Observational studyHuman brain mapping2025
Automated MRI Segmentation of Brainstem Nuclei Critical to Consciousness.
Observational study in Human brain mapping, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03504709 (Advanced MRI and EEG to Predict Recovery of Consciousness After Severe Traumatic Brain Injury), which is not on this map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Advanced MRI and EEG to Predict Recovery of Consciousness After Severe Traumatic Brain Injury
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mapping fMRI research in disorders of consciousness: a bibliometric study.Frontiers in neurologyPooled it
- Review
- Artificial intelligence could reshape research and care in disorders of consciousness.Nature reviews. Neurology · 2026Article
- A hypothalamic-brainstem activity sequence underlies arousal fluctuations during daytime drowsiness.bioRxiv : the preprint server for biology · 2026Article
- Anatomically constrained and curated cerebellar tractography (ACCURAT): an open framework and a pathway-specific neuroanatomical reference.bioRxiv : the preprint server for biology · 2026Article
- Probabilistic mapping and automated segmentation of human brainstem white matter bundles.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Reduced volume of neuromodulatory brainstem nuclei is associated with early hallucination-related vulnerability in Parkinson's disease.Brain communications · 2026Article
- Automated MRI Segmentation of Brainstem Nuclei Critical to Consciousness.Human brain mapping · 2025Observational
Corrections and comments
- Update of
Authors and funding
13 authors.
Funding
Abstract
Although substantial progress has been made in mapping the connectivity of cortical networks responsible for conscious awareness, neuroimaging analysis of subcortical networks that modulate arousal (i.e., wakefulness) has been limited by a lack of robust segmentation procedures for ascending arousal network (AAN) nuclei in the brainstem. Automated segmentation of brainstem AAN nuclei is an essential step toward elucidating the physiology of human consciousness and the pathophysiology of disorders of consciousness. We created a probabilistic atlas of 10 AAN nuclei built on diffusion MRI scans of 5 ex vivo human brain specimens imaged at 750 μm isotropic resolution. The neuroanatomic boundaries of AAN nuclei were manually annotated with reference to 200 μm 7 Tesla MRI scans in all five specimens and nucleus-specific immunostains in two of the scanned specimens. We then developed a Bayesian segmentation algorithm that utilizes the probabilistic atlas as a generative model and automatically identifies AAN nuclei in a resolution- and contrast-adaptive manner. The segmentation method displayed high accuracy when applied to in vivo T1 MRI scans of healthy individuals and patients with traumatic brain injury, as well as high test-retest reliability across T1 and T2 MRI contrasts. Finally, we show through classification and correlation assessments that the algorithm can detect volumetric changes and differences in magnetic susceptibility within AAN nuclei in patients with Alzheimer's disease and traumatic coma, respectively. We release the probabilistic atlas and Bayesian segmentation tool to advance the study of human consciousness and its disorders. Trial Registration: ClinicalTrials.gov: NCT03504709.
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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.