Heritability and reliability of automatically segmented human hippocampal formation subregions

Whelan, Christopher D., Hibar, Derrek P., van Velzen, Laura S., Zannas, Anthony S., Carrillo-Roa, Tania, McMahon, Katie, Prasad, Gautam, Kelly, Sinéad, Faskowitz, Joshua, deZubiracay, Greig, Iglesias, Juan E., van Erp, Theo G. M., Frodl, Thomas, Martin, Nicholas G., Wright, Margaret J., Jahanshad, Neda, Schmaal, Lianne, Sämann, Philipp G. and Thompson, Paul M. (2016) Heritability and reliability of automatically segmented human hippocampal formation subregions. Neuroimage, 128 125-137. doi:10.1016/j.neuroimage.2015.12.039

Author Whelan, Christopher D.
Hibar, Derrek P.
van Velzen, Laura S.
Zannas, Anthony S.
Carrillo-Roa, Tania
McMahon, Katie
Prasad, Gautam
Kelly, Sinéad
Faskowitz, Joshua
deZubiracay, Greig
Iglesias, Juan E.
van Erp, Theo G. M.
Frodl, Thomas
Martin, Nicholas G.
Wright, Margaret J.
Jahanshad, Neda
Schmaal, Lianne
Sämann, Philipp G.
Thompson, Paul M.
Title Heritability and reliability of automatically segmented human hippocampal formation subregions
Journal name Neuroimage   Check publisher's open access policy
ISSN 1053-8119
Publication date 2016-03-01
Year available 2015
Sub-type Article (original research)
DOI 10.1016/j.neuroimage.2015.12.039
Open Access Status DOI
Volume 128
Start page 125
End page 137
Total pages 13
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
Subject 2808 Neurology
2805 Cognitive Neuroscience
Abstract The human hippocampal formation can be divided into a set of cytoarchitecturally and functionally distinct subregions, involved in different aspects of memory formation. Neuroanatomical disruptions within these subregions are associated with several debilitating brain disorders including Alzheimer's disease, major depression, schizophrenia, and bipolar disorder. Multi-center brain imaging consortia, such as the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) consortium, are interested in studying disease effects on these subregions, and in the genetic factors that affect them. For large-scale studies, automated extraction and subsequent genomic association studies of these hippocampal subregion measures may provide additional insight. Here, we evaluated the test-retest reliability and transplatform reliability (1.5T versus 3T) of the subregion segmentation module in the FreeSurfer software package using three independent cohorts of healthy adults, one young (Queensland Twins Imaging Study, N=39), another elderly (Alzheimer's Disease Neuroimaging Initiative, ADNI-2, N=163) and another mixed cohort of healthy and depressed participants (Max Planck Institute, MPIP, N=598). We also investigated agreement between the most recent version of this algorithm (v6.0) and an older version (v5.3), again using the ADNI-2 and MPIP cohorts in addition to a sample from the Netherlands Study for Depression and Anxiety (NESDA) (N=221). Finally, we estimated the heritability (h(2)) of the segmented subregion volumes using the full sample of young, healthy QTIM twins (N=728). Test-retest reliability was high for all twelve subregions in the 3T ADNI-2 sample (intraclass correlation coefficient (ICC)=0.70-0.97) and moderate-to-high in the 4T QTIM sample (ICC=0.5-0.89). Transplatform reliability was strong for eleven of the twelve subregions (ICC=0.66-0.96); however, the hippocampal fissure was not consistently reconstructed across 1.5T and 3T field strengths (ICC=0.47-0.57). Between-version agreement was moderate for the hippocampal tail, subiculum and presubiculum (ICC=0.78-0.84; Dice Similarity Coefficient (DSC)=0.55-0.70), and poor for all other subregions (ICC=0.34-0.81; DSC=0.28-0.51). All hippocampal subregion volumes were highly heritable (h(2)=0.67-0.91). Our findings indicate that eleven of the twelve human hippocampal subregions segmented using FreeSurfer version 6.0 may serve as reliable and informative quantitative phenotypes for future multi-site imaging genetics initiatives such as those of the ENIGMA consortium.
Keyword Cognitive Neuroscience
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID


U01 AG024904 | U54 EB020403


Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
Official 2016 Collection
Centre for Advanced Imaging Publications
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 11 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 12 times in Scopus Article | Citations
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Created: Sat, 06 Feb 2016, 02:59:24 EST by Lorine Wilkinson on behalf of Centre for Advanced Imaging