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Connectivity of the anterior insula differentiates participants with first-episode schizophrenia spectrum disorders from controls: a machine-learning study

Published online by Cambridge University Press:  25 July 2016

P. Mikolas ,
T. Melicher ,
A. Skoch ,
M. Matejka ,
A. Slovakova ,
E. Bakstein ,
T. Hajek  and
F. Spaniel
P. Mikolas
Affiliation:
Psychiatric Hospital Bohnice, Prague, Czech Republic 3rd Faculty of Medicine, Charles University, Prague, Czech Republic National Institute of Mental Health, Klecany, Czech Republic Institute of Neuropsychiatric Care (INEP), Prague, Czech Republic
T. Melicher
Affiliation:
3rd Faculty of Medicine, Charles University, Prague, Czech Republic National Institute of Mental Health, Klecany, Czech Republic Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
A. Skoch
Affiliation:
National Institute of Mental Health, Klecany, Czech Republic MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
M. Matejka
Affiliation:
Psychiatric Hospital Bohnice, Prague, Czech Republic 3rd Faculty of Medicine, Charles University, Prague, Czech Republic National Institute of Mental Health, Klecany, Czech Republic
A. Slovakova
Affiliation:
Psychiatric Hospital Bohnice, Prague, Czech Republic 3rd Faculty of Medicine, Charles University, Prague, Czech Republic National Institute of Mental Health, Klecany, Czech Republic
E. Bakstein
Affiliation:
National Institute of Mental Health, Klecany, Czech Republic
T. Hajek*
Affiliation:
3rd Faculty of Medicine, Charles University, Prague, Czech Republic National Institute of Mental Health, Klecany, Czech Republic Dalhousie University, Department of Psychiatry, Halifax, Nova Scotia, Canada
F. Spaniel
Affiliation:
3rd Faculty of Medicine, Charles University, Prague, Czech Republic National Institute of Mental Health, Klecany, Czech Republic
*
*Address for correspondence: T. Hajek, M.D., Ph.D., Dalhousie University, Department of Psychiatry, QEII HSC, A.J. Lane Bldg, Room 3093, 5909 Veteran's Memorial Lane, Halifax, NS B3H 2E2, Canada. (Email: tomas.hajek@dal.ca)
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Abstract

Background

Early diagnosis of schizophrenia could improve the outcomes and limit the negative effects of untreated illness. Although participants with schizophrenia show aberrant functional connectivity in brain networks, these between-group differences have a limited diagnostic utility. Novel methods of magnetic resonance imaging (MRI) analyses, such as machine learning (ML), may help bring neuroimaging from the bench to the bedside. Here, we used ML to differentiate participants with a first episode of schizophrenia-spectrum disorder (FES) from healthy controls based on resting-state functional connectivity (rsFC).

Method

We acquired resting-state functional MRI data from 63 patients with FES who were individually matched by age and sex to 63 healthy controls. We applied linear kernel support vector machines (SVM) to rsFC within the default mode network, the salience network and the central executive network.

Results

The SVM applied to the rsFC within the salience network distinguished the FES from the control participants with an accuracy of 73.0% (p = 0.001), specificity of 71.4% and sensitivity of 74.6%. The classification accuracy was not significantly affected by medication dose, or by the presence of psychotic symptoms. The functional connectivity within the default mode or the central executive networks did not yield classification accuracies above chance level.

Conclusions

Seed-based functional connectivity maps can be utilized for diagnostic classification, even early in the course of schizophrenia. The classification was probably based on trait rather than state markers, as symptoms or medications were not significantly associated with classification accuracy. Our results support the role of the anterior insula/salience network in the pathophysiology of FES.

Keywords

First-episode schizophrenia spectrumfunctional connectivityfunctional magnetic resonance imagingmachine learningsalience network
Type
Original Articles
Information
Psychological Medicine , Volume 46 , Issue 13 , October 2016 , pp. 2695 - 2704
Copyright
Copyright © Cambridge University Press 2016 

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References

Alonso-Solís, A, Corripio, I, de Castro-Manglano, P, Duran-Sindreu, S, Garcia-Garcia, M, Proal, E, Nuñez-Marín, F, Soutullo, C, Alvarez, E, Gómez-Ansón, B, Kelly, C, Castellanos, FX (2012). Altered default network resting state functional connectivity in patients with a first episode of psychosis. Schizophrenia Research 139, 13–18.CrossRefGoogle ScholarPubMed
Andreasen, NC, Nopoulos, P, Magnotta, V, Pierson, R, Ziebell, S, Ho, B-C (2011). Progressive brain change in schizophrenia: a prospective longitudinal study of first-episode schizophrenia. Biological Psychiatry 70, 672–679.Google Scholar
Bora, E, Fornito, A, Radua, J, Walterfang, M, Seal, M, Wood, SJ, Yücel, M, Velakoulis, D, Pantelis, C (2011). Neuroanatomical abnormalities in schizophrenia: a multimodal voxelwise meta-analysis and meta-regression analysis. Schizophrenia Research 127, 46–57.Google Scholar
Borgwardt, S, Fusar-Poli, P (2012). Third-generation neuroimaging in early schizophrenia: translating research evidence into clinical utility. British Journal of Psychiatry: The Journal of Mental Science 200, 270–272.CrossRefGoogle ScholarPubMed
Cabral, J, Kringelbach, ML, Deco, G (2014). Exploring the network dynamics underlying brain activity during rest. Progress in Neurobiology 114, 102–131.Google Scholar
Chan, RCK, Di, X, McAlonan, GM, Gong, Q (2011). Brain anatomical abnormalities in high-risk individuals, first-episode, and chronic schizophrenia: an activation likelihood estimation meta-analysis of illness progression. Schizophrenia Bulletin 37, 177–188.Google Scholar
Chao-Gan, Y, Yu-Feng, Z (2010). DPARSF: A MATLAB toolbox for ‘pipeline’ data analysis of resting-state fMRI. Frontiers in Systems Neuroscience 4, 13.Google Scholar
Craddock, RC, Holtzheimer, PE, Hu, XP, Mayberg, HS (2009). Disease state prediction from resting state functional connectivity. Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine/Society of Magnetic Resonance in Medicine 62, 1619–1628.Google Scholar
Damoiseaux, JS, Rombouts, SARB, Barkhof, F, Scheltens, P, Stam, CJ, Smith, SM, Beckmann, CF (2006). Consistent resting-state networks across healthy subjects. Proceedings of the National Academy of Sciences 103, 13848–13853.Google Scholar
Davatzikos, C, Shen, D, Gur, RC, Wu, X, Liu, D, Fan, Y, Hughett, P, Turetsky, BI, Gur, RE (2005). Whole-brain morphometric study of schizophrenia revealing a spatially complex set of focal abnormalities. Archives of General Psychiatry 62, 1218–1227.Google Scholar
Destrieux, C, Fischl, B, Dale, A, Halgren, E (2010). Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage 53, 1–15.Google Scholar
Ellison-Wright, I, Bullmore, E (2010). Anatomy of bipolar disorder and schizophrenia: a meta-analysis. Schizophrenia Research 117, 1–12.Google Scholar
Fischl, B, Salat, DH, van der Kouwe, AJW, Makris, N, Ségonne, F, Quinn, BT, Dale, AM (2004). Sequence-independent segmentation of magnetic resonance images. NeuroImage 23 (Suppl. 1), S69–S84.Google Scholar
Franke, K, Ziegler, G, Klöppel, S, Gaser, C (2010). Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters. NeuroImage 50, 883–892.Google Scholar
Glahn, DC, Laird, AR, Ellison-Wright, I, Thelen, SM, Robinson, JL, Lancaster, JL, Bullmore, E, Fox, PT (2008). Meta-analysis of gray matter anomalies in schizophrenia: application of anatomic likelihood estimation and network analysis. Biological Psychiatry 64, 774–781.CrossRefGoogle ScholarPubMed
Goodkind, M, Eickhoff, SB, Oathes, DJ, Jiang, Y, Chang, A, Jones-Hagata, LB, Ortega, BN, Zaiko, YV, Roach, EL, Korgaonkar, MS, Grieve, SM, Galatzer-Levy, I, Fox, PT, Etkin, A (2015). Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry 72, 305–315.Google Scholar
Gould, IC, Shepherd, AM, Laurens, KR, Cairns, MJ, Carr, VJ, Green, MJ (2014). Multivariate neuroanatomical classification of cognitive subtypes in schizophrenia: a support vector machine learning approach. NeuroImage Clinical 6, 229–236.Google Scholar
Guo, W, Liu, F, Xiao, C, Yu, M, Zhang, Z, Liu, J, Zhang, J, Zhao, J (2015). Increased causal connectivity related to anatomical alterations as potential endophenotypes for schizophrenia. Medicine 94, e1493.Google Scholar
Guo, W, Xiao, C, Liu, G, Wooderson, SC, Zhang, Z, Zhang, J, Yu, L, Liu, J (2014). Decreased resting-state interhemispheric coordination in first-episode, drug-naive paranoid schizophrenia. Progress in Neuro-Psychopharmacology and Biological Psychiatry 48, 14–19.Google Scholar
Guo, X, Li, J, Wei, Q, Fan, X, Kennedy, DN, Shen, Y, Chen, H, Zhao, J (2013). Duration of untreated psychosis is associated with temporal and occipitotemporal gray matter volume decrease in treatment naïve schizophrenia. PLOS ONE 8, e83679.Google Scholar
Hajek, T, Cooke, C, Kopecek, M, Novak, T, Hoschl, C, Alda, M (2015). Using structural MRI to identify individuals at genetic risk for bipolar disorders: a 2-cohort, machine learning study. Journal of Psychiatry and Neuroscience: JPN 40, 316–324.Google Scholar
Haller, S, Lovblad, K-O, Giannakopoulos, P, Van De Ville, D (2014). Multivariate pattern recognition for diagnosis and prognosis in clinical neuroimaging: state of the art, current challenges and future trends. Brain Topography 27, 329–337.Google Scholar
Ho, B-C, Andreasen, NC, Ziebell, S, Pierson, R, Magnotta, V (2011). Long-term antipsychotic treatment and brain volumes: a longitudinal study of first-episode schizophrenia. Archives of General Psychiatry 68, 128–137.Google Scholar
Iwabuchi, SJ, Liddle, PF, Palaniyappan, L (2015). Structural connectivity of the salience-executive loop in schizophrenia. European Archives of Psychiatry and Clinical Neuroscience 265, 163–166.CrossRefGoogle ScholarPubMed
Kambeitz, J, Kambeitz-Ilankovic, L, Leucht, S, Wood, S, Davatzikos, C, Malchow, B, Falkai, P, Koutsouleris, N (2015). Detecting neuroimaging biomarkers for schizophrenia: a meta-analysis of multivariate pattern recognition studies. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology 40, 1742–1751.CrossRefGoogle ScholarPubMed
Kasparek, T, Thomaz, CE, Sato, JR, Schwarz, D, Janousova, E, Marecek, R, Prikryl, R, Vanicek, J, Fujita, A, Ceskova, E (2011). Maximum-uncertainty linear discrimination analysis of first-episode schizophrenia subjects. Psychiatry Research 191, 174–181.Google Scholar
Kay, SR, Fiszbein, A, Opler, LA (1987). The Positive and Negative Syndrome Scale (PANSS) for schizophrenia. Schizophrenia Bulletin 13, 261–276.Google Scholar
Koutsouleris, N, Meisenzahl, EM, Borgwardt, S, Riecher-Rössler, A, Frodl, T, Kambeitz, J, Köhler, Y, Falkai, P, Möller, H-J, Reiser, M, Davatzikos, C (2015). Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers. Brain: A Journal of Neurology 138, 2059–2073.Google Scholar
LaConte, S, Strother, S, Cherkassky, V, Anderson, J, Hu, X (2005). Support vector machines for temporal classification of block design fMRI data. NeuroImage 26, 317–329.Google Scholar
Lecrubier, Y, Sheehan, DV, Weiller, E, Amorim, P, Bonora, I, Harnett Sheehan, K, Janavs, J, Dunbar, GC (1997). The Mini International Neuropsychiatric Interview (MINI). A short diagnostic structured interview: reliability and validity according to the CIDI. European Psychiatry 12, 224–231.Google Scholar
Levav, I, Rutz, W (2002). The WHO World Health Report 2001 new understanding – new hope. Israel Journal of Psychiatry and Related Sciences 39, 50–56.Google Scholar
Lieberman, J, Chakos, M, Wu, H, Alvir, J, Hoffman, E, Robinson, D, Bilder, R (2001). Longitudinal study of brain morphology in first episode schizophrenia. Biological Psychiatry 49, 487–499.Google Scholar
Malla, AK, Bodnar, M, Joober, R, Lepage, M (2011). Duration of untreated psychosis is associated with orbital–frontal grey matter volume reductions in first episode psychosis. Schizophrenia Research 125, 13–20.Google Scholar
Manoliu, A, Riedl, V, Zherdin, A, Muhlau, M, Schwerthoffer, D, Scherr, M, Peters, H, Zimmer, C, Forstl, H, Bauml, J, Wohlschlager, AM, Sorg, C (2014). Aberrant dependence of default mode/central executive network interactions on anterior insular salience network activity in schizophrenia. Schizophrenia Bulletin 40, 428–437.Google Scholar
Melicher, T, Horacek, J, Hlinka, J, Spaniel, F, Tintera, J, Ibrahim, I, Mikolas, P, Novak, T, Mohr, P, Hoschl, C (2015). White matter changes in first episode psychosis and their relation to the size of sample studied: a DTI study. Schizophrenia Research 162, 22–28.Google Scholar
Menon, V, Uddin, LQ (2010). Saliency, switching, attention and control: a network model of insula function. Brain Structure and Function 214, 655–667.Google Scholar
Moran, LV, Tagamets, MA, Sampath, H, O'Donnell, A, Stein, EA, Kochunov, P, Hong, LE (2013). Disruption of anterior insula modulation of large-scale brain networks in schizophrenia. Biological Psychiatry 74, 467–474.Google Scholar
Mourao-Miranda, J, Reinders, AATS, Rocha-Rego, V, Lappin, J, Rondina, J, Morgan, C, Morgan, KD, Fearon, P, Jones, PB, Doody, GA, Murray, RM, Kapur, S, Dazzan, P (2012). Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study. Psychological Medicine 42, 1037–1047.Google Scholar
Nekovarova, T, Fajnerova, I, Horacek, J, Spaniel, F (2014). Bridging disparate symptoms of schizophrenia: a triple network dysfunction theory. Frontiers in Behavioral Neuroscience 8, 171.Google Scholar
Nieuwenhuis, M, van Haren, NEM, Hulshoff Pol, HE, Cahn, W, Kahn, RS, Schnack, HG (2012). Classification of schizophrenia patients and healthy controls from structural MRI scans in two large independent samples. NeuroImage 61, 606–612.Google Scholar
Palaniyappan, L, Simmonite, M, White, TP, Liddle, EB, Liddle, PF (2013). Neural primacy of the salience processing system in schizophrenia. Neuron 79, 814–828.Google Scholar
Penttilä, M, Jääskeläinen, E, Haapea, M, Tanskanen, P, Veijola, J, Ridler, K, Murray, GK, Barnes, A, Jones, PB, Isohanni, M, Koponen, H, Miettunen, J (2010). Association between duration of untreated psychosis and brain morphology in schizophrenia within the Northern Finland 1966 Birth Cohort. Schizophrenia Research 123, 145–152.Google Scholar
Pettersson-Yeo, W, Benetti, S, Marquand, AF, Dell'Acqua, F, Williams, SCR, Allen, P, Prata, D, McGuire, P, Mechelli, A (2013). Using genetic, cognitive and multi-modal neuroimaging data to identify ultra-high-risk and first-episode psychosis at the individual level. Psychological Medicine 43, 2547–2562.CrossRefGoogle ScholarPubMed
Rabinowitz, J, Levine, SZ, Haim, R, Häfner, H (2007). The course of schizophrenia: progressive deterioration, amelioration or both? Schizophrenia Research 91, 254–258.Google Scholar
Rasmussen, CE, Williams, CKI (2006). Gaussian Processes for Machine Learning. Adaptive Computation and Machine Learning. MIT Press: Cambridge, MA.Google Scholar
Ren, W, Lui, S, Deng, W, Li, F, Li, M, Huang, X, Wang, Y, Li, T, Sweeney, JA, Gong, Q (2013). Anatomical and functional brain abnormalities in drug-naive first-episode schizophrenia. American Journal of Psychiatry 170, 1308–1316.Google Scholar
Rocha-Rego, V, Jogia, J, Marquand, AF, Mourao-Miranda, J, Simmons, A, Frangou, S (2014). Examination of the predictive value of structural magnetic resonance scans in bipolar disorder: a pattern classification approach. Psychological Medicine 44, 519–532.Google Scholar
Sarpal, DK, Robinson, DG, Lencz, T, Argyelan, M, Ikuta, T, Karlsgodt, K, Gallego, JA, Kane, JM, Szeszko, PR, Malhotra, AK (2015). Antipsychotic treatment and functional connectivity of the striatum in first-episode schizophrenia. JAMA Psychiatry 72, 5–13.Google Scholar
Schrouff, J, Rosa, MJ, Rondina, JM, Marquand, AF, Chu, C, Ashburner, J, Phillips, C, Richiardi, J, Mourão-Miranda, J (2013). PRoNTo: pattern recognition for neuroimaging toolbox. Neuroinformatics 11, 319–337.Google Scholar
Smieskova, R, Fusar-Poli, P, Allen, P, Bendfeldt, K, Stieglitz, RD, Drewe, J, Radue, EW, McGuire, PK, Riecher-Rössler, A, Borgwardt, SJ (2009). The effects of antipsychotics on the brain: what have we learnt from structural imaging of schizophrenia? – a systematic review. Current Pharmaceutical Design 15, 2535–2549.Google Scholar
Smith, SM, Jenkinson, M, Woolrich, MW, Beckmann, CF, Behrens, TEJ, Johansen-Berg, H, Bannister, PR, De Luca, M, Drobnjak, I, Flitney, DE, Niazy, RK, Saunders, J, Vickers, J, Zhang, Y, De Stefano, N, Brady, JM, Matthews, PM (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23 (Suppl. 1), S208–S219.Google Scholar
Song, X-W, Dong, Z-Y, Long, X-Y, Li, S-F, Zuo, X-N, Zhu, C-Z, He, Y, Yan, C-G, Zang, Y-F (2011). REST: a toolkit for resting-state functional magnetic resonance imaging data processing. PLoS ONE 6, e25031.CrossRefGoogle Scholar
Spaniel, F, Tintera, J, Rydlo, J, Ibrahim, I, Kasparek, T, Horacek, J, Zaytseva, Y, Matejka, M, Fialova, M, Slovakova, A, Mikolas, P, Melicher, T, Görnerova, N, Höschl, C, Hajek, T (2016). Altered neural correlate of the self-agency experience in first-episode schizophrenia-spectrum patients: an fMRI study. Schizophrenia Bulletin 42, 916–925.Google Scholar
Sridharan, D, Levitin, DJ, Menon, V (2008). A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proceedings of the National Academy of Sciences of the USA 105, 12569–12574.Google Scholar
Sundermann, B, Herr, D, Schwindt, W, Pfleiderer, B (2014). Multivariate classification of blood oxygen level-dependent fMRI data with diagnostic intention: a clinical perspective. AJNR. American Journal of Neuroradiology 35, 848–855.Google Scholar
Swanson, N, Eichele, T, Pearlson, G, Kiehl, K, Yu, Q, Calhoun, VD (2011). Lateral differences in the default mode network in healthy controls and schizophrenia patients. Human Brain Mapping 32, 654–664.Google Scholar
Takahashi, T, Wood, SJ, Yung, AR, Phillips, LJ, Soulsby, B, McGorry, PD, Tanino, R, Zhou, S-Y, Suzuki, M, Velakoulis, D, Pantelis, C (2009). Insular cortex gray matter changes in individuals at ultra-high-risk of developing psychosis. Schizophrenia Research 111, 94–102.Google Scholar
Takayanagi, Y, Takahashi, T, Orikabe, L, Mozue, Y, Kawasaki, Y, Nakamura, K, Sato, Y, Itokawa, M, Yamasue, H, Kasai, K, Kurachi, M, Okazaki, Y, Suzuki, M (2011). Classification of first-episode schizophrenia patients and healthy subjects by automated MRI measures of regional brain volume and cortical thickness. PLoS ONE 6, e21047.Google Scholar
Tzourio-Mazoyer, N, Landeau, B, Papathanassiou, D, Crivello, F, Etard, O, Delcroix, N, Mazoyer, B, Joliot, M (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15, 273–289.Google Scholar
Venkataraman, A, Whitford, TJ, Westin, C-F, Golland, P, Kubicki, M (2012). Whole brain resting state functional connectivity abnormalities in schizophrenia. Schizophrenia Research 139, 7–12.Google Scholar
Vita, A, De Peri, L, Deste, G, Sacchetti, E (2012). Progressive loss of cortical gray matter in schizophrenia: a meta-analysis and meta-regression of longitudinal MRI studies. Translational Psychiatry 2, e190.Google Scholar
Whelan, R, Garavan, H (2014). When optimism hurts: inflated predictions in psychiatric neuroimaging. Biological Psychiatry 75, 746–748.Google Scholar
White, TP, Joseph, V, Francis, ST, Liddle, PF (2010). Aberrant salience network (bilateral insula and anterior cingulate cortex) connectivity during information processing in schizophrenia. Schizophrenia Research 123, 105–115.Google Scholar
Zanetti, MV, Schaufelberger, MS, Doshi, J, Ou, Y, Ferreira, LK, Menezes, PR, Scazufca, M, Davatzikos, C, Busatto, GF (2013). Neuroanatomical pattern classification in a population-based sample of first-episode schizophrenia. Progress in Neuro-Psychopharmacology and Biological Psychiatry 43, 116–125.Google Scholar