Dünnwald Max, Krohn Friedrich, Sciarra Alessandro, Sarkar Mousumi, Schneider Anja, Fliessbach Klaus, Kimmich Okka, Jessen Frank, Rostamzadeh Ayda, Glanz Wenzel, Incesoy Enise I, Teipel Stefan, Kilimann Ingo, Goerss Doreen, Spottke Annika, Brustkern Johanna, Heneka Michael T, Brosseron Frederic, Lüsebrink Falk, Hämmerer Dorothea, Düzel Emrah, Tönnies Klaus, Oeltze-Jafra Steffen, Betts Matthew J
Department of Neurology, Otto von Guericke University Magdeburg (OvGU), Germany.
Faculty of Computer Science, OvGU, Germany.
bioRxiv. 2024 Jul 26:2024.07.26.605356. doi: 10.1101/2024.07.26.605356.
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.
We present a Deep Learning based LC segmentation and feature extraction method: 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.
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.
Although we found excellent performance, further evaluations on more diverse datasets from clinical cohorts are required for a conclusive assessment of ELSI-Nets general applicability.
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学相关。基于磁共振成像的LC特征已显示出在体内评估LC完整性的潜力。
我们提出了一种基于深度学习的LC分割和特征提取方法：ELSI-Net，并将其应用于健康衰老和AD痴呆数据集。评估了与专家评分者的一致性以及与先前发表的LC图谱的一致性。我们旨在重现先前报道的衰老和AD痴呆中LC完整性的差异，并将提取的特征与AD病理学的脑脊液（CSF）生物标志物相关联。
ELSI-Net与专家评分者和已发表的图谱显示出高度一致性。检测到先前报道的LC完整性组间差异，并发现与CSF生物标志物的相关性。
尽管我们发现了出色的性能，但对于ELSI-Net的普遍适用性进行确定性评估还需要对来自临床队列的更多样化数据集进行进一步评估。
