Prediction of amyloid pathology in cognitively unimpaired individuals using voxel-wise analysis of longitudinal structural brain MRI

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  • dc.contributor.author Petrone, Paula M.
  • dc.contributor.author Casamitjana, Adrià
  • dc.contributor.author Falcón, Carles
  • dc.contributor.author Artigues, Miquel
  • dc.contributor.author Operto, Grégory
  • dc.contributor.author Cacciaglia, Raffaele
  • dc.contributor.author Molinuevo, José Luis
  • dc.contributor.author Vilaplana, Verónica
  • dc.contributor.author Gispert López, Juan Domingo
  • dc.contributor.author Alzheimer’s Disease Neuroimaging Initiative
  • dc.date.accessioned 2020-05-21T07:03:06Z
  • dc.date.available 2020-05-21T07:03:06Z
  • dc.date.issued 2019
  • dc.description.abstract Background: Magnetic resonance imaging (MRI) has unveiled specific alterations at different stages of Alzheimer's disease (AD) pathophysiologic continuum constituting what has been established as "AD signature". To what extent MRI can detect amyloid-related cerebral changes from structural MRI in cognitively unimpaired individuals is still an area open for exploration. Method: Longitudinal 3D-T1 MRI scans were acquired from a subset of the ADNI cohort comprising 403 subjects: 79 controls (Ctrls), 50 preclinical AD (PreAD), and 274 MCI and dementia due to AD (MCI/AD). Amyloid CSF was used as gold-standard measure with established cutoffs (< 192 pg/mL) to establish diagnostic categories. Cognitively unimpaired individuals were defined as Ctrls if were amyloid negative and PreAD otherwise. The MCI/AD group was amyloid positive. Only subjects with the same diagnostic category at baseline and follow-up visits were considered for the study. Longitudinal morphometric analysis was performed using SPM12 to calculate Jacobian determinant maps. Statistical analysis was carried out on these Jacobian maps to identify structural changes that were significantly different between diagnostic categories. A machine learning classifier was applied on Jacobian determinant maps to predict the presence of abnormal amyloid levels in cognitively unimpaired individuals. The performance of this classifier was evaluated using receiver operating characteristic curve analysis and as a function of the follow-up time between MRI scans. We applied a cost function to assess the benefit of using this classifier in the triaging of individuals in a clinical trial-recruitment setting. Results: The optimal follow-up time for classification of Ctrls vs PreAD was Δt > 2.5 years, and hence, only subjects within this temporal span are used for evaluation (15 Ctrls, 10 PreAD). The longitudinal voxel-based classifier achieved an AUC = 0.87 (95%CI 0.72-0.97). The brain regions that showed the highest discriminative power to detect amyloid abnormalities were the medial, inferior, and lateral temporal lobes; precuneus; caudate heads; basal forebrain; and lateral ventricles. Conclusions: Our work supports that machine learning applied to longitudinal brain volumetric changes can be used to predict, with high precision, the presence of amyloid abnormalities in cognitively unimpaired subjects. Used as a triaging method to identify a fixed number of amyloid-positive individuals, this longitudinal voxel-wise classifier is expected to avoid 55% of unnecessary CSF and/or PET scans and reduce economic cost by 40%.
  • dc.format.mimetype application/pdf
  • dc.identifier.citation Petrone PM, Casamitjana A, Falcon C, Artigues M, Operto G, Cacciaglia R, Molinuevo JL, Vilaplana V, Gispert JD; Alzheimer’s Disease Neuroimaging Initiative. Prediction of amyloid pathology in cognitively unimpaired individuals using voxel-wise analysis of longitudinal structural brain MRI. Alzheimers Res Ther. 2019; 11(1):72. DOI: 10.1186/s13195-019-0526-8
  • dc.identifier.doi http://dx.doi.org/10.1186/s13195-019-0526-8
  • dc.identifier.issn 1758-9193
  • dc.identifier.uri http://hdl.handle.net/10230/44631
  • dc.language.iso eng
  • dc.publisher BioMed Central
  • dc.relation.ispartof Alzheimers Res Ther. 2019; 11(1):72
  • dc.rights © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.rights.uri http://creativecommons.org/licenses/by/4.0/
  • dc.subject.keyword Jacobian determinant
  • dc.subject.keyword Longitudinal voxel-wise analysis
  • dc.subject.keyword MRI
  • dc.subject.keyword Machine learning
  • dc.subject.keyword Preclinical AD signature
  • dc.title Prediction of amyloid pathology in cognitively unimpaired individuals using voxel-wise analysis of longitudinal structural brain MRI
  • dc.type info:eu-repo/semantics/article
  • dc.type.version info:eu-repo/semantics/publishedVersion

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