Evidence map›Paper›PMID 29582505›Full record

ArticleHuman brain mapping2018

Reproducibility of myelin content-based human habenula segmentation at 3 Tesla.

Joo-Won Kim, Thomas P Naidich, Joshmi Joseph, Divya Nair, Matthew F Glasser, Rafael O'halloran, Gaelle E Doucet, Won Hee Lee, Hannah Krinsky, Alejandro Paulino and 4 more

Open access · bronzeAbstract read
In one paragraph

Article in Human brain mapping, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 pooled it
1.7field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 23 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Review
  13. Article
  14. Article
  15. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 3 institutions in 1 country.

Joo-Won KimTranslational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York.ORCID 0000-0002-0893-7696
Thomas P NaidichDepartment of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York.
Joshmi JosephTranslational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
Divya NairTranslational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
Matthew F GlasserDepartment of Neuroscience, Washington University School of Medicine, Saint Louis, Missouri.
Rafael O'halloranTranslational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
Gaelle E DoucetDepartment of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York.
Won Hee LeeDepartment of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York.
Hannah KrinskyDepartment of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York.
Alejandro PaulinoDepartment of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York.
David C GlahnDepartment of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.
Alan AnticevicDepartment of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.
Sophia FrangouDepartment of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York.
Junqian XuTranslational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York.ORCID 0000-0001-8438-2066
Icahn School of Medicine at Mount Sinai · USYale University · USWashington University in St. Louis · US

Funding

Deconstructing Psychoses Based on Patterns of Abnormal Brain ActivityR01MH104284 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI FRANGOU, SOPHIA · 2014 to 2017
$2.4M
Biotechnology and Biological Sciences Research Council BB/H008217/1Medical Research CouncilNIMH NIH HHS R01 MH104284
6 · The paper itself

Abstract

In vivo morphological study of the human habenula, a pair of small epithalamic nuclei adjacent to the dorsomedial thalamus, has recently gained significant interest for its role in reward and aversion processing. However, segmenting the habenula from in vivo magnetic resonance imaging (MRI) is challenging due to the habenula's small size and low anatomical contrast. Although manual and semi-automated habenula segmentation methods have been reported, the test-retest reproducibility of the segmented habenula volume and the consistency of the boundaries of habenula segmentation have not been investigated. In this study, we evaluated the intra- and inter-site reproducibility of in vivo human habenula segmentation from 3T MRI (0.7-0.8 mm isotropic resolution) using our previously proposed semi-automated myelin contrast-based method and its fully-automated version, as well as a previously published manual geometry-based method. The habenula segmentation using our semi-automated method showed consistent boundary definition (high Dice coefficient, low mean distance, and moderate Hausdorff distance) and reproducible volume measurement (low coefficient of variation). Furthermore, the habenula boundary in our semi-automated segmentation from 3T MRI agreed well with that in the manual segmentation from 7T MRI (0.5 mm isotropic resolution) of the same subjects. Overall, our proposed semi-automated habenula segmentation showed reliable and reproducible habenula localization, while its fully-automated version offers an efficient way for large sample analysis.

Indexed as

AdultFemaleHabenulaHumansMagnetic Resonance ImagingMaleNeuroimagingYoung Adult

Identifiers

PMID29582505
PMCPMC6033622
OpenAlexW2795338757

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.