Progress in Neuro-Psychopharmacology and Biological Psychiatry
Identification of two clusters within schizophrenia with different structural, functional and clinical characteristics
Introduction
More than 8000 genetic variants may contribute to the risk of suffering from schizophrenia (Ripke et al., 2013) and recent research supports schizophrenia subtypes characterized by both specific clinical presentation and clusters of genetic variants (Arnedo et al., 2014). Considering the likely effect of genetic variation upon brain structure (Papiol et al., 2005), distinct groups within schizophrenia might be characterized by specific patterns of cerebral alterations.
A possible approach to identify such groups is attempting data-driven (a priori independently of diagnosis) cluster segregation, starting from plausible cerebral variables: i) previously associated to schizophrenia as a group but not unanimously replicated across samples, ii) not primarily related to confounders, such as treatment or chronicity, iii) with likely genetic influence, iv) associated to neurodevelopment and v) likely related to relevant clinical and biological variables.
Cortical thickness may prove useful to this purpose, since cortical thinning has been reported in first-episode (FE) and chronic schizophrenia patients (Crespo-Facorro et al., 2011) (although not unanimously (van Haren et al., 2011)) and in antipsychotic naïve patients (Venkatasubramanian et al., 2008). Cortical thickness is highly heritable (Goldman et al., 2009), with a complex relation with genetic background in schizophrenia (Blasi et al., 2013) and early experience (Whittle et al., 2014). Moreover, cortical thickness increases during normal neurodevelopment (Schmitt et al., 2014) and v) has been associated to treatment response (Szeszko et al., 2012) and cognition (Cassidy et al., 2014) in FE patients.
Cortical curvature measures cortical folding and may reflect different neurobiological underpinnings (Ronan et al., 2011). It may contribute to the proposed clustering since higher (Falkai et al., 2007), normal (Fornito et al., 2008) and reduced (Cachia et al., 2008) gyrification indexes have been reported in schizophrenia and FE patients have shown altered gyrification (Harris et al., 2004). Furthermore, gyrification has genetic underpinnings (Piao et al., 2004) and cerebral gyrification takes place largely in the third trimester (White and Hilgetag, 2008), thus, altered gyrification may result from developmental events in this period. Finally, gyrification is associated to formation of proper cortico-cortical connections (Van Essen, 1997).
The present study has reanalyzed data from previous samples assessing cortical thickness and curvature in order to blindly investigate clustering of schizophrenia patients. Regional cortical area was also included in analyses, since it may convey different information than other structural data in schizophrenia (Rimol et al., 2012). In addition, subcortical volumes were included, given the caudate and thalamic volume association with poor-prognosis schizophrenia (Molina et al., 2010b). Regional cortical volumes were not included since they are largely explained by the corresponding area and thickness. In order to validate clusters, other data were collected, including clinical (baseline symptoms and variation with treatment) and biological (glucose metabolic rate, P300 amplitude and latency) parameters. Chronic bipolar subjects were introduced into the analyses to discard clusters being primarily related to chronicity and/or treatment. Therefore, rather than directly comparing anatomical values depending on clinical diagnosis (schizophrenia, bipolar, healthy controls), our approach was looking for biologically distinct clusters, not considering diagnosis a priori as the primary classifying factor. According to our hypothesis, clusters identified on basis of structural alteration would be expected to be treatment-independent, associated to clinical outcome and/or presentation and associated to other relevant biological markers in order to be considered as valid.
Section snippets
Sample description
Sample included 203 subjects: 121 schizophrenia (Sz) (DSM-IV criteria) patients (64 patients were FE), 22 chronic bipolar patients and 60 healthy controls (HC). Patients were recruited during a psychotic relapse (chronic patients) or first psychotic episode (FE), following their admission to a psychiatric short-term unit. After release they were treated and followed in an outpatient clinic.
MRI data were collected over a 15-year period, in the context of several research projects (identification
Cluster identification
The PCA analysis summary and the plots of the first two components for the four structural variables are shown in Fig. 1. Scatterplots in Fig. 1 clearly show two outliers, which were subsequently eliminated from remaining analyses. Also from these graphs, the PC1CURV (PCA scores in the first factor for curvature) was selected as initial index for generating separate clusters.
In the next step, PC1CURV was used to define three initial clusters: the first included 19 subjects with very large
Discussion
A function, mostly contributed by cortical curvature, segregated 20% of subjects within the schizophrenia group (cluster B), who also showed a more modest cortical thinning and hypometabolism in the left cingulate and thalamus, together with persistent negative symptoms. Cluster A schizophrenia patients showed normal structure but higher putaminal glucose metabolism than HC. Metabolism and clinical improvement were negatively related to curvature. All bipolar and HC subjects were grouped within
Financial disclosure
The authors have no conflicts of interest to declare.
Acknowledgements
This study was supported in part by grants PI08/0017 and PI011/02203 from the Carlos II Institute of the Ministry of Health (Spain), GRS 613/A/11 and 932/A/14 from the Gerencia Regional de Salud de Castilla y León and a predoctoral scholarship grant from the Consejería de Educación — Junta de Castilla y León and European Social Fund to A. Lubeiro.
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