Investigating the link between depressive symptoms and resting-state brain connectivity in people with breast cancer: A systematic review
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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.
