AIMC Topic: Psychotic Disorders

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Obesity as a Risk Factor for Accelerated Brain Ageing in First-Episode Psychosis-A Longitudinal Study.

Schizophrenia bulletin Oct 21, 2021
BACKGROUND: Obesity is highly prevalent in schizophrenia, with implications for psychiatric prognosis, possibly through links between obesity and brain structure. In this longitudinal study in first episode of psychosis (FEP), we used machine learnin...
Young Adult Adult Male Disease Progression Machine Learning Female Risk Factors Aging, Premature Magnetic Resonance Imaging Obesity Psychotic Disorders Adolescent Body Mass Index Longitudinal Studies Humans
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Robot-induced hallucinations in Parkinson's disease depend on altered sensorimotor processing in fronto-temporal network.

Science translational medicine Apr 28, 2021
Hallucinations in Parkinson's disease (PD) are disturbing and frequent non-motor symptoms and constitute a major risk factor for psychosis and dementia. We report a robotics-based approach applying conflicting sensorimotor stimulation, enabling the i...
Humans Parkinson Disease Robotics Hallucinations Psychotic Disorders Neuroimaging
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Using Natural Language Processing on Electronic Health Records to Enhance Detection and Prediction of Psychosis Risk.

Schizophrenia bulletin Mar 16, 2021
BACKGROUND: Using novel data mining methods such as natural language processing (NLP) on electronic health records (EHRs) for screening and detecting individuals at risk for psychosis.
Humans Young Adult Adult Male Middle Aged Retrospective Studies Electronic Health Records Female Models, Statistical Natural Language Processing Psychotic Disorders London Risk Assessment Adolescent Follow-Up Studies
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Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression.

JAMA psychiatry Feb 1, 2021
IMPORTANCE: Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to yo...
Psychotic Disorders Disease Susceptibility Depressive Disorder Europe Workflow Schizophrenia Adult Sensitivity and Specificity Male Longitudinal Studies Follow-Up Studies Comorbidity Time Factors Machine Learning Prognosis Female Young Adult Humans
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Towards Precision Medicine in Psychosis: Benefits and Challenges of Multimodal Multicenter Studies-PSYSCAN: Translating Neuroimaging Findings From Research into Clinical Practice.

Schizophrenia bulletin Feb 26, 2020
In the last 2 decades, several neuroimaging studies investigated brain abnormalities associated with the early stages of psychosis in the hope that these could aid the prediction of onset and clinical outcome. Despite advancements in the field, neuro...
Humans Longitudinal Studies Multicenter Studies as Topic Machine Learning Psychotic Disorders Research Design Neuroimaging Precision Medicine
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Using Machine Learning and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence.

Schizophrenia bulletin Jan 4, 2020
Despite the high level of interest in the use of machine learning (ML) and neuroimaging to detect psychosis at the individual level, the reliability of the findings is unclear due to potential methodological issues that may have inflated the existing...
Image Interpretation, Computer-Assisted Datasets as Topic Pattern Recognition, Automated Cerebral Cortex Humans Neuroimaging Machine Learning Gray Matter Adult Male Deep Learning Psychotic Disorders Female Reproducibility of Results Young Adult
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Systematic Review of Digital Phenotyping and Machine Learning in Psychosis Spectrum Illnesses.

Harvard review of psychiatry Jan 1, 2020
BACKGROUND: Digital phenotyping is the use of data from smartphones and wearables collected in situ for capturing a digital expression of human behaviors. Digital phenotyping techniques can be used to analyze both passively (e.g., sensor) and activel...
Psychotic Disorders Data Collection Humans Machine Learning Phenotype Smartphone Schizophrenia Wearable Electronic Devices Bipolar Disorder
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Annotating Temporal Relations to Determine the Onset of Psychosis Symptoms.

Studies in health technology and informatics Aug 21, 2019
For patients with a diagnosis of schizophrenia, determining symptom onset is crucial for timely and successful intervention. In mental health records, information about early symptoms is often documented only in free text, and thus needs to be extrac...
Psychotic Disorders Records Humans Electronic Health Records Natural Language Processing
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Recurrent Neural Networks in Mobile Sampling and Intervention.

Schizophrenia bulletin Mar 7, 2019
The rapid rise and now widespread distribution of handheld and wearable devices, such as smartphones, fitness trackers, or smartwatches, has opened a new universe of possibilities for monitoring emotion and cognition in everyday-life context, and for...
Machine Learning Psychotic Disorders Telemedicine Ecological Momentary Assessment Humans Neural Networks, Computer
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Brain Age in Early Stages of Bipolar Disorders or Schizophrenia.

Schizophrenia bulletin Jan 1, 2019
BACKGROUND: The greater presence of neurodevelopmental antecedants may differentiate schizophrenia from bipolar disorders (BD). Machine learning/pattern recognition allows us to estimate the biological age of the brain from structural magnetic resona...
Risk Adolescent Bipolar Disorder Schizophrenia Humans Psychotic Disorders Age Factors Young Adult Magnetic Resonance Imaging Machine Learning Diagnosis, Differential Adult Female Male
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