PRedicting the EVolution of SubjectIvE Cognitive Decline to Alzheimer’s Disease With machine learning: the PREVIEW study protocol

Abstract

AbstractBackground and aimsSubjective Cognitive Decline (SCD) is a condition in which individual complain of cognitive decline with normal performances on neuropsychological evaluation. Many studies demonstrated a higher prevalence of Alzheimer’s pathology in patients diagnosed with SCD as compared to the general population. Consequently, SCD was suggested as an early symptomatic phase of Alzheimer’s disease (AD). We will describe the study protocol of a prospective cohort study (PREVIEW) that aim to identify features and tools to accurately detect SCD patients who will progress to AD.MethodsWe will include patients self-referred to our memory clinic and diagnosed with SCD. Participants will undergo: clinical, neurologic and neuropsychological examination, estimation of cognitive reserve and depression, evaluation of personality traits,APOEandBDNFgenotyping, electroencephalography and event-related potential recording, lumbar puncture for measurement of Aβ42, t-tau, and p-tau concentration and Aβ42/Aβ40ratio. Recruited patients will have follow-up neuropsychological examination every two years. Collected data will be used to train a machine learning algorithm to define the risk of progression from SCD to MCI and AD.DiscussionThere is an urgent need to select cost-effective and easily accessible tools to identify patients at the earliest stages of the disease. Previous studies identified demographic, cognitive, genetic, neurophysiological and brain structure features to stratify SCD patients according to the risk of progression to objective cognitive decline. Nevertheless, only a few studies considered all these features together and applied machine learning approaches on SCD patients.Conclusionsthe PREVIEW study aim to identify new cost-effective disease biomarkers (e.g., EEG-derived biomarkers) and define automated algorithm to detect patients at risk for AD in a very early stage of the disease.