The oncologist · 2026
Functional brain network alterations linked to cancer-related cognitive impairment in breast cancer patients.
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Benelli Alberto, Reilly Julianne, Ortega-Márquez Jorge, Parsons Michael, Dietrich Jorg, Sprugnoli Giulia, Fregni Felipe, Santarnecchi Emiliano
Аннотация
BACKGROUND: Life expectancy for cancer patients has improved due to new therapies, but many survivors experience long-lasting side effects, including cancer-related cognitive impairment (CRCI). CRCI symptoms-such as difficulties with attention, memory, processing speed, fatigue, and concentration-can persist for years post-treatment. While clinical evidence of CRCI exists, its underlying mechanisms and neural correlates remain unclear. This study aimed to identify functional brain networks associated with CRCI in breast cancer (BC) patients using structural and functional neuroimaging data.
METHODS: We reviewed studies on CRCI-related brain changes assessed via diffusion tensor imaging (DTI, n = 282), voxel-based morphometry (VBM, n = 177), and functional MRI (fMRI, n = 328). Using the Yeo 7-network resting-state functional atlas, we evaluated the overlap between CRCI-related brain alterations and known functional networks. Overlapping percentages were calculated, and statistical independence tests assessed associations between CRCI and specific networks.
RESULTS: Based on inclusion criteria, six DTI, six VBM, and ten fMRI studies were selected. Chi-square analyses revealed significant associations between CRCI-related changes and several networks: the Dorsal Attention Network (DAN, χ2 = 17.71, P = .0001), Default Mode Network (DMN, χ2 = 21.08, P = .0000002), Frontoparietal Network (FPN, χ2 = 110.63, P = .000002), and Ventral Attention Network (VAN, χ2 = 8.203, P = .01).
CONCLUSIONS: Findings highlight network-specific alterations linked to CRCI symptoms in breast cancer survivors. These results enhance understanding of CRCI and may guide targeted therapeutic strategies.
METHODS: We reviewed studies on CRCI-related brain changes assessed via diffusion tensor imaging (DTI, n = 282), voxel-based morphometry (VBM, n = 177), and functional MRI (fMRI, n = 328). Using the Yeo 7-network resting-state functional atlas, we evaluated the overlap between CRCI-related brain alterations and known functional networks. Overlapping percentages were calculated, and statistical independence tests assessed associations between CRCI and specific networks.
RESULTS: Based on inclusion criteria, six DTI, six VBM, and ten fMRI studies were selected. Chi-square analyses revealed significant associations between CRCI-related changes and several networks: the Dorsal Attention Network (DAN, χ2 = 17.71, P = .0001), Default Mode Network (DMN, χ2 = 21.08, P = .0000002), Frontoparietal Network (FPN, χ2 = 110.63, P = .000002), and Ventral Attention Network (VAN, χ2 = 8.203, P = .01).
CONCLUSIONS: Findings highlight network-specific alterations linked to CRCI symptoms in breast cancer survivors. These results enhance understanding of CRCI and may guide targeted therapeutic strategies.