Investigating the link between depressive symptoms and resting-state brain connectivity in people with breast cancer: A systematic review
Bibliographic record
Abstract
Abstract Purpose Depressive symptoms are a common and debilitating experience among people with breast cancer (BC), often impacting quality of life and recovery. However, the neural mechanisms underlying these symptoms are unclear. This systematic review synthesised resting-state functional magnetic resonance imaging (rsfMRI) literature in BC populations to identify functional connectivity (FC) correlates of depressive symptoms. Methods A systematic search of EMBASE, PsycINFO, Medline, and CINAHL identified 27 eligible studies (15 cross-sectional, 12 longitudinal) examining the relationship between depressive symptoms and functional connectivity using rsfMRI in BC participants. Data were extracted on study design, participant characteristics, depressive symptoms, imaging acquisition, FC outcomes, and reported associations. Study quality was assessed using the Newcastle-Ottawa Scale. Findings were synthesised qualitatively. Results Across cross-sectional studies, BC participants showed elevated depressive symptoms and widespread FC alterations, predominantly patterns of dysconnectivity, compared to healthy controls. However, most studies did not find significant associations between depressive symptoms and FC. Longitudinal studies revealed dynamic trajectories in depressive symptoms and FC patterns with cancer treatment or training intervention. Conclusion While depressive symptoms are frequently reported by BC participants, the underlying neural mechanisms remain unclear, possibly due to methodological and participant heterogeneity across studies. Implications for cancer survivors Findings highlight the importance of timely and ongoing monitoring of depressive symptoms across the cancer care continuum. Future research should conduct more sensitive assessments of depressive symptomatology (e.g., ecological momentary assessment), adopt standardised rsfMRI protocols, and apply integrative network analysis. These future studies will inform rsfMRI metrics to be used as biomarkers to guide treatment at the individual cancer patient level.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".