FreeSurfer based segmentation of hippocampal subfields: a review of methods and applications, with a novel quality control procedure for ENIGMA studies and other collaborative efforts

Abstract

Structural hippocampal abnormalities are common in many neurological and psychiatric disorders, and even variation within the normal range is related to cognitive performance and other complex phenotypes such as stress sensitivity. Hippocampal subregions are increasingly studied, as automated algorithms have become available for mapping and volume quantification. In the context of the ENIGMA Consortium, several Disease Working Groups are using the FreeSurfer software to analyze hippocampal subregion (subfield) volumes in patients with major depressive disorder, bipolar disorders, posttraumatic stress disorder, and other conditions, along with data from matched controls. In this overview, we explain the algorithm’s principle, summarize measurement reliability studies, and demonstrate two additional aspects (subfield autocorrelation and volume/reliability correlation) with illustrative data. We then explain the rationale for a standardized hippocampal subfield segmentation quality control (QC) procedure for improved pipeline harmonization. To guide researchers to make optimal use of the algorithm, we discuss how global size and age effects can be modelled, how QC steps can be incorporated and how subfields may be aggregated into composite volumes. This discussion is based on a synopsis of 162 published neuroimaging studies (01/2013–12/2019) that applied the FreeSurfer hippocampal subfield segmentation in a broad range of domains including cognition and healthy aging, brain development and neurodegeneration, affective disorders, schizophrenia, childhood trauma and posttraumatic stress disorder, inflammatory and systemic disease, alcoholism, stress regulation, neurotoxicity, as well as assessments of heritability, effects of candidate genes, genome-wide association studies and epigenetic effects. Lastly, we highlight points where FreeSurfer-based hippocampal subfield studies may be optimized.

Novel characterization of the relationship between verbal list‐learning outcomes and hippocampal subfields in healthy adults

“…The lack of relation between the presubiculum and subiculum volumes and the VLT‐free recall performance in healthy young and middle‐aged adults is consistent with our literature review (see Supporting Information). Regarding CA3, its smallest volume (180–260 mm 3 ) may explain our negative result that could have been caused by a lack of sensitivity of the segmentation technique (Sämann et al, 2020). Another limitation concerns the CA4‐DG subfields, located in the internal bends of the hippocampus, whose volumes must be interpreted with caution, as stated by the Freesurfer providers (Iglesias et al, 2015).…”

“…However, the authors of the Freesurfer hippocampal subfield segmentation algorithm have demonstrated the empirical utility of the subfield segmentation based on 1 mm isotropic T1w images by showing the improved accuracy in discriminating between subjects with and without AD (Iglesias et al, 2015). As detailed in a recent review (Sämann et al, 2020), the Freesurfer hippocampal subfield segmentation with 1 mm isotropic T1w scans has been applied in a wide range of domains, including cognition in healthy and clinical populations, and show good measurement reliability and clinical validity. Others, studying young and middle‐aged adults, have shown good consistency (a Pearson's correlation > = .9) between subfield segmentation based on 1 mm isotropic T1w and the recommended high in‐plane resolution (0.4 mm) T2‐weighted (T2w) images in all subfields we examined here, except for CA3 (Broadhouse et al, 2019).…”

“…As a result, the use of the raw values may lead to distortions in the estimation of their variations. For these reasons, each subfield volume was normalized to the whole hippocampal volume (Evans et al, 2018; Izzo, Andreassen, Westlye, & van der Meer, 2020; Sämann et al, 2020; Uribe et al, 2018), and the random effects were fitted with mixed‐effect models (Carey et al, 2019). The effect of the hemispheric whole hippocampal volume on each hippocampal subfield volume was first regressed out.…”

Prediction of fluid intelligence from T1-w MRI images: A precise two-step deep learning framework

“…The traditional method for the prediction of fluid intelligence scores is to calculate features extracted with the assistance of existing computer-aided tools, and then to train a machine learning model on these features. FreeSurfer extracts the volume and thickness features describing the brain structure, which can provide more information for the prediction of fluid intelligence scores [5]. The National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) pipeline can be used to complete brain image noise reduction, correction, and feature extraction [6].…”

Genetic Specificity of Hippocampal Subfield Volumes, Relative to Hippocampal Formation, Identified in 2148 Young Adult Twins and Siblings

“…Consequently, there is a growing literature pertaining to the unique characteristics of the individual hippocampal subfields. This includes indications that associations between hippocampal volume and a range of conditions and disorders, as well as specific abilities, may be subfield-specific rather than generalized, for example, memory (Dillon et al, 2017;Zammit et al, 2017), psychopathology/disease (Averill et al, 2017;Cao et al, 2017;Das et al, 2019;Haukvik et al, 2018;Hayes et al, 2017;Maruszak & Thuret, 2014;Mueller et al, 2018;Samann et al, 2022;Tannous et al, 2018) and aging (Adler et al, 2018;Dillon et al, 2017;Malykhin et al, 2017). Thus, the hippocampal subfields are considered to be both structurally and functionally distinct.…”