Exploring the neuromagnetic signatures of cognitive decline from mild cognitive impairment to Alzheimer's disease dementia.

IF 9.7 1区医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL

EBioMedicine Pub Date : 2025-03-27 DOI:10.1016/j.ebiom.2025.105659

Sinead Gaubert, Pilar Garces, Jörg Hipp, Ricardo Bruña, Maria Eugenia Lopéz, Fernando Maestu, Delshad Vaghari, Richard Henson, Claire Paquet, Denis-Alexander Engemann

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引用次数: 0

Abstract

Background: Developing non-invasive and affordable biomarkers to detect Alzheimer's disease (AD) at a prodromal stage is essential, particularly in the context of new disease-modifying therapies. Mild cognitive impairment (MCI) is a critical stage preceding dementia, but not all patients with MCI will progress to AD. This study explores the potential of magnetoencephalography (MEG) to predict cognitive decline from MCI to AD dementia.

Methods: We analysed resting-state MEG data from the BioFIND dataset including 117 patients with MCI among whom 64 developed AD dementia (AD progression), while 53 remained cognitively stable (stable MCI), using spectral analysis. Logistic regression models estimated the additive explanation of selected clinical, MEG, and MRI variables for AD progression risk. We then built a high-dimensional classification model to combine all modalities and variables of interest.

Findings: MEG 16-38Hz spectral power, particularly over parieto-occipital magnetometers, was significantly reduced in the AD progression group. In logistic regression models, decreased MEG 16-38Hz spectral power and reduced hippocampal volume/total grey matter ratio on MRI were independently linked to higher AD progression risk. The data-driven classification model confirmed, among other factors, the complementary information of MEG covariance (AUC = 0.74, SD = 0.13) and MRI cortical volumes (AUC = 0.77, SD = 0.14) to predict AD progression. Combining all inputs led to markedly improved classification scores (AUC = 0.81, SD = 0.12).

Interpretation: These findings highlight the potential of spectral power and covariance as robust non-invasive electrophysiological biomarkers to predict AD progression, complementing other diagnostic measures, including cognitive scores and MRI.

Funding: This work was supported by: Fondation pour la Recherche Médicale (grant FDM202106013579).

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探索从轻度认知障碍到阿尔茨海默病痴呆的认知衰退的神经磁特征。

背景：开发非侵入性和负担得起的生物标志物来检测阿尔茨海默病（AD）的前驱阶段是必不可少的，特别是在新的疾病修饰疗法的背景下。轻度认知障碍（MCI）是痴呆前的关键阶段，但并非所有MCI患者都会发展为AD。本研究探讨了脑磁图（MEG）预测从MCI到AD痴呆的认知能力下降的潜力。方法：我们分析了来自BioFIND数据集的静息状态MEG数据，包括117例MCI患者，其中64例发展为AD痴呆（AD进展），53例保持认知稳定（稳定MCI），使用谱分析。Logistic回归模型估计了选定的临床、MEG和MRI变量对AD进展风险的加性解释。然后，我们建立了一个高维分类模型来结合所有感兴趣的模态和变量。结果：在AD进展组，MEG 16-38Hz频谱功率，特别是顶枕磁强计，显著降低。在logistic回归模型中，MEG 16-38Hz频谱功率降低和MRI上海马体积/总灰质比降低与AD进展风险升高独立相关。数据驱动的分类模型证实，除其他因素外，MEG协方差（AUC = 0.74, SD = 0.13）和MRI皮质体积（AUC = 0.77, SD = 0.14）的互补信息可以预测AD的进展。综合所有输入可显著提高分类评分（AUC = 0.81, SD = 0.12）。解释：这些发现强调了谱功率和协方差作为预测AD进展的强大的非侵入性电生理生物标志物的潜力，补充了其他诊断措施，包括认知评分和MRI。资助：本工作由： ()和() （）

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来源期刊

EBioMedicine Biochemistry, Genetics and Molecular Biology-General Biochemistry,Genetics and Molecular Biology

CiteScore

17.70

自引率

0.90%

发文量

579

审稿时长

5 weeks

期刊介绍： eBioMedicine is a comprehensive biomedical research journal that covers a wide range of studies that are relevant to human health. Our focus is on original research that explores the fundamental factors influencing human health and disease, including the discovery of new therapeutic targets and treatments, the identification of biomarkers and diagnostic tools, and the investigation and modification of disease pathways and mechanisms. We welcome studies from any biomedical discipline that contribute to our understanding of disease and aim to improve human health.