Test–retest reliability and longitudinal analysis of automated hippocampal subregion volumes in healthy ageing and Alzheimer's disease populations. Issue 4 (16th January 2018)

• Record Type: Journal Article
• Authors: Worker, Amanda; Dima, Danai; Combes, Anna; Crum, William R.; Streffer, Johannes; Einstein, Steven; Mehta, Mitul A.; Barker, Gareth J.; Williams, Steve C. R.; O'daly, Owen
• Is Part Of: Human brain mapping. Volume 39:Issue 4(2018)
• Page Start: 1743
• Page End: 1754
• Publication Date: 2018-01-16
• Subjects: brain structure -- FreeSurfer -- hippocampal subfields -- hippocampus -- intraclass correlation coefficient -- linear mixed effects -- Magnetic Resonance Imaging -- test‐retest reliability; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.23948
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: The hippocampal formation is a complex brain structure that is important in cognitive processes such as memory, mood, reward processing and other executive functions. Histological and neuroimaging studies have implicated the hippocampal region in neuropsychiatric disorders as well as in neurodegenerative diseases. This highly plastic limbic region is made up of several subregions that are believed to have different functional roles. Therefore, there is a growing interest in imaging the subregions of the hippocampal formation rather than modelling the hippocampus as a homogenous structure, driving the development of new automated analysis tools. Consequently, there is a pressing need to understand the stability of the measures derived from these new techniques. In this study, an automated hippocampal subregion segmentation pipeline, released as a developmental version of Freesurfer (v6.0), was applied to T1‐weighted magnetic resonance imaging (MRI) scans of 22 healthy older participants, scanned on 3 separate occasions and a separate longitudinal dataset of 40 Alzheimer's disease (AD) patients. Test–retest reliability of hippocampal subregion volumes was assessed using the intra‐class correlation coefficient (ICC), percentage volume difference and percentage volume overlap (Dice). Sensitivity of the regional estimates to longitudinal change was estimated using linear mixed effects (LME) modelling. The results show that out of the 24 hippocampal subregions, 20 had ICC scores of 0.9 or higher in both samples; these regions include the molecular layer, granule cell layer of the dentate gyrus, CA1, CA3 and the subiculum (ICC > 0.9), whilst the hippocampal fissure and fimbria had lower ICC scores (0.73–0.88). Furthermore, LME analysis of the independent AD dataset demonstrated sensitivity to group and individual differences in the rate of volume change over time in several hippocampal subregions (CA1, molecular layer, CA3, hippocampal tail, fissure and presubiculum). These results indicate that this automated segmentation method provides a robust method with which to measure hippocampal subregions, and may be useful in tracking disease progression and measuring the effects of pharmacological intervention.