Using the ATN system as a guide for the neuropsychological assessment of Alzheimer's disease. Issue 9 (21st October 2021)

• Record Type: Journal Article
• Authors: Florean, Irene; Penolazzi, Barbara; Menichelli, Alina; Pastore, Massimiliano; Cattaruzza, Tatiana; Mazzon, Giulia; Manganotti, Paolo
• Is Part Of: Journal of clinical and experimental neuropsychology. Volume 43:Issue 9(2021)
• Page Start: 926
• Page End: 943
• Publication Date: 2021-10-21
• Subjects: Alzheimer's disease -- neuropsychological assessment -- CSF biomarkers -- diagnosis -- AT(N) scheme -- ATN scheme; Neuropsychology -- Periodicals; Psychophysiology -- Periodicals; Neurosciences -- Periodicals; Psychophysiology -- Periodicals; Societies, Medical -- Periodicals; 616.89
• DOI: 10.1080/13803395.2022.2036327
• Languages: English
• ISSNs: 1380-3395

Abstract

ABSTRACT: Introduction: Many studies have attempted to determine whether Alzheimer's disease (AD) in-vivo biomarkers can predict neuropsychological performance since pathophysiological changes precede cognitive changes by several years. Nonetheless, neuropsychological measures can also detect cognitive deterioration in cognitively normal individuals with AD-positive biomarkers. Recent studies have investigated whether cognitive measures can be used as a proxy for biomarkers. This is a crucial issue since biomarker analysis is expensive, invasive, and not yet widespread in clinical practice. However, these studies have so far considered only one or two classes of AD biomarkers. Here, we aim at preliminarily evaluating whether and which neuropsychological measures can discriminate individuals that have been classified according to the full scheme of biomarkers known as ATN system. This scheme groups biomarkers as a function of the three main AD-related pathologic processes they measure (i.e., β-amyloidosis, tauopathy, and neurodegeneration) to provide an unbiased and descriptive definition of the Alzheimer's continuum. Method: Biomarkers and neuropsychological data from 78 patients (70.01 ± 9.15 years; 38 females) with suspected cognitive decline were extracted from a medical database. Participants' biomarker profiles were classified into the following ATN categories: normal AD biomarkers; Alzheimer's continuum; non-AD pathologic change. Data were analyzed using a Bayesian approach, to guarantee reliable result interpretation of data stemming from small samples. Results: The discrimination ability of each neuropsychological measure varied depending on the pairs of ATN categories compared. The best-discriminating predictor in the Alzheimer's continuum vs. normal biomarkers comparison was the figure naming ability. In contrast, in the Alzheimer's continuum vs. non-AD pathologic change comparison the best predictor was the wordlist forgetting rate. Conclusions: Although the study was exploratory in nature, the proposed methodological approach may have the potential to identify the best neuropsychological measures for estimating AD neuropathological changes, leading to a more biologically informed use of neuropsychological assessment.