Computational Approaches Applied in the Field of Neuroscience

Abstract

Dementia describes a group of symptoms linked with cognitive decline. Alzheimer’s disease (AD) is the most common form of dementia. Identifying accurate diagnostic biomarkers is a key goal. Technological advancements result in the generation of an ever-increasing volume of data. An interdisciplinary field of bioinformatics, known as machine learning (ML), allows scientists to explore and analyse said data. ML is broadly categorized into two groups: (i) unsupervised learning and (ii) supervised learning. This paper focuses on supervised learning methodologies. These approaches are not only helpful for biomarker discovery but for neuroimaging studies as well since they are able to analyse many variables simultaneously and to identify patterns in neuroimaging data. Furthermore, this paper lists several other computational approaches used for dementia care.