Brain network characteristics separating individuals at clinical high risk for psychosis into normality or psychosis
Introduction
Individuals at clinical high risk for psychosis (CHR) have been the focus of clinical research for the early detection and prevention of psychotic disorders since the 1990s (Kwon et al., 2012). Longitudinal observation of these individuals, however, showed that 36% of CHR individuals symptomatically remitted and 30% functionally recovered, whereas 30% converted to psychosis within 2 years (Schlosser et al., 2012). This tells us that a heterogeneous group of individuals are in the CHR state between the transient disturbance of mental state in youth and the prodromal stage of psychosis. If we can distinguish the individuals who will have remission or transitory nonpsychotic disorders from those who will undergo overt psychosis or persistent attenuated symptoms, then appropriate intervention in the early stage of non-specific mental distress would be possible, according to the staging model of prodromal prevention (Fusar-Poli et al., 2014).
Similar to a risk rating for cardiovascular disease or cancer, Cannon and colleagues (Cannon et al., 2016) developed a “risk calculator” for the personalized prediction of psychosis using clinical, demographic and cognitive measures and demonstrated clinical utility of this calculator (Carrión et al., 2016). In addition to these sets of clinical information, neurobiological measures can improve the individualized approach for detection and treatment in individuals at CHR. Recently, Clementz et al. (2016) reported a possible advantage of neurobiological versus clinical phenomenology for differentiating psychotic disorders. As one of the possible brain-based biomarkers, network models can provide insight into the basic structures and mechanisms that underlie mental illnesses (Park and Friston, 2013, Sporns, 2014). In patients with schizophrenia (SZ), structural and functional brain networks analyses revealed that the connection density among rich club hubs was significantly reduced, suggesting a disruption of global communication in this disease (van den Heuvel et al., 2013).
We performed a combined structural and functional imaging study using network analysis in accordance with the disconnection model of SZ (Fornito et al., 2012). Structural networks represent anatomical configurations, whereas functional networks represent the interactions among the time series of neuronal activity (Sporns, 2014). A disease-specific data analysis, termed the Healthy State Model (HSM) (Nicolau et al., 2007, Nicolau et al., 2011), was adopted to measure the error or deviation from the normal state, as well as global and local efficiencies of the network and structural-functional coupling.
We aimed to obtain the most probable classification of individual clinical high risk for psychosis into a subgroup that is similar to healthy controls and the classification of the remaining individuals into another subgroup that is similar to patients with schizophrenia, according to structural and functional network constructs. We hypothesized that the resulting subgroups would represent the respective clinical and neurocognitive features of healthy controls or patients with schizophrenia.
Section snippets
Participants and clinical assessments
The study sample consisted of 61 healthy controls (HC, aged 17–35), 57 individuals at CHR (aged 15–33) and 29 patients with SZ (aged 15–35) who participated in a study conducted at the Seoul Youth Clinic as part of the prospective and longitudinal investigation of CHR and SZ (Kwon et al., 2012). The CHR group was assessed using the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I) and the Structured Interview of Prodromal Symptoms (SIPS) (Jung et al., 2010), both of which were
Demographic and clinical characteristics of participants
As shown in Table 1, there were significant differences in age, sex ratio, and education level among the three groups. Because of the clinical characteristics of CHR individuals, the CHR group was younger and had a lower education level than the HC and SZ groups. The SZ group had fewer male participants than the HC and CHR groups. Thus, we additionally performed group comparison using covariates of age and sex to control the potential confounding effects when comparing the three groups.
The
Discussion
This combined structural and functional study demonstrated that the CHR group could be successfully separated into two subgroups, and each subgroup appeared to be similar to either HC or SZ in terms of their verbal and executive functions, as well as their network properties. The proportion of the CHR-SZ subgroup among the whole CHR group was 42% in the present study. This seems much higher than the 8% transition rate at 2 years, which was reported by 2 of the largest CHR research studies to
Disclosure
All authors report no financial relationship with any commercial interest.
Role of funding source
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (Grant no. 2016R1E1A1A02921618). The funding sources had no further role in the study design, collection, analysis, interpretation of the data, writing of the report, and in the decision to submit this report for publication. The corresponding author had full access to the data in the study and had final responsibility
Contributors
Soo-Hee Choi helped design and run the study, analyzed and interpreted the data, and wrote the manuscript. Sunghyon Kyeong analyzed the imaging data and undertook statistical analyses. Kang Ik K. Cho conducted imaging acquisition and imaging data processing. Je-Yeon Yun and Tae Young Lee administered clinical interview and managed the clinical data. Hye Yoon Park and Sung Nyun Kim reviewed and extensively commented on the first draft of the manuscript. Jun Soo Kwon undertook the study design
Conflict of interest
The authors have no financial relationship with any commercial interest or other potential conflicts of interest.
Acknowledgements
The authors would like to express our gratitude to all the individuals who kindly gave their time to participate in this research.
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