The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Associations between disrupted functional brain network topology and cognitive impairment in patients with rectal cancer during chemotherapy
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Estimating sample size in functional MRI (fMRI) neuroimaging studies: Statistical power analyses
Desmond, J. E., & Glover, G. H. (2002) · Journal of Neuroscience Methods · doi:10.1016/s0165-0270(02)00121-8 · Cited by 716
Analysis of a large fMRI cohort: statistical and methodological issues for group analyses
Thirion, B., Pinel, P., Mériaux, S., Roche, A., Dehaene, S., & Poline, J.-B. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.11.054 · Cited by 563
Hofheinz, R.-D., Wenz, F., Post, S., Matzdorff, A., Laechelt, S., Hartmann, J. T., Müller, L., Link, H., Moehler, M., Kettner, E., et al. (2012) · The Lancet Oncology · doi:10.1016/s1470-2045(12)70116-x · Cited by 509
Association of functional dorsal attention network alterations with breast cancer and chemotherapy
Shen, C.-Y., Chen, V. C.-H., Yeh, D.-C., Huang, S.-L., Zhang, X.-R., Chai, J.-W., Huang, Y.-H., Chou, M.-C., & Weng, J.-C. (2019) · Scientific Reports · doi:10.1038/s41598-018-36380-6 · Cited by 34
Research works printed but not linked: 61
- 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
- Rawla, 2019, Epidemiology of colorectal cancer: Incidence, mortality, survival, and risk factors, Przeglad Gastroenterol, 14, 89, 10.5114/pg.2018.81072 · DOI · Paper page
- Benson, 2015, Rectal cancer, version 2.2015, J Natl Compr Cancer Netw, 13, 10.6004/jnccn.2015.0087 · DOI
- 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
- Papaccio, 2020, Neoadjuvant chemotherapy in locally advanced rectal cancer, Cancers, 12, 10.3390/cancers12123611 · DOI
- Ludmir, 2017, Total neoadjuvant therapy for rectal cancer: An emerging option, Cancer, 123, 10.1002/cncr.30600 · DOI · Paper page
- 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
- 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
- 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
- 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
- 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
- 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
- Taillibert, 2016, Chemotherapy-related neurotoxicity, Curr Neurol Neurosci Rep, 16, 81, 10.1007/s11910-016-0686-x · DOI
- 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
- 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
- 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
- Raimondo, 2021, Advances in resting state fMRI acquisitions for functional connectomics, NeuroImage, 243, 118503, 10.1016/j.neuroimage.2021.118503 · DOI · Paper page
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Rubinov, 2010, Complex network measures of brain connectivity: uses and interpretations, NeuroImage, 52, 10.1016/j.neuroimage.2009.10.003 · DOI · Paper page
- 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
- Cremers, 2017, The relation between statistical power and inference in fMRI, PloS One, 12, 10.1371/journal.pone.0184923 · DOI · Paper page
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Wang, 2015, GRETNA: A graph theoretical network analysis toolbox for imaging connectomics, Front Hum Neurosci, 9, 10.3389/fnhum.2015.00386 · DOI · Paper page
- 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
- 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
- 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
- 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
- Dietrich, 2015, Chemotherapy, cognitive impairment and hippocampal toxicity, Neuroscience, 309, 10.1016/j.neuroscience.2015.06.016 · DOI
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Tang, 2013, Identifying individuals with antisocial personality disorder using resting-state FMRI, PloS One, 8, 10.1371/journal.pone.0060652 · DOI
- Tao, 2013, Depression uncouples brain hate circuit, Mol Psychiatry, 18, 10.1038/mp.2011.127 · DOI · Paper page
- 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
- 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
- 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
- Publisher
- Frontiers Media SA
- Published
- 2022-11-24
- Volume
- 12
- ISSN
- 2234-943X
- Cited by
- 5
- References
- 4
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- 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
- The paper
- 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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.