Abstract
Major depressive disorder (MDD) is frequently characterized as a disorder of the disconnection syndrome. Diffusion tensor imaging (DTI) has played a critical role in supporting this view, with much investigation providing a large amount of evidence of structural connectivity abnormalities in the disorder. Recent research on the human connectome combined neuroimaging techniques with graph theoretic methods to highlight the disrupted topological properties of large-scale structural brain networks under depression, involving global metrics (e.g., global and local efficiencies), and local nodal properties (e.g., degree and betweenness), as well as other related metrics, including a modular structure, assortativity, and (rich) hubs. Here, we review the studies of white matter networks in the case of MDD with the application of these techniques, focusing principally on the consistent findings and the clinical significance of DTI-based network research, while discussing the key methodological issues that frequently arise in the field. The already published literature shows that MDD is associated with a widespread structural connectivity deficit. Topological alteration of structural brain networks in the case of MDD points to decreased overall connectivity strength and reduced global efficiency as well as decreased small-worldness and network resilience. These structural connectivity disturbances entail potential functional consequences, although the relationship between the two is very sophisticated and requires further investigation. In summary, the present study comprehensively maps the structural connectomic disturbances in patients with MDD across the entire brain, which adds important weight to the view suggesting connectivity abnormalities of this disorder and highlights the potential of network properties as diagnostic biomarkers in the psychoradiology field. Several common methodological issues of the study of DTI-based networks are discussed, involving sample heterogeneity and fiber crossing problems and the tractography algorithms. Finally, suggestions for future perspectives, including imaging multimodality, a longitudinal study and computational connectomics, in the further study of white matter networks under depression are given. Surmounting these challenges and advancing the research methods will be required to surpass the simple mapping of connectivity changes to illuminate the underlying psychiatric pathological mechanism.
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References
Jia Z, Huang X, Wu Q, Zhang T, Lui S, Zhang J et al (2010) High-field magnetic resonance imaging of suicidality in patients with major depressive disorder. Am J Psychiatry 167(11):1381–1390
North CS, Baron D, Chen AF (2018) Prevalence and predictors of postdisaster major depression: convergence of evidence from 11 disaster studies using consistent methods. J Psychiatr Res 102:96–101
Chen T, Kendrick KM, Wang J, Wu M, Li K, Huang X et al (2017) Anomalous single-subject based morphological cortical networks in drug-naive, first-episode major depressive disorder. Hum Brain Mapp 38(5):2482–2494
Gong Q, He Y (2015) Depression, neuroimaging and connectomics: a selective overview. Biol Psychiatry 77(3):223–235
Lui S, Zhou XJ, Sweeney JA, Gong Q (2016) Psychoradiology: the frontier of neuroimaging in psychiatry. Radiology 281(2):357–372
van Beek EJR, Kuhl C, Anzai Y, Desmond P, Ehman RL, Gong Q et al (2019) Value of MRI in medicine: more than just another test? J Magn Reson Imaging: JMRI 49(7):e14–e25
Tymofiyeva O, Connolly CG, Ho TC, Sacchet MD, Henje Blom E, LeWinn KZ et al (2017) DTI-based connectome analysis of adolescents with major depressive disorder reveals hypoconnectivity of the right caudate. J Affect Disord 207:18–25
Ho TC, Sacchet MD, Connolly CG, Margulies DS, Tymofiyeva O, Paulus MP et al (2017) Inflexible functional connectivity of the dorsal anterior cingulate cortex in adolescent major depressive disorder. Neuropsychopharmacology: Off Publ Am Coll Neuropsychopharmacol 42(12):2434–2445
Menon V (2011) Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci 15(10):483–506
Connolly CG, Wu J, Ho TC, Hoeft F, Wolkowitz O, Eisendrath S et al (2013) Resting-state functional connectivity of subgenual anterior cingulate cortex in depressed adolescents. Biol Psychiatry 74(12):898–907
Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3):186–198
Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52(3):1059–1069
Fields RD (2008) White matter matters. Sci Am 298(3):42–49
Sporns O, Tononi G, Kotter R (2005) The human connectome: a structural description of the human brain. PLoS Comput Biol 1(4):e42
Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ et al (2008) Mapping the structural core of human cerebral cortex. PLoS Biol 6(7):e159
Gong G, Rosa-Neto P, Carbonell F, Chen ZJ, He Y, Evans AC (2009) Age- and gender-related differences in the cortical anatomical network. J Neurosci Off J Soc Neurosci 29(50):15684–15693
Hagmann P, Kurant M, Gigandet X, Thiran P, Wedeen VJ, Meuli R et al (2007) Mapping human whole-brain structural networks with diffusion MRI. PLoS One 2(7):e597
Achard S, Bullmore ET (2007) Efficiency and cost of economical brain functional networks. PLoS Comput Biol 3(2):174–183
Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440–442
Latora V, Marchiori M (2001) Efficient behavior of small-world networks. Phys Rev Lett 87(19):198701
Newman ME (2006) Modularity and community structure in networks. Proc Natl Acad Sci U S A 103(23):8577–8582
Freeman LC (1978) Centrality in social networks conceptual clarification. Soc Networks 1(3):215–239
van Haren NEM, Schnack HG, Cahn W, van den Heuvel MP, Lepage C, Collins L et al (2011) Changes in cortical thickness during the course of illness in schizophrenia. Arch Gen Psychiatry 68(9):871–880
Schmidt A, Crossley NA, Harrisberger F, Smieskova R, Lenz C, Riecher-Rossler A et al (2017) Structural network disorganization in subjects at clinical high risk for psychosis. Schizophr Bull 43(3):583–591
Zalesky A, Fornito A, Harding IH, Cocchi L, Yucel M, Pantelis C et al (2010) Whole-brain anatomical networks: does the choice of nodes matter? NeuroImage 50(3):970–983
Suo X, Lei D, Li L, Li W, Dai J, Wang S et al (2018) Psychoradiological patterns of small-world properties and a systematic review of connectome studies of patients with 6 major psychiatric disorders. J Psychiatry Neurosci: JPN 43(5):170214
Bai F, Shu N, Yuan Y, Shi Y, Yu H, Wu D et al (2012) Topologically convergent and divergent structural connectivity patterns between patients with remitted geriatric depression and amnestic mild cognitive impairment. J Neurosci Off J Soc Neurosci 32(12):4307–4318
Mai N, Zhong X, Chen B, Peng Q, Wu Z, Zhang W et al (2017) Weight rich-club analysis in the white matter network of late-life depression with memory deficits. Front Aging Neurosci 9:279
Wang Z, Yuan Y, You J, Zhang Z (2019) Disrupted structural brain connectome underlying the cognitive deficits in remitted late-onset depression. Brain Imaging Behav 14:1600
Chen JH, Yao ZJ, Qin JL, Yan R, Hua LL, Lu Q (2016) Aberrant global and regional topological organization of the fractional anisotropy-weighted brain structural networks in major depressive disorder. Chin Med J 129(6):679–689
Long Z, Duan X, Wang Y, Liu F, Zeng L, Zhao JP et al (2015) Disrupted structural connectivity network in treatment-naive depression. Prog Neuro-Psychopharmacol Biol Psychiatry 56:18–26
Sporns O, Zwi JD (2004) The small world of the cerebral cortex. Neuroinformatics 2(2):145–162
Bai F, Zhang Z, Watson DR, Yu H, Shi Y, Yuan Y et al (2009) Abnormal integrity of association fiber tracts in amnestic mild cognitive impairment. J Neurol Sci 278(1–2):102–106
Caeyenberghs K, Duprat R, Leemans A, Hosseini H, Wilson PH, Klooster D et al (2019) Accelerated intermittent theta burst stimulation in major depression induces decreases in modularity: a connectome analysis. Netw Neurosci (Cambridge, Mass) 3(1):157–172
Yao Z, Zou Y, Zheng W, Zhang Z, Li Y, Yu Y et al (2019) Structural alterations of the brain preceded functional alterations in major depressive disorder patients: evidence from multimodal connectivity. J Affect Disord 253:107–117
Zheng K, Wang H, Li J, Yan B, Liu J, Xi Y et al (2019) Structural networks analysis for depression combined with graph theory and the properties of fiber tracts via diffusion tensor imaging. Neurosci Lett 694:34–40
Park CH, Wang SM, Lee HK, Kweon YS, Lee CT, Kim KT et al (2014) Affective state-dependent changes in the brain functional network in major depressive disorder. Soc Cogn Affect Neurosci 9(9):1404–1412
Li X, Steffens DC, Potter GG, Guo H, Song S, Wang L (2017) Decreased between-hemisphere connectivity strength and network efficiency in geriatric depression. Hum Brain Mapp 38(1):53–67
Chen VC, Shen CY, Liang SH, Li ZH, Tyan YS, Liao YT et al (2016) Assessment of abnormal brain structures and networks in major depressive disorder using morphometric and connectome analyses. J Affect Disord 205:103–111
Vaessen MJ, Jansen JF, Vlooswijk MC, Hofman PA, Majoie HJ, Aldenkamp AP et al (2012) White matter network abnormalities are associated with cognitive decline in chronic epilepsy. Cereb Cortex 22(9):2139–2147
Lu Y, Shen Z, Cheng Y, Yang H, He B, Xie Y et al (2017) Alternations of white matter structural networks in first episode untreated major depressive disorder with short duration. Front Psych 8:205
van den Heuvel MP, Sporns O (2011) Rich-club organization of the human connectome. J Neurosci Off J Soc Neurosci 31(44):15775–15786
Charlton RA, Leow A, GadElkarim J, Zhang A, Ajilore O, Yang S et al (2015) Brain connectivity in late-life depression and aging revealed by network analysis. Am J Geriatr Psychiatry: Off J Am Assoc Geriatr Psychiatry 23(6):642–650
Ajilore O, Lamar M, Kumar A (2014) Association of brain network efficiency with aging, depression, and cognition. Am J Geriatr Psychiatry 22(2):102–110
Davis J, Maes M, Andreazza A, McGrath JJ, Tye SJ, Berk M (2015) Towards a classification of biomarkers of neuropsychiatric disease: from encompass to compass. Mol Psychiatry 20(2):152–153
Weickert CS, Weickert TW, Pillai A, Buckley PF (2013) Biomarkers in schizophrenia: a brief conceptual consideration. Dis Markers 35(1):3–9
Sun YT, Chen TL, He D, Dong ZQ, Cheng BC, Wang S et al (2019) Research progress of biological markers for depression based on psychoradiology and artificial intelligence. Prog Biochem Biophys 46(9):879–899
Jiang X, Shen Y, Yao J, Zhang L, Xu L, Feng R et al (2019) Connectome analysis of functional and structural hemispheric brain networks in major depressive disorder. Transl Psychiatry 9(1):136
Sacchet MD, Prasad G, Foland-Ross LC, Thompson PM, Gotlib IH (2014) Elucidating brain connectivity networks in major depressive disorder using classification-based scoring. Proc IEEE Int Symp Biomed Imaging 2014:246–249
Sacchet MD, Prasad G, Foland-Ross LC, Thompson PM, Gotlib IH (2015) Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory. Front Psych 6:21
Liu LY, Xu XP, Luo LY, Zhu CQ, Li YP, Wang PR et al (2019) Brain connectomic associations with traditional Chinese medicine diagnostic classification of major depressive disorder: a diffusion tensor imaging study. Chin Med 14:15
Jones DK, Cercignani M (2010) Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed 23(7):803–820
Jones DK, Knosche TR, Turner R (2013) White matter integrity, fiber count, and other fallacies: the do’s and dont’s of diffusion MRI. NeuroImage 73:239–254
Wedeen VJ, Wang RP, Schmahmann JD, Benner T, Tseng WY, Dai G et al (2008) Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers. NeuroImage 41(4):1267–1277
Iturria-Medina Y, Sotero RC, Canales-RodrÃguez EJ, Alemán-Gómez Y, Melie-GarcÃa L (2008) Studying the human brain anatomical network via diffusion-weighted MRI and graph theory. NeuroImage 40(3):1064–1076
Bassett DS, Gazzaniga MS (2011) Understanding complexity in the human brain. Trends Cogn Sci 15(5):200–209
Leuze CW, Anwander A, Bazin PL, Dhital B, Stuber C, Reimann K et al (2014) Layer-specific intracortical connectivity revealed with diffusion MRI. Cereb Cortex 24(2):328–339
McNab JA, Jbabdi S, Deoni SC, Douaud G, Behrens TE, Miller KL (2009) High resolution diffusion-weighted imaging in fixed human brain using diffusion-weighted steady state free precession. NeuroImage 46(3):775–785
Takahashi E, Song JW, Folkerth RD, Grant PE, Schmahmann JD (2013) Detection of postmortem human cerebellar cortex and white matter pathways using high angular resolution diffusion tractography: a feasibility study. NeuroImage 68:105–111
Deco G, Ponce-Alvarez A, Mantini D, Romani GL, Hagmann P, Corbetta M (2013) Resting-state functional connectivity emerges from structurally and dynamically shaped slow linear fluctuations. J Neurosci Off J Soc Neurosci 33(27):11239–11252
Lord LD, Stevner AB, Deco G, Kringelbach ML (2017) Understanding principles of integration and segregation using whole-brain computational connectomics: implications for neuropsychiatric disorders. Philos Transact A Math Phys Eng Sci 375(2096):20160283
Deco G, Kringelbach ML (2014) Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders. Neuron 84(5):892–905
Ritter P, Schirner M, McIntosh AR, Jirsa VK (2013) The virtual brain integrates computational modeling and multimodal neuroimaging. Brain Connect 3(2):121–145
Cabral J, Hugues E, Kringelbach ML, Deco G (2012) Modeling the outcome of structural disconnection on resting-state functional connectivity. NeuroImage 62(3):1342–1353
Raj A, Kuceyeski A, Weiner M (2012) A network diffusion model of disease progression in dementia. Neuron 73(6):1204–1215
Korgaonkar MS, Fornito A, Williams LM, Grieve SM (2014) Abnormal structural networks characterize major depressive disorder: a connectome analysis. Biol Psychiatry 76(7):567–574
Qin J, Wei M, Liu H, Yan R, Luo G, Yao Z, Lu Q (2014) Abnormal brain anatomical topological organization of the cognitive-emotional and the frontoparietal circuitry in major depressive disorder. Magn Reson Med 72(5):1397–1407
Myung W, Han CE, Fava M, Mischoulon D, Papakostas GI, Heo JY, Kim KW, Kim ST, Kim DJ, Kim DK, Seo SW, Seong JK, Jeon HJ (2016) Reduced frontalsubcortical white matter connectivity in association with suicidal ideation in major depressive disorder. Transl Psychiatry 6(6):e835
Liu H, Zhao K, Shi J, Chen Y, Yao Z, Lu Q (2018) Topological properties of brain structural networks represent early predictive characteristics for the occurrence of bipolar disorder in patients with major depressive disorder: a 7-year prospective longitudinal study. Front Psych 9:704
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Chen, T., Chen, Z., Gong, Q. (2021). White Matter-Based Structural Brain Network of Major Depression. In: Kim, YK. (eds) Major Depressive Disorder. Advances in Experimental Medicine and Biology, vol 1305. Springer, Singapore. https://doi.org/10.1007/978-981-33-6044-0_3
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