Early Diagnostic Tool for Alzheimer's Disease on the Anvil

A team of researchers has found a promising diagnostic tool to help in the early diagnosis of Alzheimer as well assess the severity of the disease.

Alzheimer's is a common disease among older people. Though numerous studies have been done on this disorder no early detection method has been found so far. Almost 60 per cent to 80 percent of all dementia cases are diagnosed with Alzheimer's.

People who suffer from Alzheimer's disease currently undergo a neuropsychological testing the results of which are sometimes inconclusive and difficult to interpret.

Studies conducted earlier have identified the regions of cerebral cortex that is mainly affected in the initial stages of the disease.

A new study  done by Professor Tiago H. Falk of INRS's Centre Énergie Materiaux Telecommunications looked at the electroencephalograms (EEGs) or brain waves of people with the disease.  The research participants included 27 healthy individuals, 22 people with moderate Alzheimer's and 27 people with mild Alzheimer's. Dr Falk along with a team of researchers compared the electroencephalograms of healthy individuals, individuals with mild Alzheimer's and those with moderate level of the disease. On analyzing the data the team noticed a statistically significant difference in the three groups. With the help of an algorithm the researchers dissected the brain waves of different frequencies.

Professor Falk said, "What makes this algorithm innovative is that it characterizes the changes in temporal dynamics of the patients' brain waves. The findings show that healthy individuals have different patterns than those with mild Alzheimer's disease. We also found a difference between patients with mild levels of the disease and those with moderate Alzheimer's."

In order to validate the model, the research team has shared the algorithm on the NeuroAccelerator.org online data analysis portal. It is the first open source algorithm that is posted which the researchers around the globe can have an access to.

The article appears in the journal PLOS ONE.