Schizophrenia

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning.

Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from n...

Dynamic ElecTronic hEalth reCord deTection (DETECT) of individuals at risk of a first episode of psychosis: a case-control development and validation study.

BACKGROUND: Many individuals who will experience a first episode of psychosis (FEP) are not detected...

Feature optimization method for machine learning-based diagnosis of schizophrenia using magnetoencephalography.

BACKGROUND: When many features and a small number of clinical data exist, previous studies have used...

Pragmatics, Theory of Mind and executive functions in schizophrenia: Disentangling the puzzle using machine learning.

OBJECTIVE: Schizophrenia is associated with a severe impairment in the communicative-pragmatic domai...

Diagnosing schizophrenia with network analysis and a machine learning method.

OBJECTIVE: Schizophrenia is a chronic and debilitating neuropsychiatric disorder. It has been sugges...

Development and Validation of a Machine Learning Individualized Treatment Rule in First-Episode Schizophrenia.

IMPORTANCE: Little guidance exists to date on how to select antipsychotic medications for patients w...

A Multidimensional Neural Maturation Index Reveals Reproducible Developmental Patterns in Children and Adolescents.

Adolescence is a time of extensive neural restructuring, leaving one susceptible to atypical develop...

Predicting the individual risk of psychosis conversion in at-risk mental state (ARMS): a multivariate model reveals the influence of nonpsychotic prodromal symptoms.

To improve the prediction of the individual risk of conversion to psychosis in UHR subjects, by cons...

Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual.

Schizophrenia is a severe psychiatric disorder associated with both structural and functional brain ...

A review on neural network models of schizophrenia and autism spectrum disorder.

This survey presents the most relevant neural network models of autism spectrum disorder and schizop...

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...

Analysis of risk factor domains in psychosis patient health records.

BACKGROUND: Readmission after discharge from a hospital is disruptive and costly, regardless of the ...

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 buildin...

Face Hallucination Using Cascaded Super-Resolution and Identity Priors.

In this paper we address the problem of hallucinating high-resolution facial images from low-resolut...

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 conne...

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 sch...

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 re...

Phasic dopamine release identification using convolutional neural network.

Dopamine has a major behavioral impact related to drug dependence, learning and memory functions, as...

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 di...

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 ...