EEG-based classification of alzheimer’s disease and frontotemporal dementia using functional connectivity

By: Tjaša Mlinarič, Arne Van Den Kerchove, Zoe I. Barinaga & Marc M. Van Hulle

The Story

Brain activity recorded by electroencephalography (EEG) is an accessible method to evaluate cognitive functions. We investigated whether resting-state EEG can help in diagnosing two types of dementia: Alzheimer's disease and frontotemporal dementia. These dementias are often hard to differentiate based on symptoms alone, but require different therapy. We designed a machine learning model that relies on various metrics of connectivity to characterize and interpret the affected brain networks, and to classify them using novel techniques from the field of Brain-Computer Interfacing.

The Impact

We trained and evaluated our method on a large open dataset and found that it can effectively detect the presence of both dementias. While Alzheimer's disease and frontotemporal dementia share significant overlapping characteristics, our approach also makes advancements towards distinguishing them from each other and highlights the brain networks involved in this task.

VSC Contribution

Massive parallel computation on VSC cluster drastically sped prototyping and enabled proper validation of our results.

Read the full scientific publication in Springer Nature here

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