Machine Learning Classification Identifies Cerebellar Contributions to Early and Moderate Cognitive Decline in Alzheimer's Disease.
Alzheimer’s disease
MCI (mild cognitive impairment)
cerebellum
dementia
gray matter (GM)
machine learning
mild moderate AD
white matter (WM)
Journal
Frontiers in aging neuroscience
ISSN: 1663-4365
Titre abrégé: Front Aging Neurosci
Pays: Switzerland
ID NLM: 101525824
Informations de publication
Date de publication:
2020
2020
Historique:
received:
01
01
2020
accepted:
28
09
2020
entrez:
26
11
2020
pubmed:
27
11
2020
medline:
27
11
2020
Statut:
epublish
Résumé
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 gray or white matter components involved, remains unclear. We used MRI machine learning-based classification to assess the contribution of two tissue components [volume fraction myelin (VFM), and gray matter (GM) volume] within the whole brain, the neocortex, the whole cerebellum as well as its anterior and posterior parts and their predictive contribution to the first two stages of AD and typically aging controls. While classification accuracy increased with AD stages, VFM was the best predictor for all early stages of dementia when compared with typically aging controls. However, we document overall higher cerebellar prediction accuracy when compared to the whole brain with distinct structural signatures of higher anterior cerebellar contribution to mild cognitive impairment (MCI) 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.
Identifiants
pubmed: 33240072
doi: 10.3389/fnagi.2020.524024
pmc: PMC7669549
doi:
Types de publication
Journal Article
Langues
eng
Pagination
524024Informations de copyright
Copyright © 2020 Bruchhage, Correia, Malloy, Salloway and Deoni.
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