Quantitative Longitudinal Predictions of Alzheimer’s Disease by Multi-Modal Predictive Learning

Article type: Research Article

Authors: Prakash, Mithilesh^{a; *} | Abdelaziz, Mahmoud^b | Zhang, Linda^c | Strange, Bryan A.^{c; d} | Tohka, Jussi^a | for the Alzheimer’s Disease Neuroimaging Initiative¹

Affiliations: [a] University of Eastern Finland, A.I. Virtanen Institute for Molecular Sciences, Kuopio, Finland | [b] Zewail City of Science and Technology, Giza, Egypt | [c] Department of Neuroimaging, Alzheimer’s Disease Research Centre, Reina Sofia-CIEN Foundation, Madrid, Spain | [d] Laboratory for Clinical Neuroscience, CTB, Universidad Politécnica de Madrid, Madrid, Spain

Correspondence: [*] Correspondence to: Mithilesh Prakash, PhD, A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, P.O.B. 1627, FI-70211 Kuopio, Finland. E-mail: mithilesh.prakash@uef.fi.

Note: [1 ] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (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: Background:Quantitatively predicting the progression of Alzheimer’s disease (AD) in an individual on a continuous scale, such as the Alzheimer’s Disease Assessment Scale-cognitive (ADAS-cog) scores, is informative for a personalized approach as opposed to qualitatively classifying the individual into a broad disease category. Objective:To evaluate the hypothesis that the multi-modal data and predictive learning models can be employed for future predicting ADAS-cog scores. Methods:Unimodal and multi-modal regression models were trained on baseline data comprised of demographics, neuroimaging, and cerebrospinal fluid based markers, and genetic factors to predict future ADAS-cog scores for 12, 24, and 36 months. We subjected the prediction models to repeated cross-validation and assessed the resulting mean absolute error (MAE) and cross-validated correlation (ρ) of the model. Results:Prediction models trained on multi-modal data outperformed the models trained on single modal data in predicting future ADAS-cog scores (MAE12, 24 & 36 months= 4.1, 4.5, and 5.0, ρ12, 24 & 36 months= 0.88, 0.82, and 0.75). Including baseline ADAS-cog scores to prediction models improved predictive performance (MAE12, 24 & 36 months= 3.5, 3.7, and 4.6, ρ12, 24 & 36 months= 0.89, 0.87, and 0.80). Conclusion:Future ADAS-cog scores were predicted which could aid clinicians in identifying those at greater risk of decline and apply interventions at an earlier disease stage and inform likely future disease progression in individuals enrolled in AD clinical trials.

Keywords: Alzheimer’s disease, machine learning, magnetic resonance imaging, multi-modal imaging, neuropsychology

DOI: 10.3233/JAD-200906

Journal: Journal of Alzheimer's Disease, vol. 79, no. 4, pp. 1533-1546, 2021