von Gal Alessandro, Papa Dario, D'Auria Marco, Piccardi Laura
Department of Psychology, Sapienza University of Rome, Rome, Italy.
San Raffaele Cassino Hospital, Cassino (FR), Italy.
J Alzheimers Dis. 2025 Jul;106(1):18-37. doi: 10.1177/13872877251337770. Epub 2025 May 6.
BackgroundDepressive comorbidity in neurodegeneration has been shown to predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD). However, its pathophysiology is not completely understood.ObjectiveHere, we characterize aberrant functional resting state networks (RSNs) characterizing depressive comorbidity in both AD and MCI.MethodsWe conducted a systematic literature review on Scopus, PubMed, and Web of Science to extract experiments that compared resting state scans of depressed and non-depressed MCI or AD patients. We employed Activation Likelihood Estimation (ALE) meta-analysis on eligible studies resulting from the search, to describe regions of significant co-activation across studies.ResultsThe systematic search resulted in 17 experiments, with 303 participants in total. The ALE yielded 10 clusters of significant co-activation distributed in the five major RSNs and across cortico-basal ganglia-thalamic circuits.ConclusionsDepressive comorbidity in neurodegeneration presents signature aberrant resting-state fluctuations. Understanding these within- and between-network alterations may be useful for future diagnostic and therapeutic applications.
背景
神经退行性疾病中的抑郁共病已被证明可预测从轻度认知障碍（MCI）向阿尔茨海默病（AD）的转化。然而，其病理生理学尚未完全明确。
目的
在此，我们描述在AD和MCI中表征抑郁共病的异常静息态功能网络（RSN）。
方法
我们在Scopus、PubMed和Web of Science上进行了系统的文献综述，以提取比较抑郁和非抑郁MCI或AD患者静息态扫描的实验。我们对搜索得到的符合条件的研究采用激活似然估计（ALE）元分析，以描述各研究中显著共激活的区域。
结果
系统检索得到17项实验，共303名参与者。ALE分析产生了10个显著共激活簇，分布在五个主要RSN以及皮质 - 基底神经节 - 丘脑回路中。
结论
神经退行性疾病中的抑郁共病呈现出特征性的异常静息态波动。了解这些网络内和网络间的改变可能对未来的诊断和治疗应用有用。
