AIMC Topic: Psychotic Disorders
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A symptom network structure of the psychosis spectrum.
Schizophrenia research
Feb 22, 2017
Current diagnostic systems mainly focus on symptoms needed to classify patients with a specific mental disorder and do not take into account the variation in co-occurring symptoms and the interaction between the symptoms themselves. The innovative ne...
Deep dreaming, aberrant salience and psychosis: Connecting the dots by artificial neural networks.
Schizophrenia research
Jan 24, 2017
Why some individuals, when presented with unstructured sensory inputs, develop altered perceptions not based in reality, is not well understood. Machine learning approaches can potentially help us understand how the brain normally interprets sensory ...
Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia.
Scientific reports
Dec 12, 2016
Neuroimaging-based models contribute to increasing our understanding of schizophrenia pathophysiology and can reveal the underlying characteristics of this and other clinical conditions. However, the considerable variability in reported neuroimaging ...
Using clinical information to make individualized prognostic predictions in people at ultra high risk for psychosis.
Schizophrenia research
Dec 4, 2016
Recent studies have reported an association between psychopathology and subsequent clinical and functional outcomes in people at ultra-high risk (UHR) for psychosis. This has led to the suggestion that psychopathological information could be used to ...
Precuneus functioning differentiates first-episode psychosis patients during the fantasy movie Alice in Wonderland.
Psychological medicine
Oct 25, 2016
BACKGROUND: While group-level functional alterations have been identified in many brain regions of psychotic patients, multivariate machine-learning methods provide a tool to test whether some of such alterations could be used to differentiate an ind...
Individual prediction of long-term outcome in adolescents at ultra-high risk for psychosis: Applying machine learning techniques to brain imaging data.
Human brain mapping
Oct 4, 2016
An important focus of studies of individuals at ultra-high risk (UHR) for psychosis has been to identify biomarkers to predict which individuals will transition to psychosis. However, the majority of individuals will prove to be resilient and go on t...
Using neuroimaging to help predict the onset of psychosis.
NeuroImage
Apr 1, 2016
The aim of this review is to assess the potential for neuroimaging measures to facilitate prediction of the onset of psychosis. Research in this field has mainly involved people at 'ultra-high risk' (UHR) of psychosis, who have a very high risk of de...
Classification of first-episode psychosis in a large cohort of patients using support vector machine and multiple kernel learning techniques.
NeuroImage
Dec 12, 2015
First episode psychosis (FEP) patients are of particular interest for neuroimaging investigations because of the absence of confounding effects due to medications and chronicity. Nonetheless, imaging data are prone to heterogeneity because for exampl...
Prediction of psychosis using neural oscillations and machine learning in neuroleptic-naïve at-risk patients.
The world journal of biological psychiatry : the official journal of the World Federation of Societies of Biological Psychiatry
Oct 9, 2015
OBJECTIVES: This study investigates whether abnormal neural oscillations, which have been shown to precede the onset of frank psychosis, could be used towards the individualised prediction of psychosis in clinical high-risk patients.
Predictors of schizophrenia spectrum disorders in early-onset first episodes of psychosis: a support vector machine model.
European child & adolescent psychiatry
Aug 11, 2014
Identifying early-onset schizophrenia spectrum disorders (SSD) at a very early stage remains challenging. To assess the diagnostic predictive value of multiple types of data at the emergence of early-onset first-episode psychosis (FEP), various suppo...
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