Improved Prediction of Imminent Progression to Clinically Significant Memory Decline Using Surface Multivariate Morphometry Statistics and Sparse Coding

Article type: Research Article

Authors: Stonnington, Cynthia M.^{a; *} | Wu, Jianfeng^b | Zhang, Jie^b | Shi, Jie^b | Bauer III, Robert J.^c | Devadas, Vivek^c | Su, Yi^c | Locke, Dona E.C.^a | Reiman, Eric M.^c | Caselli, Richard J.^d | Chen, Kewei^c | Wang, Yalin^b | for the Alzheimer’s Disease Neuroimaging Initiative¹

Affiliations: [a] Department of Psychiatry and Psychology, Mayo Clinic, Scottsdale, AZ, USA | [b] School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA | [c] Banner Alzheimer’s Institute, Phoenix, AZ, USA | [b] Department of Neurology, Mayo Clinic, Scottsdale, AZ, USA

Correspondence: [*] Correspondence to: Cynthia M. Stonnington, M.D., Department of Psychiatry and Psychology, Mayo Clinic, 13400 E Shea Blvd., Scottsdale, AZ 85259, USA. Tel.: +1 480 301 4853; Fax: +1 480 301 6258; E-mail: [email protected].

Note: [1] Some of the 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: Background:Besides their other roles, brain imaging and other biomarkers of Alzheimer’s disease (AD) have the potential to inform a cognitively unimpaired (CU) person’s likelihood of progression to mild cognitive impairment (MCI) and benefit subject selection when evaluating promising prevention therapies. We previously described that among baseline FDG-PET and MRI measures known to be preferentially affected in the preclinical and clinical stages of AD, hippocampal volume was the best predictor of incident MCI within 2 years (79%sensitivity/78%specificity), using standard automated MRI volumetric algorithmic programs, binary logistic regression, and leave-one-out procedures. Objective:To improve the same prediction by using different hippocampal features and machine learning methods, cross-validated via two independent and prospective cohorts (Arizona and ADNI). Methods:Patch-based sparse coding algorithms were applied to hippocampal surface features of baseline TI-MRIs from 78 CU adults who subsequently progressed to amnestic MCI in approximately 2 years (“progressors”) and 80 matched adults who remained CU for at least 4 years (“nonprogressors”). Nonprogressors and progressors were matched for age, sex, education, and apolipoprotein E4 allele dose. We did not include amyloid or tau biomarkers in defining MCI. Results:We achieved 92%prediction accuracy in the Arizona cohort, 92%prediction accuracy in the ADNI cohort, and 90%prediction accuracy when combining the two demographically distinct cohorts, as compared to 79%(Arizona) and 72%(ADNI) prediction accuracy using hippocampal volume. Conclusion:Surface multivariate morphometry and sparse coding, applied to individual MRIs, may accurately predict imminent progression to MCI even in the absence of other AD biomarkers.

Keywords: Alzheimer’s disease, magnetic resonance imaging, mild cognitive impairment, prediction, prognosis

DOI: 10.3233/JAD-200821

Journal: Journal of Alzheimer's Disease, vol. 81, no. 1, pp. 209-220, 2021