Sad Mood Bridges Depressive Symptoms and Cognitive Performance in Community-Dwelling Older Adults: A Network Approach
Bibliographic record
Abstract
Abstract Background and Objectives Depression and cognitive impairment are common and often coexist in older adults. The network theory of mental disorders provides a novel approach to understanding the pathways between depressive symptoms and cognitive domains and the potential “bridge” that links and perpetuates both conditions. This study aimed to identify pathways and bridge symptoms between depressive symptoms and cognitive domains in older adults. Research Design and Methods Data were derived from 2,792 older adults aged 60 years and older with mild and more severe depressive symptoms from the community in Hong Kong. Depressive symptoms were assessed using the Patient Health Questionnaire (PHQ-9) and cognition using the Montreal Cognitive Assessment 5-minute protocol (MoCA-5min). Summary descriptive statistics were calculated, followed by network estimation using graphical LASSO, community detection, centrality analysis using bridge expected influence (BEI), and network stability analyses to assess the structure of the PHQ-9 and MoCA-5min items network, the pathways, and the bridge symptoms. Results Participants (mean age = 77.3 years, SD = 8.5) scored 8.2 (SD = 3.4) on PHQ-9 and 20.3 (SD = 5.4) on MoCA-5min. Three independent communities were identified in PHQ-9 and MoCA-5min items, suggesting that depression is not a uniform entity (2 communities) and has differential connections with cognition. The network estimation results suggested that the 2 most prominent connections between depressive symptoms and cognitive domains were: (1) anhedonia with executive functions/language and (2) sad mood with memory. Among all depressive symptoms, sad mood had the highest BEI, bridging depressive symptoms and cognitive domains. Discussion and Implications Sad mood seems to be the pathway between depression and cognition in this sample of older Chinese. This finding highlights the importance of sad mood as a potential mechanism for the co-occurrence of depression and cognitive impairment, implying that intervention targeting sad mood might have rippling effects on cognitive health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".