Your algorithm might think the hippocampus grows in Alzheimer's disease: Caveats of longitudinal automated hippocampal volumetry.

TitleYour algorithm might think the hippocampus grows in Alzheimer's disease: Caveats of longitudinal automated hippocampal volumetry.
Publication TypeJournal Article
Year of Publication2017
AuthorsSankar T, Park MTae M, Jawa T, Patel R, Bhagwat N, Voineskos AN, Lozano AM, M Chakravarty M
Corporate AuthorsAlzheimer's Disease Neuroimaging Initiative
JournalHum Brain Mapp
Volume38
Issue6
Pagination2875-2896
Date Published2017 Jun
ISSN1097-0193
Abstract

Hippocampal atrophy rate-measured using automated techniques applied to structural MRI scans-is considered a sensitive marker of disease progression in Alzheimer's disease, frequently used as an outcome measure in clinical trials. Using publicly accessible data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we examined 1-year hippocampal atrophy rates generated by each of five automated or semiautomated hippocampal segmentation algorithms in patients with Alzheimer's disease, subjects with mild cognitive impairment, or elderly controls. We analyzed MRI data from 398 and 62 subjects available at baseline and at 1 year at MRI field strengths of 1.5 T and 3 T, respectively. We observed a high rate of hippocampal segmentation failures across all algorithms and diagnostic categories, with only 50.8% of subjects at 1.5 T and 58.1% of subjects at 3 T passing stringent segmentation quality control. We also found that all algorithms identified several subjects (between 2.94% and 48.68%) across all diagnostic categories showing increases in hippocampal volume over 1 year. For any given algorithm, hippocampal "growth" could not entirely be explained by excluding patients with flawed hippocampal segmentations, scan-rescan variability, or MRI field strength. Furthermore, different algorithms did not uniformly identify the same subjects as hippocampal "growers," and showed very poor concordance in estimates of magnitude of hippocampal volume change over time (intraclass correlation coefficient 0.319 at 1.5 T and 0.149 at 3 T). This precluded a meaningful analysis of whether hippocampal "growth" represents a true biological phenomenon. Taken together, our findings suggest that longitudinal hippocampal volume change should be interpreted with considerable caution as a biomarker. Hum Brain Mapp 38:2875-2896, 2017. © 2017 Wiley Periodicals, Inc.

DOI10.1002/hbm.23559
Alternate JournalHum Brain Mapp
PubMed ID28295799
PubMed Central IDPMC5447460
Grant ListU24 RR021382 / RR / NCRR NIH HHS / United States
R01 MH056584 / MH / NIMH NIH HHS / United States
U01 AG024904 / AG / NIA NIH HHS / United States
P01 AG003991 / AG / NIA NIH HHS / United States
P50 AG005681 / AG / NIA NIH HHS / United States
R01 AG021910 / AG / NIA NIH HHS / United States
P50 MH071616 / MH / NIMH NIH HHS / United States