Four Types of Alzheimer's Disease and How Machine Learning Helped Identify Them

Alzheimer's Disease remains one of the most common brain disorders affecting people, especially the elderly, worldwide - and a new study reports that there's not only one, but there are four different types of these progressive brain disorders.

The currently irreversible brain condition has been characterized by slowly declining memory, cognitive capabilities, which eventually lead to the incapability to perform even the simplest type. As mankind learns more about this disease, the better we can address this condition and hopefully in the near future, develop a cure for it. This makes the new discovery particularly important progress in the study of the disease.

A report appearing in the latest Nature Medicine, published last April 29, presents findings from an international team of researchers - including those from the McGill University in Canada, the King's College London in the UK, Skåne University Hospital in Sweden, Yonsei University College of Medicine in South Korea, as well as members of AVID Radiopharmaceuticals and the Alzheimer's Disease Neuroimaging Initiative.

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Finding Distinct Types Using Technology

In the study titled "Four distinct trajectories of tau deposition identified in Alzheimer's disease," researchers explain how Alzheimer's disease is "characterized by the spread of tau pathology throughout the cerebral cortex."

The brain has a member of the microtubule-associated family called the "Tau protein," which is involved in a number of neurodegenerative diseases like Parkinson's and Alzheimer's disease.

Tau pathology refers to the existence of a pathological aggregation of these proteins in the neurofibrillary tangles (NFTs). These misshapen proteins and the pattern of how they get tangled have long been previously believed to be more or less similar to people having neurodegenerative disease.

This particular phenomenon, which develops in cases of Alzheimer's disease, was examined by the researchers with help from specially-developed machine learning algorithms. The machine learning tool was trained to analyze brain scans of 1,143 people - a mixed data set of healthy brains and those diagnosed with Alzheimer's disease.

Four Subtypes of Alzheimer's Disease

"We identified four clear patterns of tau pathology that became distinct over time," said Oskar Hansson, co-author of the study and a neurologist from the Clinical Memory Unit at the Lund University, in a press release from the Swedish university.

Hansson additionally explains that the prevalence of the subgroups was anywhere from 18 to 30 percent of the cases in the study. This means that all of the subtypes of the disease appear to be almost equally common, with no single subtype dominating over the others.

The first variant, Subtype 1: Limbic, was found in 33 percent of the cases. It was characterized by pathologic tau spread mostly within the brain's temporal lobe and is affecting patient memory. It is followed by the Subtype, MTL-Sparing, which was present in 18 percent of the cases and spreads across other sections of the cerebral cortex. Under these cases, memory problems become less common but are dominated by difficulties in planning and performing actions.

The third, Subtype 3: Posterior, was found in 30 percent of the cases - tau proteins spreading in the visual cortex, which is the brain's region for processing eyesight. In this case, patients experience difficulties in orientation, depth and distance perception, and processing shapes. The last one, Subtype 4: L Temporal, was only detected in 19 percent of cases and is asymmetrically spread in the left hemisphere, affecting speech and language.

"We now have reason to reevaluate the concept of typical Alzheimer's, and in the long run also the methods we use to assess the progression of the disease," commented Jacob Vogel, co-author of the study from McGill University.