Abstract
Schizophrenia is a complex disorder associated with aberrant brain functional connectivity. This study aims to demonstrate the relation of heterogeneous symptomatology in this disorder to distinct brain connectivity patterns within the triple-network model. The study sample comprised 300 first-episode antipsychotic-naive patients with schizophrenia (FES) and 301 healthy controls (HCs). At baseline, resting-state functional magnetic resonance imaging data were captured for each participant, and concomitant neurocognitive functions were evaluated outside the scanner. Clinical information of 49 FES in the discovery dataset were reevaluated at a 6-week follow-up. Differential features between FES and HCs were selected from triple-network connectivity profiles. Cutting-edge unsupervised machine learning algorithms were used to define patient subtypes. Clinical and cognitive variables were compared between patient subgroups. Two FES subgroups with differing triple-network connectivity profiles were identified in the discovery dataset and confirmed in an independent hold-out cohort. One patient subgroup appearing to have more severe clinical symptoms was distinguished by salience network (SN)-centered hypoconnectivity, which was associated with greater impairments in sustained attention. The other subgroup exhibited hyperconnectivity and manifested greater deficits in cognitive flexibility. The SN-centered hypoconnectivity subgroup had more persistent negative symptoms at the 6-week follow-up than the hyperconnectivity subgroup. The present study illustrates that clinically relevant cognitive subtypes of schizophrenia may be associated with distinct differences in connectivity in the triple-network model. This categorization may foster further analysis of the effects of therapy on these network connectivity patterns, which may help to guide therapeutic choices to effectively reach personalized treatment goals.
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References
Menon V. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn. Sci. 2011;15:483–506.
Palaniyappan L, Deshpande G, Lanka P, Rangaprakash D, Iwabuchi S, Francis S, et al. Effective connectivity within a triple network brain system discriminates schizophrenia spectrum disorders from psychotic bipolar disorder at the single-subject level. Schizophr. Res. 2019;214:24–33.
Wotruba D, Michels L, Buechler R, Metzler S, Theodoridou A, Gerstenberg M, et al. Aberrant coupling within and across the default mode, task-positive, and salience network in subjects at risk for psychosis. Schizophr. Bull. 2014;40:1095–104.
Wu, D. & Jiang, T. Schizophrenia-related abnormalities in the triple network: a meta-analysis of working memory studies. Brain Imaging Behav. 2019. https://doi.org/10.1007/s11682-019-00071-1.
Manoliu A, Riedl V, Zherdin A, Muhlau M, Schwerthoffer D, Scherr M, et al. Aberrant dependence of default mode/central executive network interactions on anterior insular salience network activity in schizophrenia. Schizophr Bull. 2014;40:428–37.
Supekar K, Cai W, Krishnadas R, Palaniyappan L, Menon V. Dysregulated brain dynamics in a triple-network saliency model of schizophrenia and its relation to psychosis. Biol Psychiatry. 2019;85:60–9.
Nekovarova T, Fajnerova I, Horacek J, Spaniel F. Bridging disparate symptoms of schizophrenia: a triple network dysfunction theory. Front Behav Neurosci. 2014;8:171.
Manoliu A, Riedl V, Doll A, Bauml JG, Muhlau M, Schwerthoffer D, et al. Insular dysfunction reflects altered between-network connectivity and severity of negative symptoms in schizophrenia during psychotic remission. Front Hum Neurosci. 2013;7:216.
Palaniyappan L, Mallikarjun P, Joseph V, White TP, Liddle PF. Regional contraction of brain surface area involves three large-scale networks in schizophrenia. Schizophr Res. 2011;129:163–8.
Gong Q, Hu X, Pettersson-Yeo W, Xu X, Lui S, Crossley N, et al. Network-level dysconnectivity in drug-naive first-episode psychosis: dissociating transdiagnostic and diagnosis-specific alterations. Neuropsychopharmacology 2017;42:933–40.
Tsuang MT, Lyons MJ, Faraone SV. Heterogeneity of schizophrenia. Conceptual models and analytic strategies. Br J Psychiatry. 1990;156:17–26.
Sun H, Lui S, Yao L, Deng W, Xiao Y, Zhang W, et al. Two patterns of white matter abnormalities in medication-naive patients with first-episode schizophrenia revealed by diffusion tensor imaging and cluster analysis. JAMA Psychiatry. 2015;72:678–86.
Dwyer DB, Cabral C, Kambeitz-Ilankovic L, Sanfelici R, Kambeitz J, Calhoun V, et al. Brain subtyping enhances the neuroanatomical discrimination of schizophrenia. Schizophr Bull. 2018;44:1060–69.
Chand GB, Dwyer DB, Erus G, Sotiras A, Varol E, Srinivasan D, et al. Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning. Brain 2020;143:1027–38.
Bak N, Ebdrup BH, Oranje B, Fagerlund B, Jensen MH, During SW, et al. Two subgroups of antipsychotic-naive, first-episode schizophrenia patients identified with a Gaussian mixture model on cognition and electrophysiology. Transl Psychiatry. 2017;7:e1087.
Viviano JD, Buchanan RW, Calarco N, Gold JM, Foussias G, Bhagwat N, et al. Resting-state connectivity biomarkers of cognitive performance and social function in individuals with schizophrenia spectrum disorder and healthy control subjects. Biol Psychiatry. 2018;84:665–74.
Kindler J, Lim CK, Weickert CS, Boerrigter D, Galletly C, Liu D, et al. Dysregulation of kynurenine metabolism is related to proinflammatory cytokines, attention, and prefrontal cortex volume in schizophrenia. Mol Psychiatry. 2019. https://doi.org/10.1038/s41380-019-0401-9.
Kranz TM, Berns A, Shields J, Rothman K, Walsh-Messinger J, Goetz RR, et al. Phenotypically distinct subtypes of psychosis accompany novel or rare variants in four different signaling genes. EBioMedicine 2016;6:206–14.
Han W, Sorg C, Zheng C, Yang Q, Zhang X, Ternblom A, et al. Low-rank network signatures in the triple network separate schizophrenia and major depressive disorder. NeuroImage Clin. 2019;22:101725.
Williams LM. Precision psychiatry: a neural circuit taxonomy for depression and anxiety. Lancet Psychiatry. 2016;3:472–80.
Kay SR, Flszbein A, Opfer LA. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr Bull. 1987;13:261.
Liang S, Brown MRG, Deng W, Wang Q, Ma X, Li M, et al. Convergence and divergence of neurocognitive patterns in schizophrenia and depression. Schizophr Res. 2018;192:327–34.
Andreasen NC, Pressler M, Nopoulos P, Miller D, Ho BC. Antipsychotic dose equivalents and dose-years: a standardized method for comparing exposure to different drugs. Biol Psychiatry. 2010;67:255–62.
Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. FSL. NeuroImage. 2012;62:782–90.
Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 2012;59:2142–54.
Sridharan D, Levitin DJ, Menon V. A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc Natl Acad Sci USA. 2008;105:12569–74.
Von Luxburg U. A tutorial on spectral clustering. Stat Comput. 2007;17:395–416.
Calinski T, Harabasz J. A dendrite method for cluster analysis. Commun Stat Theory Methods. 1974;3:1–27.
Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987;20:53–65.
Van Craenendonck T, Blockeel H. Using internal validity measures to compare clustering algorithms. ICML [C]. Lille, France: 2015:1–8.
Liu Y, Hayes DN, Nobel A, Marron JS. Statistical significance of clustering for high-dimension, low–sample size data. J Am Stat Assoc. 2008;103:1281–93.
Leucht S. Measurements of response, remission, and recovery in schizophrenia and examples for their clinical application. J Clin Psychiatry. 2014;75:8–14.
Menon V, Uddin LQ. Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct. 2010;214:655–67.
Sambataro F, Blasi G, Fazio L, Caforio G, Taurisano P, Romano R, et al. Treatment with olanzapine is associated with modulation of the default mode network in patients with Schizophrenia. Neuropsychopharmacology 2010;35:904–12.
Wang Y, Tang W, Fan X, Zhang J, Geng D, Jiang K, et al. Resting-state functional connectivity changes within the default mode network and the salience network after antipsychotic treatment in early-phase schizophrenia. Neuropsychiatr Dis Treat. 2017;13:397–406.
Lui S, Li T, Deng W, Jiang L, Wu Q, Tang H, et al. Short-term effects of antipsychotic treatment on cerebral function in drug-naive first-episode schizophrenia revealed by “resting state” functional magnetic resonance imaging. Arch Gen Psychiatry. 2010;67:783–92.
De Rossi P, Chiapponi C, Spalletta G. Brain functional effects of psychopharmacological treatments in schizophrenia: a network-based functional perspective beyond neurotransmitter systems. Curr Neuropharmacol. 2015;13:435–44.
Yu M, Dai Z, Tang X, Wang X, Zhang X, Sha W, et al. Convergence and divergence of brain network dysfunction in deficit and non-deficit schizophrenia. Schizophr Bull. 2017;43:1315–28.
Vita A, Minelli A, Barlati S, Deste G, Giacopuzzi E, Valsecchi P, et al. Treatment-resistant schizophrenia: genetic and neuroimaging correlates. Front Pharm. 2019;10:402.
Ganella EP, Bartholomeusz CF, Seguin C, Whittle S, Bousman C, Phassouliotis C, et al. Functional brain networks in treatment-resistant schizophrenia. Schizophr Res. 2017;184:73–81.
Dickinson D, Harvey PD. Systemic hypotheses for generalized cognitive deficits in schizophrenia: a new take on an old problem. Schizophr Bull. 2009;35:403–14.
Douw L, Wakeman DG, Tanaka N, Liu H, Stufflebeam SM. State-dependent variability of dynamic functional connectivity between frontoparietal and default networks relates to cognitive flexibility. Neuroscience 2016;339:12–21.
Vatansever D, Manktelow AE, Sahakian BJ, Menon DK, Stamatakis EA. Cognitive flexibility: a default network and basal ganglia connectivity perspective. Brain Connect. 2016;6:201–7.
Lefebvre S, Demeulemeester M, Leroy A, Delmaire C, Lopes R, Pins D, et al. Network dynamics during the different stages of hallucinations in schizophrenia. Hum Brain Mapp. 2016;37:2571–86.
Liao W, Fan YS, Yang S, Li J, Duan X, Cui Q, et al. Preservation effect: cigarette smoking acts on the dynamic of influences among unifying neuropsychiatric triple networks in schizophrenia. Schizophr Bull. 2019;45:1242–50.
Acknowledgements
We warmly thank Professor Nan-kuei Chen (Radiology Medical Research Lab, University of Arizona) for helping with the fMRI preprocessing analysis. We also warmly thank all the participants for their participation in the study.
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All authors have made significant scientific contributions to this manuscript. T.L., W.D., and X.M. designed the study and wrote the protocol. S.L., Q.W., and X.L. managed the literature searches and analyses. H.R., C.Z., M.L., and Y.M. conducted interviews with participants. H.Y., W.W., Y.M., and L.Z. gathered the data. S.L. undertook the statistical analysis with some help from C.-G.Y., Q.W., and A.J.G., and S.L. wrote the first draft of the manuscript. A.J.G., T.L., X.D., and C.-G.Y. were involved in the revision and completion of the work. All authors contributed to and approved the final manuscript.
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Liang, S., Wang, Q., Greenshaw, A.J. et al. Aberrant triple-network connectivity patterns discriminate biotypes of first-episode medication-naive schizophrenia in two large independent cohorts. Neuropsychopharmacol. 46, 1502–1509 (2021). https://doi.org/10.1038/s41386-020-00926-y
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DOI: https://doi.org/10.1038/s41386-020-00926-y
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