Detecting Amyloid Positivity Using Morphometric Magnetic Resonance Imaging

Article type: Research Article

Authors: Pereira, Helena Rico^{a; b} | Diogo, Vasco Sá^{a; c} | Prata, Diana^{a; d; e; 1; *} | Ferreira, Hugo Alexandre^{a; 1} | for the Alzheimer’s Disease Neuroimaging Initiative

Affiliations: [a] Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências da Universidade de Lisboa, Lisbon, Portugal | [b] Faculdade de Ciências e Tecnologia e UNINOVA-CTS, Universidade Nova de Lisboa, Caparica, Portugal | [c] Instituto Universitário de Lisboa (Iscte-IUL), CIS-Iscte, Lisbon, Portugal | [d] Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK | [e] Laboratório de Instrumentação, Engenharia Biomédica e da Física das Radiações, No pólo da Universidade Nova (LIBPhys-UNL), Lisbon, Portugal

Correspondence: [*] Correspondence to: Diana Prata, PhD, Campo Grande, C1 building, 3th floor; 1749-016 Lisbon, Portugal. E-mails: dmprata@fc.ul.pt, diana.prata@kcl.ac.uk.

Note: [1] These authors contributed equally to this work.

Abstract: Background:Early detection of amyloid-β (Aβ) positivity is essential for an accurate diagnosis and treatment of Alzheimer’s disease (AD), but it is currently costly and/or invasive. Objective:We aimed to classify Aβ positivity (Aβ+) using morphometric features from magnetic resonance imaging (MRI), a more accessible and non-invasive technique, in two clinical population scenarios: one containing AD, mild cognitive impairment (MCI) and cognitively normal (CN) subjects, and another only cognitively impaired subjects (AD and MCI). Methods:Demographic, cognitive (Mini-Mental State Examination [MMSE] scores), regional morphometry MRI (volumes, areas, and thicknesses), and derived morphometric graph theory (GT) features from all subjects (302 Aβ+, age: 73.3±7.2, 150 male; 246 Aβ–, age: 71.1±7.1, 131 male) were combined in different feature sets. We implemented a machine learning workflow to find the best Aβ+ classification model. Results:In an AD+MCI+CN population scenario, the best-performing model selected 120 features (107 GT features, 12 regional morphometric features and the MMSE total score) and achieved a negative predictive value (NPVadj) of 68.4%, and a balanced accuracy (BAC) of 66.9%. In a AD+MCI scenario, the best model obtained NPVadj of 71.6%, and BAC of 70.7%, using 180 regional morphometric features (98 volumes, 52 areas and 29 thicknesses from temporal, parietal, and frontal brain regions). Conclusions:Although with currently limited clinical applicability, regional MRI morphometric features have clinical usefulness potential for detecting Aβ status, which may be augmented by a combination with cognitive data when cognitively normal subjects make up a substantial part of the population presenting for diagnosis.

Keywords: Alzheimer’s disease, amyloid-β, dementia, diagnostic imaging, machine learning

DOI: 10.3233/JAD-240366

Journal: Journal of Alzheimer's Disease, vol. 101, no. 4, pp. 1293-1305, 2024