Schizophrenia

Comprehensive evaluation of pipelines for classification of psychiatric disorders using multi-site resting-state fMRI datasets.

Objective classification biomarkers that are developed using resting-state functional magnetic resonance imaging (rs-fMRI) data are expected to contribute to more effective treatment for psychiatric disorders. Unfortunately, no widely accepted biomarkers are available at present, partially because of the large variety of analysis pipelines for their development. In this study, we comprehensively e...

Neurofind: using deep learning to make individualised inferences in brain-based disorders.

Within precision psychiatry, there is a growing interest in normative models given their ability to parse heterogeneity. While they are intuitive and informative, the technical expertise and resources required to develop normative models may not be accessible to most researchers. Here we present Neurofind, a new freely available tool that bridges this gap by wrapping sound and previously tested me...

Joint Driver State Classification Approach: Face Classification Model Development and Facial Feature Analysis Improvement.

Driver drowsiness remains a critical factor in road safety, necessitating the development of robust detection methodologies. This study presents a dua...

Interpretable modality-specific and interactive graph convolutional network on brain functional and structural connectomes.

Both brain functional connectivity (FC) and structural connectivity (SC) provide distinct neural mechanisms for cognition and neurological disease. In...

Identifying periphery biomarkers of first-episode drug-naïve patients with schizophrenia using machine-learning-based strategies.

Schizophrenia is a complex mental disorder. Accurate diagnosis and classification of schizophrenia has always been a major challenge in clinic due to ...

Generative Artificial Intelligence and Misinformation Acceptance: An Experimental Test of the Effect of Forewarning About Artificial Intelligence Hallucination.

Generative artificial intelligence (AI) tools could create statements that are seemingly plausible but factually incorrect. This is referred to as AI ...

Electroencephalogram (EEG) Based Fuzzy Logic and Spiking Neural Networks (FLSNN) for Advanced Multiple Neurological Disorder Diagnosis.

Neurological disorders are a major global health concern that have a substantial impact on death rates and quality of life. accurately identifying a n...

Deep learning imputes DNA methylation states in single cells and enhances the detection of epigenetic alterations in schizophrenia.

DNA methylation (DNAm) is a key epigenetic mark with essential roles in gene regulation, mammalian development, and human diseases. Single-cell techno...

Multi-feature fusion method combining brain functional connectivity and graph theory for schizophrenia classification and neuroimaging markers screening.

BACKGROUND: The abnormalities in brain functional connectivity (FC) and graph topology (GT) in patients with schizophrenia (SZ) are unclear. Researche...

Artificial intelligence in neurovascular decision-making: a comparative analysis of ChatGPT-4 and multidisciplinary expert recommendations for unruptured intracranial aneurysms.

In the multidisciplinary treatment of cerebrovascular diseases, specialists from different disciplines strive to develop patient-specific treatment re...

Predicting mental health disparities using machine learning for African Americans in Southeastern Virginia.

This study examined mental health disparities among African Americans using AI and machine learning for outcome prediction. Analyzing data from Africa...

Efficient Neural Network Classification of Parkinson's Disease and Schizophrenia Using Resting-State EEG Data.

Timely identification of Parkinson's disease and schizophrenia is crucial for the effective management and enhancement of patients' quality of life. T...

Predicting antipsychotic responsiveness using a machine learning classifier trained on plasma levels of inflammatory markers in schizophrenia.

We apply machine learning techniques to navigate the multifaceted landscape of schizophrenia. Our method entails the development of predictive models,...

Exploring Potential Medications for Alzheimer's Disease with Psychosis by Integrating Drug Target Information into Deep Learning Models: A Data-Driven Approach.

Approximately 50% of Alzheimer's disease (AD) patients develop psychotic symptoms, leading to a subtype known as psychosis in AD (AD + P), which is as...

Lurasidone uses and dosages in Spain: RETROLUR, a real-world retrospective analysis using artificial intelligence.

INTRODUCTION: Lurasidone is used for schizophrenia and bipolar depression in many countries, yet there is a lack of existing literature about its use,...

Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network.

Previous deep learning-based brain network research has made significant progress in understanding the pathophysiology of schizophrenia. However, it i...

Estimating the Prevalence of Schizophrenia in the General Population of Japan Using an Artificial Neural Network-Based Schizophrenia Classifier: Web-Based Cross-Sectional Survey.

BACKGROUND: Estimating the prevalence of schizophrenia in the general population remains a challenge worldwide, as well as in Japan. Few studies have ...

Preliminary investigation of an artificial intelligence-based cognitive behavioral therapy training tool.

We developed an asynchronous online cognitive behavioral therapy (CBT) training tool that provides artificial intelligence- (AI-) enabled feedback to ...

A comparative machine learning study of schizophrenia biomarkers derived from functional connectivity.

Functional connectivity holds promise as a biomarker of schizophrenia. Yet, the high dimensionality of predictive models trained on functional connect...

Predicting conversion to psychosis using machine learning: response to Cannon.

BACKGROUND: We previously reported that machine learning could be used to predict conversion to psychosis in individuals at clinical high risk (CHR) f...