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
Year: 2020
Type: review
Abstract: Structural hippocampal abnormalities are common in many neurological and psychiatric disorders, and variation in hippocampal measures 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 Enhancing Neuro Imaging Genetics through Meta Analysis Consortium, several Disease Working Groups are using the FreeSurfer software to analyze hippocampal subregion (subfield) volumes in patients with neurological and psychiatric conditions along with data from matched controls. In this overview, we explain the algorithm's principles, 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 modeled, 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, psychosis, stress regulation, neurotoxicity, epilepsy, inflammatory disease, childhood adversity and posttraumatic stress disorder, and candidate and whole genome (epi-)genetics. Finally, we highlight points where FreeSurfer-based hippocampal subfield studies may be optimized.
Source: Human Brain Mapping
DOI: 10.1002/hbm.25326
Authors: Philipp G. Sämann, Juan Eugenio Iglesias, Boris Gutman, Dominik Grotegerd, Ramona Leenings, Claas Flint, Udo Dannlowski, Emily K. Clarke‐Rubright, Rajendra A. Morey, Theo G.M. van Erp, Christopher D. Whelan, Laura K. M. Han, Laura S. van Velzen, Bo Cao, Jean C. Augustinack, Paul M. Thompson, Neda Jahanshad, Lianne Schmaal
Institutions: Max Planck Institute of Psychiatry, Harvard University, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, MIT Computer Science and Artificial Intelligence Laboratory, University College London, Massachusetts Institute of Technology, Illinois Institute of Technology, University of Münster, Duke University, VA Mid-Atlantic Health Care Network, University of California, Irvine, University of Southern California, Amsterdam Neuroscience, GGZ inGeest, Amsterdam University Medical Centers, The University of Melbourne, Orygen, University of Alberta
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,200
Funders: Deutsche Forschungsgemeinschaft, Interdisziplinäres Zentrum für Klinische Forschung, Universitätsklinikum Würzburg, National Institutes of Health, National Institute on Aging, National Institute of Mental Health, National Institute of Biomedical Imaging and Bioengineering, NIH Clinical Center, H2020 European Research Council
Awards: G1116979012, G1255536505, G1340315266, G1677387308, G179444772, G238323396, Building a pathology-validated neuroimaging tool for Alzheimer's Disease, G2831939486, G6073498883, ENIGMA Center for Worldwide Medicine, Imaging & Genomics, G648958512, G6733821374, G8874029884, G9000210308, Genomic Architecture of Functional Brain Networks in PTSD, G961545024
OpenAlex ID: W3114793870