-weeks with active or sham tRNS delivered over the lesioned hemisphere motor representation. tRNS was triggered to coincide with a voluntary movement attempt, ceasing after 5-s. At this point, peripheral nerve stimulation enabled full range reaching. To determine feasibility, we considered adverse events, training outcomes, clinical outcomes, corticospinal tract (CST) structural integrity, and reflections on training through in-depth interviews from each individual case.

Results

Two participants received active and two sham tRNS. There were no adverse events. All training sessions were completed, repetitive practice performed and clinically relevant improvements across motor outcomes demonstrated. The amount of improvement varied across individuals and appeared to be independent of group allocation and CST integrity.

Conclusion

Reaching training that includes tRNS timed to coincide with generation of voluntary motor commands is feasible. Clinical improvements were possible even in the most severely affected individuals as evidenced by CST integrity.

Trial registration

This study was registered on the Australian and New Zealand Clinical Trials Registry (ANZCTR) http://www.ANZCTR.org.au/ACTRN12614000952640.aspx. Registration date 4 September 2014, first participant date 9 September 2014

Background

It is estimated that 30% of stroke survivors have severe upper limb impairment [1], whereby the functional capacity of the paretic arm is diminished to the extent that it cannot be moved against gravity [2]. For these individuals, who do not have sufficient movement with which to work, the provision of effective therapy can be challenging. The associated consequences are poor prospects for recovery [3], limited rehabilitation opportunities [4], and ultimately reduced quality of life (QoL) [5]. Yet, if task-oriented practice can be made possible by some means, there exists the potential to promote motor recovery, and in turn make a significant positive impact upon individual QoL and alleviate burden of care. In seeking to achieve levels of task-oriented practice beyond those that are possible through traditional therapy alone, attention has therefore turned to enabling technologies, including “assistive” devices, and adjuvant methods such as peripheral nerve and brain stimulation.

Best evidence syntheses [67] suggest that goal-directed movements can be assisted by minimizing the mechanical degrees of freedom to be controlled, in combination with the augmentation of voluntary muscle activity via peripheral nerve stimulation of target muscles, or the use of mechanical actuators. To encourage positive changes in motor performance, the capacity to increase task difficulty through small, yet incremental progressions and provision of meaningful real-time visual and auditory feedback have also been highlighted [89]. The authors have previously sought to implement these principles, using the Sensorimotor Active Rehabilitation Training of the Arm (SMART Arm) device to promote functional recovery in severely impaired stroke survivors [8910]. It has been shown that 4-weeks (12-h) of community-based training of reaching in people greater than 6-months post stroke improved upper limb function (and increased reaching distance) [8], enhanced the specificity of muscle recruitment (elevated ratio of biceps to triceps activation during reaching) [11], and accentuated corticospinal reactivity (decreased motor evoked potential [MEP] onset latency) [12]. Of particular interest in the context of the current study is the observation that not all individuals achieved functional gains. In these cases, the intrinsic neurobiological reserve of the injured brain may have been insufficient for repetitive training alone to drive recovery of motor function.

A variety of non-invasive brain stimulation (NIBS) techniques are now being used with the aim of altering the excitability of brain networks that have the potential to be engaged during the execution of motor tasks. The most commonly applied NIBS techniques are transcranial-direct current stimulation (tDCS) and repetitive-transcranial magnetic stimulation (rTMS) [13]. In general, the application of these techniques is predicated on the assumption that by altering the state of circuits within (contralateral) primary motor cortex (M1) in a manner that produces sustained increases in the excitability of corticospinal projections to the impaired limb (or by decreasing the excitability of circuits in the M1 ipsilateral to the impaired limb), therapeutic gains will be realised. The fact that these approaches have limited efficacy in severely impaired stroke survivors notwithstanding [14], there exist other forms of therapeutic NIBS that are motivated by a different premise.

It is well established that in some circumstances, the addition of random interference or noise, enhances the detection of weak stimuli, or the information content of a signal (e.g., trains of action potentials) [15]. In light of this phenomenon, it has been proposed that the application of transcranial random noise stimulation (tRNS) may boost the adaptive potential of cortical tissue [16]. The present investigation is motivated by the conjecture that: if the delivery of random noise stimulation is timed to occur simultaneously with the generation of voluntary motor commands, it may serve to amplify functional adaptations invoked by the intrinsic neural activity.

Implemented through a triple-blind pilot randomised control design, the specific aim of this study was to establish the feasibility of delivering tRNS, timed to coincide with the generation of the voluntary motor commands, in the context of reaching movements performed by individuals with chronic and severe upper limb paresis after stroke. Recognising that the response to any therapeutic intervention is constrained by the state of pathways that can convey signals from the brain to the periphery, diffusion-weighted magnetic resonance imaging (DW-MRI) was performed to characterize the structural integrity of the descending corticospinal tract (CST) projections for each participant.

Continue —>  Repetitive reaching training combined with transcranial Random Noise Stimulation in stroke survivors with chronic and severe arm paresis is feasible: a pilot, triple-blind, randomised case series | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 1 Representation of the training setup including horizontal reaching track, trunk restraint, visual feedback, transcranial random noise stimulation application, and electrical stimulation application to lateral head of triceps

 

Fig. 2 Corticospinal tract streamline reconstructions: the corticospinal tract is indicated for each of the four participants, displayed on coronal (x view) slices of T1 weighted anatomical scans with direction encoded fractional anisotropy (FA) colour maps superimposed. Images are shown in radiological format (ie. right on the image is the patient’s left side). The reconstructed streamlines for the corticospinal tract are also superimposed, and indicated by red circles. The posterior limb of the internal capsule (PLIC) within the corticospinal tract was the region of interest that was delineated manually for each scan, using anatomical landmarks. No tracts were detected in the PLIC region in the right hemisphere for P03, or the left hemisphere for P04

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[ARTICLE] Can Neurological Biomarkers of Brain Impairment Be Used to Predict Poststroke Motor Recovery? – Full Text

Background. There is growing interest to establish recovery biomarkers, especially neurological biomarkers, in order to develop new therapies and prediction models for the promotion of stroke rehabilitation and recovery. However, there is no consensus among the neurorehabilitation community about which biomarker(s) have the highest predictive value for motor recovery. Objective. To review the evidence and determine which neurological biomarker(s) meet the high evidence quality criteria for use in predicting motor recovery. Methods. We searched databases for prognostic neuroimaging/neurophysiological studies. Methodological quality of each study was assessed using a previously employed comprehensive 15-item rating system. Furthermore, we used the GRADE approach and ranked the overall evidence quality for each category of neurologic biomarker. Results. Seventy-one articles met our inclusion criteria; 5 categories of neurologic biomarkers were identified: diffusion tensor imaging (DTI), transcranial magnetic stimulation (TMS), functional magnetic resonance imaging (fMRI), conventional structural MRI (sMRI), and a combination of these biomarkers. Most studies were conducted with individuals after ischemic stroke in the acute and/or subacute stage (~70%). Less than one-third of the studies (21/71) were assessed with satisfactory methodological quality (80% or more of total quality score). Conventional structural MRI and the combination biomarker categories ranked “high” in overall evidence quality. Conclusions. There were 3 prevalent methodological limitations: (a) lack of cross-validation, (b) lack of minimal clinically important difference (MCID) for motor outcomes, and (c) small sample size. More high-quality studies are needed to establish which neurological biomarkers are the best predictors of motor recovery after stroke. Finally, the quarter-century old methodological quality tool used here should be updated by inclusion of more contemporary methods and statistical approaches.

There is growing interest in establishing stroke recovery biomarkers. Researchers define stroke recovery biomarkers as surrogate indicators of disease state that can have predictive value for recovery or treatment response.1 Specifically, previous studies have suggested that better understanding of neurological biomarkers, derived from brain imaging and neurophysiological assessments, is likely to move stroke rehabilitation research forward.1,2

Recovery biomarkers acquired during the acute and subacute phases (acute—within 1 week after onset; subacute—between 1 week and 3 months after onset) may be vital to set attainable neurorehabilitation goals and to choose proper therapeutic approaches based on the recovery capacity. Furthermore, motor recovery prediction using neurological biomarkers in the chronic phase (more than 3 months after onset) can be useful to determine whether an individual will benefit from specific therapeutic interventions applied after the normal period of rehabilitation has ended. Hence, use of recovery biomarkers is likely to improve customization of physical interventions for individual stroke survivors regarding their capacity for recovery, and to facilitate development of new neurorehabilitation approaches.

There have been fundamental changes in recovery biomarkers from simple clinical behavioral biomarkers to brain imaging and neurophysiological biomarkers. In particular, a number of recent studies have shown that neurologic biomarkers (ie, neuroimaging and/or neurophysiological measures of brain) are more predictive of motor recovery than clinical behavioral biomarkers.35

Although there is some evidence that neurological biomarkers are more valuable as predictors of motor recovery than clinical behavioral biomarkers, there are significant gaps between the published evidence and clinical usage. First, there is no consensus on which specific neurological biomarkers would be best for prediction models.4,6,7 Viable neurological biomarker of motor recovery have evolved from lesion size and location, prevalent in the early 1990s8 to more contemporary complex brain network analysis variables.9 Despite this evolution, there is a paucity of high-level evidence for determining the most critical neurological biomarkers of motor recovery. A number of literature reviews and systematic reviews of studies published since the 1990s aimed to identify the most appropriate biomarkers of motor recovery or functional independence.8,1012 Among these reviews, only one by Schiemanck and colleagues8 assessed the evidence quality of neurologic biomarkers, while many focused on clinical measures (ie, clinical motor and/or functional measures).11 Their review was limited to only 13 studies that employed structural magnetic resonance imaging (sMRI) measures of lesion volume as neurologic biomarkers. Besides lesion volume derived from structural MRI, there are other viable neurological biomarkers of brain impairment. Therefore, this systematic review includes a broad set of relevant biomarkers for consideration as critical predictors for inclusion in motor recovery prediction models.

Furthermore, there is some evidence to suggest that multivariate prediction models that use neurological biomarkers in addition to clinical outcome measures are more accurate than those that use clinical outcome measures alone.2,13 However there is still no consensus about whether incorporating behavioral and neurological predictors in a multimodal prediction model is superior (ie, more accurate) to a univariate model that includes either behavioral or neurological predictors alone.

Taken together, this systematic review has 2 aims. The first is to conduct a critical and systematic comparison of selected studies to determine which neurological biomarker(s) is likely to have sufficient high-level evidence in order to render the most accurate prediction of motor recovery after stroke. The second aim is to identify whether adding clinical measures along with neurological biomarkers in the model improves the accuracy of the model compared to the models that use neurological biomarkers alone.

Continue —> Can Neurological Biomarkers of Brain Impairment Be Used to Predict Poststroke Motor Recovery? A Systematic Review – Aug 08, 2016

Figure

Figure 1. Evidence search strategy diagram.

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[Abstract] Recovery of functional connectivity of the sensorimotor network after surgery for diffuse low-grade gliomas involving the supplementary motor area.

Abstract

OBJECTIVE

The supplementary motor area (SMA) syndrome is a well-studied lesional model of brain plasticity involving the sensorimotor network. Patients with diffuse low-grade gliomas in the SMA may exhibit this syndrome after resective surgery. They experience a temporary loss of motor function, which completely resolves within 3 months. The authors used functional MRI (fMRI) resting state analysis of the sensorimotor network to investigate large-scale brain plasticity between the immediate postoperative period and 3 months’ follow-up.

METHODS

Resting state fMRI was performed preoperatively, during the immediate postoperative period, and 3 months postoperatively in 6 patients with diffuse low-grade gliomas who underwent partial surgical excision of the SMA. Correlation analysis within the sensorimotor network was carried out on those 3 time points to study modifications of its functional connectivity.

RESULTS

The results showed a large-scale reorganization of the sensorimotor network. Interhemispheric connectivity was decreased in the postoperative period, and increased again during the recovery process. Connectivity between the lesion side motor area and the contralateral SMA rose to higher values than in the preoperative period. Intrahemispheric connectivity was decreased during the immediate postoperative period and had returned to preoperative values at 3 months after surgery.

CONCLUSIONS

These results confirm the findings reported in the existing literature on the plasticity of the SMA, showing large-scale modifications of the sensorimotor network, at both inter- and intrahemispheric levels. They suggest that interhemispheric connectivity might be a correlate of SMA syndrome recovery.

Source : Recovery of functional connectivity of the sensorimotor network after surgery for diffuse low-grade gliomas involving the supplementary motor area

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[WEB SITE] What to Expect from an MRI

 

MRI MachineWhat Exactly is an MRI?

Magnetic resonance imaging (MRI) uses magnetic resonance to take detailed, high resolution images of your body that other procedures such as X-rays are not able to detect. An MRI does not produce a photo but rather a map of specific parts of your body which are highly valuable to your prognosis, diagnosis, and future treatment plan.

I recently had my first MRI during a yearly follow-up with my provider to monitor scoliosis issues with my spine. Below is the information I learned during the MRI process including reasons your doctor may recommend the procedure, questions you should ask, benefits vs. risks, preparing for the procedure, what you should expect during the scan, and following up with your provider afterwards.

Reasons Your Provider may Recommend an MRI:

  • To get a more accurate picture of your body.
  • To identify specific abnormalities.
  • To recommend the most appropriate treatment plan.
  • To determine if further tests or procedures are required.
  • To defer or more accurately recommended surgery.

Questions You Should Ask before the Scan

If your doctor has recommended an MRI scan, below are some questions you should ask and ensure you fully understand prior to undergoing the procedure:

  • Will the scan be covered by my insurance?

Your provider may not be able to answer this question, but he or she can direct you to the billing department and the right people who can get you the financial answers you need.

  • Is this absolutely necessary or are there alternatives?

As with any recommended procedure, it’s important to ask your doctor if this is the right treatment, necessary to move forward with your care, and if there are any alternatives you should consider.

Continue —->  What to Expect from an MRI.

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