Improved Prediction Of Imminent Progression To Clinically Significant Memory Decline Using Surface Multivariate Morphometry Statistics And Sparse Coding

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, crossvalidated 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.