ArticleCerebral cortex communications2020
Automatic Segmentation of the Dorsal Claustrum in Humans Using in vivo High-Resolution MRI.
Article in Cerebral cortex communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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.
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.
Who cites it
10 citing papers in PubMed, 18 citations in OpenAlex.
- Multiscale characterization of the human claustrum from histology to MRI.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Article
- Comprehensive Segmentation of Deep Grey Nuclei From Structural MRI Data.Human brain mapping · 2025Article
- A Contrast-Agnostic Method for Ultra-High Resolution Claustrum Segmentation.Human brain mapping · 2025Article
- The Role of the Claustrum in Parkinson's Disease and Vascular Parkinsonism: A Matter of Network?Life (Basel, Switzerland) · 2025Review
- High-resolution dataset of manual claustrum segmentation.Data in brief · 2024Article
- Efficient Claustrum Segmentation in T2-weighted Neonatal Brain MRI Using Transfer Learning from Adult Scans.Clinical neuroradiology · 2022Article
- Mapping microstructural gradients of the human striatum in normal aging and Parkinson's disease.Science advances · 2022Article
- Automated claustrum segmentation in human brain MRI using deep learning.Human brain mapping · 2021Article
- The mystery of claustral neural circuits and recent updates on its role in neurodegenerative pathology.Behavioral and brain functions : BBF · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The claustrum is a thin sheet of neurons enclosed by white matter and situated between the insula and the putamen. It is highly interconnected with sensory, frontal, and subcortical regions. The deep location of the claustrum, with its fine structure, has limited the degree to which it could be studied in vivo. Particularly in humans, identifying the claustrum using magnetic resonance imaging (MRI) is extremely challenging, even manually. Therefore, automatic segmentation of the claustrum is an invaluable step toward enabling extensive and reproducible research of the anatomy and function of the human claustrum. In this study, we developed an automatic algorithm for segmenting the human dorsal claustrum in vivo using high-resolution MRI. Using this algorithm, we segmented the dorsal claustrum bilaterally in 1068 subjects of the Human Connectome Project Young Adult dataset, a publicly available high-resolution MRI dataset. We found good agreement between the automatic and manual segmentations performed by 2 observers in 10 subjects. We demonstrate the use of the segmentation in analyzing the covariation of the dorsal claustrum with other brain regions, in terms of macro- and microstructure. We identified several covariance networks associated with the dorsal claustrum. We provide an online repository of 1068 bilateral dorsal claustrum segmentations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.