An artificial neural network model for clinical score prediction in Alzheimer disease using structural neuroimaging measures

An artificial neural network model for clinical score prediction in Alzheimer disease using structural neuroimaging measures

J Psychiatry Neurosci 2019;44(4):246-260 | PDF | Appendix

Nikhil Bhagwat, MSc; Jon Pipitone, MSc; Aristotle N. Voineskos, MD, PhD; M. Mallar Chakravarty, PhD; Alzheimer’s Disease Neuroimaging Initiative

Background: The development of diagnostic and prognostic tools for Alzheimer disease is complicated by substantial clinical heterogeneity in prodromal stages. Many neuroimaging studies have focused on case–control classification and predicting conversion from mild cognitive impairment to Alzheimer disease, but predicting scores from clinical assessments (such as the Alzheimer’s Disease Assessment Scale or the Mini Mental State Examination) using MRI data has received less attention. Predicting clinical scores can be crucial in providing a nuanced prognosis and inferring symptomatic severity.

Methods: We predicted clinical scores at the individual level using a novel anatomically partitioned artificial neural network (APANN) model. The model combined input from 2 structural MRI measures relevant to the neurodegenerative patterns observed in Alzheimer disease: hippocampal segmentations and cortical thickness. We evaluated the performance of the APANN model with 10 rounds of 10-fold cross-validation in 3 experiments, using cohorts from the Alzheimer’s Disease Neuroimaging Initiative (ADNI): ADNI1, ADNI2 and ADNI1 + 2.

Results: Pearson correlation and root mean square error between the actual and predicted scores on the Alzheimer’s Disease Assessment Scale (ADNI1: r = 0.60; ADNI2: r = 0.68; ADNI1 + 2: r = 0.63) and Mini Mental State Examination (ADNI1: r = 0.52; ADNI2: r = 0.55; ADNI1 + 2: r = 0.55) showed that APANN can accurately infer clinical severity from MRI data.

Limitations: To rigorously validate the model, we focused primarily on large cross-sectional baseline data sets with only proof-of-concept longitudinal results.

Conclusion: The APANN provides a highly robust and scalable framework for predicting clinical severity at the individual level using high-dimensional, multimodal neuroimaging data.


Submitted Feb. 1, 2018; Revised Apr. 19, 2018; Accepted Aug. 1, 2018; Published online Feb. 5, 2019

Acknowledgements: N. Bhagwat receives support from the Alzheimer Society of Canada. A. Voineskos is funded by the Canadian Institutes of Health Research, the Ontario Mental Health Foundation, the Brain and Behavior Research Foundation and the National Institute of Mental Health (R01MH099167 and R01MH102324). M. Chakravarty is funded by the Weston Brain Institute, the Alzheimer Society of Canada, the Michael J. Fox Foundation for Parkinson’s Research, the Canadian Institutes of Health Research, the Natural Sciences and Engineering Research Council of Canada and Fondation de Recherches Santé Québec. Computations were performed on the GPC supercomputer at the SciNet HPC Consortium and the Kimel Family Translational ImagingGenetics Research (TIGR) Lab computing cluster. SciNet is funded by the Canada Foundation for Innovation under the auspices of Compute Canada, the Government of Ontario, the Ontario Research Fund Research Excellence Program and the University of Toronto. The TIGR Lab cluster is funded by the Canada Foundation for Innovation Research Hospital Fund. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI; National Institutes of Health Grant U01 AG024904), and ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from Abbott; the Alzheimer’s Association; the Alzheimer’s Drug Discovery Foundation; Amorfix Life Sciences Ltd.; AstraZeneca; Bayer HealthCare; BioClinica Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research Development LLC.; Johnson & Johnson Pharmaceutical Research Development LLC; Medpace Inc.; Merck & Co. Inc.; Meso Scale Diagnostics LLC; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research provides funds to support ADNI clinical sites in Canada. Private-sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is Rev March 26, 2012, coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. The ADNI data are disseminated by the Laboratory for NeuroImaging at the University of California, Los Angeles. This research was also supported by National Institutes of Health grants P30 AG010129 and K01 AG030514.

Affiliations: From the Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ont. (Bhagwat, Chakravarty); the Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Que. (Bhagwat, Chakravarty); the Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ont. (Bhagwat, Pipitone, Voineskos); the Department of Psychiatry, University of Toronto, Toronto, Ont. (Voineskos); and the Department of Psychiatry, McGill University, Montreal, Que. (Chakravarty), Canada.

Compteting interests: None declared.

Contributors: N. Bhagwat, J. Pipitone and M. Chakravarty designed the study. Data were collected by the Alzheimer’s Disease Neuroimaging Initiative, and all authors participated in data analysis. N. Bhagwat and J. Pipitone wrote the article, which all authors reviewed. All authors approved the final version to be published and can certify that no other individuals not listed as authors have made substantial contributions to the paper.

DOI: 10.1503/cjs.180016

Correspondence to: N. Bhagwat, Cerebral Imaging Centre, Douglas Mental Health University Institute, 6875 Lasalle Blvd, Montreal, QC H4H 1R3; nikhil153@gmail.com