Principal component analysis of brain metabolism in symptomatic Alzheimer's disease patients: A positron emission tomography with 18F‐fluorodeoxyglucose (PET‐FDG) study: Neuroimaging / Optimal neuroimaging measures for tracking disease progression. (7th December 2020)

• Record Type: Journal Article
• Authors: Demey, Ignacio; Mendez, Patricio Alexis Chrem; Bérgamo, Yanina; Urrutia, Leandro; Campos, Jorge; Falasco, German; Sevlever, Gustavo; Allegri, Ricardo F; Vazquez, Silvia
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 5
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.038843
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Patients with symptomatic Alzheimer's Disease (AD) usually present alterations in their cerebral metabolism measured by Positron Emission Tomography with 18F‐fluorodeoxyglucose (PET‐FDG). In the amnestic AD phenotype, the regional differences reported between patients with Mild Cognitive Impairment due to Alzheimer's Disease (MCI‐AD) and AD Dementia (ADD) have not been consistent. Within the ATN scheme, this study detects Neurodegeneration (N). There are no published studies that correlated the metabolic value of each cortical region and the quantification of cognitive performance. Objective: To analyze brain metabolism by regions measured by Positron Emission Tomography with PET‐FDG in patients with MCI‐AD and ADD, and correlate the regional metabolic values with the severity of neuropsychological clinical variables. Method: In this cross‐sectional study, 24 patients with MCI‐AD and 22 with ADD were included, all right‐handed and PET–PIB positive, matched by sex, age, and education, with differences in different cognitive variables (p<0.01). All were studied with PET‐FDG with automated parcellation using the Klein and Tourville atlas. Differences between groups (Anova) were analyzed, and a Principal Component (PC) analysis was performed to summarize the vectors of higher variability. Vectors were compared through diagnoses and correlated with neuropsychological evaluation and other clinical aspects. Result: Patients with ADD showed lower metabolism than patients with MCI‐AD (p<0.05) in different regions of interest. Four PCs were identified that summarized 84.77% of the metabolism variability of the cortices analyzed. No significant differences were found in the magnitude of the CP between MCI‐AD and ADD patients (p>0.05). The first CP represented the magnitude of the overall metabolism and correlated with the age of the patients (p:0.04), the second CP weighted negatively frontal and positively occipital metabolism, and correlated negatively with semantic fluency (p:0.02), phonological fluency (p:0.04) and TMT‐A (p:0.003). The third CP considered parahippocampal regions and correlated with recognition in verbal episodic memory (p:0.01). Conclusion: Dimension reduction analysis represents a novel approach to summarize neuroimaging data. In this sample of patients with PET‐PIB positive and symptomatic AD, CP extraction demonstrates a close correlation between regional metabolism and performance in neuropsychological tests associated with those regions.