Schizophrenia

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 investigate whether the structural brain patterns in first episode psychosis (FEP) patients would be more similar to patients with chronic schizophrenia (SCZ) or healthy controls (HC), from a schizophrenia model perspective. Brain regions volumetric data were estimated by using MRI images of SCZ and FEP patie...

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 negative symptoms and changes in tryptophan catabolite (TRYCAT) patterning. The aim of this study is to delineate the associations between DAPS and psychosis, hostility, excitation, and mannerism (PHEM) symptoms, cognitive tests as measured using the Consortium to Establish a Registry for Alzheimer's Dis...

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 be associated with insular pathology. Music inter...

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 items and to validate an early psychosis screener (EP...

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 events remains difficult and time-consuming. This...

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

UNLABELLED: Schizophrenia is associated with heterogeneous clinical symptoms and neuroanatomical alterations. In this work, we aim to disentangle the ...

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) patients from healthy controls (HC) using magnetic re...

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 physiological data supporting and a unified data analys...

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 studies, one important question that remains large...

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 externally observed behavioural symptoms due to the de...

Does this robot have a mind? Schizophrenia patients' mind perception toward humanoid robots.

A Deep-Dream Virtual Reality Platform for Studying Altered Perceptual Phenomenology.

Altered states of consciousness, such as psychotic or pharmacologically-induced hallucinations, provide a unique opportunity to examine the mechanisms...

Working Memory and Decision-Making in a Frontoparietal Circuit Model.

Working memory (WM) and decision-making (DM) are fundamental cognitive functions involving a distributed interacting network of brain areas, with the ...

Influence of facial feedback during a cooperative human-robot task in schizophrenia.

Rapid progress in the area of humanoid robots offers tremendous possibilities for investigating and improving social competences in people with social...

Neurocognitive Graphs of First-Episode Schizophrenia and Major Depression Based on Cognitive Features.

Neurocognitive deficits are frequently observed in patients with schizophrenia and major depressive disorder (MDD). The relations between cognitive fe...

Improving individual predictions: Machine learning approaches for detecting and attacking heterogeneity in schizophrenia (and other psychiatric diseases).

Psychiatric diseases are very heterogeneous both in clinical manifestation and etiology. With the recent rise of using machine learning techniques to ...

Individualized prediction of psychosis in subjects with an at-risk mental state.

Early intervention strategies in psychosis would significantly benefit from the identification of reliable prognostic biomarkers. Pattern classificati...

A state-independent network of depressive, negative and positive symptoms in male patients with schizophrenia spectrum disorders.

Depressive symptoms occur frequently in patients with schizophrenia. Several factor analytical studies investigated the associations between positive,...

A Collaborative Approach to Identifying Social Media Markers of Schizophrenia by Employing Machine Learning and Clinical Appraisals.

BACKGROUND: Linguistic analysis of publicly available Twitter feeds have achieved success in differentiating individuals who self-disclose online as h...

Abnormal regional homogeneity as a potential imaging biomarker for adolescent-onset schizophrenia: A resting-state fMRI study and support vector machine analysis.

OBJECTIVE: Structural and functional abnormalities have been reported in the brain of patients with adolescent-onset schizophrenia (AOS). The brain re...