Shared and specific patterns of structural brain connectivity across affective and psychotic disorders



      Altered brain structural connectivity has been implicated in the pathophysiology of psychiatric disorders including schizophrenia (SZ), bipolar disorder (BD) and major depressive disorder (MDD). However, it is unknown which part of these connectivity abnormalities are disorder-specific and which are shared across the spectrum of psychotic and affective disorders. We investigated common and distinct brain connectivity alterations in a large sample (n= 1743) of SZ, BD, and MDD patients and healthy controls (HC).


      This study examines diffusion-weighted-imaging-based structural connectome topology in 720 MDD patients, 112 BD patients, 69 SZ patients and 842 healthy controls (mean age of all subjects: 35.7 years). Graph theory-based network analysis was employed to investigate connectome organization. Machine learning algorithms were trained to classify groups based on their structural connectivity matrices.


      Groups differed significantly in the network metrics global efficiency, clustering, present edges, and global connectivity strength with a converging pattern of alterations between diagnoses (e.g., efficiency: HC>MDD>BD>SZ, pFDR = .028). Subnetwork analysis revealed a common core of edges that were affected across all three disorders, but also revealed differences between disorders. Machine learning algorithms could not discriminate between disorders, but could discriminate each diagnosis from healthy controls. Furthermore, dysconnectivity patterns were found most pronounced in patients with an early disease onset irrespective of diagnosis.


      We demonstrate shared and specific signatures of structural white matter dysconnectivity in SZ, BD, and MDD, leading to commonly reduced network efficiency. These results demonstrate compromised brain communication across a spectrum of major psychiatric disorders.

      Key words

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      1. Bleuler E (1924): Textbook of Psychiatry. New York: The Macmillan Company.

        • de Lange S.C.
        • Scholtens L.H.
        • van den Berg L.H.
        • Boks M.P.
        • Bozzali M.
        • Cahn W.
        • et al.
        Shared vulnerability for connectome alterations across psychiatric and neurological brain disorders.
        Nat Hum Behav. 2019; 3: 988-998
        • van den Heuvel M.P.
        • Scholtens L.H.
        • de Reus M.A.
        • Kahn R.S.
        Associated Microscale Spine Density and Macroscale Connectivity Disruptions in Schizophrenia.
        Biol Psychiatry. 2016; 80: 293-301
        • Sporns O.
        The human connectome: A complex network.
        Annals of the New York Academy of Sciences. 2011; 1224: 109-125
        • Bullmore E.
        • Sporns O.
        Complex brain networks: graph theoretical analysis of structural and functional systems.
        Nat Rev Neurosci. 2009; 10: 186-198
        • van den Heuvel M.P.
        • Sporns O.
        A cross-disorder connectome landscape of brain dysconnectivity.
        Nat Rev Neurosci. 2019; 1
        • Anttila V.
        • Bulik-Sullivan B.
        • Finucane H.K.
        • Walters R.K.
        • Bras J.
        • Duncan L.
        • et al.
        Analysis of shared heritability in common disorders of the brain.
        Science. 2018; 80: 360
        • Caspi A.
        • Houts R.M.
        • Belsky D.W.
        • Goldman-Mellor S.J.
        • Harrington H.
        • Israel S.
        • et al.
        The p factor: One general psychopathology factor in the structure of psychiatric disorders?.
        Clin Psychol Sci. 2014; 2: 119-137
        • van den Heuvel M.M.P.M.
        • Sporns O.
        • Collin G.
        • Scheewe T.
        • Mandl R.C.W.R.
        • Cahn W.
        • et al.
        Abnormal rich club organization and functional brain dynamics in schizophrenia.
        JAMA Psychiatry. 2013; 70: 783-792
        • Wang Q.
        • Su T.P.
        • Zhou Y.
        • Chou K.H.
        • Chen I.Y.
        • Jiang T.
        • Lin C.P.
        Anatomical insights into disrupted small-world networks in schizophrenia.
        Neuroimage. 2012; 59: 1085-1093
        • Nabulsi L.
        • McPhilemy G.
        • Kilmartin L.
        • O’Hora D.
        • O’Donoghue S.
        • Forcellini G.
        • et al.
        Bipolar Disorder and Gender Are Associated with Frontolimbic and Basal Ganglia Dysconnectivity: A Study of Topological Variance Using Network Analysis.
        Brain Connect. 2019; 9: 745-759
        • Collin G.
        • Heuvel MP Van Den
        • Abramovic L.
        • Vreeker A.
        • Reus MA De
        • Haren NEM Van
        • et al.
        Brain network analysis reveals affected connectome structure in bipolar I disorder.
        Hum Brain Mapp. 2016; 134: 122-134
        • Xu D.
        • Xu G.
        • Zhao Z.
        • Sublette M.E.
        • Miller J.M.
        • Mann J.J.
        Diffusion tensor imaging brain structural clustering patterns in major depressive disorder.
        Hum Brain Mapp. 2021; 42: 5023-5036
        • Korgaonkar M.S.
        • Fornito A.
        • Williams L.M.
        • Grieve S.M.
        Abnormal structural networks characterize major depressive disorder: A connectome analysis.
        Biol Psychiatry. 2014; 76: 567-574
        • Yao Z.
        • Zou Y.
        • Zheng W.
        • Zhang Z.
        • Li Y.
        • Yu Y.
        • et al.
        Structural alterations of the brain preceded functional alterations in major depressive disorder patients: Evidence from multimodal connectivity.
        J Affect Disord. 2019; 253: 107-117
        • Cha J.
        • Spielberg J.M.
        • Hu B.
        • Altinay M.
        • Anand A.
        Differences in network properties of the structural connectome in bipolar and unipolar depression.
        Psychiatry Res Neuroimaging. 2022; 321
        • Wang S.
        • Gong G.
        • Zhong S.
        • Duan J.
        • Yin Z.
        • Chang M.
        • et al.
        Neurobiological commonalities and distinctions among 3 major psychiatric disorders: a graph theoretical analysis of the structural connectome.
        J Psychiatry Neurosci. 2020; 45: 15-22
        • O’Donoghue S.
        • Holleran L.
        • Cannon D.M.
        • McDonald C.
        Anatomical dysconnectivity in bipolar disorder compared with schizophrenia: A selective review of structural network analyses using diffusion MRI.
        J Affect Disord. 2017; 209: 217-228
      2. Taquet M, Smith SM, Prohl AK, Peters JM, Warfield SK, Scherrer B, Harrison PJ (n.d.): A structural brain network of genetic vulnerability to psychiatric illness. 26. Retrieved May 18, 2020, from

        • Vogelbacher C.
        • Möbius T.W.D.
        • Sommer J.
        • Schuster V.
        • Dannlowski U.
        • Kircher T.
        • et al.
        The Marburg-Münster Affective Disorders Cohort Study (MACS): A quality assurance protocol for MR neuroimaging data.
        Neuroimage. 2018; 172: 450-460
        • Kircher T.
        • Wöhr M.
        • Nenadic I.
        • Schwarting R.
        • Schratt G.
        • Alferink J.
        • et al.
        Neurobiology of the major psychoses: a translational perspective on brain structure and function—the FOR2107 consortium.
        Eur Arch Psychiatry Clin Neurosci. 2018, September; (in press: 0)
      3. Wittchen H-U, Wunderlich U, Gruschwitz S, Zaudig M (1997): Strukturiertes Klinisches Interview fuer DSM-VI (SKID). Goettingen, Hogrefe. Goettingen: Hogrefe.

        • Lange SC de
        • Heuvel MP van den
        Structural and functional connectivity reconstruction with CATO - A Connectivity Analysis TOolbox.
        bioRxiv. 2021.05.31; (2021)446012
        • Andersson J.
        • SkareS
        A model-based method for retrospective correction of geometric distortions in diffusion-weighted EPI.
        Neuroimage. 2002; 16: 177-199
        • Hagmann P.
        • Cammoun L.
        • Gigandet X.
        • Meuli R.
        • Honey C.J.
        • Van Wedeen J.
        • Sporns O.
        Mapping the structural core of human cerebral cortex.
        PLoS Biol. 2008;
        • Cammoun L.
        • Gigandet X.
        • Meskaldji D.
        • Thiran J.P.
        • Sporns O.
        • Do K.Q.
        • et al.
        Mapping the human connectome at multiple scales with diffusion spectrum MRI.
        J Neurosci Methods. 2012;
        • Mori S.
        • Van Zijl P.P.C.M.
        Fiber tracking: Principles and strategies - A technical review. NMR in Biomedicine.
        NMR Biomed. 2002; 15: 468-480
        • Sarwar T.
        • Ramamohanarao K.
        • Zalesky A.
        Mapping connectomes with diffusion MRI: deterministic or probabilistic tractography?.
        Magn Reson Med. 2019; 81: 1368-1384
        • Zalesky A.
        • Fornito A.
        • Cocchi L.
        • Gollo L.L.
        • van den Heuvel M.P.
        • Breakspear M.
        Connectome sensitivity or specificity: which is more important?.
        Neuroimage. 2016; 142: 407-420
        • de Reus M.A.
        • van den Heuvel M.P.
        Estimating false positives and negatives in brain networks.
        Neuroimage. 2013;
        • Rubinov M.
        • Sporns O.
        Complex network measures of brain connectivity: uses and interpretations.
        Neuroimage. 2010; 52: 1059-1069
        • Van Den Heuvel M.P.
        • Scholtens L.H.
        • Van Der Burgh H.K.
        • Agosta F.
        • Alloza C.
        • Arango C.
        • et al.
        10kin1day: A bottom-up neuroimaging initiative.
        Front Neurol. 2019; 10
      4. IBM Corp. (2019): IBM SPSS Statistics for Windows, Version 26.0. 2019. pp 1–5.

      5. The Mathworks, Inc. MATLAB, Version 9.6 2019 (2019): MATLAB 2019b - MathWorks. Www.Mathworks.Com/Products/Matlab.

      6. van Rossum G, Drake FL (2009): Python 3 Reference Manual. Scotts Valley, CA.

        • Benjamini Y.
        • Hochberg Y.
        Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.
        J R Stat Soc. 1995; 57: 289-300
        • Zalesky A.
        • Fornito A.
        • Bullmore E.T.
        Network-based statistic: Identifying differences in brain networks.
        Neuroimage. 2010; 53: 1197-1207
        • Kessler R.C.
        • Amminger G.P.
        • Aguilar-Gaxiola S.
        • Alonso J.
        • Lee S.
        • Üstün T.B.
        Age of onset of mental disorders: A review of recent literature.
        Current Opinion in Psychiatry. 2007; 20: 359-364
        • Immonen J.
        • Jääskeläinen E.
        • Korpela H.
        • Miettunen J.
        Age at onset and the outcomes of schizophrenia: A systematic review and meta-analysis.
        Early Intervention in Psychiatry. 2017; 11: 453-460
        • Janssen R.J.
        • Mourão-Miranda J.
        • Schnack H.G.
        Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning.
        Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. 2018; 3: 798-808
        • Arbabshirani M.R.
        • Plis S.
        • Sui J.
        • Calhoun V.D.
        Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.
        Neuroimage. 2017; 145: 137-165
        • Kambeitz J.
        • Kambeitz-Ilankovic L.
        • Leucht S.
        • Wood S.
        • Davatzikos C.
        • Malchow B.
        • et al.
        Detecting neuroimaging biomarkers for schizophrenia: a meta-analysis of multivariate pattern recognition studies.
        Neuropsychopharmacology. 2015; 40: 1742-1751
        • Sarica A.
        • Cerasa A.
        • Quattrone A.
        Random forest algorithm for the classification of neuroimaging data in Alzheimer’s disease: A systematic review.
        Frontiers in Aging Neuroscience. 2017; 9
        • Leenings R.
        • Winter N.R.
        • Plagwitz L.
        • Holstein V.
        • Ernsting J.
        • Sarink K.
        • et al.
        PHOTONAI-A Python API for rapid machine learning model development.
        PLoS One. 2021; 16
        • Schnack H.G.
        • Kahn R.S.
        Detecting neuroimaging biomarkers for psychiatric disorders: Sample size matters.
        Front Psychiatry. 2016; 7: 50
        • Flint C.
        • Cearns M.
        • Opel N.
        • Redlich R.
        • Mehler D.M.A.
        • Emden D.
        • et al.
        Systematic misestimation of machine learning performance in neuroimaging studies of depression.
        Neuropsychopharmacology. 2021; 46: 1510-1517
        • Nunes A.
        • Schnack H.G.
        • Ching C.R.K.
        • Agartz I.
        • Akudjedu T.N.
        • Alda M.
        • et al.
        Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group.
        Mol Psychiatry 2018. 2018; 259 25: 2130-2143
        • Liu H.
        • Zhao K.
        • Shi J.
        • Chen Y.
        • Yao Z.
        • Lu Q.
        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 Psychiatry. 2018;
        • Collin G.
        • de Nijs J.
        • Hulshoff Pol H.E.
        • Cahn W.
        • van den Heuvel M.P.
        Connectome organization is related to longitudinal changes in general functioning, symptoms and IQ in chronic schizophrenia.
        Schizophr Res. 2016; 173: 166-173
        • Herzog D.P.
        • Wagner S.
        • Engelmann J.
        • Treccani G.
        • Dreimüller N.
        • Müller M.B.
        • et al.
        Early onset of depression and treatment outcome in patients with major depressive disorder.
        J Psychiatr Res. 2021; 139: 150-158
        • Allen M.
        • Poggiali D.
        • Whitaker K.
        • Marshall T.R.
        • Kievit R.A.
        Raincloud plots: A multi-platform tool for robust data visualization.
        Wellcome Open Res. 2019; 4
      7. Dwyer T, Fornito A, Pham TN, Shi M, Smith N, Manley J, Klapperstueck M (2017): NeuroMArVL. Monash University. Retrieved March 30, 2022, from

        • Xia M.
        • Wang J.
        • He Y.
        BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics.
        PLoS One. 2013; 8