A virtual brain helps decrypt epilepsy

Researchers at CNRS, INSERM, Aix-Marseille University and AP-HM have just created a virtual brain that can reconstitute the brain of a person affected by epilepsy for the first time. From this work we understand better how the disease works and can also better prepare for surgery.

Worldwide, one percent of the population suffers from epilepsy. The disease affects individuals differently, so personalized diagnosis and treatment are important. Currently we have few ways to understand the pathology’s mechanisms of action, and mainly use visual interpretation of an MRI and electroencephalogram. This is especially difficult because 50% of patients do not present anomalies visible in MRI, so the cause of their epilepsy is unknown.
Researchers have succeeded for the first time in developing a personalized virtual brain, by designing a base “template” and adding individual patient information, such as the specific way the brain’s regions are organized and connected in each individual. Mathematical models that cause cerebral activity can be tested on the virtual brain. In this way, scientists have been able to reproduce the place where epilepsy seizures initiate and how they propagate. This brain therefore has real value in predicting how seizures occur in each patient, which could lead to much more precise diagnosis.

Moreover, 30% of epileptic patients do not respond to drugs, so their only hope remains surgery. This is effective if the surgeon has good indications of where to operate. The virtual brain gives surgeons a virtual “platform.” In this way they can determine where to operate while avoiding invasive procedures, and especially prepare for the operation by testing different surgical possibilities, seeing which would be most effective and what the consequences would be, something that is obviously impossible to do on the patient.
In the long run, the team’s goal is to provide personalized medicine for the brain, by offering virtual, tailored, therapeutic solutions that are specific for each patient. The researchers are currently working on clinical trials to demonstrate the predictive value of their discovery. This technology is also being tested on other pathologies that affect the brain, such as strokes, Alzheimer’s, degenerative neurological diseases, and multiple sclerosis.

A press release can be found on The National Center for Scientific Research (CNRS) website.

Artificial Intelligence May Aid in Alzheimer’s Diagnosis

Machine learning is a type of artificial intelligence that allows computer programs to learn when exposed to new data without being programmed. Now, researchers in The Netherlands have coupled machine learning methods with a special MRI technique that measures the perfusion, or tissue absorption rate, of blood throughout the brain to detect early forms of dementia, such as mild cognitive impairment (MCI), Radiology.

     

"MRI can help with the diagnosis of Alzheimer's disease," said principal investigator Alle Meije Wink, Ph.D., from the VU University Medical Centre in Amsterdam. "However, the early diagnosis of Alzheimer's disease is problematic."

Scientists have long known that Alzheimer's disease is a gradual process and that the brain undergoes functional changes before the structural changes associated with the disease show up on imaging results. Physicians have no definitive way of identifying who has early dementia or which cases of mild cognitive impairment will progress to Alzheimer's disease.

"With standard diagnostic MRI, we can see advanced Alzheimer's disease, such as atrophy of the hippocampus," Dr. Meije Wink said. "But at that point, the brain tissue is gone and there's no way to restore it. It would be helpful to detect and diagnose the disease before it's too late."

For the new study, the researchers applied machine learning methods to special type of MRI called arterial spin labeling (ASL) imaging. ASL MRI is used to create images called perfusion maps, which show how much blood is delivered to various regions of the brain.

The automated machine learning program is taught to recognize patterns in these maps to distinguish among patients with varying levels of cognitive impairment and predict the stage of Alzheimer's disease in new (unseen) cases.

The study included 260 of 311 participants from the Alzheimer Center of the VU University Medical Center dementia cohort who underwent ASL MRI between October 2010 and November 2012.

The study group included 100 patients diagnosed with probable Alzheimer's disease, 60 patients with mild cognitive impairment (MCI) and 100 patients with subjective cognitive decline (SCD), and 26 healthy controls.

SCD and MCI are considered to be early stages of the dementia process and are diagnosed based on the severity of cognitive symptoms, including memory loss and thought- and decision-making problems.

The automated system was able to distinguish effectively among participants with Alzheimer's disease, MCI and SCD. Using classifiers based on the automated machine learning training, the researchers were then able to predict the Alzheimer's diagnosis or progression of single patients with a high degree of accuracy, ranging from 82 percent to 90 percent.

"ASL is a promising alternative functional biomarker for the early diagnosis of Alzheimer's disease," Dr. Meije Wink said.

He added that the application of automated machine learning methods would be useful as a potential screening tool.

"ASL MRI can identify brain changes that appear early in disease process, when there's a window of opportunity for intervention," Dr. Meije Wink said. "If the disease process from SCD to MCI to Alzheimer's disease could be intercepted or slowed, this technique could play a role in screening."

More information can be found from RSNA website.