Alterations of structural-functional connectivity coupling in older adults with depressive symptoms
Ting Li1,2, Haishuo Xia3, Shaokun Zhao4
1State Key Laboratory of Cognitive Neuroscience and Learning, https://ror.org/022k4wk35Beijing Normal University, Beijing, China.
Background:
Prior research indicates that both structural and functional networks are compromised in older adults experiencing depressive symptoms. However, the potential impact of abnormal interactions between brain structure and function remains unclear. This study investigates alterations in structural-functional connectivity coupling (SFC) among older adults with depressive symptoms, and explores how these changes differ depending on the presence of physiological comorbidities.
Methods:
We used multimodal neuroimaging data (dMRI/rs-fMRI) from 415 older adults with depressive symptoms and 415 age-matched normal controls. Subgroups were established within the depressive group based on the presence of hypertension, hyperlipidemia, diabetes, cerebrovascular disease, and sleep disorders. We examined group and subgroup differences in SFC and tracked its alterations in relation to symptom progression.
Results:
Older adults with depressive symptoms showed significantly increased SFC in the ventral attention network compared with normal controls. Moreover, changes in SFC within the subcortical network, especially in the left amygdala, were closely linked to symptom progression. Subgroup analyses further revealed heterogeneity in SFC changes, with certain physiological health factors, such as metabolic diseases and sleep disorders, contributing to distinct neural mechanisms underlying depressive symptoms in this population.
Conclusions:
This study identifies alterations in SFC related to depressive symptoms in older adults, primarily within the ventral attention and subcortical networks. Subgroup analyses highlight the heterogeneous SFC changes associated with metabolic diseases and sleep disorders. These findings highlight SFC may serve as potential markers for more personalized interventions, ultimately improving the clinical management of depression in older adults.
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