Schizophrenia

Guest editorial: Special issue on machine learning in schizophrenia.

Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [S]GTPγS Binding Assays.

G-protein-coupled receptors (GPCRs), also known as 7-transmembrane receptors, are the single largest class of drug targets. Consequently, a large amount of preclinical assays having GPCRs as molecular targets has been released to public sources like the Chemical European Molecular Biology Laboratory (ChEMBL) database. These data are also very complex covering changes in drug chemical structure and...

Analysis of risk factor domains in psychosis patient health records.

BACKGROUND: Readmission after discharge from a hospital is disruptive and costly, regardless of the reason. However, it can be particularly problemati...

Clinical-learning versus machine-learning for transdiagnostic prediction of psychosis onset in individuals at-risk.

Predicting the onset of psychosis in individuals at-risk is based on robust prognostic model building methods including a priori clinical knowledge (a...

Face Hallucination Using Cascaded Super-Resolution and Identity Priors.

In this paper we address the problem of hallucinating high-resolution facial images from low-resolution inputs at high magnification factors. We appro...

Multivariate classification of schizophrenia and its familial risk based on load-dependent attentional control brain functional connectivity.

Patients with schizophrenia (SCZ), as well as their unaffected siblings (SIB), show functional connectivity (FC) alterations during performance of tas...

Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder.

Obsessive and Compulsive Symptoms (OCS) or Obsessive Compulsive Disorder (OCD) in the context of schizophrenia or related disorders are of clinical im...

Generalizability of machine learning for classification of schizophrenia based on resting-state functional MRI data.

Machine learning has increasingly been applied to classification of schizophrenia in neuroimaging research. However, direct replication studies and st...

Phasic dopamine release identification using convolutional neural network.

Dopamine has a major behavioral impact related to drug dependence, learning and memory functions, as well as pathologies such as schizophrenia and Par...

A Multi-Domain Connectome Convolutional Neural Network for Identifying Schizophrenia From EEG Connectivity Patterns.

OBJECTIVE: We exploit altered patterns in brain functional connectivity as features for automatic discriminative analysis of neuropsychiatric patients...

Development and validation of multivariable prediction models of remission, recovery, and quality of life outcomes in people with first episode psychosis: a machine learning approach.

BACKGROUND: Outcomes for people with first-episode psychosis are highly heterogeneous. Few reliable validated methods are available to predict the out...

Using machine learning to explain the heterogeneity of schizophrenia. Realizing the promise and avoiding the hype.

Despite extensive research and prodigious advances in neuroscience, our comprehension of the nature of schizophrenia remains rudimentary. Our failure ...

Identifying schizophrenia subgroups using clustering and supervised learning.

Schizophrenia has a 1% incidence rate world-wide and those diagnosed present with positive (e.g. hallucinations, delusions), negative (e.g. apathy, as...

Characterizing functional regional homogeneity (ReHo) as a B-SNIP psychosis biomarker using traditional and machine learning approaches.

BACKGROUND: Recently, a biologically-driven psychosis classification (B-SNIP Biotypes) was derived using brain-based cognitive and electrophysiologica...

The risks of risk. Regulating the use of machine learning for psychosis prediction.

Recent advances in Machine Learning (ML) have the potential to revolutionise psychosis prediction and psychiatric assessment. This article has two obj...

Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data.

BACKGROUND: Current fMRI-based classification approaches mostly use functional connectivity or spatial maps as input, instead of exploring the dynamic...

Classification of schizophrenia and normal controls using 3D convolutional neural network and outcome visualization.

BACKGROUND: The recent deep learning-based studies on the classification of schizophrenia (SCZ) using MRI data rely on manual extraction of feature ve...

Validation of oxidative stress assay for schizophrenia.

Accumulating evidence implicates oxidative stress in a range of diseases, yet no objective measurement has emerged that characterizes the global natur...

Identification of the gene signature reflecting schizophrenia's etiology by constructing artificial intelligence-based method of enhanced reproducibility.

AIMS: As one of the most fundamental questions in modern science, "what causes schizophrenia (SZ)" remains a profound mystery due to the absence of ob...

3D-CNN based discrimination of schizophrenia using resting-state fMRI.

MOTIVATION: This study reports a framework to discriminate patients with schizophrenia and normal healthy control subjects, based on magnetic resonanc...