Elsevier

Schizophrenia Research

Volume 190, December 2017, Pages 107-114
Schizophrenia Research

Brain network characteristics separating individuals at clinical high risk for psychosis into normality or psychosis

https://doi.org/10.1016/j.schres.2017.03.028Get rights and content

Abstract

We aimed to separate individuals at clinical high risk for psychosis (CHR) state into subgroups according to neurobiological characteristics using structural and functional network constructs and examine their clinical characteristics. Structural diffusion tensor imaging and resting-state functional magnetic resonance imaging were performed in 61 healthy controls (HC), 57 individuals at CHR and 29 patients with schizophrenia (SZ). The main outcome was a likelihood ratio calculated from measures of structural and functional network efficiencies, coupling strength of structural and functional networks, and a disease-specific data analysis, resulting in the most probable classification of CHR into HC or SZ. The likelihood ratios revealed that 33 individuals at CHR were likely similar to HC (CHR-HC), and the remaining 24 CHR individuals were similar to SZ (CHR-SZ). The CHR subgroups were comparable to each other in demographic characteristics and clinical symptoms. However, the verbal and executive functions of CHR-HC were similar to those of HC, and those of CHR-SZ similar to SZ. Additionally, CHR-SZ was more responsive to treatment than CHR-HC during the follow-up period. By combining structural and functional data, we could detect the vulnerable population and provide an active intervention in the early phase of the CHR state.

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.

References (41)

  • M. Aas et al.

    A systematic review of cognitive function in first-episode psychosis, including a discussion on childhood trauma, stress, and inflammation

    Front. Psych.

    (2013)
  • S. Achard et al.

    Efficiency and cost of economical brain functional networks

    PLoS Comput. Biol.

    (2007)
  • A. Anticevic et al.

    Association of thalamic dysconnectivity and conversion to psychosis in youth and young adults at elevated clinical risk

    JAMA Psychiat.

    (2015)
  • E. Bora et al.

    Cognitive deficits in youth with familial and clinical high risk to psychosis: a systematic review and meta-analysis

    Acta Psychiatr. Scand.

    (2014)
  • E. Bullmore et al.

    Complex brain networks: graph theoretical analysis of structural and functional systems

    Nat. Rev. Neurosci.

    (2009)
  • T.D. Cannon et al.

    An individualized risk calculator for research in prodromal psychosis

    Am. J. Psychiatry

    (2016)
  • R.E. Carrión et al.

    Personalized prediction of psychosis: external validation of the NAPLS-2 psychosis risk calculator with the EDIPPP project

    Am. J. Psychiatry

    (2016)
  • B.A. Clementz et al.

    Identification of distinct psychosis biotypes using brain-based biomarkers

    Am. J. Psychiatry

    (2016)
  • G. Collin et al.

    Impaired rich club connectivity in unaffected siblings of schizophrenia patients

    Schizophr. Bull.

    (2014)
  • B.A. Cornblatt et al.

    Psychosis prevention: a modified clinical high risk perspective from the Recognition and Prevention (RAP) program

    Am. J. Psychiatry

    (2015)
  • Cited by (7)

    • Discovery and Validation of Prediction Algorithms for Psychosis in Youths at Clinical High Risk

      2020, Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
      Citation Excerpt :

      Essentially, the human brain can be considered as an elegant system with an optimal balance between information integration and segregation in order to maximally facilitate neural processing (54,55). Several studies have demonstrated that disrupted balance between information integration and segregation is likely to be an early predictor for psychosis (56–59). Specifically, network efficiency, a critical measure for information integration ability of a given network, has been found to be significantly decreased in converters among the CHR-P population at baseline (56,57).

    • White Matter Microstructure across the Psychosis Spectrum

      2020, Trends in Neurosciences
      Citation Excerpt :

      To achieve sufficient sample sizes, such studies are often carried out through large scale consortiums such as the North American Longitudinal Prodromal Study (NAPLS) or the European Prediction of Psychosis (EPOS) study. Recent high-risk studies have found WM alterations, primarily decreases in FA, to be present even in these relatively early stages of illness [39,41,52,53]. Moreover, there appear to be differences in the maturational patterns, or patterns of change across time in these populations.

    • Microstructural white matter network-connectivity in individuals with psychotic disorder, unaffected siblings and controls

      2019, NeuroImage: Clinical
      Citation Excerpt :

      While research in first-degree relatives on LE is limited to this one study (and reports on CC are missing), research on rich club (a tightly interconnected core of regions) organization of structural networks suggests impaired connectivity in siblings (Collin et al., 2014; Schmidt et al., 2016) and parents (Zhao et al., 2017) of patients with psychotic disorder. Studies on help-seeking individuals with an at-risk mental state for psychotic disorder (help-seeking individuals with affective or substance use disorder and a degree of psychosis admixture, some of whom will have a poor prognosis) showed preserved (Schmidt et al., 2016) as well as reduced GE (Choi et al., 2017; Drakesmith et al., 2015). To date, there are no studies examining density in siblings.

    View all citing articles on Scopus
    View full text