Modern neuroimaging techniques in the assessment of changes in the brain connectome in patients with postmastectomy syndrome

Relevance. Currently, neuropsychiatric disorders are considered one of the main ones in the pathogenesis of postmastectomy syndrome. Modern neuroimaging techniques — functional (fMRI) and diffusion tensor (DTI) magnetic resonance imaging — allow us to identify functional and structural changes in the brain connectome in patients with postmastectomy syndrome caused by a complex of neuropsychiatric disorders.

The purpose of the study. To evaluate changes in the functional and structural connectome of the brain in patients with postmastectomy syndrome using fMRI and DTI techniques.

Materials and methods. The study was carried out on a tomograph with a magnetic field induction of 3.0 T. 46 patients with neurological disorders in the long-term postoperative period (more than 6 months) after radical mastectomy, chemotherapeutic and/or radiation treatment of breast cancer were examined.

Results. According to the results of the intergroup statistical analysis, all 46 patients with postmastectomy syndrome had differences in functional connectivity in the default mode network and quantitative fractional anisotropy in the white matter tracts of the brain compared with the control group (p < 0.01).

Conclusion. The use of fMRI and DTI in patients with postmastectomy syndrome makes it possible to identify changes in the brain connectome correlating with neurological disorders and a decrease in the quality of life of patients. The results obtained will allow improving treatment and rehabilitation approaches in patients receiving treatment for breast cancer.

1. DeSantis CE, Bray F, Ferlay J, et al. International Variation in Female Breast Cancer Incidence and Mortality Rates. Cancer Epidemiol Biomarkers Prev. 2015 Oct;24(10):1495–506. DOI: 10.1158/1055-9965.EPI-15-0535.

2. Fakhari S, Atashkhoei S, Pourfathi H, et al. Postmastectomy Pain Syndrome. International Journal of Women’s Health and Reproduction Sciences. 2017;5(1):18–23. https://doi.org/10.15296/ijwhr.2017.04

3. Tishakova VE, Filonenko EV, Chissov VI, et al. Physical methods of rehabilitation in cancer patients after combined modality treatment for breast cancer. Biomedical Photonics. 2017;6(1):28–37. (In Russ.)

4. Stepanova AM, Merzlyakova AM, Khulamkhanova MM, et al. Postmastectomy syndrome: secondary lymphedema of the upper extremities after combined treatment of breast cancer (literature review and own results) // Modern oncology. — 2018. — Vol. 20. — No 2. — P. 45–49. (In Russ.). DOI: 10.26442/1815-1434_2018.2.45-49

5. Wisotzky E, Hanrahan N, Lione TP, et al. Deconstructing Postmastectomy Syndrome: Implications for Physiatric Management. Phys Med Rehabil Clin N Am. 2017 Feb;28(1):153–169. DOI: 10.1016/j.pmr.2016.09.003.

6. Beyaz SG, et al. Postmastectomy Pain: A Crosssectional Study of Prevalence, Pain Characteristics, and Effects on Quality of Life. Chin Med J (Engl). 2016 Jan 5;129(1):66–71.

7. Capuco A, Urits I, Orhurhu V, et al. A Comprehensive Review of the Diagnosis, Treatment, and Management of Postmastectomy Pain Syndrome. Curr Pain Headache Rep. 2020 Jun 11;24(8):41. DOI: 10.1007/s11916-020-00876-6.

8. Obmanov IV, Yarygin ML, Shmyrev VI, et al. Neurological disorders in breast cancer patients after surgical treatment. Journal of Neurology and Psychiatry named after S. S. Korsakov. 2015;115(8):42–44. (In Russ.)

9. Stubblefield MD, Keole N. Upper Body Pain and Functional Disorders in Patients With Breast Cancer. PM&R. 2014;6:170–183. https://doi.org/10.1016/j.pmrj.2013.08.605

10. Fomberstein K, Qadri S, Ramani R. Functional MRI and pain. Curr Opin Anaesthesiol. 2013 Oct;26(5):588– 93. DOI: 10.1097/01.aco.0000433060.59939.fe.

11. Legrain V, Iannetti GD, Plaghki L, et al. The pain matrix reloaded: a salience detection system for the body. Prog Neurobiol. 2011. Vol. 93. No 1. P. 111–24. DOI: 10.1016/j.pneurobio

12. Lepekhina AS, Pospelova ML, Efimtsev AYu, et al. Clinical and neuroimaging comparison of the dynamics of functional connectivity of the brain in patients with chronic tension headache. Modern problems of science and education. — 2021. — № 3 (In Russ.)

13. Shikhkerimov RK, Savin AA, Welsher LZ, et al. Pathology of the brachial neurovascular bundle in clinical manifestations of post-mastectomy syndrome. Bulletin of the National Medical and Surgical Center named after N. I. Pirogov, 2011; 6 (4), 86–90. (In Russ.)

14. Magomedov MM, Khalitov IA, Mikhailova BI, et al. Psychoemotional problems of patients with postmastectomy syndrome. Modern problems of science and education. — 2009. — № 9 (In Russ.)

15. Maslyakov VV, Levina VA, Nagaeva EYu. Quality of life and postoperative rehabilitation of breast cancer patients // Medical Bulletin of the North Caucasus. 2014. №1 (33). (In Russ.) http://doi.org/10.14300/mnnc.2014.09007

16. Efimova MYu, Ivanova NE, Alekseeva TM, et al. Pathophysiological bases of restoration of higher cortical functions // Modern problems of science and education. — 2019. — № 3.

17. Hänggi J, Fövenyi L, Liem F, et al. The hypothesis of neuronal interconnectivity as a function of brain size-a general organization principle of the human connectome. Front Hum Neurosci. 2014 Nov 11;8:915. DOI: 10.3389/fnhum.2014.00915.

18. Lv H, Wang Z, Tong E, et al. Resting-State Functional MRI: Everything That Nonexperts Have Always Wanted to Know. ANJR. American Journal of Neuroradiology. 2018. V. 39. No. 8. P. 390–1399. https://doi.org/10.3174/ajnr.A5527.

19. Mak LE, Minuzzi L, MacQueen G, et al. The Default Mode Network in Healthy Individuals: A Systematic Review and Meta-Analysis. Brain Connect. 2017. V. 7. No. 1. P. 25–33. https://doi.org/10.1089/brain.2016.0438

20. Baliki MN, Mansour AR, Baria AT, et al. Functional reorganization of the default mode network across chronic pain conditions. PLoS One. 2014. Vol. 9. No. 9: e106133. DOI: 10.1371/journal.pone.0106133.

21. Chung MK, Hanson JL, Adluru N, et al. Integrative Structural Brain Network Analysis in Diffusion Tensor Imaging // Brain Connectivity. — 2017. — Vol. 7, No 6. — P. 331–346.

22. Trivedi R, Rathore RKS, Gupta RK. Review: Clinical application of diffusion tensor imaging. Indian J Radiol Imaging. 2008;18(1):45–52. DOI:10.4103/0971-3026.38505.

23. O’Donnell LJ, Westin CF. An introduction to diffusion tensor image analysis. Neurosurg Clin N Am. 2011;22(2):185-viii. DOI:10.1016/j.nec.2010.12.004.

24. Zhang Y, Vakhtin AA, Jennings JS, et al. Diffusion tensor tractography of brainstem fibers and its application in pain. PLoS One. 2020 Feb 18;15(2):e0213952. DOI: 10.1371/journal.pone.0213952.

25. Liao Y, Huang X, Wu Q, et al. Is depression a disconnection syndrome? Meta-analysis of diffusion tensor imaging studies in patients with MDD. J Psychiatry Neurosci. 2013;38(1):49–56. DOI:10.1503/jpn.110180.

26. Jao CW, Soong BW, Huang CW, et al. Diffusion Tensor Magnetic Resonance Imaging for Differentiating Multiple System Atrophy Cerebellar Type and Spinocerebellar Ataxia Type 3. Brain Sci. 2019 Dec 3;9(12):354. DOI: 10.3390/brainsci9120354..

27. Efimtsev AYu, Fokin VA, Trufanov AG, et al. Lesion of myelinated fibers in Parkinson’s disease: possibilities of diffusion tensor magnetic resonance tractography. Visualization in Medicine 2016, 1(1), 22–29. (In Russ.)

28. Stouten-Kemperman MM, de Ruiter MB, Koppelmans V, et al. Neurotoxicity in breast cancer survivors ≥10 years post-treatment is dependent on treatment type // Brain Imaging and Behavior. — 2015. — Vol. 9. — P. 275–284.

29. Kesler SR, Watson CL, Blayney DW. Brain network alterations and vulnerability to simulated neurodegeneration in breast cancer // Neurobiol Aging. — 2015. — Vol. 36, No 8. — P. 2429–2442.

30. Li TY, Chen VC, Yeh DC, et al. Investigation of chemotherapy-induced brain structural alterations in breast cancer patients with generalized q-sampling MRI and graph theoretical analysis. BMC Cancer. 2018;18(1):1211. Published 2018 Dec 4. DOI:10.1186/s12885-018-5113-z.

31. Yeh FC, Badre D, Verstynen T. Connectometry: A statistical approach harnessing the analytical potential of the local connectome. Neuroimage. 2016 Jan 15;125:162–171. DOI: 10.1016/j.neuroimage.2015.10.053.

32. Yeh FC, Verstynen TD, Wang Y, et al. Deterministic diffusion fiber tracking improved by quantitative anisotropy. PLoS One. 2013 Nov 15;8(11):e80713. DOI: 10.1371/journal.pone.0080713.

33. Yeh FC, Panesar S, Barrios J, et al. Automatic Removal of False Connections in Diffusion MRI Tractography Using Topology-Informed Pruning (TIP). Neurotherapeutics. 2019 Jan;16(1):52–58. DOI: 10.1007/s13311-018-0663-y.

34. Cai S, Chong T, Zhang Y, et al. Alzheimer’s Disease Neuroimaging Initiative. Altered Functional Connectivity of Fusiform Gyrus in Subjects with Amnestic Mild Cognitive Impairment: A Resting-State fMRI Study. FrontHumNeurosci. 2015 Aug27;9:471. DOI: 10.3389/fnhum.2015.00471.

35. Simone Kühn, Jürgen Gallinat, Resting-State Brain Activity in Schizophrenia and Major Depression: A Quantitative Meta-Analysis, Schizophrenia Bulletin, Volume 39, Issue 2, March 2013, Pages 358–365, https://doi.org/10.1093/schbul/sbr151

36. Brownstone RM, Chopek JW. Reticulospinal Systems for Tuning Motor Commands. Front Neural Circuits. 2018;12:30. Published 2018 Apr 18. DOI:10.3389/fncir.2018.00030.

37. Kwon HG, Hong JH, Hong CP, et al. Dentatorubrothalamic tract in human brain: diffusion tensor tractography study. Neuroradiology. 2011 Oct;53(10):787–91. DOI: 10.1007/s00234-011-0878-7.

38. Petersen KJ, Reid JA, Chakravorti S, et al. Structural and functional connectivity of the nondecussating dentato-rubro-thalamic tract. Neuroimage. 2018 Aug 1;176:364–371. DOI: 10.1016/j.neuroimage.2018.04.074.