Affiliations: [a] Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, Barcelona, Spain | [b] Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain | [c] Centro de Investigación Biomédica en Red Bioingenieria, Biomateriales y Nanomedicina (CIBER-BBN), Spain | [d] Alzheimer’s Disease and Other Cognitive Disorders Unit, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pii Sunyer (IDIBAPS), Barcelona, Spain | [e] CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
Correspondence:
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Correspondence to: Dr. Juan Domingo Gispert, BarcelonaBeta Brain Research Centre - Pasqual Maragall Foundation, C/ Wellington 30, 08005 Barcelona, Spain. Tel.: +34 93 3160990;E-mail: jdgispert@barcelonabeta.org and Dr. Veronica Vilaplana, Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, C/ Jordi Girona 1-3, edifici D5 Campus Nord UPC, 08034 Barcelona, Spain. E-mail: veronica.vilaplana@upc.edu.
Note: [1] These authors contributed equally to this work.
Note: [2] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
Abstract: The identification of healthy individuals harboring amyloid pathology represents one important challenge for secondary prevention clinical trials in Alzheimer’s disease (AD). Consequently, noninvasive and cost-efficient techniques to detect preclinical AD constitute an unmet need of critical importance. In this manuscript, we apply machine learning to structural MRI (T1 and DTI) of 96 cognitively normal subjects to identify amyloid-positive ones. Models were trained on public ADNI data and validated on an independent local cohort. Used for subject classification in a simulated clinical trial setting, the proposed method is able to save 60% of unnecessary CSF/PET tests and to reduce 47% of the cost of recruitment. This recruitment strategy capitalizes on available MR scans to reduce the overall amount of invasive PET/CSF tests in prevention trials, demonstrating a potential value as a tool for preclinical AD screening. This protocol could foster the development of secondary prevention strategies for AD.
