AUTHOR=Bruchhage Muriel M. K. , Correia Stephen , Malloy Paul , Salloway Stephen , Deoni Sean 

TITLE=Machine Learning Classification Identifies Cerebellar Contributions to Early and Moderate Cognitive Decline in Alzheimer’s Disease

JOURNAL=Frontiers in Aging Neuroscience

VOLUME=Volume 12 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2020.524024

DOI=10.3389/fnagi.2020.524024

ISSN=1663-4365

ABSTRACT=Alzheimer’s disease (AD) is one of the most common forms of dementia, marked by progressively degrading cognitive function. Although cerebellar changes occur throughout AD progression, its involvement and predictive contribution in its earliest stages as well as the tissue types involved remains unclear. We used MRI machine learning based classification to assess the contribution of two tissue types (myelin water fraction and grey matter) within the whole brain, the whole cerebellum as well as its anterior and posterior parts and their predictive contribution to the first two stages of AD and typically ageing controls. While classification accuracy increased with staging, WMF was the best predictor for all early stages of dementia when compared with typically ageing controls. However, we document overall stronger cerebellar classification when compared to the whole brain with distinct structural signatures of higher anterior cerebellar contribution to mild cognitive impairment and higher posterior cerebellar contribution to mild/moderate stages of AD for each tissue property. Based on these different cerebellar profiles and their unique contribution to early disease stages, we propose a refined model of cerebellar contribution to early AD development.