A machine-learning approach for monitoring the progression of Alzheimer's disease medrxivpreprint alzheimers disease AD machinelearning ML artificialintelligence AI health
By Neha MathurJul 19 2023Reviewed by Benedette Cuffari, M.Sc. In a recent article posted to the medRxiv* preprint server, researchers evaluate the predictive value of a multidimensional machine learning approach for monitoring the progression of Alzheimer’s disease .
Background AD patients often experience significant changes in their personality as the disease progresses. These include increased neuroticism, as well as reduced agreeableness, extraversion, conscientiousness, and openness. About the study To train the ML approach for the current study, the researchers utilized voxel-wise resting state functional magnetic resonance imaging , the ‘Big Five’ personality traits, depression, anxiety, apolipoprotein E genotype, and CSF biomarkers.
Study findings All feature sets demonstrated DA significantly above chance, with the highest DA observed for four feature sets including personality extended, CSF consisting of tTau, pTau, and Aβ42/40 ratio, ApoE, as well as depression and anxiety scores. Notably, mPerAF performed above chance; however, its performance did not vary from the base model. Most previous DMN studies focused on functional connectivity, rather than voxel-wise amplitude measures, such as mPerAF; therefore, the predictive ability of resting-state fMRI of the DMN for AD led to inconsistent findings. The current study approach resembled an automated diagnostic process and did not make dichotomous decisions across groups.
Conclusions None of the combinations of the evaluated feature sets consistently attained superior class accuracies for all participant groups. CSF biomarkers most effectively classified participants with mild AD, whereas depression and anxiety scores combined effectively classified healthy controls, thus reaffirming that late-life depression is a prodrome of AD.
Belgique Dernières Nouvelles, Belgique Actualités
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