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Default Mode Network Connectivity Is Associated With Executive and Language Performance in Mild Cognitive Impairment Publisher Pubmed



Nasiri H ; Mohammadtaheri B ; Khosravi F ; Ghadiminia N ; Saberian P ; Mohammadian M ; Shakeri S ; Hendudari F ; Siyah Rood YK ; Hassanpoor A ; Sadat S ; Bagheri F ; Mayeli M
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Source: Neuroscience Letters Published:2026


Abstract

AbstractDisruptions in default mode network (DMN) connectivity are well documented in Alzheimer’s disease (AD), yet their associations with specific cognitive domains remain unclear. This study examined relationships between anterior and posterior DMN functional connectivity and memory, executive function, and language performance across the AD continuum. We conducted a cross-sectional analysis of resting-state fMRI and composite cognitive scores from 154 participants (61 cognitively normal, 68 mild cognitive impairment [MCI], and 25 AD). DMN connectivity metrics were derived from regional voxelwise correlations within anterior (aDMN) and posterior (pDMN) subdivisions. Associations between DMN measures and cognitive domains were assessed using multiple linear regression adjusted for age, sex, and years of education, with correction for multiple comparisons. No DMN measure was significantly associated with memory performance in any diagnostic group after correction. In the MCI group, executive and language performance were associated with anterior–posterior DMN connectivity, with weaker coupling linked to poorer performance across these domains. No significant DMN–cognition associations were observed in the cognitively normal or AD groups. After additional adjustment for white matter hyperintensities, only anterior–posterior DMN connectivity remained significantly associated with executive and language performance in the MCI group. Overall, DMN connectivity–cognition relationships were domain-specific and most evident in MCI, supporting the concept of a transitional stage in which network-level functional organization is related to cognitive performance. © 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.