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Neurobiol Aging. 2017 Nov;59:80-90. doi: 10.1016/j.neurobiolaging.2017.06.027. Epub 2017 Jul 11.

Identifying incipient dementia individuals using machine learning and amyloid imaging.

Author information

1
Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging (MCSA), Douglas Research Institute, McGill University, Montreal, Quebec, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
2
Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging (MCSA), Douglas Research Institute, McGill University, Montreal, Quebec, Canada.
3
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
4
McGill University Research Centre for Studies in Aging (MCSA), Douglas Research Institute, McGill University, Montreal, Quebec, Canada; Douglas Research Institute, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Neurology & Neurosurgery, McGill University, Montreal, Quebec, Canada.
5
Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging (MCSA), Douglas Research Institute, McGill University, Montreal, Quebec, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; McGill University Research Centre for Studies in Aging (MCSA), Douglas Research Institute, McGill University, Montreal, Quebec, Canada; Douglas Research Institute, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Neurology & Neurosurgery, McGill University, Montreal, Quebec, Canada. Electronic address: pedro.rosa@mcgill.ca.

Abstract

Identifying individuals destined to develop Alzheimer's dementia within time frames acceptable for clinical trials constitutes an important challenge to design studies to test emerging disease-modifying therapies. Although amyloid-β protein is the core pathologic feature of Alzheimer's disease, biomarkers of neuronal degeneration are the only ones believed to provide satisfactory predictions of clinical progression within short time frames. Here, we propose a machine learning-based probabilistic method designed to assess the progression to dementia within 24 months, based on the regional information from a single amyloid positron emission tomography scan. Importantly, the proposed method was designed to overcome the inherent adverse imbalance proportions between stable and progressive mild cognitive impairment individuals within a short observation period. The novel algorithm obtained an accuracy of 84% and an under-receiver operating characteristic curve of 0.91, outperforming the existing algorithms using the same biomarker measures and previous studies using multiple biomarker modalities. With its high accuracy, this algorithm has immediate applications for population enrichment in clinical trials designed to test disease-modifying therapies aiming to mitigate the progression to Alzheimer's disease dementia.

KEYWORDS:

Alzheimer's disease; Amyloid; Mild cognitive impairment; Prediction; Random forest; Random under sampling

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