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Abstract #2043

USING MACHINE LEARNING TO CLASSIFY EARLY STAGES OF COGNITIVE DECLINE FROM TYPICAL AGEING - THE CEREBELLUM MORE THAN JUST A BYSTANDER

Muriel Marisa Katharina Bruchhage1,2, Stephen Correla3, Paul Malloy4, Stephen Salloway5, and Sean Deoni2,6

1Centre for Neuroimaging Sciences, King's College London, London, United Kingdom, 2Advanced Baby Imaging Lab, Memorial Hospital of Rhode Island, Providence, RI, United States, 3Veterans Affairs Medical Center, Providence, RI, United States, 4Neurology, Butler Hospital, Providence, RI, United States, 5Human Behavior and Psychiatry, Warren Alpert Medical School at Brown University, Providence, RI, United States, 6Warren Alpert Medical School at Brown University, Providence, RI, United States

Alzheimer’s disease (AD) is one of the most common forms of dementia, marked by progressively degrading cognitive function. The cerebellum plays a role in AD development, but its predictive contribution to early stages of AD remains unclear. We used MRI machine learning based classification within myelin and grey matter of the whole, anterior and posterior cerebellum and the whole brain, between individuals within the first two early stages of dementia and typically ageing controls. Our findings suggest myelin and grey matter loss in early stages of AD, with distinct patterns of anterior and posterior cerebellar atrophy for each tissue property.

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