AIMC Topic: Psychotic Disorders
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Towards a new classification of stable phase schizophrenia into major and simple neuro-cognitive psychosis: Results of unsupervised machine learning analysis.
Journal of evaluation in clinical practice
May 23, 2018
RATIONALE: Deficit schizophrenia, as defined by the Schedule for Deficit Syndrome, may represent a distinct diagnostic class defined by neurocognitive impairments coupled with changes in IgA/IgM responses to tryptophan catabolites (TRYCATs). Adequate...
Quality of Life
Psychiatric Status Rating Scales
Inflammation
Unsupervised Machine Learning
Female
Adult
Male
Middle Aged
Humans
Schizophrenic Psychology
Cytokines
Behavioral Symptoms
Cognitive Reserve
Neurocognitive Disorders
Neuroimmunomodulation
Patient Acuity
Schizophrenia
Psychotic Disorders
Tryptophan
Investigating brain structural patterns in first episode psychosis and schizophrenia using MRI and a machine learning approach.
Psychiatry research. Neuroimaging
Mar 6, 2018
In this study, we employed the Maximum Uncertainty Linear Discriminant Analysis (MLDA) to investigate whether the structural brain patterns in first episode psychosis (FEP) patients would be more similar to patients with chronic schizophrenia (SCZ) o...
The Early Psychosis Screener (EPS): Quantitative validation against the SIPS using machine learning.
Schizophrenia research
Jan 19, 2018
Machine learning techniques were used to identify highly informative early psychosis self-report items and to validate an early psychosis screener (EPS) against the Structured Interview for Psychosis-risk Syndromes (SIPS). The Prodromal Questionnaire...
Individualized prediction of psychosis in subjects with an at-risk mental state.
Schizophrenia research
Sep 19, 2017
Early intervention strategies in psychosis would significantly benefit from the identification of reliable prognostic biomarkers. Pattern classification methods have shown the feasibility of an early diagnosis of psychosis onset both in clinical and ...
A state-independent network of depressive, negative and positive symptoms in male patients with schizophrenia spectrum disorders.
Schizophrenia research
Aug 23, 2017
Depressive symptoms occur frequently in patients with schizophrenia. Several factor analytical studies investigated the associations between positive, negative and depressive symptoms and reported difficulties differentiating between these symptom do...
BrainAGE score indicates accelerated brain aging in schizophrenia, but not bipolar disorder.
Psychiatry research. Neuroimaging
May 24, 2017
BrainAGE (brain age gap estimation) is a novel morphometric parameter providing a univariate score derived from multivariate voxel-wise analyses. It uses a machine learning approach and can be used to analyse deviation from physiological developmenta...
Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis.
PloS one
Apr 20, 2017
A relatively large number of studies have investigated the power of structural magnetic resonance imaging (sMRI) data to discriminate patients with schizophrenia from healthy controls. However, very few of them have also included patients with bipola...
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 ...
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