[en] [en] INTRODUCTION: Recent studies support the identification of valid subtypes within schizophrenia and bipolar disorder using cluster analysis. Our aim was to identify meaningful biotypes of psychosis based on network properties of the electroencephalogram. We hypothesized that these parameters would be more altered in a subgroup of patients also characterized by more severe deficits in other clinical, cognitive, and biological measurements.
METHODS: A clustering analysis was performed using the electroencephalogram-based network parameters derived from graph-theory obtained during a P300 task of 137 schizophrenia (of them, 35 first episodes) and 46 bipolar patients. Both prestimulus and modulation of the electroencephalogram were included in the analysis. Demographic, clinical, cognitive, structural cerebral data, and the modulation of the spectral entropy of the electroencephalogram were compared between clusters. Data from 158 healthy controls were included for further comparisons.
RESULTS: We identified two clusters of patients. One cluster presented higher prestimulus connectivity strength, clustering coefficient, path-length, and lower small-world index compared to controls. The modulation of clustering coefficient and path-length parameters was smaller in the former cluster, which also showed an altered structural connectivity network and a widespread cortical thinning. The other cluster of patients did not show significant differences with controls in the functional network properties. No significant differences were found between patients´ clusters in first episodes and bipolar proportions, symptoms scores, cognitive performance, or spectral entropy modulation.
CONCLUSION: These data support the existence of a subgroup within psychosis with altered global properties of functional and structural connectivity.
Disciplines :
Psychiatry
Author, co-author :
Fernández-Linsenbarth, Inés; Psychiatry Department, School of Medicine, University of Valladolid, Valladolid, Spain
Planchuelo-Gómez, Álvaro ; Imaging Processing Laboratory, University of Valladolid, Valladolid, Spain
Beño-Ruiz-de-la-Sierra, Rosa M; Psychiatry Department, School of Medicine, University of Valladolid, Valladolid, Spain
Díez, Alvaro; Psychiatry Department, School of Medicine, University of Valladolid, Valladolid, Spain
Arjona, Antonio; Psychiatry Department, School of Medicine, University of Valladolid, Valladolid, Spain
Pérez, Adela; Psychiatry Service, Clinical Hospital of Valladolid, Valladolid, Spain
Rodríguez-Lorenzana, Alberto; School of Psychology, Universidad de Las Américas, Quito, Ecuador
Del Valle, Pilar; Psychiatry Service, Clinical Hospital of Valladolid, Valladolid, Spain
de Luis-García, Rodrigo; Imaging Processing Laboratory, University of Valladolid, Valladolid, Spain
Mascialino, Guido; School of Psychology, Universidad de Las Américas, Quito, Ecuador
Holgado-Madera, Pedro; Psychiatry Service, Doce de Octubre University Hospital, Madrid, Spain
Gomez-Pilar, Javier; Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
Nunez Novo, Pablo ; University of Valladolid > Biomedical Engineering Group
Bote-Boneaechea, Berta; Psychiatry Service, University Hospital of Salamanca, Salamanca, Spain
Zambrana-Gómez, Antonio; Psychiatry Service, University Hospital of Salamanca, Salamanca, Spain
Roig-Herrero, Alejandro; Psychiatry Department, School of Medicine, University of Valladolid, Valladolid, Spain
Molina, Vicente ; Psychiatry Department, School of Medicine, University of Valladolid, Valladolid, Spain ; Psychiatry Service, Clinical Hospital of Valladolid, Valladolid, Spain
This work was supported by the following grants: “Instituto de Salud Carlos III” grant number PI18/00178, “Gerencia Regional de Salud de Castilla y León” grant number GRS 1721/A/18, and by predoctoral grants from the “Consejería de Educación ‐ Junta de Castilla y León” and the European Social Fund grant numbers VA‐183‐18 to Inés Fernández‐Linsenbarth, 376062 to Álvaro Planchuelo‐Gómez, and VA‐223‐19 to Rosa M. Beño‐Ruiz‐de‐la‐Sierra. We appreciate the collaboration of patients and healthy controls in our research.
Arnedo, J., Svrakic, D. M., Del Val, C., Romero-Zaliz, R., Hernández-Cuervo, H., Fanous, A. H., Pato, M. T., Pato, C. N., De Erausquin, G. A., Cloninger, C. R., & Zwir, I. (2015). Uncovering the hidden risk architecture of the schizophrenias: Confirmation in three independent genome-wide association studies. American Journal of Psychiatry, 172(2), 139–153. https://doi.org/10.1176/appi.ajp.2014.14040435
Bachiller, A., Díez, A., Suazo, V., Domínguez, C., Ayuso, M., Hornero, R., Poza, J., & Molina, V. (2014). Decreased spectral entropy modulation in patients with schizophrenia during a P300 task. European Archives of Psychiatry and Clinical Neuroscience, 264(6), 533–543. https://doi.org/10.1007/s00406-014-0488-6
Bastos, A. M., & Schoffelen, J. M. (2016). A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Frontiers in Systems Neuroscience, 9, 1–23. https://doi.org/10.3389/fnsys.2015.00175
Bob, P., Palus, M., Susta, M., & Glaslova, K. (2008). EEG phase synchronization in patients with paranoid schizophrenia. Neuroscience Letters, 447(1), 73–77. https://doi.org/10.1016/j.neulet.2008.09.055
Brugger, S. P., & Howes, O. D. (2017). Heterogeneity and homogeneity of regional brain structure in schizophrenia: A meta-analysis. JAMA Psychiatry, 74(11), 1104–1111. https://doi.org/10.1001/jamapsychiatry.2017.2663
Brunner, C., Billinger, M., Seeber, M., Mullen, T. R., & Makeig, S. (2016). Volume conduction influences scalp-based connectivity estimates. Frontiers in Computational Neuroscience, 10, 1–4. https://doi.org/10.3389/fncom.2016.00121
Buzsáki, G., & Draguhn, A. (2004). Neuronal olscillations in cortical networks. Science, 304, 1926–1929. https://doi.org/10.1126/science.1099745
Cea-Cañas, B., Gomez-Pilar, J., Núñez, P., Rodríguez-Vázquez, E., De Uribe, N., Díez, Á., Pérez-Escudero, A., & Molina, V. (2020). Connectivity strength of the EEG functional network in schizophrenia and bipolar disorder. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 98, 109801. https://doi.org/10.1016/j.pnpbp.2019.109801
Charrad, M., Ghazzali, N., Boiteau, V., & Niknafs, A. (2014). Nbclust: An R package for determining the relevant number of clusters in a data set. Journal of Statistical Software, 61(6), 1–36. https://doi.org/10.18637/jss.v061.i06
Chelune, G. J., & Baer, R. A. (1986). Developmental norms for the Wisconsin card sorting test. Journal of Clinical and Experimental Neuropsychology, 8(3), 219–228. https://doi.org/10.1080/01688638608401314
Clementz, B. A., Sweeney, J. A., Hamm, J. P., Ivleva, E. I., Ethridge, L. E., Pearlson, G. D., Keshavan, M. S., & Tamminga, C. A. (2016). Identification of distinct psychosis biotypes using brain-based biomarkers. American Journal of Psychiatry, 173(4), 373–384. https://doi.org/10.1176/appi.ajp.2015.14091200
Dale, A. M., Fischl, B., & Sereno, M. I. (1999). Cortical surface-based analysis: I. Segmentation and surface reconstruction. Neuroimage, 9(2), 179–194. https://doi.org/10.1006/nimg.1998.0395
Dehaene, S., & Changeux, J. P. (2011). Experimental and theoretical approaches to conscious processing. Neuron, 70, 200–227. https://doi.org/10.1016/j.neuron.2011.03.018
Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., Albert, M. S., & Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage, 31, 968–980. https://doi.org/10.1016/j.neuroimage.2006.01.021
Di, X., Gohel, S., Kim, E. H., & Biswal, B. B. (2013). Task vs. rest-different network configurations between the coactivation and the resting-state brain networks. Frontiers in Human Neuroscience, 7(Sep), 1–9. https://doi.org/10.3389/fnhum.2013.00493
Fernández-Linsenbarth, I., Planchuelo-Gómez, Á., Díez, Á., Arjona-Valladares, A., De Luis, R., Martín-Santiago, Ó., Benito-Sánchez, J. A., Pérez-Laureano, Á., González-Parra, D., Montes-Gonzalo, C., Melero-Lerma, R., Morante, S. F., Sanz-Fuentenebro, J., Gómez-Pilar, J., Núñez-Novo, P., & Molina, V. (2021). Neurobiological underpinnings of cognitive subtypes in psychoses: A cross-diagnostic cluster analysis. Schizophrenia Research, 229, 102–111. https://doi.org/10.1016/j.schres.2020.11.013
Fuentes Durá, I., Peris, M. R., Vivó, C. D., & Ruiz, J. C. R. (2010). Versión abreviada del WAIS-III para su uso en la evaluación de pacientes con diagnóstico de esquizofrenia. Psicothema, 22(2), 202–207. www.psicothema.com
Gomez-Pilar, J., De Luis-García, R., Lubeiro, A., De La Red, H., Poza, J., Núñez, P., Hornero, R., & Molina, V. (2018). Relations between structural and EEG-based graph metrics in healthy controls and schizophrenia patients. Human Brain Mapping, 39(8), 3152–3165. https://doi.org/10.1002/hbm.24066
Gomez-Pilar, J., De Luis-García, R., Lubeiro, A., De Uribe, N., Poza, J., Núñez, P., Ayuso, M., Hornero, R., & Molina, V. (2018). Deficits of entropy modulation in schizophrenia are predicted by functional connectivity strength in the theta band and structural clustering. NeuroImage: Clinical, 18, 382–389. https://doi.org/10.1016/j.nicl.2018.02.005
Gomez-Pilar, J., Lubeiro, A., Poza, J., Hornero, R., Ayuso, M., Valcárcel, C., Haidar, K., Blanco, J. A., & Molina, V. (2017). Functional EEG network analysis in schizophrenia: Evidence of larger segregation and deficit of modulation. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 76, 116–123. https://doi.org/10.1016/j.pnpbp.2017.03.004
Hassan, M., Dufor, O., Merlet, I., Berrou, C., & Wendling, F. (2014). EEG source connectivity analysis: From dense array recordings to brain networks. Plos One, 9(8), e105041. https://doi.org/10.1371/journal.pone.0105041
Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012). FSL. Neuroimage, 62(2), 782–790. https://doi.org/10.1016/j.neuroimage.2011.09.015
Kaufman, L., & Rousseeuw, P. (1990). In Clustering large applications (Program CLARA). L. Kaufman & P. Rousseeuw (Eds.), Finding Groups in Data, Wiley series in probability and statistics (pp. 126–163). Wiley–Blackwell.
Kay, S. R., Fiszbein, A., & Opler, L. A. (1987). The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophrenia Bulletin, 13(2), 261–276. https://doi.org/10.1093/schbul/13.2.261
Kirkpatrick, B., Strauss, G. P., Nguyen, L., Fischer, B. A., Daniel, D. G., Cienfuegos, A., & Marder, S. R. (2011). The brief negative symptom scale: Psychometric properties. Schizophrenia Bulletin, 37(2), 300–305. https://doi.org/10.1093/schbul/sbq059
Lachaux, J., Rodriguez, E., Martinerie, J., & Varela, F. J. (1999). Measuring phase synchrony in brain signals. Human Brain Mapping, 8(4), 194–208. h ttps://doi.org/10.1002/(SICI)1097-0193(1999)8:4<194::AID-HBM4>3.0.CO;2-C
Lubeiro, A., de Luis-García, R., Rodríguez, M., Álvarez, A., de la Red, H., & Molina, V. (2017). Biological and cognitive correlates of cortical curvature in schizophrenia. Psychiatry Research - Neuroimaging, 270, 68–75. https://doi.org/10.1016/j.pscychresns.2017.10.011
Lubeiro, A., Rueda, C., Hernández, J. A., Sanz, J., Sarramea, F., & Molina, V. (2016). Identification of two clusters within schizophrenia with different structural, functional and clinical characteristics. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 64, 79–86. https://doi.org/10.1016/j.pnpbp.2015.06.015
Mayer, J. D., Salovey, P., Caruso, D. R., & Sitarenios, G. (2003). Measuring emotional intelligence with the MSCEIT V2.0 work-life strategies. Emotion, 3(1), 97–105. https://doi.org/10.1037/1528-3542.3.1.97
Michel, C. M., Murray, M. M., Lantz, G., Gonzalez, S., Spinelli, L., & Grave De Peralta, R. (2004). EEG source imaging. Clinical Neurophysiology, 115(10), 2195–2222. https://doi.org/10.1016/j.clinph.2004.06.001
Molina, V., Lubeiro, A., de Luis-García, R., & Gómez-Pilar, J. (2020). Deficit of entropy modulation of the EEG: A biomarker for the altered function in schizophrenia and bipolar disorder? The Journal of Neuropsychiatry and Clinical Neurosciences, 45(190032), 322–333.
Molina, V., Reig, S., Sanz, J., Palomo, T., Benito, C., Sarramea, F., Pascau, J., Sánchez, J., Martín-Loeches, M., Muñoz, F., & Desco, M. (2008). Differential clinical, structural and P300 parameters in schizophrenia patients resistant to conventional neuroleptics. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 32(1), 257–266. https://doi.org/10.1016/j.pnpbp.2007.08.017
Molina, V., Bachiller, A., Gomez-Pilar, J., Lubeiro, A., Hornero, R., Cea-Cañas, B., Valcárcel, C., Haidar, M.-K., & Poza, J. (2018). Deficit of entropy modulation of the EEG in schizophrenia associated to cognitive performance and symptoms. A replication study. Schizophrenia Research, 195, 334–342. https://doi.org/10.1016/j.schres.2017.08.057
Molina, V., & Blanco, J. A. (2013). A Proposal for reframing schizophrenia research. Journal of Nervous and Mental Disease, 201, 744–752. https://doi.org/10.1097/NMD.0b013e3182a21444
Planchuelo-Gómez, Á., Lubeiro, A., Núñez-Novo, P., Gomez-Pilar, J., De Luis-García, R., Del Valle, P., Martín-Santiago, Ó., Pérez-Escudero, A., & Molina, V. (2020). Identificacion of MRI-based psychosis subtypes: Replication and refinement. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 100, 109907. https://doi.org/10.1016/j.pnpbp.2020.109907
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. https://doi.org/10.1016/0377-0427(87)90125-7
Segarra, N., Bernardo, M., Gutierrez, F., Justicia, A., Fernadez-Egea, E., Allas, M., Safont, G., Contreras, F., Gascon, J., Soler-Insa, P. A., Menchon, J. M., Junque, C., & Keefe, R. S. E. (2011). Spanish validation of the Brief Assessment in Cognition in Schizophrenia (BACS) in patients with schizophrenia and healthy controls. European Psychiatry, 26(2), 69–73. https://doi.org/10.1016/j.eurpsy.2009.11.001
Severiano, A., Carriço, J. A., Robinson, D. A., Ramirez, M., & Pinto, F. R. (2011). Evaluation of Jackknife and Bootstrap for defining confidence intervals for pairwise agreement measures. Plos One, 6(5), e19539. https://doi.org/10.1371/journal.pone.0019539
Spencer, K. M., Nestor, P. G., Niznikiewicz, M. A., Salisbury, D. F., Shenton, M. E., & McCarley, R. W. (2003). Abnormal neural synchrony in schizophrenia. Journal of Neuroscience, 23(19), 7407–7411. https://doi.org/10.1523/jneurosci.23-19-07407.2003
Stam, C. J., & van Straaten, E. C. W. (2012). The organization of physiological brain networks. Clinical Neurophysiology, 123, 1067–1087. https://doi.org/10.1016/j.clinph.2012.01.011
Sun, H., Lui, S.u, Yao, L., Deng, W., Xiao, Y., Zhang, W., Huang, X., Hu, J., Bi, F., Li, T., Sweeney, J. A., & Gong, Q. (2015). Two patterns of white matter abnormalities in medication-naive patients with first-episode schizophrenia revealed by diffusion tensor imaging and cluster analysis. JAMA Psychiatry, 72(7), 678–686. https://doi.org/10.1001/jamapsychiatry.2015.0505
Torrence, C., & Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79(1), 61–78. https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2
Uhlhaas, P. J., & Singer, W. (2010). Abnormal neural oscillations and synchrony in schizophrenia. Nature Reviews Neuroscience, 11(2), 100–113. https://doi.org/10.1038/nrn2774
van Diessen, E., Senders, J., Jansen, F. E., Boersma, M., & Bruining, H. (2015). Increased power of resting-state gamma oscillations in autism spectrum disorder detected by routine electroencephalography. European Archives of Psychiatry and Clinical Neuroscience, 265(6), 537–540. https://doi.org/10.1007/s00406-014-0527-3
Varela, F., Lachaux, J. P., Rodriguez, E., & Martinerie, J. (2001). The brainweb: Phase synchronization and large-scale integration. Nature Reviews Neuroscience, 2(4), 229–239. https://doi.org/10.1038/35067550
Volk, D. W., Matsubara, T., Li, S., Sengupta, E. J., Georgiev, D., Minabe, Y., Sampson, A., Hashimoto, T., & Lewis, D. A. (2012). Deficits in transcriptional regulators of cortical parvalbumin neurons in schizophrenia. American Journal of Psychiatry, 169(10), 1082–1091. https://doi.org/10.1176/appi.ajp.2012.12030305
Weinberg, D., Lenroot, R., Jacomb, I., Allen, K., Bruggemann, J., Wells, R., Balzan, R., Liu, D., Galletly, C., Catts, S. V., Weickert, C. S., & Weickert, T. W. (2016). Cognitive subtypes of schizophrenia characterized by differential brain volumetric reductions and cognitive decline. JAMA Psychiatry, 73(12), 1251–1259. https://doi.org/10.1001/jamapsychiatry.2016.2925
Wheeler, A. L., Wessa, M., Szeszko, P. R., Foussias, G., Chakravarty, M. M., Lerch, J. P., Derosse, P., Remington, G., Mulsant, B. H., Linke, J., Malhotra, A. K., & Voineskos, A. N. (2015). Further neuroimaging evidence for the deficit subtype of schizophrenia: A cortical connectomics analysis. JAMA Psychiatry, 72(5), 446–455. https://doi.org/10.1001/jamapsychiatry.2014.3020
