Schizophrenia

Is It Ethical to Use Prognostic Estimates from Machine Learning to Treat Psychosis?

Machine learning is a method for predicting clinically relevant variables, such as opportunities for...

A rapid LC-MS/MS method for simultaneous determination of quetiapine and duloxetine in rat plasma and its application to pharmacokinetic interaction study.

Combinations of new antidepressants like duloxetine and second-generation antipsychotics like quetia...

Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images.

The human cerebellum plays an essential role in motor control, is involved in cognitive function (i....

Reading the (functional) writing on the (structural) wall: Multimodal fusion of brain structure and function via a deep neural network based translation approach reveals novel impairments in schizophrenia.

This work presents a novel approach to finding linkage/association between multimodal brain imaging ...

Is there a symptomatic distinction between the affective psychoses and schizophrenia? A machine learning approach.

Dubiety exists over whether clinical symptoms of schizophrenia can be distinguished from affective p...

Drug Repositioning for Schizophrenia and Depression/Anxiety Disorders: A Machine Learning Approach Leveraging Expression Data.

Development of new medications is a lengthy and costly process, and drug repositioning might help to...

Quantum-like behavior without quantum physics II. A quantum-like model of neural network dynamics.

In earlier work, we laid out the foundation for explaining the quantum-like behavior of neural syste...

Machine learning improved classification of psychoses using clinical and biological stratification: Update from the bipolar-schizophrenia network for intermediate phenotypes (B-SNIP).

Towards a new classification of stable phase schizophrenia into major and simple neuro-cognitive psychosis: Results of unsupervised machine learning analysis.

RATIONALE: Deficit schizophrenia, as defined by the Schedule for Deficit Syndrome, may represent a d...

Machine learning classification of first-episode schizophrenia spectrum disorders and controls using whole brain white matter fractional anisotropy.

BACKGROUND: Early diagnosis of schizophrenia could improve the outcome of the illness. Unlike classi...

Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI.

BACKGROUND: A lack of a sufficiently large sample at single sites causes poor generalizability in au...

Investigating brain structural patterns in first episode psychosis and schizophrenia using MRI and a machine learning approach.

In this study, we employed the Maximum Uncertainty Linear Discriminant Analysis (MLDA) to investigat...

In Schizophrenia, Depression, Anxiety, and Physiosomatic Symptoms Are Strongly Related to Psychotic Symptoms and Excitation, Impairments in Episodic Memory, and Increased Production of Neurotoxic Tryptophan Catabolites: a Multivariate and Machine Learning Study.

The depression, anxiety and physiosomatic symptoms (DAPS) of schizophrenia are associated with negat...

Music Intervention Leads to Increased Insular Connectivity and Improved Clinical Symptoms in Schizophrenia.

Schizophrenia is a syndrome that is typically accompanied by delusions and hallucinations that might...

The Early Psychosis Screener (EPS): Quantitative validation against the SIPS using machine learning.

Machine learning techniques were used to identify highly informative early psychosis self-report ite...

Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity.

Despite significant progress in the field, the detection of fMRI signal changes during hallucinatory...

Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning? A multi-method and multi-dataset study.

Machine learning is a powerful tool that has previously been used to classify schizophrenia (SZ) pat...

Neuroanatomical heterogeneity of schizophrenia revealed by semi-supervised machine learning methods.

UNLABELLED: Schizophrenia is associated with heterogeneous clinical symptoms and neuroanatomical alt...

Automatic schizophrenic discrimination on fNIRS by using complex brain network analysis and SVM.

BACKGROUND: Schizophrenia is a kind of serious mental illness. Due to the lack of an objective physi...

Support vector machine-based classification of first episode drug-naïve schizophrenia patients and healthy controls using structural MRI.

Although regional brain deficits have been demonstrated in schizophrenia patients by structural MRI ...

A novel fuzzy rough selection of non-linearly extracted features for schizophrenia diagnosis using fMRI.

BACKGROUND AND OBJECTIVES: Schizophrenia is a severe brain disorder primarily diagnosed through exte...