Centre for Youth Mental Health - Research Publications

Permanent URI for this collection

Search Results

Now showing 1 - 2 of 2
  • Item
    No Preview Available
    Development of the PSYCHS: Positive SYmptoms and Diagnostic Criteria for the CAARMS Harmonized with the SIPS.
    Woods, SW ; Parker, S ; Kerr, MJ ; Walsh, BC ; Wijtenburg, SA ; Prunier, N ; Nunez, AR ; Buccilli, K ; Mourgues-Codern, C ; Brummitt, K ; Kinney, KS ; Trankler, C ; Szacilo, J ; Colton, B-L ; Ali, M ; Haidar, A ; Billah, T ; Huynh, K ; Ahmed, U ; Adery, LL ; Corcoran, CM ; Perkins, DO ; Schiffman, J ; Perez, J ; Mamah, D ; Ellman, LM ; Powers, AR ; Coleman, MJ ; Anticevic, A ; Fusar-Poli, P ; Kane, JM ; Kahn, RS ; McGorry, PD ; Bearden, CE ; Shenton, ME ; Nelson, B ; Calkins, ME ; Hendricks, L ; Bouix, S ; Addington, J ; McGlashan, TH ; Yung, AR ; Accelerating Medicines Partnership Schizophrenia, ( 2023-05-02)
    AIM: To harmonize two ascertainment and severity rating instruments commonly used for the clinical high risk syndrome for psychosis (CHR-P): the Structured Interview for Psychosis-risk Syndromes (SIPS) and the Comprehensive Assessment of At-Risk Mental States (CAARMS). METHODS: The initial workshop is described in the companion report from Addington et al. After the workshop, lead experts for each instrument continued harmonizing attenuated positive symptoms and criteria for psychosis and CHR-P through an intensive series of joint videoconferences. RESULTS: Full harmonization was achieved for attenuated positive symptom ratings and psychosis criteria, and partial harmonization for CHR-P criteria. The semi-structured interview, named P ositive SY mptoms and Diagnostic Criteria for the C AARMS H armonized with the S IPS (PSYCHS), generates CHR-P criteria and severity scores for both CAARMS and SIPS. CONCLUSION: Using the PSYCHS for CHR-P ascertainment, conversion determination, and attenuated positive symptom severity rating will help in comparing findings across studies and in meta-analyses.
  • Item
    No Preview Available
    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.