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Associations between disrupted functional brain network topology and cognitive impairment in patients with rectal cancer during chemotherapy

Guo, Yesong, Liu, Siwen, Yan, Fei, Yin, Na, Ni, Jie, Li, Chenchen, Pan, Xuan, Ma, Rong, Wu, Jianzhong, Li, Shengwei, Li, Xiaoyou·2022·Frontiers in Oncology

Abstract

Introduction: Cognitive impairment has been identified in patients with non-central nervous system cancer received chemotherapy. Chemotherapy-induced changes in the brain are considered as the possible causes of the cognitive deficits of patients. This study aimed to explore chemotherapy-related functional brain changes and cognitive impairment in rectal cancer (RC) patients who had just finished chemotherapy treatment. Methods: In this study, RC patients after chemotherapy (on the day patients received the last dose of chemotherapy) (n=30) and matched healthy controls (HCs) (n=30) underwent cognitive assessments, structural magnetic resonance imaging (MRI) and resting-state functional MRI. The functional brain networks were constructed by thresholding the partial correlation matrices of 90 brain regions in the Anatomical Automatic Labeling template and the topologic properties were evaluated by graph theory analysis. Moreover, correlations between altered topological measures and scores of cognitive scales were explored in the patient group. Results: Compared with HCs, RC patients had lower scores of cognitive scales. The functional brain network had preserved small-world topological features but with a tendency towards higher path length in the whole network. In addition, patients had decreased nodal global efficiency (E glo(i)) in the left superior frontal gyrus (dorsolateral), superior frontal gyrus (orbital part), inferior frontal gyrus (opercular part), inferior frontal gyrus (triangular part) and right inferior frontal gyrus (triangular part). Moreover, values of E glo(i) in the superior and inferior frontal gyrus were positively associated with cognitive function in the patient group. Conclusion: These results suggested that cognitive impairment was associated with disruptions of the topological organization in functional brain networks of RC patients who had just finished chemotherapy, which provided new insights into the pathophysiology underlying acute effects of chemotherapy on cognitive function.

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Research works printed but not linked: 61

  1. Ferlay, 2015, Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012, Int J Cancer, 136, 10.1002/ijc.29210 · DOI · Paper page
  2. Rawla, 2019, Epidemiology of colorectal cancer: Incidence, mortality, survival, and risk factors, Przeglad Gastroenterol, 14, 89, 10.5114/pg.2018.81072 · DOI · Paper page
  3. Benson, 2015, Rectal cancer, version 2.2015, J Natl Compr Cancer Netw, 13, 10.6004/jnccn.2015.0087 · DOI
  4. Maringe, 2013, Stage at diagnosis and colorectal cancer survival in six high-income countries: A population-based study of patients diagnosed during 2000-2007, Acta Oncol (Stockholm Sweden), 52, 10.3109/0284186X.2013.764008 · DOI · Paper page
  5. Papaccio, 2020, Neoadjuvant chemotherapy in locally advanced rectal cancer, Cancers, 12, 10.3390/cancers12123611 · DOI
  6. Ludmir, 2017, Total neoadjuvant therapy for rectal cancer: An emerging option, Cancer, 123, 10.1002/cncr.30600 · DOI · Paper page
  7. Peeters, 2007, The TME trial after a median follow-up of 6 years: Increased local control but no survival benefit in irradiated patients with resectable rectal carcinoma, Ann Surg, 246, 693, 10.1097/01.sla.0000257358.56863.ce · DOI · Paper page
  8. van Gijn, 2011, Preoperative radiotherapy combined with total mesorectal excision for resectable rectal cancer: 12-year follow-up of the multicentre, randomised controlled TME trial, Lancet Oncol, 12, 10.1016/S1470-2045(11)70097-3 · DOI · Paper page
  9. Ma, 2019, Is adjuvant chemotherapy necessary for locally advanced rectal cancer patients with pathological complete response after neoadjuvant chemoradiotherapy and radical surgery? a systematic review and meta-analysis, Int J Colorect Dis, 34, 10.1007/s00384-018-3181-9 · doi:10.1007/s00384-018-3181-9
  10. Roselló, 2018, The role of chemotherapy in localized and locally advanced rectal cancer: A systematic revision, Cancer Treat Rev, 63, 10.1016/j.ctrv.2018.01.001 · DOI
  11. Conroy, 2021, Neoadjuvant chemotherapy with FOLFIRINOX and preoperative chemoradiotherapy for patients with locally advanced rectal cancer (UNICANCER-PRODIGE 23): A multicentre, randomised, open-label, phase 3 trial, Lancet Oncol, 22, 10.1016/S1470-2045(21)00079-6 · DOI · Paper page
  12. Rödel, 2015, Oxaliplatin added to fluorouracil-based preoperative chemoradiotherapy and postoperative chemotherapy of locally advanced rectal cancer (the German CAO/ARO/AIO-04 study): Final results of the multicentre, open-label, randomised, phase 3 trial, Lancet Oncol, 16, 10.1016/S1470-2045(15)00159-X · DOI · Paper page
  13. Taillibert, 2016, Chemotherapy-related neurotoxicity, Curr Neurol Neurosci Rep, 16, 81, 10.1007/s11910-016-0686-x · DOI
  14. Hodgson, 2013, A meta-analysis of the effects of chemotherapy on cognition in patients with cancer, Cancer Treat Rev, 39, 297, 10.1016/j.ctrv.2012.11.001 · DOI · Paper page
  15. Niu, 2021, Chemotherapy-induced grey matter abnormalities in cancer survivors: A voxel-wise neuroimaging meta-analysis, Brain Imaging Behav, 15, 10.1007/s11682-020-00402-7 · DOI
  16. Li, 2018, Longitudinal assessment of chemotherapy-induced changes in brain and cognitive functioning: A systematic review, Neurosci Biobehav Rev, 92, 10.1016/j.neubiorev.2018.05.019 · DOI
  17. Raimondo, 2021, Advances in resting state fMRI acquisitions for functional connectomics, NeuroImage, 243, 118503, 10.1016/j.neuroimage.2021.118503 · DOI · Paper page
  18. Ibrahim, 2021, Diagnostic power of resting-state fMRI for detection of network connectivity in alzheimer's disease and mild cognitive impairment: A systematic review, Hum Brain Mapp, 42, 10.1002/hbm.25369 · DOI · Paper page
  19. Farràs-Permanyer, 2015, Mild cognitive impairment and fMRI studies of brain functional connectivity: The state of the art, Front Psychol, 6, 10.3389/fpsyg.2015.01095 · DOI
  20. Feng, 2019, Chemotherapy-induced brain changes in breast cancer survivors: Evaluation with multimodality magnetic resonance imaging, Brain Imaging Behav, 13, 10.1007/s11682-019-00074-y · DOI
  21. Zhang, 2020, Chemotherapy-induced functional changes of the default mode network in patients with lung cancer, Brain Imaging Behav, 14, 10.1007/s11682-018-0030-y · DOI
  22. Van der Gucht, 2020, Effects of a mindfulness-based intervention on cancer-related cognitive impairment: Results of a randomized controlled functional magnetic resonance imaging pilot study, Cancer, 126, 10.1002/cncr.33074 · DOI
  23. Farahani, 2019, Application of graph theory for identifying connectivity patterns in human brain networks: A systematic review, Front Neurosci, 13, 10.3389/fnins.2019.00585 · DOI · Paper page
  24. Xu, 2021, The integrated understanding of structural and functional connectomes in depression: A multimodal meta-analysis of graph metrics, J Affect Disord, 295, 10.1016/j.jad.2021.08.120 · DOI
  25. Rubinov, 2010, Complex network measures of brain connectivity: uses and interpretations, NeuroImage, 52, 10.1016/j.neuroimage.2009.10.003 · DOI · Paper page
  26. Lazarou, 2019, Is brain connectome research the future frontier for subjective cognitive decline? A systematic review, Clin Neurophysiol, 130, 10.1016/j.clinph.2019.07.004 · DOI
  27. Cremers, 2017, The relation between statistical power and inference in fMRI, PloS One, 12, 10.1371/journal.pone.0184923 · DOI · Paper page
  28. Yarkoni, 2009, Big correlations in little studies: Inflated fMRI correlations reflect low statistical power-commentary on vul et al. (2009), Perspect Psychol Sci, 4, 10.1111/j.1745-6924.2009.01127.x · DOI · Paper page
  29. Mitrushina, 1991, Reliability and validity of the mini-mental state exam in neurologically intact elderly, J Clin Psychol, 47, 10.1002/1097-4679(199107)47:4<537::AID-JCLP2270470411>3.0.CO;2-9 · doi:10.1002/1097-4679(199107)47:4<537::aid-jclp2270470411>3.0.co;2-9
  30. Nasreddine, 2005, The Montreal cognitive assessment, MoCA: A brief screening tool for mild cognitive impairment, J Am Geriatr Soc, 53, 10.1111/j.1532-5415.2005.53221.x · DOI · Paper page
  31. Cheung, 2013, Linguistic validation of functional assessment of cancer therapy-cognitive function (FACT-cog): Methodological concerns, Support Care Cancer, 21, 10.1007/s00520-012-1631-4 · doi:10.1007/s00520-012-1631-4
  32. Chao-Gan, 2010, DPARSF: A MATLAB toolbox for "Pipeline" data analysis of resting-state fMRI, Front Syst Neurosci, 4, 10.3389/fnsys.2010.00013 · DOI · Paper page
  33. Tzourio-Mazoyer, 2002, Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain, NeuroImage, 15, 10.1006/nimg.2001.0978 · DOI · Paper page
  34. Liu, 2022, Topological abnormalities of pallido-Thalamo-Cortical circuit in functional brain network of patients with nonchemotherapy with non-small cell lung cancer, Front Neurol, 13, 10.3389/fneur.2022.821470 · DOI
  35. Wang, 2015, GRETNA: A graph theoretical network analysis toolbox for imaging connectomics, Front Hum Neurosci, 9, 10.3389/fnhum.2015.00386 · DOI · Paper page
  36. Cao, 2017, Early development of functional network segregation revealed by connectomic analysis of the preterm human brain, Cereb Cortex (New York NY: 1991), 27, 10.1093/cercor/bhw038 · DOI · Paper page
  37. Zhang, 2011, Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder, Biol Psychiatry, 70, 10.1016/j.biopsych.2011.05.018 · DOI · Paper page
  38. Ono, 2015, A meta-analysis of cognitive impairment and decline associated with adjuvant chemotherapy in women with breast cancer, Front Oncol, 5, 10.3389/fonc.2015.00059 · DOI
  39. Lv, 2020, Pathogenesis, assessments, and management of chemotherapy-related cognitive impairment (CRCI): An updated literature review, J Oncol, 2020, 3942439, 10.1155/2020/3942439 · DOI
  40. Dietrich, 2015, Chemotherapy, cognitive impairment and hippocampal toxicity, Neuroscience, 309, 10.1016/j.neuroscience.2015.06.016 · DOI
  41. Cascella, 2018, Chemotherapy-related cognitive impairment: Mechanisms, clinical features and research perspectives, Recenti Progressi Med, 109, 10.1701/3031.30 · doi:10.1701/3031.30
  42. Saykin, 2013, Neuroimaging biomarkers and cognitive function in non-CNS cancer and its treatment: Current status and recommendations for future research, Brain Imaging Behav, 7, 10.1007/s11682-013-9283-7 · DOI
  43. Reuter-Lorenz, 2013, Cognitive function and breast cancer: Promise and potential insights from functional brain imaging, Breast Cancer Res Treat, 137, 33, 10.1007/s10549-012-2266-3 · DOI
  44. Chen, 2016, Convergent and divergent intranetwork and internetwork connectivity patterns in patients with remitted late-life depression and amnestic mild cognitive impairment, Cortex J Devoted Study Nervous Syst Behav, 83, 194, 10.1016/j.cortex.2016.08.001 · DOI
  45. Eyler, 2019, Resting state abnormalities of the default mode network in mild cognitive impairment: A systematic review and meta-analysis, J Alzheimer's Dis, 70, 10.3233/JAD-180847 · DOI
  46. Bezdicek, 2018, Mild cognitive impairment disrupts attention network connectivity in parkinson's disease: A combined multimodal MRI and meta-analytical study, Neuropsychologia, 112, 10.1016/j.neuropsychologia.2018.03.011 · DOI
  47. Cascone, 2021, Frontoparietal network resilience is associated with protection against cognitive decline in parkinson's disease, Commun Biol, 4, 1021, 10.1038/s42003-021-02478-3 · DOI
  48. Mak, 2017, The default mode network in healthy individuals: A systematic review and meta-analysis, Brain Connectivity, 7, 25, 10.1089/brain.2016.0438 · DOI · Paper page
  49. Wang, 2018, Aberrant default mode network in amnestic mild cognitive impairment: A meta-analysis of independent component analysis studies, Neurol Sci, 39, 10.1007/s10072-018-3306-5 · DOI
  50. Yuan, 2021, Convergent functional changes of default mode network in mild cognitive impairment using activation likelihood estimation, Front Aging Neurosci, 13, 10.3389/fnagi.2021.708687 · DOI
  51. Buckner, 2008, The brain's default network: anatomy, function, and relevance to disease, Ann New York Acad Sci, 1124, 1, 10.1196/annals.1440.011 · DOI · Paper page
  52. Liu, 2020, Altered rich-club organization and regional topology are associated with cognitive decline in patients with frontal and temporal gliomas, Front Hum Neurosci, 14, 10.3389/fnhum.2020.00023 · DOI
  53. Zhang, 2021, Associations of the disrupted functional brain network and cognitive function in end-stage renal disease patients on maintenance hemodialysis: A graph theory-based study of resting-state functional magnetic resonance imaging, Front Hum Neurosci, 15, 10.3389/fnhum.2021.716719 · DOI
  54. Liu, 2020, Fiber connectivity density in cerebral small-vessel disease patients with mild cognitive impairment and cerebral small-vessel disease patients with normal cognition, Front Neurosci, 14, 10.3389/fnins.2020.00083 · DOI
  55. Cai, 2020, Altered patterns of phase position connectivity in default mode subnetwork of subjective cognitive decline and amnestic mild cognitive impairment, Front Neurosci, 14, 10.3389/fnins.2020.00185 · DOI
  56. Mao, 2021, Disrupted balance of long and short-range functional connectivity density in alzheimer's disease (AD) and mild cognitive impairment (MCI) patients: A resting-state fMRI study, Ann Trans Med, 9, 65, 10.21037/atm-20-7019 · DOI
  57. Tang, 2013, Identifying individuals with antisocial personality disorder using resting-state FMRI, PloS One, 8, 10.1371/journal.pone.0060652 · DOI
  58. Tao, 2013, Depression uncouples brain hate circuit, Mol Psychiatry, 18, 10.1038/mp.2011.127 · DOI · Paper page
  59. Esposito, 2018, Modifications in resting state functional anticorrelation between default mode network and dorsal attention network: Comparison among young adults, healthy elders and mild cognitive impairment patients, Brain Imaging Behav, 12, 10.1007/s11682-017-9686-y · DOI
  60. Wang, 2019, Abnormal functional connectivity in cognitive control network, default mode network, and visual attention network in Internet addiction: A resting-state fMRI study, Front Neurol, 10, 10.3389/fneur.2019.01006 · DOI
  61. Esposito, 2018, The role of body image and self-perception in anorexia nervosa: The neuroimaging perspective, J Neuropsychol, 12, 41, 10.1111/jnp.12106 · DOI

Metadata

Authors
Guo, Y., Liu, S., Yan, F., Yin, N., Ni, J., Li, C., Pan, X., Ma, R., Wu, J., Li, S., & Li, X.
Venue
Frontiers in Oncology
Published
2022-11-24
Volume
12
ISSN
2234-943X
Cited by
5
References
4

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CoChat. (2026). Citation check: Associations between disrupted functional brain network topology and cognitive impairment in patients with rectal cancer during chemotherapy. https://app.cochat.ai/papers/10.3389/fonc.2022.927771
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Guo, Y., Liu, S., Yan, F., Yin, N., Ni, J., Li, C., Pan, X., Ma, R., Wu, J., Li, S., & Li, X. (2022). Associations between disrupted functional brain network topology and cognitive impairment in patients with rectal cancer during chemotherapy. Frontiers in Oncology. https://doi.org/10.3389/fonc.2022.927771

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