期刊
AGING & MENTAL HEALTH
卷 -, 期 -, 页码 -出版社
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/13607863.2023.2179972
关键词
Late-life depression; cognitive control; functional connectivity; Stroop test; Salience network
This study aimed to compare the functional connectivity of large-scale brain networks during a cognitive control task with emotional stimuli in older adults with and without a history of late-life depression (LLD). The results revealed that LLD patients exhibited reduced functional connectivity between certain networks during the processing of incongruent emotional stimuli. The findings expand on the network-based LLD model and suggest the salience network as a potential target for future interventions.
ObjectivesLate-life depression (LLD) is a common and debilitating disorder. Previously, resting-state studies have revealed abnormal functional connectivity (FC) of brain networks in LLD. Since LLD is associated with emotional-cognitive control deficits, the aim of this study was to compare FC of large-scale brain networks in older adults with and without a history of LLD during a cognitive control task with emotional stimuli.MethodsCross-sectional case-control study. Twenty participants diagnosed with LLD and 37 never-depressed adults 60-88 years of age underwent functional magnetic resonance imaging during an emotional Stroop task. Network-region-to-region FC was assessed with seed regions in the default mode, the frontoparietal, the dorsal attention, and the salience networks.ResultsFC between salience and sensorimotor network regions and between salience and dorsal attention network regions were reduced in LLD patients compared to controls during the processing of incongruent emotional stimuli. The normally positive FC between these networks were negative in LLD patients and inversely correlated with vascular risk and white matter hyperintensities.ConclusionsEmotional-cognitive control in LLD is associated with aberrant functional coupling between salience and other networks. This expands on the network-based LLD model and proposes the salience network as a target for future interventions.
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