Centre for Youth Mental Health - Research Publications

Permanent URI for this collection

http://hdl.handle.net/11343/250

Filters

2020 2
1 1
Reset filters

Settings
Statistics
Citations
Author: Nelson, Christopher × Author: Allott, Kelly × Author: Mallawaarachchi, Sumudu ×

Search Results

Now showing 1 - 2 of 2
  • 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.
  • Item
    Thumbnail Image
    Cognitive functioning in ultra -high risk for psychosis individuals with and without depression: Secondary analysis of findings from the NEURAPRO randomized clinical trial
    Mallawaarachchi, SR ; Amminger, GP ; Farhall, J ; Bolt, LK ; Nelson, B ; Yuen, HP ; McGorry, PD ; Markulev, C ; Schaefer, MR ; Mossaheb, N ; Schloegelhofer, M ; Smesny, S ; Hickie, IB ; Berger, GE ; Chen, EYH ; de Haan, L ; Nieman, DH ; Nordentoft, M ; Riecher-Roessler, A ; Verma, S ; Thompson, A ; Yung, AR ; Allott, KA (ELSEVIER, 2020-04-01)
    Neurocognitive impairments are well established in both ultra-high risk (UHR) for psychosis and major depressive disorder (MDD). Despite this understanding, investigation of neurocognitive deficits in UHR individuals with MDD and its association with MDD within this population, has been scarce. Hence, this study aimed to examine any differences in neurocognition at baseline between those with MDD at baseline and those with no history of MDD, as well as determine whether neurocognitive variables are significantly associated with meeting criteria for MDD at follow-up, while controlling for relevant clinical variables, within a UHR cohort. Data analysis was conducted on 207 participants whose baseline neurocognition was assessed using Brief Assessment of Cognition for Schizophrenia, as part of a trial of omega-3 fatty acids (NEURAPRO) for UHR individuals. While baseline MDD was the strongest predictor, poorer verbal memory and higher verbal fluency were significantly associated with MDD at 12 months (p = .04 and 0.026, respectively). Further, higher processing speed was significantly associated with MDD at medium-term follow-up (p = .047). These findings outline that neurocognitive skills were independently associated with meeting criteria for MDD at follow-up within UHR individuals, with novel findings of better verbal fluency and processing speed being linked to MDD outcomes. Hence, neurocognitive performance should be considered as a marker of risk for MDD outcomes and a target for management of MDD in UHR.