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

IF 4 Q1 CLINICAL NEUROLOGY

Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring Pub Date : 2025-05-12 eCollection Date: 2025-04-01 DOI:10.1002/dad2.70118

Max Dünnwald, Friedrich Krohn, Alessandro Sciarra, Mousumi Sarkar, Anja Schneider, Klaus Fliessbach, Okka Kimmich, Frank Jessen, Ayda Rostamzadeh, Wenzel Glanz, Enise I Incesoy, Stefan Teipel, Ingo Kilimann, Doreen Goerss, Annika Spottke, Johanna Brustkern, Michael T Heneka, Frederic Brosseron, Falk Lüsebrink, Dorothea Hämmerer, Emrah Düzel, Klaus Tönnies, Steffen Oeltze-Jafra, Matthew J Betts

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引用次数: 0

Abstract

Introduction: The 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.

Methods: We 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.

Results: ELSI-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.

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使用ELSI-Net对衰老和阿尔茨海默病痴呆中的蓝斑座进行全自动核磁共振分析。

蓝斑（LC）与阿尔茨海默病（AD）等神经退行性疾病的发展和病理生理有关。基于磁共振成像的LC特征显示出在体内评估LC完整性的潜力。方法：提出了一种基于深度学习的LC分割和特征提取方法，称为基于集成的蓝斑分割网络（ELSI-Net），并将其应用于健康老龄化和AD痴呆数据集。评估了与专家评价者和先前出版的LC地图集的协议。我们的目的是重现先前报道的衰老和阿尔茨海默病痴呆中LC完整性的差异，并将提取的特征与阿尔茨海默病病理的脑脊液（CSF）生物标志物联系起来。结果：ELSI-Net与专家评分者和已发表的地图集具有较高的一致性。检测了先前报道的LC完整性组差异，并发现了与CSF生物标志物的相关性。讨论：虽然我们发现了出色的性能，但需要对来自临床队列的更多样化的数据集进行进一步的评估，以结论性地评估ELSI-Net的一般适用性。重点：我们提供了一个全面的评估全自动蓝斑基因（LC）分割方法称为集成为基础的蓝斑基因分割网络（ELSI-Net）在衰老和阿尔茨海默病（AD）痴呆。ELSI-Net优于以前的工作，并显示出与手动评级和先前发表的LC图谱高度一致。先前的ELSI-Net重复显示LC组在衰老和AD方面存在差异。ELSI-Net的LC掩膜体积与AD病理的脑脊液生物标志物相关。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring Medicine-Psychiatry and Mental Health

CiteScore

7.80

自引率

7.50%

发文量

101

审稿时长

8 weeks

期刊介绍： Alzheimer''s & Dementia: Diagnosis, Assessment & Disease Monitoring (DADM) is an open access, peer-reviewed, journal from the Alzheimer''s Association® that will publish new research that reports the discovery, development and validation of instruments, technologies, algorithms, and innovative processes. Papers will cover a range of topics interested in the early and accurate detection of individuals with memory complaints and/or among asymptomatic individuals at elevated risk for various forms of memory disorders. The expectation for published papers will be to translate fundamental knowledge about the neurobiology of the disease into practical reports that describe both the conceptual and methodological aspects of the submitted scientific inquiry. Published topics will explore the development of biomarkers, surrogate markers, and conceptual/methodological challenges. Publication priority will be given to papers that 1) describe putative surrogate markers that accurately track disease progression, 2) biomarkers that fulfill international regulatory requirements, 3) reports from large, well-characterized population-based cohorts that comprise the heterogeneity and diversity of asymptomatic individuals and 4) algorithmic development that considers multi-marker arrays (e.g., integrated-omics, genetics, biofluids, imaging, etc.) and advanced computational analytics and technologies.