A proposal for reframing schizophrenia research

Search for schizophrenia and bipolar biotypes using functional network properties

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.

Subtyping Schizophrenia Patients Based on Patterns of Structural Brain Alterations

Schizophrenia is a complex and heterogeneous syndrome. Whether quantitative imaging biomarkers can identify discrete subgroups of patients as might be used to foster personalized medicine approaches for patient care remains unclear. Cross-sectional structural MR images of 163 never-treated first-episode schizophrenia patients (FES) and 133 chronically ill patients with midcourse schizophrenia from the Bipolar and Schizophrenia Network for Intermediate Phenotypes (B-SNIP) consortium and a total of 403 healthy controls were recruited. Morphometric measures (cortical thickness, surface area, and subcortical structures) were extracted for each subject and then the optimized subtyping results were obtained with nonsupervised cluster analysis. Three subgroups of patients defined by distinct patterns of regional cortical and subcortical morphometric features were identified in FES. A similar three subgroup pattern was identified in the independent dataset of patients from the multi-site B-SNIP consortium. Similarities of classification patterns across these two patient cohorts suggest that the 3-group typology is relatively stable over the course of illness. Cognitive functions were worse in subgroup 1 with midcourse schizophrenia than those in subgroup 3. These findings provide novel insight into distinct subgroups of patients with schizophrenia based on structural brain features. Findings of different cognitive functions among the subgroups support clinical differences in the MRI-defined illness subtypes. Regardless of clinical presentation and stage of illness, anatomic MR subgrouping biomarkers can separate neurobiologically distinct subgroups of schizophrenia patients, which represent an important and meaningful step forward in differentiating subtypes of patients for studies of illness neurobiology and potentially for clinical trials.

Analysis of KCNH2 and CACNA1C schizophrenia risk genes on EEG functional network modulation during an auditory odd-ball task

A deficit in task-related functional connectivity modulation from electroencephalogram (EEG) has been described in schizophrenia. The use of measures of neuronal connectivity as an intermediate phenotype may allow identifying genetic factors involved in these deficits, and therefore, establishing underlying pathophysiological mechanisms. Genes involved in neuronal excitability and previously associated with the risk for schizophrenia may be adequate candidates in relation to functional connectivity alterations in schizophrenia. The objective was to study the association of two genes of voltage-gated ion channels (CACNA1C and KCNH2) with the functional modulation of the cortical networks measured with EEG and graph-theory parameter during a cognitive task, both in individuals with schizophrenia and healthy controls. Both CACNA1C (rs1006737) and KCNH2 (rs3800779) were genotyped in 101 controls and 50 schizophrenia patients. Small-world index (SW) was calculated from EEG recorded during an odd-ball task in two different temporal windows (pre-stimulus and response). Modulation was defined as the difference in SW between both windows. Genetic, group and their interaction effects on SW in the pre-stimulus window and in modulation were evaluated using ANOVA. The CACNA1C genotype was not associated with SW properties. KCNH2 was significantly associated with SW modulation. Healthy subjects showed a positive SW modulation irrespective of the KCNH2 genotype, whereas within patients allele-related differences were observed. Patients carrying the KCNH2 risk allele (A) presented a negative SW modulation and non-carriers showed SW modulation similar to the healthy subjects. Our data suggest that KCNH2 genotype contributes to the efficient modulation of brain electrophysiological activity during a cognitive task in schizophrenia patients.

Quantification of Graph Complexity Based on the Edge Weight Distribution Balance: Application to Brain Networks

The aim of this study was to introduce a novel global measure of graph complexity: Shannon graph complexity (SGC). This measure was specifically developed for weighted graphs, but it can also be applied to binary graphs. The proposed complexity measure was designed to capture the interplay between two properties of a system: the ‘information’ (calculated by means of Shannon entropy) and the ‘order’ of the system (estimated by means of a disequilibrium measure). SGC is based on the concept that complex graphs should maintain an equilibrium between the aforementioned two properties, which can be measured by means of the edge weight distribution. In this study, SGC was assessed using four synthetic graph datasets and a real dataset, formed by electroencephalographic (EEG) recordings from controls and schizophrenia patients. SGC was compared with graph density (GD), a classical measure used to evaluate graph complexity. Our results showed that SGC is invariant with respect to GD and independent of node degree distribution. Furthermore, its variation with graph size [Formula: see text] is close to zero for [Formula: see text]. Results from the real dataset showed an increment in the weight distribution balance during the cognitive processing for both controls and schizophrenia patients, although these changes are more relevant for controls. Our findings revealed that SGC does not need a comparison with null-hypothesis networks constructed by a surrogate process. In addition, SGC results on the real dataset suggest that schizophrenia is associated with a deficit in the brain dynamic reorganization related to secondary pathways of the brain network.

Sensibilidad en el Reconocimiento de Emociones Faciales Como Endofenotipo de Esquizofrenia

<p class="MsoNormal" style="line-height: 200%;">Los endofenotipos son rasgos presentes antes de la aparición de un trastorno y podrían ser útiles para identificar genes de susceptibilidad. Se determinó si personas con esquizofrenia y sus familiares de primer grado no afectados tenían un desempeño menor que los controles en la Tarea de Multitransformación de Expresión Emocional, que mide reconocimiento de emociones faciales. Las personas con esquizofrenia y sus familiares mostraron menor sensibilidad o requirieron más intensidad para identificar emociones que los controles. La exactitud para identificar emociones fue similar entre familiares y controles, pero menor en aquellos con esquizofrenia. Esto sugiere que la sensibilidad para el reconocimiento de emociones faciales es un endofenotipo de la esquizofrenia.</p>

The neurobiology of relapse in schizophrenia

Dopamine's proposed role in psychosis proved a starting point in our understanding of the neurobiology of relapse, fitting given the central role positive symptoms play. This link is reflected in early work examining neurotransmitter metabolite and drug (e.g. amphetamine, methylphenidate) challenge studies as a means of better understanding relapse and predictors. Since, lines of investigation have expanded (e.g. electrophysiological, immunological, hormonal, stress), an important step forward if relapse per se is the question. Arguably, perturbations in dopamine represent the final common pathway in psychosis but it is evident that, like schizophrenia, relapse is heterogeneous and multidimensional. In understanding the neurobiology of relapse, greater gains are likely to be made if these distinctions are acknowledged; for example, efforts to identify trait markers might better be served by distinguishing primary (i.e. idiopathic) and secondary (e.g. substance abuse, medication nonadherence) forms of relapse. Similarly, it has been suggested that relapse is 'neurotoxic', yet individuals do very well on clozapine after multiple relapses and the designation of treatment resistance. An alternative explanation holds that schizophrenia is characterized by different trajectories, at least to some extent biologically and/or structurally distinguishable from the outset, with differential patterns of response and relapse. Just as with schizophrenia, it seems naïve to conceptualize the neurobiology of relapse as a singular process. We propose that it is shaped by the form of illness and in place from the outset, modified by constitutional factors like resilience, as well as treatment, and confounded by secondary forms of relapse.

Gender identity disorder and schizophrenia: neurodevelopmental disorders with common causal mechanisms?

Gender identity disorder (GID), recently renamed gender dysphoria (GD), is a rare condition characterized by an incongruity between gender identity and biological sex. Clinical evidence suggests that schizophrenia occurs in patients with GID at rates higher than in the general population and that patients with GID may have schizophrenia-like personality traits. Conversely, patients with schizophrenia may experience alterations in gender identity and gender role perception. Neurobiological research, including brain imaging and studies of finger length ratio and handedness, suggests that both these disorders are associated with altered cerebral sexual dimorphism and changes in cerebral lateralization. Various mechanisms, such as Toxoplasma infection, reduced levels of brain-derived neurotrophic factor (BDNF), early childhood adversity, and links with autism spectrum disorders, may account for some of this overlap. The implications of this association for further research are discussed.