AIMC Topic: Schizophrenia

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Big data in severe mental illness: the role of electronic monitoring tools and metabolomics.

Personalized medicine
There is an increasing interest in the development of effective early detection and intervention strategies in severe mental illness (SMI). Ideally, these efforts should lead to the delineation of accurate staging models of SMI enabling personalized ...

Inner speech.

Wiley interdisciplinary reviews. Cognitive science
Inner speech travels under many aliases: the inner voice, verbal thought, thinking in words, internal verbalization, "talking in your head," the "little voice in the head," and so on. It is both a familiar element of first-person experience and a psy...

Multi-dimensional predictions of psychotic symptoms via machine learning.

Human brain mapping
The diagnostic criteria for schizophrenia comprise a diverse range of heterogeneous symptoms. As a result, individuals each present a distinct set of symptoms despite having the same overall diagnosis. Whilst previous machine learning studies have pr...

Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks.

JMIR mHealth and uHealth
BACKGROUND: Schizophrenia spectrum disorders (SSDs) are chronic conditions, but the severity of symptomatic experiences and functional impairments vacillate over the course of illness. Developing unobtrusive remote monitoring systems to detect early ...

Reconfiguration of αmplitude driven dominant coupling modes (DoCM) mediated by α-band in adolescents with schizophrenia spectrum disorders.

Progress in neuro-psychopharmacology & biological psychiatry
Electroencephalography (EEG) based biomarkers have been shown to correlate with the presence of psychotic disorders. Increased delta and decreased alpha power in psychosis indicate an abnormal arousal state. We investigated brain activity across the ...

A deep learning model for detecting mental illness from user content on social media.

Scientific reports
Users of social media often share their feelings or emotional states through their posts. In this study, we developed a deep learning model to identify a user's mental state based on his/her posting information. To this end, we collected posts from m...

Automated design and optimization of multitarget schizophrenia drug candidates by deep learning.

European journal of medicinal chemistry
Complex neuropsychiatric diseases such as schizophrenia require drugs that can target multiple G protein-coupled receptors (GPCRs) to modulate complex neuropsychiatric functions. Here, we report an automated system comprising a deep recurrent neural ...

A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRI.

Sensors (Basel, Switzerland)
Segmentation of the hippocampus (HC) in magnetic resonance imaging (MRI) is an essential step for diagnosis and monitoring of several clinical situations such as Alzheimer's disease (AD), schizophrenia and epilepsy. Automatic segmentation of HC struc...

Computing schizophrenia: ethical challenges for machine learning in psychiatry.

Psychological medicine
Recent advances in machine learning (ML) promise far-reaching improvements across medical care, not least within psychiatry. While to date no psychiatric application of ML constitutes standard clinical practice, it seems crucial to get ahead of these...