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Mental Health in the 21st Century

Mental Health in the 21st Century

According to experts, more than 800,000 people commit suicide every year. This is a huge number and it is getting larger, with suicide being the second leading cause of death in the world for those from 15 to 29 years old. However, even though this number is startling, many are still afraid to take mental health seriously. Whether it is because of fear, stigma, embarrassment, or ignorance, people are just not getting it. Instead of treating mental illnesses, they are blaming it on behaviour or personality issues. 

Violence and Mental Health

What does this mean for future generations? Well, there may be less of them to worry about if the suicide rate continues on like it is. And it is not just about suicide. A large number of people suffering from mental health conditions end up hurting others before hurting themselves. What with all of the mass killings in the news recently, mental illness has jumped into position as one of the major hot topics. As many of those committing these crimes are being found to have mental illnesses, the public wants to know what we are going to do about it. 

Neurological Disorders

Mental health studies have been done to determine the cause of some of these mental conditions and have found that neurological disorders and brain injuries could be two of the problems. For example, Alzheimer’s disorder and Huntington’s disease have been determined to be caused by neurological damage. Whether it is a brain injury or a genetic flaw, these abnormalities can cause severe disability in many. Stroke is the second most common cause of mortality in older people and over 1/3rd of them who survive are disabled permanently. 

Breaking the Silence

While it is important to find a cure for these and other mental health disorders, it is even more important to get those with these disorders to acknowledge that they have them. Many with depression suffer in silence and self-medicate with drugs or alcohol while struggling to maintain a normal life. Those with anxiety disorders are trying to go about their lives without letting anyone know how stressed out they are, making them even more stressed out. These people are the ones who can end up having a breakdown of some kind and without treatment, may end up taking their own lives. 

The Cures Act

A new bill called the 21st Century Cures Act was introduced in 2016, which is focused on reforming mental health care. With new strategies addressing serious mental illnesses such as Alzheimer’s disorder and schizophrenia, the Cures Act includes the assertive community treatment (ACT) model. This provides a team of professionals who are on call 24 hours a day, seven days a week to expand outpatient treatment as well as inpatient care. Some of the other benefits include:

  • Paying for inpatient psychiatric care
  • Treating criminals with serious mental illness instead of incarcerating them
  • Reduces red tape that slows progress of scientific projects for cures
  • Identifies programs to promote innovation
  • Highlights the impact of disease and treatment on patient’s lives to get the FDA to evaluate new drugs
  • Enhances the impact of data
  • Advances the clinical research
  • Forces states to use at least 10% of their mental health grants to fund early intervention programs
  • Supports major fights for cures
  • Speeds up the initiation of research and new discoveries

Researchers are also noticing a connection between mental and physical health, which shows more of a need for mental health care in the United States as well. In the 20th century, life expectancy had increased due to better health care and advances in treatment for heart disease and cancer. However, by the end of the 20th century it was becoming obvious that even with the breakthrough treatments, physical health was being affected by mental health. Whether it be from suicide, secondary disease of the liver, heart, or lung from drug or alcohol use, or a comorbidity issue, mental health and physical health seem to go hand in hand.

New Mental Health Help

In the 21st century, we have seen a plethora of new mental health care progressions such as virtual reality headgear for phobias and other illnesses, mental health apps, new drugs and ways to use them, and online mental health care. 

Virtual Reality (VR) Headgear

In the past two decades, VR headgear has been used for exposure therapy to help those who have phobias. For example, those with acrophobia, which is the fear of heights, are being safely exposed to their fear with VR headgear to help them overcome those fears. This experience has been extremely successful and along with treatment from a therapist, patients have been able to overcome their phobias within just a few sessions. 

Mental Health Apps

The amount of mental health apps available to the public has gone from a dozen to thousands in the past decade and they are extremely popular. The choices vary from meditation apps such as Breathe2Relax, Headspace, and Smiling Mind to mood apps such as Happify, Mood Tools, and Mood Kit. There are even apps that teach cognitive-behavioural therapy (CBT) such as Anxiety Coach, iCBT, and Live OCD Free. The apps range from free to $19.99 per month, depending on the services they offer. For example, some of the apps provide instant access to a mental health professional so you can get help with anything whenever you need it. One of the main benefits of these apps is the anonymity they offer as people are still not completely comfortable talking about mental health care. 

Wearables and Other Technology

From wristbands and watches to high tech earrings, technology has jumped on the wearable bandwagon. It may have started with the smartwatch that tracked your moods but now it has transformed into mood sensors and bands that can read your emotions. For example, there are bands that you wear on your wrists that read your pulse and can tell how anxious you are by body temperature and even how much you sweat. There are also clip-ons that you wear that read your breathing and heart rate all day and keeps track of how your moods change. You can even get a headset that will train your brain how to respond to stress and other emotions. 

New Drugs

Medication is also being improved with technology. A new type of drug that just hit the research labs is potentially able to prevent neurons from dying with metabolism changes. This new medication can be used to treat neurological disorders such as Alzheimer’s disorder, amyotrophic lateral sclerosis, stroke, traumatic brain injury, and Huntington’s disease. The new nanotechnology is confirmed to be able to inhibit and prevent cell death and reduce brain infarctions while protecting neurons from plaques. 

Online Mental Health Care

The use of online mental health care has been going on for some time now and with online counselling platforms such as BetterHelp, individuals are able to talk to a licensed mental health care expert 24 hours a day, seven days a week, 365 days a year. No appointment needed and you do not even have to leave your home to do it. This can be especially beneficial to those who have transportation or mobility issues, financial issues, or those with anxiety or other mental issues that make it difficult for them to go out. For example, those with post-traumatic stress disorder (PTSD) who have had anxiety attacks from being in a crowded place will have a much easier time talking to a therapist from home on their cellphone or tablet. 

Easier and Cost-Efficient

Similarly, those with severe depression sometimes have trouble getting out of bed, let along making an appointment and going to see a therapist. With online therapy, there is no need to go out, you do not have to wait for an appointment, and you save money because the cost is just a fraction of what traditional therapy costs. Online therapy with BetterHelp is $40 to $70 per week compared to $200 to $400 per hour for traditional face to face counselling. The cost is so much lower because the expenses are lower for the therapists. They do not have to pay for an office, staff, or other office expenses and can see more patients since they are not bound to only seeing seven patients a day for one hour each. They can talk to as many patients as they want since they are not confined by appointments. 

Find What Works

Whether you are struggling with depression, anxiety, or have a loved one suffering from Alzheimer’s disorder or another neurological illness, there are many new treatments coming. And whether you choose to use the old traditional type of treatment or one of the new technological advancements, it is important to get the help you need when you need it. Contact a mental health care professional today to get treatment. With all of the advances in medical and mental health care, you are sure to be able to find what fits you best.

Creation of new brain cells plays underappreciated role in Alzheimer’s

Creation of new brain cells plays underappreciated role in Alzheimer’s

Much of the research on the underlying causes of Alzheimer’s disease focuses on amyloid beta (Aß), a protein that accumulates in the brain as the disease progresses. Excess Aß proteins form clumps or “plaques” that disrupt communication between brain cells and trigger inflammation, eventually leading to widespread loss of neurons and brain tissue.

Aß plaques will continue to be a major focus for Alzheimer’s researchers. However, innovative research by neuroscientists at the University of Chicago looks at another process that plays an underappreciated role in the progression of the disease.

In a new study published in the Journal of Neuroscience, Prof. Sangram Sisodia, a leading expert on the biology of Alzheimer’s disease, and his colleagues show how in genetic forms of Alzheimer’s, a process called neurogenesis—the creation of new brain cells—can be disrupted by the brain’s own immune cells.

Some types of early onset, hereditary Alzheimer’s are caused by mutations in two genes called presenilin 1 (PS1) and presenilin 2 (PS2). Previous research has shown that when healthy mice are placed into an “enriched” environment where they can exercise, play and interact, they have a huge increase in new brain cells being created in the hippocampus—the part of the brain that is important for memory. But when mice carrying mutations to PS1 and PS2 are placed in an enriched environment, they don’t show the same increase in new brain cells. They also start to show signs of anxiety, a symptom often reported by people with early onset Alzheimer’s.

This led Sisodia to think that something besides genetics had a role to play. He suspected that the process of neurogenesis in mice both with and without Alzheimer’s mutations also could be influenced by other cells that interact with the newly forming brain cells.

The researchers focused on microglia, a kind of immune cell in the brain that usually repairs synapses, destroys dying cells and clears out excess Aß proteins. When the researchers gave the mice a drug that causes microglial cells to die, neurogenesis returned to normal. The mice with presenilin mutations were then placed into an enriched environment and they were fine; they didn’t show any memory deficits or signs of anxiety, and they were creating the normal, expected number of new neurons.

“It’s the most astounding result to me,” said Sisodia, the Thomas Reynolds Sr. Family Professor of Neurosciences at UChicago. “Once you wipe out the microglia, all these deficits that you see in these mice with the mutations are completely restored. You get rid of one cell type, and everything is back to normal.”

“It’s the most astounding result to me … You get rid of one cell type, and everything is back to normal.”

—Prof. Sangram Sisodia

Sisodia thinks the microglia could be overplaying their immune system role in this case. Alzheimer’s disease normally causes inflammation in the microglia, so when they encounter newly formed brain cells with presenilin mutations they may overreact and kill them off prematurely. He feels that this discovery about the microglia’s role opens another important avenue toward understanding the biology of Alzheimer’s disease.

“I’ve been studying amyloid for 30 years, but there’s something else going on here, and the role of neurogenesis is really underappreciated,” he said. “This is another way to understand the biology of these genes that we know significantly affect the progression of disease and loss of memory.”

Additional authors include Sylvia Ortega-Martinez, Nisha Palla, Xiaoqiong Zhang and Erin Lipman from the University of Chicago.

Citation: “Deficits in Enrichment-Dependent Neurogenesis and Enhanced Anxiety Behaviors Mediated by Expression of Alzheimer’s Disease-Linked Ps1 Variants Are Rescued by Microglial Depletion.” Journal of Neuroscience, Aug. 21, 2019. DOI: 10.1523/JNEUROSCI.0884-19.2019

Materials provided by the University of Chicago

Tissue model reveals role of blood-brain barrier in Alzheimer’s

Tissue model reveals role of blood-brain barrier in Alzheimer’s

Beta-amyloid plaques, the protein aggregates that form in the brains of Alzheimer’s patients, disrupt many brain functions and can kill neurons. They can also damage the blood-brain barrier — the normally tight border that prevents harmful molecules in the bloodstream from entering the brain.

MIT engineers have now developed a tissue model that mimics beta-amyloid’s effects on the blood-brain barrier, and used it to show that this damage can lead molecules such as thrombin, a clotting factor normally found in the bloodstream, to enter the brain and cause additional damage to Alzheimer’s neurons.

“We were able to show clearly in this model that the amyloid-beta secreted by Alzheimer’s disease cells can actually impair barrier function, and once that is impaired, factors are secreted into the brain tissue that can have adverse effects on neuron health,” says Roger Kamm, the Cecil and Ida Green Distinguished Professor of Mechanical and Biological Engineering at MIT.

The researchers also used the tissue model to show that a drug that restores the blood-brain barrier can slow down the cell death seen in Alzheimer’s neurons.

Kamm and Rudolph Tanzi, a professor of neurology at Harvard Medical School and Massachusetts General Hospital, are the senior authors of the study, which appears in the August 12 issue of the journal Advanced Science. MIT postdoc Yoojin Shin is the paper’s lead author.

Barrier breakdown

The blood vessel cells that make up the blood-brain barrier have many specialized proteins that help them to form tight junctions — cellular structures that act as a strong seal between cells.

Alzheimer’s patients often experience damage to brain blood vessels caused by beta-amyloid proteins, an effect known as cerebral amyloid angiopathy (CAA). It is believed that this damage allows harmful molecules to get into the brain more easily. Kamm decided to study this phenomenon, and its role in Alzheimer’s, by modeling brain and blood vessel tissue on a microfluidic chip.

“What we were trying to do from the start was generate a model that we could use to understand the interactions between Alzheimer’s disease neurons and the brain vasculature,” Kamm says. “Given the fact that there’s been so little success in developing therapeutics that are effective against Alzheimer’s, there has been increased attention paid to CAA over the last couple of years.”

His lab began working on this project several years ago, along with researchers at MGH who had engineered neurons to produce large amounts of beta-amyloid proteins, just like the brain cells of Alzheimer’s patients.

Led by Shin, the researchers devised a way to grow these cells in a microfluidic channel, where they produce and secrete beta-amyloid protein. On the same chip, in a parallel channel, the researchers grew brain endothelial cells, which are the cells that form the blood-brain barrier. An empty channel separated the two channels while each tissue type developed.

After 10 days of cell growth, the researchers added collagen to the central channel separating the two tissue types, which allowed molecules to diffuse from one channel to the other. They found that within three to six days, beta-amyloid proteins secreted by the neurons began to accumulate in the endothelial tissue, which led the cells to become leakier. These cells also showed a decline in proteins that form tight junctions, and an increase in enzymes that break down the extracellular matrix that normally surrounds and supports blood vessels.

As a result of this breakdown in the blood-brain barrier, thrombin was able to pass from blood flowing through the leaky vessels into the Alzheimer’s neurons. Excessive levels of thrombin can harm neurons and lead to cell death.

“We were able to demonstrate this bidirectional signaling between cell types and really solidify things that had been seen previously in animal experiments, but reproduce them in a model system that we can control with much more detail and better fidelity,” Kamm says.

Plugging the leaks

The researchers then decided to test two drugs that have previously been shown to solidify the blood-brain barrier in simpler models of endothelial tissue. Both of these drugs are FDA-approved to treat other conditions. The researchers found that one of these drugs, etodolac, worked very well, while the other, beclomethasone, had little effect on leakiness in their tissue model.

In tissue treated with etodolac, the blood-brain barrier became tighter, and neurons’ survival rates improved. The MIT and MGH team is now working with a drug discovery consortium to look for other drugs that might be able to restore the blood-brain barrier in Alzheimer’s patients.

“We’re starting to use this platform to screen for drugs that have come out of very simple single cell screens that we now need to validate in a more complex system,” Kamm says. “This approach could offer a new potential form of Alzheimer’s treatment, especially given the fact that so few treatments have been demonstrated to be effective.”

Materials provided by Massachusetts Institute of Technology

Model predicts cognitive decline due to Alzheimer’s, up to two years out

Model predicts cognitive decline due to Alzheimer’s, up to two years out

A new model developed at MIT can help predict if patients at risk for Alzheimer’s disease will experience clinically significant cognitive decline due to the disease, by predicting their cognition test scores up to two years in the future.

The model could be used to improve the selection of candidate drugs and participant cohorts for clinical trials, which have been notoriously unsuccessful thus far. It would also let patients know they may experience rapid cognitive decline in the coming months and years, so they and their loved ones can prepare.

Pharmaceutical firms over the past two decades have injected hundreds of billions of dollars into Alzheimer’s research. Yet the field has been plagued with failure: Between 1998 and 2017, there were 146 unsuccessful attempts to develop drugs to treat or prevent the disease, according to a 2018 report from the Pharmaceutical Research and Manufacturers of America. In that time, only four new medicines were approved, and only to treat symptoms. More than 90 drug candidates are currently in development.

Studies suggest greater success in bringing drugs to market could come down to recruiting candidates who are in the disease’s early stages, before symptoms are evident, which is when treatment is most effective. In a paper to be presented next week at the Machine Learning for Health Care conference, MIT Media Lab researchers describe a machine-learning model that can help clinicians zero in on that specific cohort of participants.

They first trained a “population” model on an entire dataset that included clinically significant cognitive test scores and other biometric data from Alzheimer’s patients, and also healthy individuals, collected between biannual doctor’s visits. From the data, the model learns patterns that can help predict how the patients will score on cognitive tests taken between visits. In new participants, a second model, personalized for each patient, continuously updates score predictions based on newly recorded data, such as information collected during the most recent visits.

Experiments indicate accurate predictions can be made looking ahead six, 12, 18, and 24 months. Clinicians could thus use the model to help select at-risk participants for clinical trials, who are likely to demonstrate rapid cognitive decline, possibly even before other clinical symptoms emerge. Treating such patients early on may help clinicians better track which antidementia medicines are and aren’t working.

“Accurate prediction of cognitive decline from six to 24 months is critical to designing clinical trials,” says Oggi Rudovic, a Media Lab researcher. “Being able to accurately predict future cognitive changes can reduce the number of visits the participant has to make, which can be expensive and time-consuming. Apart from helping develop a useful drug, the goal is to help reduce the costs of clinical trials to make them more affordable and done on larger scales.”

Joining Rudovic on the paper are: Yuria Utsumi, an undergraduate student, and Kelly Peterson, a graduate student, both in the Department of Electrical Engineering and Computer Science; Ricardo Guerrero and Daniel Rueckert, both of Imperial College London; and Rosalind Picard, a professor of media arts and sciences and director of affective computing research in the Media Lab.

Population to personalization

For their work, the researchers leveraged the world’s largest Alzheimer’s disease clinical trial dataset, called Alzheimer’s Disease Neuroimaging Initiative (ADNI). The dataset contains data from around 1,700 participants, with and without Alzheimer’s, recorded during semiannual doctor’s visits over 10 years.

Data includes their AD Assessment Scale-cognition sub-scale (ADAS-Cog13) scores, the most widely used cognitive metric for clinical trials of Alzheimer’s disease drugs. The test assesses memory, language, and orientation on a scale of increasing severity up to 85 points. The dataset also includes MRI scans, demographic and genetic information, and cerebrospinal fluid measurements.

In all, the researchers trained and tested their model on a sub-cohort of 100 participants, who made more than 10 visits and had less than 85 percent missing data, each with more than 600 computable features. Of those participants, 48 were diagnosed with Alzheimer’s disease. But data are sparse, with different combinations of features missing for most of the participants.

To tackle that, the researchers used the data to train a population model powered by a “nonparametric” probability framework, called Gaussian Processes (GPs), which has flexible parameters to fit various probability distributions and to process uncertainties in data. This technique measures similarities between variables, such as patient data points, to predict a value for an unseen data point — such as a cognitive score. The output also contains an estimate for how certain it is about the prediction. The model works robustly even when analyzing datasets with missing values or lots of noise from different data-collecting formats.

But, in evaluating the model on new patients from a held-out portion of participants, the researchers found the model’s predictions weren’t as accurate as they could be. So, they personalized the population model for each new patient. The system would then progressively fill in data gaps with each new patient visit and update the ADAS-Cog13 score prediction accordingly, by continuously updating the previously unknown distributions of the GPs. After about four visits, the personalized models significantly reduced the error rate in predictions. It also outperformed various traditional machine-learning approaches used for clinical data.

Learning how to learn

But the researchers found the personalized models’ results were still suboptimal. To fix that, they invented a novel “metalearning” scheme that learns to automatically choose which type of model, population or personalized, works best for any given participant at any given time, depending on the data being analyzed. Metalearning has been used before for computer vision and machine translation tasks to learn new skills or adapt to new environments rapidly with a few training examples. But this is the first time it’s been applied to tracking cognitive decline of Alzheimer’s patients, where limited data is a main challenge, Rudovic says.

The scheme essentially simulates how the different models perform on a given task — such as predicting an ADAS-Cog13 score — and learns the best fit. During each visit of a new patient, the scheme assigns the appropriate model, based on the previous data. With patients with noisy, sparse data during early visits, for instance, population models make more accurate predictions. When patients start with more data or collect more through subsequent visits, however, personalized models perform better.

This helped reduce the error rate for predictions by a further 50 percent. “We couldn’t find a single model or fixed combination of models that could give us the best prediction,” Rudovic says. “So, we wanted to learn how to learn with this metalearning scheme. It’s like a model on top of a model that acts as a selector, trained using metaknowledge to decide which model is better to deploy.”

Next, the researchers are hoping to partner with pharmaceutical firms to implement the model into real-world Alzheimer’s clinical trials. Rudovic says the model can also be generalized to predict various metrics for Alzheimer’s and other diseases.

Materials provided by Massachusetts Institute of Technology

Alzheimer's detection by virtual reality

Virtual reality can spot navigation problems in early Alzheimer’s disease

Virtual reality (VR) can identify early Alzheimer’s disease more accurately than ‘gold standard’ cognitive tests currently in use, suggests new research from the University of Cambridge.

We’ve wanted to do this for years, but it’s only now that virtual reality technology has evolved to the point that we can readily undertake this research in patients

–Dennis Chan

The study highlights the potential of new technologies to help diagnose and monitor conditions such as Alzheimer’s disease, which affects more than 525,000 people in the UK.

In 2014, Professor John O’Keefe of UCL was jointly awarded the Nobel Prize in Physiology or Medicine for ‘discoveries of cells that constitute a positioning system in the brain’. Essentially, this means that the brain contains a mental ‘satnav’ of where we are, where we have been, and how to find our way around.

A key component of this internal satnav is a region of the brain known as the entorhinal cortex. This is one of the first regions to be damaged in Alzheimer’s disease, which may explain why ‘getting lost’ is one of the first symptoms of the disease. However, the pen-and-paper cognitive tests used in clinic to diagnose the condition are unable to test for navigation difficulties.

In collaboration with Professor Neil Burgess at UCL, a team of scientists at the Department of Clinical Neurosciences at the University of Cambridge led by Dr Dennis Chan, previously Professor O’Keefe’s PhD student, developed and trialled a VR navigation test in patients at risk of developing dementia. The results of their study are published today in the journal Brain.

In the test, a patient dons a VR headset and undertakes a test of navigation while walking within a simulated environment. Successful completion of the task requires intact functioning of the entorhinal cortex, so Dr Chan’s team hypothesised that patients with early Alzheimer’s disease would be disproportionately affected on the test.

The team recruited 45 patients with mild cognitive impairment (MCI) from the Cambridge University Hospitals NHS Trust Mild Cognitive Impairment and Memory Clinics. Patients with MCI typically exhibit memory impairment, but while MCI can indicate early Alzheimer’s, it can also be caused by other conditions such as anxiety and even normal aging. As such, establishing the cause of MCI is crucial for determining whether affected individuals are at risk of developing dementia in the future.

The researchers took samples of cerebrospinal fluid (CSF) to look for biomarkers of underlying Alzheimer’s disease in their MCI patients, with 12 testing positive. The researchers also recruited 41 age-matched healthy controls for comparison.

All of the patients with MCI performed worse on the navigation task than the healthy controls. However, the study yielded two crucial additional observations. First, MCI patients with positive CSF markers – indicating the presence of Alzheimer’s disease, thus placing them at risk of developing dementia – performed worse than those with negative CSF markers at low risk of future dementia.

Secondly, the VR navigation task was better at differentiating between these low and high risk MCI patients than a battery of currently-used tests considered to be gold standard for the diagnosis of early Alzheimer’s.

“These results suggest a VR test of navigation may be better at identifying early Alzheimer’s disease than tests we use at present in clinic and in research studies,” says Dr Chan.

VR could also help clinical trials of future drugs aimed at slowing down, or even halting, progression of Alzheimer’s disease. Currently, the first stage of drug trials involves testing in animals, typically mouse models of the disease. To determine whether treatments are effective, scientists study their effect on navigation using tests such as a water maze, where mice have to learn the location of hidden platforms beneath the surface of opaque pools of water. If new drugs are found to improve memory on this task, they proceed to trials in human subjects, but using word and picture memory tests. This lack of comparability of memory tests between animal models and human participants represents a major problem for current clinical trials.

“The brain cells underpinning navigation are similar in rodents and humans, so testing navigation may allow us to overcome this roadblock in Alzheimer’s drug trials and help translate basic science discoveries into clinical use,” says Dr Chan. “We’ve wanted to do this for years, but it’s only now that VR technology has evolved to the point that we can readily undertake this research in patients.”

In fact, Dr Chan believes technology could play a crucial role in diagnosing and monitoring Alzheimer’s disease. He is working with Professor Cecilia Mascolo at Cambridge’s Centre for Mobile, Wearable Systems and Augmented Intelligence to develop apps for detecting the disease and monitoring its progression. These apps would run on smartphones and smartwatches. As well as looking for changes in how we navigate, the apps will track changes in other everyday activities such as sleep and communication.

“We know that Alzheimer’s affects the brain long before symptoms become apparent,” says Dr Chan. “We’re getting to the point where everyday tech can be used to spot the warning signs of the disease well before we become aware of them.

“We live in a world where mobile devices are almost ubiquitous, and so app-based approaches have the potential to diagnose Alzheimer’s disease at minimal extra cost and at a scale way beyond that of brain scanning and other current diagnostic approaches.”

The VR research was funded by the Medical Research Council and the Cambridge NIHR Biomedical Research Centre. The app-based research is funded by the Wellcome, the European Research Council and the Alan Turing Institute.

Howett, D, Castegnaro, A, et al. Differentiation of mild cognitive impairment using an entorhinal cortex based test of VR navigation. Brain; 28 May 2019; DOI: 10.1093/brain/awz116

Materials provided by University of Cambridge