Data-Driven Phenotyping of Alzheimer’s Disease under Epigenetic Conditions Using Partial Volume Correction of PET Studies and Manifold Learning

• Published in: Biomedicines Vol. 11; no. 2; p. 273
• Main Authors: Campanioni, Silvia, González-Nóvoa, José A., Busto, Laura, Agís-Balboa, Roberto Carlos, Veiga, César
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 19.01.2023; MDPI
• Subjects: Alzheimer's disease; Artificial intelligence; Brain research; Cognitive ability; Computational neuroscience; data-driven AD phenotyping; Datasets; Dementia; Dementia disorders; Discriminant analysis; Embedding; Environmental effects; Epigenetics; Magnetic resonance imaging; manifold learning; Methods; Neurodegenerative diseases; Neuroimaging; Partial Volume Correction (PVC); Patients; PET; Phenotypes; Phenotyping; Physiological aspects; Positron emission tomography; Software packages; Spain; Partial Volume Correction (PVC); PET; artificial intelligence; data-driven AD phenotyping; manifold learning
• ISSN: 2227-9059; 2227-9059
• DOI: 10.3390/biomedicines11020273

Alzheimer’s disease (AD) is the most common form of dementia. An increasing number of studies have confirmed epigenetic changes in AD. Consequently, a robust phenotyping mechanism must take into consideration the environmental effects on the patient in the generation of phenotypes. Positron Emission Tomography (PET) is employed for the quantification of pathological amyloid deposition in brain tissues. The objective is to develop a new methodology for the hyperparametric analysis of changes in cognitive scores and PET features to test for there being multiple AD phenotypes. We used a computational method to identify phenotypes in a retrospective cohort study (532 subjects), using PET and Magnetic Resonance Imaging (MRI) images and neuropsychological assessments, to develop a novel computational phenotyping method that uses Partial Volume Correction (PVC) and subsets of neuropsychological assessments in a non-biased fashion. Our pipeline is based on a Regional Spread Function (RSF) method for PVC and a t-distributed Stochastic Neighbor Embedding (t-SNE) manifold. The results presented demonstrate that (1) the approach to data-driven phenotyping is valid, (2) the different techniques involved in the pipelines produce different results, and (3) they permit us to identify the best phenotyping pipeline. The method identifies three phenotypes and permits us to analyze them under epigenetic conditions.