Detecting amyloid positivity from MRI scans using comprehensible convolutional neural networks
Year: 2024
Type: article
Abstract: Background Analysis of neuroimaging data based on convolutional neural networks (CNNs) can improve detection of clinically relevant characteristics of patients with Alzheimer’s disease (AD). Previously, our group developed a CNN‐based approach for detecting AD via magnetic resonance imaging (MRI) scans and for identifying features that are relevant to the decision of the network. In the current study, we aimed to evaluate the potential utility of applying this approach to MRI scans to assist in the identification of individuals at high risk for amyloid positivity to aid in the selection of study samples and case finding for treatment. Method In the current analysis, we have trained a CNN to detect amyloid positivity using MRI scans from 1461 Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants (498 cognitively normal participants, 103 participants with significant memory concern, 640 participants with mild cognitive impairment, and 220 participants with AD dementia). Amyloid positivity was assessed via amyloid PET scans obtained with [18F]florbetapir or [18F]florbetaben and quantified on a Centiloid scale. A threshold of 24.1 CL categorized 46% of participants as amyloid‐positive. The modeling approach was evaluated using 10‐fold cross‐validation, the number of epochs in training was set to 10. Result For each of 10 cross‐validation folds, we selected a model state corresponding to an epoch showing best performance in the validation partition. Balanced accuracy across these models ranged from 0.62 to 0.72 with an average of 0.68 (SD = 0.03). Conclusion We used a previously established approach to train CNNs for detecting amyloid positivity using MRI scans. Such models, particularly when tuned to have low rates of false negatives, have a potential to enhance identification of patients who would benefit from more in‐depth assessments, which could then inform antibody treatment. We are conducting ongoing work to improve and characterize the modeling approach, including evaluation of relevance maps which indicate importance of brain regions for detecting amyloid positivity. Future work will evaluate the role of amyloid positivity threshold selection. Planned analyses also include validation in independent data such as the German DZNE ‐ Longitudinal Cognitive Impairment and Dementia Study (DELCODE) dataset.
Source: Alzheimer s & Dementia
DOI: 10.1002/alz.089044
Authors: Fedor Levin, Qingyue Li, Devesh Singh, Katharina Buerger, Klaus Fliessbach, Michael T. Heneka, Luca Kleineidam, Christoph Laske, Robert Perneczky, Oliver Peters, Josef Priller, Alfredo Ramirez, Anja Schneider, Annika Spottke, Michael Wagner, Jens Wiltfang, Emrah Düzel, Frank Jessen, Stefan Teipel, Martin Dyrba, the DELCODE Study Group
Institutions: German Center for Neurodegenerative Diseases, Universitätsmedizin Rostock, Institut für Schlaganfall- und Demenzforschung, Ludwig-Maximilians-Universität München, University of Bonn, University of Luxembourg, Luxembourg Centre for Systems Biomedicine, Hertie Institute for Clinical Brain Research, University of Tübingen, LMU Klinikum, Munich Cluster for Systems Neurology, Humboldt-Universität zu Berlin, Freie Universität Berlin, Charité - Universitätsmedizin Berlin, UK Dementia Research Institute, Stiftung Charité, Technical University of Munich, University of Edinburgh, University of Cologne, Cologne Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases, University Hospital Cologne, Universitätsmedizin Göttingen, University of Aveiro, University of Göttingen, Otto-von-Guericke-Universität Magdeburg
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,000
OpenAlex ID: W4406209815