Quantifying numerical and spatial reliability of hippocampal and amygdala subdivisions in FreeSurfer

Kahhale, Isabella; Buser, Nicholas J.; Madan, Christopher R.; Hanson, Jamie L.

Abstract

On-going, large-scale neuroimaging initiatives can aid in uncovering neurobiological causes and correlates of poor mental health, disease pathology, and many other important conditions. As projects grow in scale with hundreds, even thousands, of individual participants and scans collected, quantification of brain structures by automated algorithms is becoming the only truly tractable approach. Here, we assessed the spatial and numerical reliability for newly deployed automated segmentation of hippocampal subfields and amygdala nuclei in FreeSurfer 7. In a sample of participants with repeated structural imaging scans (N = 928), we found numerical reliability (as assessed by intraclass correlations, ICCs) was reasonable. Approximately 95% of hippocampal subfields had “excellent” numerical reliability (ICCs ≥ 0.90), while only 67% of amygdala subnuclei met this same threshold. In terms of spatial reliability, 58% of hippocampal subfields and 44% of amygdala subnuclei had Dice coefficients ≥ 0.70. Notably, multiple regions had poor numerical and/or spatial reliability. We also examined correlations between spatial reliability and person-level factors (e.g., participant age; T1 image quality). Both sex and image scan quality were related to variations in spatial reliability metrics. Examined collectively, our work suggests caution should be exercised for a few hippocampal subfields and amygdala nuclei with more variable reliability. Graphical Abstract:

Citation

Kahhale, I., Buser, N. J., Madan, C. R., & Hanson, J. L. (2023). Quantifying numerical and spatial reliability of hippocampal and amygdala subdivisions in FreeSurfer. Brain Informatics, 10(1), Article 9. https://doi.org/10.1186/s40708-023-00189-5

• Journal Article Type: Article
• Acceptance Date: Mar 24, 2023
• Online Publication Date: Apr 7, 2023
• Publication Date: 2023
• Deposit Date: Jun 12, 2023
• Publicly Available Date: Jun 12, 2023
• Journal: Brain Informatics
• Print ISSN: 2198-4018
• Electronic ISSN: 2198-4026
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Issue: 1
• Article Number: 9
• DOI: https://doi.org/10.1186/s40708-023-00189-5
• Keywords: Research, Amygdala, Hippocampus, Automated segmentation, FreeSurfer, FreeSurfer 7.1
• Public URL: https://nottingham-repository.worktribe.com/output/19454522
• Publisher URL: https://braininformatics.springeropen.com/articles/10.1186/s40708-023-00189-5
• Additional Information: Received: 9 November 2022; Accepted: 24 March 2023; First Online: 7 April 2023; : ; : The authors have no conflicts of interest to disclose.