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.
Highlights
The hippocampal formation is a brain region forming part of the limbic system that has been implicated in many psychiatric conditions, including major depressive disorder (MDD), schizophrenia (SCZ), posttraumatic stress disorder (PTSD) and Alzheimer’s disease (AD) (Arnold, 1997; Bartsch, 2012; Chakos et al, 2005; Du et al, 2001; Kempton, Salvador, Munafo, Geddes, Simmons, Frangou, & Williams, 2011; Ku€hn & Gallinat, 2013; Laakso et al, 1998; Videbech & Ravnkilde, 1957)
The results from this study show that almost all hippocampal subregion segmentations achieve high intra-class correlation coefficient (ICC) scores (ICC > 0.85), after longitudinal processing compared to just over half after cross-sectional processing
To our knowledge this is the first study to assess the test–retest reliability using ICC of automated hippocampal subregion segmentation applied to cross-sectional and longitudinal data using Freesurfer’s pipeline, in two independent datasets consisting of three separate timepoints, spanning 4 weeks in healthy controls and 6 weeks in AD patients
Summary
The hippocampal formation is a brain region forming part of the limbic system that has been implicated in many psychiatric conditions, including major depressive disorder (MDD), schizophrenia (SCZ), posttraumatic stress disorder (PTSD) and Alzheimer’s disease (AD) (Arnold, 1997; Bartsch, 2012; Chakos et al, 2005; Du et al, 2001; Kempton, Salvador, Munafo, Geddes, Simmons, Frangou, & Williams, 2011; Ku€hn & Gallinat, 2013; Laakso et al, 1998; Videbech & Ravnkilde, 1957). Limitations in magnetic resonance imaging (MRI) acquisition, resolution and segmentation have meant that in vivo neuroimaging studies have typically been forced to model the hippocampus as a homogenous structure This approach has been successful in identifying the hippocampus as a region that is sensitive to disease processes and reduced hippocampal volume is evident in many neurological and psychiatric conditions (Small, Schobel, Buxton, Witter, & Barnes, 2011). A pipeline released as part of the FreeSurfer package (v6.0) (Iglesias et al, 2015) and is compatible with Freesurfer v5.3 (developmental version available at https://surfer.nmr.mgh.harvard.edu/fswiki/Hippocampal Subfields), offers the possibility of automated segmentation of hippocampal subregions, utilising a probabilistic atlas that has been built from manual segmentation of in vivo and ultra-high resolution ex vivo data This method is recommended for use with a T1-weighted and highresolution T2-weighted MRI scan, but can be applied to a standard T1-weighted scan alone. The primary aim of the study was to provide metrics on the betweensession reliability of automated hippocampal subregion segmentation on standard T1-weighted MRI data using ICC, percentage volume difference and percentage volume overlap (Dice)
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