AIMC Topic: Schizophrenia

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Evaluation of deep convolutional neural networks for in situ hybridization gene expression image representation.

PloS one
High resolution in situ hybridization (ISH) images of the brain capture spatial gene expression at cellular resolution. These spatial profiles are key to understanding brain organization at the molecular level. Previously, manual qualitative scoring ...

Evaluation of the correlation between gaze avoidance and schizophrenia psychopathology with deep learning-based emotional recognition.

Asian journal of psychiatry
OBJECTIVE: To investigate the correlation between gaze avoidance and psychopathology in patients with schizophrenia through eye movement measurements in real-life interpersonal situations.

A deep learning approach in automated detection of schizophrenia using scalogram images of EEG signals.

Physical and engineering sciences in medicine
This study presents a method with high accuracy performance that aims to automatically detect schizophrenia (SZ) from electroencephalography (EEG) records. Unlike related literature studies using traditional machine learning algorithms, the features ...

Plasma neurofilament light chain protein is not increased in treatment-resistant schizophrenia and first-degree relatives.

The Australian and New Zealand journal of psychiatry
OBJECTIVE: Schizophrenia, a complex psychiatric disorder, is often associated with cognitive, neurological and neuroimaging abnormalities. The processes underlying these abnormalities, and whether a subset of people with schizophrenia have a neuropro...

Diagnosis of Schizophrenia Based on Deep Learning Using fMRI.

Computational and mathematical methods in medicine
Schizophrenia is a brain disease that frequently occurs in young people. Early diagnosis and treatment can reduce family burdens and reduce social costs. There is no objective evaluation index for schizophrenia. In order to improve the classification...

Hollow-tree super: A directional and scalable approach for feature importance in boosted tree models.

PloS one
PURPOSE: Current limitations in methodologies used throughout machine-learning to investigate feature importance in boosted tree modelling prevent the effective scaling to datasets with a large number of features, particularly when one is investigati...

Color painting predicts clinical symptoms in chronic schizophrenia patients via deep learning.

BMC psychiatry
BACKGROUND: Individuals with psychiatric disorders perceive the world differently. Previous studies indicated impaired color vision and weakened color discrimination ability in psychotic patients. Examining the paintings from psychotic patients can m...

Causal pathways to social and occupational functioning in the first episode of schizophrenia: uncovering unmet treatment needs.

Psychological medicine
BACKGROUND: We aimed to identify unmet treatment needs for improving social and occupational functioning in early schizophrenia using a data-driven causal discovery analysis.

Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction.

Nature communications
A promise of genomics in precision medicine is to provide individualized genetic risk predictions. Polygenic risk scores (PRS), computed by aggregating effects from many genomic variants, have been developed as a useful tool in complex disease resear...

Machine learning approach to measurement of criticism: The core dimension of expressed emotion.

Journal of family psychology : JFP : journal of the Division of Family Psychology of the American Psychological Association (Division 43)
Expressed emotion (EE), a measure of the family's emotional climate, is a fundamental measure in caregiving research. A core dimension of EE is the level of criticism expressed by the caregiver to the care recipient, with a high level of criticism a ...