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

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.

• Authors: Cynthia M Stonnington, Jianfeng Wu, Jie Zhang, Jie Shi, Robert J Bauer Iii, Vivek Devadas, Yi Su, Dona E C Locke, Eric M Reiman, Richard J Caselli, Kewei Chen, Yalin Wang, Alzheimer’s Disease Neuroimaging Initiative
• Journal: Journal of Alzheimer's disease : JAD (J Alzheimers Dis) Vol. 81 Issue 1 Pg. 209-220 ( 2021) ISSN: 1875-8908 [Electronic] Netherlands
• PMID: 33749642 (Publication Type: Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't)

Topics

• Aged
• Aged, 80 and over
• Algorithms
• Alzheimer Disease (diagnostic imaging)
• Cognitive Dysfunction (diagnostic imaging)
• Disease Progression
• Female
• Hippocampus (diagnostic imaging)
• Humans
• Machine Learning
• Magnetic Resonance Imaging
• Male
• Middle Aged
• Neuroimaging (methods)
• Positron-Emission Tomography
• Prognosis
• Prospective Studies
• Sensitivity and Specificity