Jorik D Elberse, Amin Saberi, Reihaneh Ahmadi, Monir Changizi, Hanwen Bi, Felix Hoffstaedter, Bryce A Mander, Simon B Eickhoff, Masoud Tahmasian, Alzheimer's Disease Neuroimaging Initiative
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We measured the gray matter volume (GMV), structural covariance (SC), degrees centrality (DC), and functional connectivity (FC), testing the effect and interaction of insomnia symptoms and diagnosis on each index. Subsequently, we performed a within-group linear regression across each network and ROI. Finally, we correlated observed abnormalities with changes in cognitive and affective scores.</p><p><strong>Results: </strong>Insomnia symptoms were associated with FC alterations across all groups. The AD group also demonstrated an interaction between insomnia and diagnosis. Within-group analyses revealed that in CN and MCI, insomnia symptoms were characterized by within-network hyperconnectivity, while in AD, within- and between-network hypoconnectivity was ubiquitous. SC and GMV alterations were nonsignificant in the presence of insomnia symptoms, and DC indices only showed network-level alterations in the CEN of AD individuals. 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引用次数: 0
摘要
研究目的:失眠症状在阿尔茨海默病(AD)的发病过程中非常普遍,但人们对其相互作用的神经生物学基础知之甚少。在此,我们对默认模式网络(DMN)、显著性网络(SN)和中央执行网络(CEN)内部和之间的大脑结构和功能进行了评估:我们从 ADNI 数据库中选取了 320 名受试者,按照他们的诊断分为认知功能正常(CN)、轻度认知功能障碍(MCI)和 AD(有和无自我报告的失眠症状)。我们测量了灰质体积(GMV)、结构协方差(SC)、度数中心性(DC)和功能连接性(FC),测试了失眠症状和诊断对各项指标的影响和交互作用。随后,我们对每个网络和 ROI 进行了组内线性回归。最后,我们将观察到的异常与认知和情感评分的变化相关联:结果:在所有组别中,失眠症状都与 FC 改变有关。AD组还显示出失眠与诊断之间的交互作用。组内分析显示,在 CN 和 MCI 组中,失眠症状的特点是网络内连接性过高,而在 AD 组中,网络内和网络间的连接性过低无处不在。SC和GMV的改变在出现失眠症状时并不显著,而DC指数仅在AD患者的CEN中显示出网络水平的改变。DMN和CEN枢纽内部和之间的FC异常还与所有群体的认知功能下降以及AD患者的抑郁症状增加有关:我们得出的结论是,临床注意力缺失症患者具有独特的失眠相关功能改变模式,这凸显了这两种疾病之间的深刻相互作用。
The interplay between insomnia symptoms and Alzheimer's disease across three main brain networks.
Study objectives: Insomnia symptoms are prevalent along the trajectory of Alzheimer's disease (AD), but the neurobiological underpinning of their interaction is poorly understood. Here, we assessed structural and functional brain measures within and between the default mode network (DMN), salience network, and central executive network (CEN).
Methods: We selected 320 participants from the ADNI database and divided them by their diagnosis: cognitively normal (CN), Mild Cognitive Impairment (MCI), and AD, with and without self-reported insomnia symptoms. We measured the gray matter volume (GMV), structural covariance (SC), degrees centrality (DC), and functional connectivity (FC), testing the effect and interaction of insomnia symptoms and diagnosis on each index. Subsequently, we performed a within-group linear regression across each network and ROI. Finally, we correlated observed abnormalities with changes in cognitive and affective scores.
Results: Insomnia symptoms were associated with FC alterations across all groups. The AD group also demonstrated an interaction between insomnia and diagnosis. Within-group analyses revealed that in CN and MCI, insomnia symptoms were characterized by within-network hyperconnectivity, while in AD, within- and between-network hypoconnectivity was ubiquitous. SC and GMV alterations were nonsignificant in the presence of insomnia symptoms, and DC indices only showed network-level alterations in the CEN of AD individuals. Abnormal FC within and between DMN and CEN hubs was additionally associated with reduced cognitive function across all groups, and increased depressive symptoms in AD.
Conclusions: We conclude that patients with clinical AD present with a unique pattern of insomnia-related functional alterations, highlighting the profound interaction between both conditions.
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