Schizophrenia

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 objective gene markers. The reproducibility of the gene signatures identified by independent studies is found to be extremely low due to the incapability of available feature selection methods and the lack of measurement on validating signatures' robus...

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 resonance imaging (MRI) of the brain. Resting-state functional MRI data from a total of 144 subjects (72 patients with schizophrenia and 72 healthy controls) was obtained from a publicly available dataset using a three-dimensional convolution neural network ...

Machine-learning based brain age estimation in major depression showing no evidence of accelerated aging.

Molecular biological findings indicate that affective disorders are associated with processes akin to accelerated aging of the brain. The use of the B...

SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse.

Synapses are fundamental information-processing units of the brain, and synaptic dysregulation is central to many brain disorders ("synaptopathies"). ...

Machine learning identifies unaffected first-degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients.

Schizophrenia (SCZ) patients and their unaffected first-degree relatives (FDRs) share similar functional neuroanatomy. However, it remains largely unk...

Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis.

A psychological disorder is a mutilation state of the body that intervenes the imperative functioning of the mind or brain. In the last few years, the...

Identifying psychosis spectrum disorder from experience sampling data using machine learning approaches.

The ubiquity of smartphones opened up the possibility of widespread use of the Experience Sampling Method (ESM). The method is used to collect longitu...

Semantic Face Hallucination: Super-Resolving Very Low-Resolution Face Images with Supplementary Attributes.

Given a tiny face image, existing face hallucination methods aim at super-resolving its high-resolution (HR) counterpart by learning a mapping from an...

Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning.

Face hallucination is a domain-specific super-resolution problem that aims to generate a high-resolution (HR) face image from a low-resolution (LR) in...

Analyzing DNA methylation patterns in subjects diagnosed with schizophrenia using machine learning methods.

Schizophrenia is a common mental disorder with high heritability. It is genetically complex and to date more than a hundred risk loci have been identi...

Relative importance of symptoms, cognition, and other multilevel variables for psychiatric disease classifications by machine learning.

This study used machine-learning algorithms to make unbiased estimates of the relative importance of various multilevel data for classifying cases wit...

Brain Morphometry Methods for Feature Extraction in Random Subspace Ensemble Neural Network Classification of First-Episode Schizophrenia.

Machine learning (ML) is a growing field that provides tools for automatic pattern recognition. The neuroimaging community currently tries to take adv...

Predicting hospital-acquired pneumonia among schizophrenic patients: a machine learning approach.

BACKGROUND: Medications are frequently used for treating schizophrenia, however, anti-psychotic drug use is known to lead to cases of pneumonia. The p...

Predicting one-year outcome in first episode psychosis using machine learning.

BACKGROUND: Early illness course correlates with long-term outcome in psychosis. Accurate prediction could allow more focused intervention. Earlier in...

Low-rank network signatures in the triple network separate schizophrenia and major depressive disorder.

Brain imaging studies have revealed that functional and structural brain connectivity in the so-called triple network (i.e., default mode network (DMN...

The Early Psychosis Screener for Internet (EPSI)-SR: Predicting 12 month psychotic conversion using machine learning.

INTRODUCTION: A faster and more accurate self-report screener for early psychosis is needed to promote early identification and intervention.

Resting-state anticorrelated networks in Schizophrenia.

Converging evidences from different lines of research suggest abnormalities in functional brain connectivity in schizophrenia. While positively correl...

Machine learning for predicting psychotic relapse at 2 years in schizophrenia in the national FACE-SZ cohort.

BACKGROUND: Predicting psychotic relapse is one of the major challenges in the daily care of schizophrenia.

From cognitive and clinical substrates to functional profiles: Disentangling heterogeneity in schizophrenia.

The relationship between neurocognition and functioning among patients with schizophrenia is well documented. However, integrating neuropsychological,...

Disease comorbidity-guided drug repositioning: a case study in schizophrenia.

UNLABELLED: The key to any computational drug repositioning is the availability of relevant data in machine-understandable format. While large amount ...