Search for schizophrenia and bipolar biotypes using functional network properties. Issue 12 (10th November 2021)

• Record Type: Journal Article
• Authors: Fernández‐Linsenbarth, Inés; Planchuelo‐Gómez, Álvaro; Beño‐Ruiz‐de‐la‐Sierra, Rosa M.; Díez, Alvaro; Arjona, Antonio; Pérez, Adela; Rodríguez‐Lorenzana, Alberto; del Valle, Pilar; de Luis‐García, Rodrigo; Mascialino, Guido; Holgado‐Madera, Pedro; Segarra‐Echevarría, Rafael; Gomez‐Pilar, Javier; Núñez, Pablo; Bote‐Boneaechea, Berta; Zambrana‐Gómez, Antonio; Roig‐Herrero, Alejandro; Molina, Vicente
• Is Part Of: Brain and behavior. Volume 11:Issue 12(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-10
• Subjects: biotypes -- bipolar disorder -- diffusion -- electroencephalogram -- network -- schizophrenia; Neurology -- Periodicals; Neurosciences -- Periodicals; Psychology -- Periodicals; Psychiatry -- Periodicals; 616.8005
• DOI: 10.1002/brb3.2415
• Languages: English
• ISSNs: 2162-3279

Abstract

Abstract: 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. Abstract : In this study, our aim was to contribute to a better understanding of the biological heterogeneity in psychoses. For this purpose, we conducted a cluster analysis to identify patients' subgroups based on the characteristics of their functional network, assessed wit electroencephalogram (EEG) data and methods derived from graph‐theory. Our cluster analysis yielded a two‐cluster solution, where one cluster showed significant alterations in their functional network and a significantly altered structural connectivity network with a widespread cortical thinning, and the other cluster showed a normal functional network.