Centre for Youth Mental Health - Research Publications

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    Associations of state or trait dissociation with severity of psychopathology in young people with borderline personality disorder
    Salmon, AP ; Nicol, K ; Kaess, M ; Jovev, M ; Betts, JK ; Chanen, AM ( 2022-11-07)
    Background: State and trait dissociation are associated with borderline personality disorder (BPD) severity and severity of commonly co-occurring mental health symptoms. Although these distinct constructs do not consistently co-occur in experimental settings, they are frequently reported as the same construct, namely dissociation. This study aimed to investigate the co-occurrence of state and trait dissociation among young people with BPD and to examine whether state or trait dissociation were associated with symptom severity in this population. Methods: State dissociation was induced using a stressful behavioural task in a clinical sample of 55 young people (aged 15–25 years) with three or more BPD features. Diagnoses, state and trait dissociation, BPD severity and severity of posttraumatic stress disorder (PTSD), depressive, and stress symptoms were assessed by self-report or research interview. Results: A chi-square test of independence showed a strong association between state and trait dissociation. Bonferroni corrected t-tests showed that state dissociation was significantly associated with PTSD symptom severity and likely associated with BPD severity and severity of depressive and stress symptoms. Trait dissociation was not associated with symptom severity or severity of BPD features. Conclusions: These findings highlight the need to distinguish between state and trait dissociation in personality disorder research. They suggest that state dissociation might be an indicator of higher severity of psychopathology in young people with BPD.
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    The effectiveness of peer support from a person with lived experience of mental health challenges for young people with anxiety and depression: A systematic review
    Simmons, MB ; Cartner, S ; MacDonald, R ; Whitson, S ; Bailey, A ; Brown, E ( 2022-05-20)
    Background: Peer support has become increasingly popular in adult mental health services to complement existing care provided by clinicians with formal training. The same is now happening to support young people with anxiety and depression but it is unclear what the evidence base of peer support in this population is. Methods: A systematic search was conducted with the Orygen Evidence Finder database, Embase, MEDLINE, and PsycInfo from January 1980 to July 2021. Controlled trials of interventions aimed to educate or treat young people (mean age between 14-24) to improve their mental health, which were delivered by a peer worker with lived experienced of mental health challenges were included. Outcomes related to depression or anxiety were extracted. Study quality was rated using the Critical Appraisal Skills Programme. Results: Eight randomised controlled trials with 1,885 participants were included, with six undertaken in high income countries. One targeted depression and anxiety, another targeted stigma-distress in youth mental illness, one aimed at first episode psychosis, four studies for preventing eating disorders and one aimed at drug misuse. One study successfully reduced anxiety and depression, another reduced depression only, four reported reductions in negative affect, with the final two measuring, but not having a significant impact on depression. Study quality was rated as ‘good’ overall. Discussion: Despite the uptake of youth peer support globally, there is limited evidence from controlled trials of the effect of peer support related interventions on anxiety and depression. Further rigorously designed trials of peer delivered interventions for young people experiencing anxiety and depression need to be conducted with a focus on understanding the mechanisms of action underpinning peer support. In the absence of sufficient evidence, we propose potential mechanisms to guide future research into how peer support is an active ingredient for youth anxiety and depression.
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    Characterization and Prediction of Clinical Pathways of Vulnerability to Psychosis through Graph Signal Processing
    Sandini, C ; Zöller, D ; Schneider, M ; Tarun, A ; Armando, M ; Nelson, B ; Nelson, B ; Mallawaarachchi, SR ; Amminger, P ; Farhall, J ; Bolt, L ; Yuen, HP ; Markulev, C ; Schäfer, M ; Mossaheb, N ; Schlögelhofer, M ; Smesny, S ; Hickie, I ; Berger, GE ; Chen, EYH ; de Haan, L ; Nieman, D ; Nordentoft, M ; Riecher-Rössler, A ; Verma, S ; Thompson, A ; Yung, AR ; Allott, K ; McGorry, P ; Van De Ville, D ; Eliez, S ( 2020)
    There is a growing recognition that psychiatric symptoms have the potential to causally interact with one another. Particularly in the earliest stages of psychopathology dynamic interactions between symptoms could contribute heterogeneous and cross-diagnostic clinical evolutions. Current clinical approaches attempt to merge clinical manifestations that co-occur across subjects and could therefore significantly hinder our understanding of clinical pathways connecting individual symptoms. Network approaches have the potential to shed light on the complex dynamics of early psychopathology. In the present manuscript we attempt to address 2 main limitations that have in our opinion hindered the application of network approaches in the clinical setting. The first limitation is that network analyses have mostly been applied to cross-sectional data, yielding results that often lack the intuitive interpretability of simpler categorical or dimensional approaches. Here we propose an approach based on multi-layer network analysis that offers an intuitive low-dimensional characterization of longitudinal pathways involved in the evolution of psychopathology, while conserving high-dimensional information on the role of specific symptoms. The second limitation is that network analyses typically characterize symptom connectivity at the level of a population, whereas clinical practice deals with symptom severity at the level of the individual. Here we propose an approach based on graph signal processing that exploits knowledge of network interactions between symptoms to predict longitudinal clinical evolution at the level of the individual. We test our approaches in two independent samples of individuals with genetic and clinical vulnerability for developing psychosis.