Hippocampal grading provides higher Alzheimer’s Disease prediction accuracy than hippocampal volume

Abstract Background Finding an early biomarker of Alzheimer’s disease (AD) is essential to develop and implement early treatments. Much research has focused on using hippocampal volume to measure neurodegeneration in aging and Alzheimer’s disease (AD). However, a new method to measure hippocampal change, known as hippocampal grading, has shown enhanced predictive power in older adults. It is unknown whether this method can capture hippocampal changes at each progressive stage of AD better than hippocampal volume. The goal of this study was to determine if hippocampal grading is more strongly associated with group differences between normal controls (NC), early MCI (eMCI), late (lMCI), and AD than hippocampal volume. Methods Data from 1666 Alzheimer’s Disease Neuroimaging Initiative older adults with baseline MRI scans were included in the first set of analyses (513 normal controls NC, 269 eMCI, 556 lMCI, and 328 AD). Sub-analyses were also completed using only those that were amyloid positive (N=834; 179 NC, 148 eMCI, 298 lMCI, and 209 AD). We compared seven different classification techniques to classify participants into their correct cohort using 10-fold cross-validation. The following classifiers were applied: support vector machines, decision trees, k-nearest neighbors, error-correcting output codes, binary Gaussian kernel, binary linear, and random forest. These multiple classifiers enable comparison to other research and examination of the most suitable classifier for Scoring by Nonlocal Image Patch Estimator (SNIPE) grading, SNIPE volume, and Freesurfer volume. This model was then validated in the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL). Results SNIPE grading provided the highest classification accuracy over SNIPE volume and Freesurfer volume for all classifications in both the full sample and amyloid positive sample. When classifying NC from AD, SNIPE grading provided an accuracy of 89% for the full sample and 87% for the amyloid positive group. Much lower accuracies of 65% and 46% were obtained when using Freesurfer in the full sample and amyloid positive sample, respectively. Similar accuracies were obtained in the AIBL validation cohort for SNIPE grading (NC vs AD: 90% classification accuracy). Conclusion These findings suggest that SNIPE grading offers increased prediction accuracy compared to both SNIPE volume and Freesurfer volume. SNIPE grading offers promise as a means to classify between people with and without AD. Future research is needed to determine the predictive power of grading at detecting conversion to MCI and AD in amyloid positive cognitively normal older adults (i.e., early in the AD continuum). Key points HC grading may better classify different disease cohorts than HC volume Higher prediction accuracy was obtained for HC grading than HC volume HC grading offers promise as a method to detect declines in aging and Alzheimer’s