Cerebellum and nucleus caudatus asymmetry in major depressive disorder

Year 2022, Volume: 6 Issue: 4, 470 - 475, 01.04.2022

Burak Okumuş Mert Besenek Doğancan Sönmez Fatma Beyazal Çeliker Cicek Hocaoglu

https://doi.org/10.28982/josam.939233

Abstract

Background/Aim: The relationship between major depressive disorder (MDD) and specific brain regions was investigated using neuroimaging methods. Although the findings show structural hemispheric asymmetry, research has often focused on the specific brain region involved in MDD. This study aimed to investigate asymmetry in the brain regions of MDD patients for the first time with volBrain, which is a fully automated segmentation technique.
Methods: Our study was designed as a case-control study. Structural asymmetry was evaluated using the current web-based fully automated segmentation algorithm, volBrain, that analyzes volumetric T1 axial magnetic resonance imaging data. Sixteen cases with MDD and 14 healthy controls were analyzed. For comparison of continuous data between binary groups, an independent T-test was used for data that follow a normal distribution and Mann–Whitney U (MWU) test was used for data that did not follow a normal distribution while categorical data were evaluated using Chi-square test (or Fisher’s exact test when needed).
Results: There was no significant difference in terms of gender (χ2 [1, n = 30] = 0.117, P = 0.732), education level (2 [1, n = 30] = 0.002; P = 0.961] and marital status (P = 0.596, Fisher exact chi-square test). However, both groups were found to be similar in terms of age (P = 0.608, MWU test). Right/left nucleus caudatus volume ratios (P = 0.028, MWU test) and right/left cerebellum volume ratios were significantly smaller in the case group (P = 0.006, independent T-test). When the volumes of the right and left parts were compared, only the volume of the right globus pallidus was larger (statistically significant) in the case group (P = 0.008, independent T-test).
Conclusion: In line with our hypothesis, our study supports the notion of cortico–striatal–pallidal–thalamic circuit abnormalities in current MDD research and found that some regions in this phase may contain structural asymmetry. In addition, this study contributed to the literature consisting of studies that have examined the relationship between cerebellum and MDD by adding that the cerebellum may show structural asymmetry. The results of our study suggest that research using volBrain may be beneficial to patients with MDD. Current web-based fully automatic segmentation algorithms can restrict both the rater-induced differences in manual segmentation applications and the differences that various segmentation algorithms can create. The challenge of multicenter research can be overcome by using web-based fully automated segmentation volumetry systems and data containing the same standardized magnetic resonance imaging (MRI) acquisition parameters because it is easy for clinicians around the world to access web-based fully automated segmentation volumetry systems. Research on fully automatic segmentation techniques might be the driving force behind fully understanding biological foundations of MDD in the future.

Keywords

Asymmetrical, Cerebellum, Depression, Nucleus caudatus, volBrain

References

• 1. Cizza G, Ronsaville DS, Kleitz H, Eskandari F, Mistry S, Torvik S, et al. Clinical subtypes of depression are associated with specific metabolic parameters and circadian endocrine profiles in women: The power study. PLoS ONE. 2012;7(1):1-9.
• 2. Jiang X, Shen Y, Yao J, Zhang L, Xu L, Feng R, et al. Connectome analysis of functional and structural hemispheric brain networks in major depressive disorder. Transl psychiatry. 2019;9:1-12.
• 3. Fang Y, Mao R. Introduction. In: Fang Y, editor. Depressive Disorders: Mechanisms, Measurement and Management. 1st ed. Springer Nature Singapore Pte Ltd.; 2019. p.1–19.
• 4. Masdeu JC. Neuroimaging in psychiatric disorders. Neurotherapeutics. 2011; 8:93–102.
• 5. Peng D, Yao Z. Neuroimaging Advance in Depressive Disorder. In: Fang Y, editor. Depressive Disorders: Mechanisms, Measurement and Management. 1st ed. Springer Nature Singapore Pte Ltd.; 2019. p.59–85.
• 6. Pizzagalli DA, Holmes AJ, Dillon DG, Goetz EL, Birk JL, Bogdan R, et al. Reduced caudate and nucleus accumbens response to rewards in unmedicated individuals with major depressive disorder. Am J Psychiatry. 2009;166:702–10.
• 7. Han KM, De Berardis D, Fornaro M, Kim YK. Differentiating between bipolar and unipolar depression in functional and structural MRI studies. Prog. Neuro-Psychopharmacol. Biol. Psychiatry. 2019;91:20–7.
• 8. De Kovel CGF, Aftanas L, Aleman A, Alexander-Bloch AF, Baune BT, Brack I, et al. No alterations of brain structural asymmetry in major depressive disorder: An ENIGMA consortium analysis. Am J Psychiatry. 2019;176:1039–49.
• 9. Pereira DM, Khan A. Brain Lateralization of Emotional Processing in Depression. In: Breznoscakova D, editor. Depression. IntechOpen; 2017;25–33.
• 10. Murrough JW, Collins KA, Fields J, DeWilde KE, Phillips ML, Mathew SJ, et al. Regulation of neural responses to emotion perception by ketamine in individuals with treatment-resistant major depressive disorder. Transl psychiatry. 2015;5:1-7.
• 11. Grieve SM, Korgaonkar MS, Koslow SH, Gordon E, Williams LM. Widespread reductions in gray matter volume in depression. NeuroImage Clin. 2013;3:332–9.
• 12. Choi KW, Han KM, Kim H, Kim A, Kang W, Kang Y, et al. Comparison of shape alterations of the thalamus and caudate nucleus between drug-naïve major depressive disorder patients and healthy controls. J Affect Disord. 2020;264:279–85.
• 13. Bruder GE, Stewart JW, Hellerstein D, Alvarenga JE, Alschuler D, McGrath PJ. Abnormal functional brain asymmetry in depression: Evidence of biologic commonality between major depression and dysthymia. Psychiatry Res. 2012;196:250–4.
• 14. Zuo Z, Ran S, Wang Y, Li C, Han Q, Tang Q, et al. Asymmetry in cortical thickness and subcortical volume in treatment-naïve major depressive disorder. NeuroImage Clin. 2019;21:1–6.
• 15. Rashid B, Calhoun V. Towards a brain-based predictome of mental illness. Hum Brain Mapp. 2020;41:3468–535.
• 16. Bruder GE, Stewart JW, McGrath PJ. Right brain, left brain in depressive disorders: Clinical and theoretical implications of behavioral, electrophysiological and neuroimaging findings. Neurosci biobehav rev. 2017;78:178–91.
• 17. Andreescu C, Butters MA, Begley A, Rajji T, Wu M, Meltzer CC, et al. Gray matter changes in late life depression - A structural MRI analysis. Neuropsychopharmacology. 2008;33:2566–72.
• 18. Zhang H, Li L, Wu M, Chen Z, Hu X, Chen Y, et al. Brain gray matter alterations in first episodes of depression: A meta-analysis of whole-brain studies. Neurosci Biobehav Rev. 2016;60:43–50.
• 19. Bora E, Fornito A, Pantelis C, Yücel M. Gray matter abnormalities in Major Depressive Disorder: A meta-analysis of voxel based morphometry studies. J Affect Disord. 2012;138:9–18.
• 20. Pizzagalli DA. Frontocingulate dysfunction in depression: Toward biomarkers of treatment response. Neuropsychopharmacology. 2011;36:183–206.
• 21. Minichino A, Bersani FS, Trabucchi G, Albano G, Primavera M, Chiaie RD, et al. The role of cerebellum in unipolar and bipolar depression: A review of the main neurobiological findings. Riv Psichiatr. 2014;49:124–31.
• 22. Pierce JE, Péron J. The basal ganglia and the cerebellum in human emotion. Soc Cogn Affect Neurosci. 2020;15:599–613.
• 23. Escalona PR, Early B, McDonald WM, Doraiswamy PM, Shah SA, Husain MM, et al. Reduction of cerebellar volume in major depression: A controlled MRI study. Depression. 1993;1:156–8.
• 24. Peng J, Liu J, Nie B, Li Y, Shan B, Wang G, et al. Cerebral and cerebellar gray matter reduction in first-episode patients with major depressive disorder: A voxel-based morphometry study. Eur J Radiol. 2011;80:395–9.
• 25. Akudjedu TN, Nabulsi L, Makelyte M, Scanlon C, Hehir S, Casey H, et al. A comparative study of segmentation techniques for the quantification of brain subcortical volume. Brain imaging behav. 2018;12:1678–95.
• 26. Doring TM, Kubo TTA, Cruz LCH, Juruena MF, Fainberg J, Domingues RC, et al. Evaluation of hippocampal volume based on MR imaging in patients with bipolar affective disorder applying manual and automatic segmentation techniques. J Magn Reson Imaging. 2011;33:565–72.
• 27. Van Erp TG, Hibar DP, Rasmussen JM, Glahn DC, Pearlson GD, Andreassen OA, et al. Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol Psychiatry. 2016;21:547–53.
• 28. Franke B, Stein JL, Ripke S, Anttila V, Hibar DP, van Hulzen KJE, et al. Schizophrenia Working Group of the Psychiatric Genomics Consortium: ENIGMA Consortium, O'Donovan MC, Thompson PM, Neale BM, Medland SE, Sullivan PF. Genetic influences on schizophrenia and subcortical brain volumes: Large-scale proof of concept. Nat Neurosci. 2016;19:420–31.
• 29. Hannoun S, Tutunji R, El Homsi M, Saaybi S, Hourani R. Automatic Thalamus Segmentation on Unenhanced 3D T1 Weighted Images: Comparison of Publicly Available Segmentation Methods in a Pediatric Population. Neuroinformatics. 2019;17:443–50.
• 30. Manjon J V, Coupe P. volBrain: An Online MRI Brain Volumetry System. Front neuroinform. 2016;10:1-14.
• 31. Næss-Schmidt E, Tietze A, Blicher JU, Petersen M, Mikkelsen IK, Coupé P, et al. Automatic thalamus and hippocampus segmentation from MP2RAGE: comparison of publicly available methods and implications for DTI quantification. Int J Comput Assist Radiol Surg. 2016;11:1979–91.
• 32. Hedderich DM, Spiro JE, Goldhardt O, Kaesmacher J, Wiestler B, Yakushev I, et al. Increasing Diagnostic Accuracy of Mild Cognitive Impairment due to Alzheimer’s Disease by User-Independent, Web-Based Whole-Brain Volumetry. J Alzheimers Dis. 2018;65:1459–67.
• 33. Özkürkçügil A, Aydemir Ö, Yıldız M, Esen Danacı A, Köroğlu E. DSM-IV Eksen I Bozuklukları için Yapılandırılmış Klinik Görüşmenin Türkçeye Uyarlanması ve Güvenilirlik Çalışması. İlaç ve Tedavi Dergisi. 1999;12:233–6.