Evidence map›Paper›PMID 40365469›Full record

ArticleAlzheimer's & dementia (Amsterdam, Netherlands)

Fully automated MRI-based analysis of the locus coeruleus in aging and Alzheimer's disease dementia using ELSI-Net.

Max Dünnwald, Friedrich Krohn, Alessandro Sciarra, Mousumi Sarkar, Anja Schneider, Klaus Fliessbach, Okka Kimmich, Frank Jessen, Ayda Rostamzadeh, Wenzel Glanz and 14 more

Abstract read
In one paragraph

Article in Alzheimer's & dementia (Amsterdam, Netherlands). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

24 authors.

Max DünnwaldDepartment of Neurology Otto von Guericke University Magdeburg (OvGU) Magdeburg Germany.ORCID https://orcid.org/0000-0003-3838-3345
Friedrich KrohnInstitute of Cognitive Neurology and Dementia Research OvGU Magdeburg Germany.
Alessandro SciarraDepartment of Neurology Otto von Guericke University Magdeburg (OvGU) Magdeburg Germany.
Mousumi SarkarInstitute of Cognitive Neurology and Dementia Research OvGU Magdeburg Germany.
Anja SchneiderGerman Center for Neurodegenerative Diseases (DZNE), Bonn Bonn Germany.
Klaus FliessbachGerman Center for Neurodegenerative Diseases (DZNE), Bonn Bonn Germany.
Okka KimmichGerman Center for Neurodegenerative Diseases (DZNE), Bonn Bonn Germany.
Frank JessenGerman Center for Neurodegenerative Diseases (DZNE), Bonn Bonn Germany.
Ayda RostamzadehDepartment of Psychiatry Medical Faculty University of Cologne Cologne Germany.
Wenzel GlanzGerman Center for Neurodegenerative Diseases (DZNE) Magdeburg Germany.
Enise I IncesoyInstitute of Cognitive Neurology and Dementia Research OvGU Magdeburg Germany.
Stefan TeipelGerman Center for Neurodegenerative Diseases (DZNE) Rostock Germany.
Ingo KilimannGerman Center for Neurodegenerative Diseases (DZNE) Rostock Germany.
Doreen GoerssGerman Center for Neurodegenerative Diseases (DZNE) Rostock Germany.
Annika SpottkeGerman Center for Neurodegenerative Diseases (DZNE), Bonn Bonn Germany.
Johanna BrustkernGerman Center for Neurodegenerative Diseases (DZNE), Bonn Bonn Germany.
Michael T HenekaLuxembourg Centre for Systems Biomedicine (LCSB) University of Luxembourg Esch-sur-Alzette Luxembourg.
Frederic BrosseronGerman Center for Neurodegenerative Diseases (DZNE), Bonn Bonn Germany.
Falk LüsebrinkGerman Center for Neurodegenerative Diseases (DZNE) Magdeburg Germany.
Dorothea HämmererInstitute of Cognitive Neurology and Dementia Research OvGU Magdeburg Germany.
Emrah DüzelInstitute of Cognitive Neurology and Dementia Research OvGU Magdeburg Germany.
Klaus TönniesFaculty of Computer Science OvGU Magdeburg Germany.
Steffen Oeltze-JafraPeter L. Reichertz Institute for Medical Informatics Hannover Medical School Hannover Germany.
Matthew J BettsInstitute of Cognitive Neurology and Dementia Research OvGU Magdeburg Germany.

Funding

Representational dynamics for flexible learning in complex environmentsR01MH126971 · NIMH · BROWN UNIVERSITY · PI Matthew Nassar · 2022 to 2026
$3.0M
NIMH NIH HHS R01 MH126971
6 · The paper itself

Abstract

introductionThe locus coeruleus (LC) is linked to the development and pathophysiology of neurodegenerative diseases such as Alzheimer's disease (AD). Magnetic resonance imaging-based LC features have shown potential to assess LC integrity in vivo.

methodsWe present a deep learning-based LC segmentation and feature extraction method called Ensemble-based Locus Coeruleus Segmentation Network (ELSI-Net) and apply it to healthy aging and AD dementia datasets. Agreement to expert raters and previously published LC atlases were assessed. We aimed to reproduce previously reported differences in LC integrity in aging and AD dementia and correlate extracted features to cerebrospinal fluid (CSF) biomarkers of AD pathology.

resultsELSI-Net demonstrated high agreement to expert raters and published atlases. Previously reported group differences in LC integrity were detected and correlations to CSF biomarkers were found. DISCUSSION: Although we found excellent performance, further evaluations on more diverse datasets from clinical cohorts are required for a conclusive assessment of ELSI-Net's general applicability. Highlights: We provide a thorough evaluation of a fully automatic locus coeruleus (LC) segmentation method termed Ensemble-based Locus Coeruleus Segmentation Network (ELSI-Net) in aging and Alzheimer's disease (AD) dementia.ELSI-Net outperforms previous work and shows high agreement with manual ratings and previously published LC atlases.ELSI-Net replicates previously shown LC group differences in aging and AD.ELSI-Net's LC mask volume correlates with cerebrospinal fluid biomarkers of AD pathology.

Indexed as

biomarkerdeep learninglocus coeruleusmagnetic resonance imagingsegmentation

Identifiers

PMID40365469
PMCPMC12069022

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.