Schizophrenia

Predicting the outcome of psychological treatments for borderline personality disorder and posttraumatic stress disorder: a machine learning approach to predict long-term outcome of Narrative Exposure Therapy vs. Dialectical Behavioral Therapy based treatment.

: A comorbidity between Borderline Personality Disorder (BPD) and Posttraumatic Stress Disorder (PTSD) is common, severely disabling, and hard to treat. The choice of an optimal psychotherapy based on patient characteristics remains challenging. This study develops models to predict the outcome of two psychotherapies for comorbid BPD and PTSD.: Data from two trials comparing Narrative Exposure The...

Tailored pharmacotherapy monitoring in Parkinson's disease and Schizophrenia using a rapid and sensitive α-Synuclein assay.

BACKGROUND: While Parkinson's disease is a low dopamine neurodegenerative disorder, Schizophrenia is considered a high dopamine psychiatric disorder. Pharmacological interventions that are directed to normalize dopamine concentrations in the mid-brain for an extended duration lead to unintended consequences. Parkinson's disease patients experience psychosis, and Schizophrenia patients develop extr...

AI-driven early diagnosis of specific mental disorders: a comprehensive study.

One of the areas where artificial intelligence (AI) technologies are used is the detection and diagnosis of mental disorders. AI approaches, including...

Visualizing functional network connectivity differences using an explainable machine-learning method.

. Functional network connectivity (FNC) estimated from resting-state functional magnetic resonance imaging showed great information about the neural m...

Predictors of psychotic experiences among adolescents with obsessive-compulsive symptoms: A data-driven machine learning approach.

AIM: Prediction of future psychosis in individuals with obsessive and compulsive (OC) symptoms is crucial for treatment choice, but only a few predict...

Biological age prediction in schizophrenia using brain MRI, gut microbiome and blood data.

The study of biological age prediction using various biological data has been widely explored. However, single biological data may offer limited insig...

Predictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depression.

Identifying predictors of treatment response to repetitive transcranial magnetic stimulation (rTMS) remain elusive in treatment-resistant depression (...

Brain circuits that regulate social behavior.

Social interactions are essential for the survival of individuals and the reproduction of populations. Social stressors, such as social defeat and iso...

Understanding contraceptive switching rationales from real world clinical notes using large language models.

Understanding reasons for treatment switching is of significant medical interest, but these factors are often only found in unstructured clinical note...

Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application.

BACKGROUND: Early identification of Schizophrenia Spectrum Disorder (SSD) is crucial for effective intervention and prognosis improvement. Previous ne...

Machine learning-based differentiation of schizophrenia and bipolar disorder using multiscale fuzzy entropy and relative power from resting-state EEG.

Schizophrenia (SZ) and bipolar disorder (BD) pose diagnostic challenges due to overlapping clinical symptoms and genetic factors, often resulting in m...

Classification of schizophrenia based on RAnet-ET: resnet based attention network for eye-tracking.

There is a notable need of quantifiable and objective methods for the classification of schizophrenia. Patients with schizophrenia exhibit atypical ey...

Establishing a Validation Framework of Treatment Discontinuation in Claims Data Using Natural Language Processing and Electronic Health Records.

Measuring medication discontinuation in claims data primarily relies on the gaps between prescription fills, but such definitions are rarely validated...

Patient-specific MRI super-resolution via implicit neural representations and knowledge transfer.

Magnetic resonance imaging (MRI) is a non-invasive imaging technique that provides high soft tissue contrast, playing a vital role in disease diagnosi...

Association of the characteristics of brain magnetic resonance imaging with genes related to disease onset in schizophrenia patients.

BACKGROUND: Schizophrenia (SCH) is a complex neurodevelopmental disorder, whose pathogenesis is not fully elucidated. This article aims to reveal dise...

An ensemble approach using multidimensional convolutional neural networks in wavelet domain for schizophrenia classification from sMRI data.

Schizophrenia is a complicated mental condition marked by disruptions in thought processes, perceptions, and emotional responses, which can cause seve...

Deep Learning-Assisted SERS for Therapeutic Drug Monitoring of Clozapine in Serum on Plasmonic Metasurfaces.

Clozapine is widely regarded as one of the most effective therapeutics for treatment-resistant schizophrenia. Despite its proven efficacy, the therape...

GPT-4 generated psychological reports in psychodynamic perspective: a pilot study on quality, risk of hallucination and client satisfaction.

BACKGROUND: Recently, there have been active proposals on how to utilize large language models (LLMs) in the fields of psychiatry and counseling. It w...

Exploring the significance of the frontal lobe for diagnosis of schizophrenia using explainable artificial intelligence and group level analysis.

Schizophrenia (SZ) is a complex mental disorder characterized by a profound disruption in cognition and emotion, often resulting in a distorted percep...

The More, the Better? Evaluating the Role of EEG Preprocessing for Deep Learning Applications.

The last decade has witnessed a notable surge in deep learning applications for electroencephalography (EEG) data analysis, showing promising improvem...