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Hum Brain Mapp. 2017 Jun;38(6):2875-2896. doi: 10.1002/hbm.23559. Epub 2017 Mar 15.

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

Author information

1
Division of Neurosurgery, Department of Surgery, University of Alberta, Alberta, Canada.
2
Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada.
3
Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
4
Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada.
5
Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada.
6
Kimel Family Translational Imaging Genetics Research Laboratory, Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.
7
Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
8
Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.
9
Department of Psychiatry, McGill University, Montreal, Quebec, Canada.

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.

KEYWORDS:

Alzheimer's disease; MRI; atrophy; hippocampus; volumetry

PMID:
28295799
PMCID:
PMC5447460
DOI:
10.1002/hbm.23559
[Indexed for MEDLINE]
Free PMC Article

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