Posts Tagged neuroimaging

[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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[OPINION ARTICLE] Can Functional Magnetic Resonance Imaging Generate Valid Clinical Neuroimaging Reports? – Full Text

imageRoland Beisteiner* Study Group Clinical fMRI, High Field MR Center, Department of Neurology, Medical University of Vienna, Vienna, Austria

A highly critical issue for applied neuroimaging in neurology—and particularly for functional neuroimaging—concerns the question of validity of the final clinical result. Within a clinical context, the question of “validity” often equals the question of “instantaneous repeatability,” because clinical functional neuroimaging is done within a specific pathophysiological framework. Here, not only every brain is different but also every pathology is different, and most importantly, individual pathological brains may rapidly change in short time.

Within the brain mapping community, the problem of validity and repeatability of functional neuroimaging results has recently become a major issue. In 2016, the Committee on Best Practice in Data Analysis and Sharing from the Organization for Human Brain Mapping (OHBM) created recommendations for replicable research in neuroimaging, focused on magnetic resonance imaging and functional magnetic resonance imaging (fMRI). Here, “replication” is defined as “Independent researchers use independent data and … methods to arrive at the same original conclusion.” “Repeatability” is defined as repeated investigations performed “with the same method on identical test/measurement items in the same test or measuring facility by the same operator using the same equipment within short intervals of time” (ISO 3534-2:2006 3.3.5). An intermediate position between replication and repeatability is defined for “reproducibility”: repeated investigations performed “with the same method on identical test/measurement items in different test or measurement facilities with different operators using different equipment” (ISO 3534-2:2006 3.3.10). Further definitions vary depending on the focus, be it the “measurement stability,” the “analytical stability,” or the “generalizability” over subjects, labs, methods, or populations.

The whole discussion was recently fueled by an PNAS article published by Eklund et al. (1), which claims that certain results achieved with widely used fMRI software packages may generate false-positive results, i.e., show brain activation where is none. More specifically, when looking at activation clusters defined by the software as being significant (clusterwise inference), the probability of a false-positive brain activation is not 5% but up to 70%. This was true for group as well as single subject data (2). The reason lies in an “imperfect” model assumption about the distribution of the spatial autocorrelation of functional signals over the brain. A squared exponential distribution was assumed but found not to be correct for the empirical data. This article received heavy attention and discussion in scientific and public media and a major Austrian newspaper titled “Doubts about thousands of brain research studies.” A recent PubMed analysis indicates already 69 publications citing the Eklund work. Critical comments by Cox et al. (3)—with focusing on the AFNI software results—criticize the authors for “their emphasis on reporting the single worst result from thousands of simulation cases,” which “greatly exaggerated the scale of the problem.” Other groups extended the work. With regard to the fact that “replicability of individual studies is an acknowledged limitation,” Eickhoff et al. (4) suggest that “Coordinate-based meta-analysis offers a practical solution to this limitation.” They claim that meta-analyses allow “filtering and consolidating the enormous corpus of functional and structural neuroimaging results” but also describe “errors in multiple-comparison corrections” in GingerALE, a software package for coordinate-based meta-analysis. One of their goals is to “exemplify and promote an open approach to error management.” More generally and probably also triggered by the Eklund paper, Nissen et al. (5) discuss the current situation that “Science is facing a ‘replication crisis’.” They focus on the publicability of negative results and model “the community’s confidence in a claim as a Markov process with successive published results shifting the degree of belief.” Important findings are, that “unless a sufficient fraction of negative results are published, false claims frequently can become canonized as fact” and “Should negative results become easier to publish … true and false claims would be more readily distinguished.”

As a consequence of this discussion, public skepticism about the validity of clinical functional neuroimaging arose. At first sight, this seems to be really bad news for clinicians. However, at closer inspection, it turns out that particularly the clinical neuroimaging community has already long been aware of the problems with standard (“black box”) analyses of functional data recorded from compromised patients with largely variable pathological brains. Quite evidently, methodological assumptions as developed for healthy subjects and implemented in standard software packages may not always be valid for distorted and physiologically altered brains. There are specific problems for clinical populations and particularly for defining the functional status of an individual brain (as opposed to a “group brain” in group studies). With task-based fMRI—the most important clinical application—the major problems may be categorized in “patient problems” and “methodological problems.”

Critical patient problems concern:

  • – Patient compliance may change quickly and considerably.
  • – The patient may “change” from 1 day to the other (altered vigilance, effects of pathology and medication, mood changes—depression, exhaustion).
  • – The clinical state may “change” considerably from patient to patient (despite all having the same diagnosis). This is primarily due to location and extent of brain pathology and compliance capabilities.

Critical methodological problems concern:

  • – Selection of clinically adequate experimental paradigms (note paresis, neglect, aphasia).
  • – Performance control (particularly important in compromised patients).
  • – Restriction of head motion (in patients artifacts may be very large).
  • – Clarification of the signal source (microvascular versus remote large vessel effects).
  • – Large variability of the contrast to noise ratio from run to run.
  • – Errors with inter-image registration of brains with large pathologies.
  • – Effects of data smoothing, definition of adequate functional regions of interest, and definition of essential brain activations.
  • – Difficult data interpretation requires specific clinical fMRI expertise and independent validation of the local hardware and software performance (preferably with electrocortical stimulation).

All these problems have to be recognized and specific solutions have to be developed depending on the question at hand—generation of an individual functional diagnosis or performance of a clinical group study. To discuss such problems and define solutions, clinical functional neuroimagers have already assembled early (Austrian Society for fMRI,1 American Society of Functional Neuroradiology2) and just recently the Alpine Chapter from the OHBM3 was established with a dedicated focus on applied neuroimaging. Starting in the 1990s (6), this community published a considerable number of clinical methodological investigations focused on the improvement of individual patient results and including studies on replication, repeatability, and reproducibility [compare (7)]. Early examples comprise investigations on fMRI signal sources (8), clinical paradigms (9), reduction of head motion artifacts (10), and fMRI validation studies (11, 12). Of course the primary goal of this clinical research is improvement of the validity of the final clinical result. One of the suggested clinical procedures focuses particularly on instantaneous replicability as a measure of validity [Risk Map Technique (1315); see Figure 1] with successful long-term clinical use. This procedure was developed for presurgical fMRI and minimizes methodological assumptions to stay as close to the original data as possible. This is done by avoiding data smoothing and normalization procedures and minimization of head motion artifacts by helmet fixation (avoiding artifacts instead of correcting them). It is interesting to note that in the Eklund et al. (1) analysis it was also the method with minimal assumptions (a non-parametric permutation), which was the only one that achieved correct (nominal) results. The two general ideas of the risk map technique are (a) to use voxel replicability as a criterion for functionally most important voxels (extracting activation foci = voxels with largest risk for a functional deficit when lesioned) and (b) to consider regional variability of brain conditions (e.g., close to tumor) by variation of the hemodynamic response functions (step function/HRF/variable onset latencies) and thresholds. The technique consists only of few steps, which can easily be realized by in house programming: (i) Record up to 20 short runs of the same task type to allow checking of repeatability. (ii) Define a reference function (e.g., step function with a latency of 1 TR). (iii) Calculate Pearson correlation r for every voxel and every run. (iv) Color code voxels according to their reliability at a given correlation threshold (e.g., r > 0.5): yellow voxels >75%, orange voxels >50%, red voxels >25% of runs need to be active. (v) Repeat (i)–(iv) with different reference functions (to our experience, a classical HRF and two step functions with different latencies are sufficient to evaluate most patients) and at different correlation thresholds (e.g., r > 0.2 to r > 0.9). The clinical fMRI expert performs a comprehensive evaluation of all functional maps with consideration of patient pathology, patient performance, and the distribution and level of artifacts [compare descriptions in Ref. (13, 15)]. The final clinical result is illustrated in Figure 1, and a typical interpretation would be: most reliable activation of the Wernicke area is found with a step function of 1 TR latency and shown at Pearson correlation r > 0.5. It is important to note that risk maps extract the most active voxel(s) within a given brain area and judgment of a “true” activation extent is not possible. However, due to the underlying neurophysiological principles [gradients of functional representations (16)], it is questionable whether “true” activation extents of fMRI activations can be defined with any technique.

 

Figure 1. Example for a missing language activation (Wernicke activity, white arrow) with a “black box” standard analysis (right, SPM12 applying motion regression and smoothing, voxelwise inference FWE <0.05, standard k = 25) using an overt language design [described in Ref. (17)]. Wernicke activity is detectable with the clinical risk map analysis (left) based on activation replicability (yellow = most reliabel voxels). Patient with left temporoparietal tumor.

The importance to check individual patient data from various perspectives instead of relying on a “standard statistical significance value,” which may not correctly reflect the individual patients signal situation, has also been stressed by other authors [e.g., Ref. (18)]. Of course, clinical fMRI—as all other applied neuroimaging techniques—requires clinical fMRI expertise and particularly pathophysiological expertise to be able to conceptualize where to find what, depending on the pathologies of the given brain. One should be aware that full automatization is currently not possible neither for a comparatively simple analysis of a chest X-ray nor for applied neuroimaging. In a clinical context, error estimations still need to be supported by the fMRI expert and cannot be done by an algorithm alone. As a consequence, the international community started early with offering dedicated clinical methodological courses (compare http://oegfmrt.org or http://ohbmbrainmappingblog.com/blog/archives/12-2016). Meanwhile, there are enough methodological studies that enable an experienced clinical fMRI expert to safely judge the possibilities and limitations for a valid functional report in a given patient with his/her specific pathologies and compliance situation. Of course, this also requires adequate consideration of the local hard- and software. Therefore and particularly when considering the various validation studies, neither for patients nor for doctors there is a need to raise “doubts about clinical fMRI studies” but instead good reason to “keep calm and scan on.”4

Author Contributions

The author confirms being the sole contributor of this work and approved it for publication.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The methodological developments have been supported by the Austrian Science Fund (KLI455, KLI453, P23611) and Cluster Grants of the Medical University of Vienna and the University of Vienna, Austria.

Footnotes

References

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Source: Frontiers | Can Functional Magnetic Resonance Imaging Generate Valid Clinical Neuroimaging Reports? | Neurology

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[Abstract] Test-retest reliability of prefrontal transcranial Direct Current Stimulation (tDCS) effects on functional MRI connectivity in healthy subjects

Highlights

• Prefrontal non-invasive brain stimulation targeting specific brain circuits has the potential to be applied in therapeutic settings but reliability, validity and generalisability have to be evaluated.

 

• This is the first study investigating the test-retest reliability of prefrontal tDCS-induced resting-state functional-connectivity (RS fcMRI) modulations.

 

• Analyses of individual RS-fcMRI responses to active tDCS across three single sessions revealed no to low reliability, whereas reliability of RS-fcMRI baselines and RS-fcMRI responses to sham tDCS was low to moderate.

 

• Our pilot data can be used to plan future imaging studies investigating rs-fcMRI effects of prefrontal tDCS.

Abstract

Transcranial Direct Current Stimulation (tDCS) of the prefrontal cortex (PFC) can be used for probing functional brain connectivity and meets general interest as novel therapeutic intervention in psychiatric and neurological disorders. Along with a more extensive use, it is important to understand the interplay between neural systems and stimulation protocols requiring basic methodological work. Here, we examined the test-retest (TRT) characteristics of tDCS-induced modulations in resting-state functional-connectivity MRI (RS fcMRI). Twenty healthy subjects received 20 minutes of either active or sham tDCS of the dorsolateral PFC (2 mA, anode over F3 and cathode over F4, international 10–20 system), preceded and ensued by a RS fcMRI (10 minutes each). All subject underwent three tDCS sessions with one-week intervals in between. Effects of tDCS on RS fcMRI were determined at an individual as well as at a group level using both ROI-based and independent-component analyses (ICA). To evaluate the TRT reliability of individual active-tDCS and sham effects on RS fcMRI, voxel-wise intra-class correlation coefficients (ICC) of post-tDCS maps between testing sessions were calculated. For both approaches, results revealed low reliability of RS fcMRI after active tDCS (ICC(2,1) = −0.09 – 0.16). Reliability of RS fcMRI (baselines only) was low to moderate for ROI-derived (ICC(2,1) = 0.13 – 0.50) and low for ICA-derived connectivity (ICC(2,1) = 0.19 – 0.34). Thus, for ROI-based analyses, the distribution of voxel-wise ICC was shifted to lower TRT reliability after active, but not after sham tDCS, for which the distribution was similar to baseline. The intra-individual variation observed here resembles variability of tDCS effects in motor regions and may be one reason why in this study robust tDCS effects at a group level were missing. The data can be used for appropriately designing large scale studies investigating methodological issues such as sources of variability and localisation of tDCS effects.

 

Source: Test-retest reliability of prefrontal transcranial Direct Current Stimulation (tDCS) effects on functional MRI connectivity in healthy subjects

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