Structure and stability of symptoms in first episode psychosis: a longitudinal network approach

Early psychosis is characterised by heterogeneity in illness trajectories, where outcomes remain poor for many. Understanding psychosis symptoms and their relation to illness outcomes, from a novel network perspective, may help to delineate psychopathology within early psychosis and identify pivotal targets for intervention. Using network modelling in first episode psychosis (FEP), this study aimed to identify: (a) key central and bridge symptoms most influential in symptom networks, and (b) examine the structure and stability of the networks at baseline and 12-month follow-up. Data on 1027 participants with FEP were taken from the National EDEN longitudinal study and used to create regularised partial correlation networks using the ‘EBICglasso’ algorithm for positive, negative, and depressive symptoms at baseline and at 12-months. Centrality and bridge estimations were computed using a permutation-based network comparison test. Depression featured as a central symptom in both the baseline and 12-month networks. Conceptual disorganisation, stereotyped thinking, along with hallucinations and suspiciousness featured as key bridge symptoms across the networks. The network comparison test revealed that the strength and bridge centralities did not differ significantly between the two networks (C = 0.096153; p = 0.22297). However, the network structure and connectedness differed significantly from baseline to follow-up (M = 0.16405, p = <0.0001; S = 0.74536, p = 0.02), with several associations between psychosis and depressive items differing significantly by 12 months. Depressive symptoms, in addition to symptoms of thought disturbance (e.g. conceptual disorganisation and stereotyped thinking), may be examples of important, under-recognized treatment targets in early psychosis, which may have the potential to lead to global symptom improvements and better recovery.

Significant Subgraph Mining for Neural Network Inference with Multiple Comparisons Correction

We describe how the recently introduced method of significant subgraph mining can be employed as a useful tool in network comparison. It is applicable whenever the goal is to compare two sets of unweighted graphs and to determine differences in the processes that generate them. We provide an extension of the method to dependent graph generating processes as the occur for example in within-subject experimental designs. Furthermore, we present an extensive investigation of error-statistical properties of the method in simulation using Erdos-Renyi models and in empirical data. In particular, we perform an empirical power analysis for transfer entropy networks inferred from resting state MEG data comparing autism spectrum patients with neurotypical controls. From this analysis one may estimate that the appropriate sample size for similar studies should be chosen in the order of n=60 per group or larger.

Sex Differences in Depressive Symptom Networks Among Community-Dwelling Older Adults in South Korea: A Network Comparison Test

This cross-sectional study investigated the sex differences in depressive symptom networks among community-dwelling older adults in Korea. The analysis was based on the 2019 Korean Community Health Survey data targeting older adults aged 65 years or older. Using network analysis, depressive symptom networks were constructed according to the items listed in the Patient Health Questionnaire-9 for propensity score-matched male (n = 1,885) and female groups (n = 2,848). Strength centrality and network stability were tested. A network comparison test was performed to compare the global strength, network structure, and specific edge strength between the networks. Symptoms central to the network were similar between sexes, which were suicidal ideation, hopelessness, and psychomotor retardation/agitation. However, the global structure (S = 0.67, p = .008) and network structure (M = 0.11, p = .043) differed between sexes. The female symptom network showed more strengthened edges (Smale = 2.00; Sfemale = 2.66). Particularly, four edges—loss of interest–hopelessness (E = 0.09, p = .016), sleep disturbance–low energy/fatigue (E = 0.11, p = .005), loss of interest–concentration difficulty (E = 0.05, p = .047), and worthlessness–concentration difficulty (E = 0.08, p = .045)—were more pronounced in the female network. Our results may help guide future research and clinical interventions for female depression.

Null-model-based network comparison reveals core associations

Microbial network construction and analysis is an important tool in microbial ecology. Such networks are often constructed from statistically inferred associations and may not represent ecological interactions. Hence, microbial association networks are error prone and do not necessarily reflect true community structure. We have developed anuran, a toolbox for investigation of noisy networks with null models. Such models allow researchers to generate data under the null hypothesis that all associations are random, supporting identification of nonrandom patterns in groups of association networks. This toolbox compares multiple networks to identify conserved subsets (core association networks, CANs) and other network properties that are shared across all networks. We apply anuran to a time series of fecal samples from 20 women to demonstrate the existence of CANs in a subset of the sampled individuals. Moreover, we use data from the Global Sponge Project to demonstrate that orders of sponges have a larger CAN than expected at random. In conclusion, this toolbox is a resource for investigators wanting to compare microbial networks across conditions, time series, gradients, or hosts.

Out-of-hospital cardiac arrest due to hanging: a retrospective analysis

BackgroundThere has been little research into the prehospital management of cardiac arrest following hanging despite it being among the most prevalent methods of suicide worldwide. The aim of this study was to report the characteristics, resuscitative treatment and outcomes of patients managed in the prehospital environment for cardiac arrest secondary to hanging and compare these with all-cause out-of-hospital cardiac arrest (OHCA).MethodsData from a UK ambulance service cardiac arrest registry were extracted for all cases in which treatment was provided for OHCA due to hanging between 1 January 2013 and 30 June 2018. Cases were linked to outcome data obtained from the Trauma Audit and Research Network. Comparison of the cohort was made to previously published data from a UK study of all-cause OHCA with 95% CIs calculated for the proportional difference between the studies in selected presentation and outcome variables.Results189 cases were identified. 95 patients were conveyed to hospital and four of these survived to discharge. 50 patients were conveyed despite absence of a spontaneous circulation and none of these patients survived. While only three patients were initially in a shockable rhythm, DC shocks were administered in 20 cases. There was one case of failed ventilation prompting front-of-neck access for oxygenation. By comparison with all-cause OHCA the proportion of patients with a spontaneous circulation at hospital handover was similar (27.0% vs 27.5%; 0.5% difference, 95% CI −5.9% to 6.8%, p=0.882) but survival to hospital discharge was significantly lower (2.2% vs 8.4%; 6.2% difference, 95% CI 4.1% to 8.3%, p=0.002).ConclusionClinical outcomes following OHCA due to hanging are poor, particularly when patients are transported while in cardiac arrest. Failure to ventilate was uncommon, and clinicians should be alert to the possibility of shockable rhythms developing during resuscitation.