Schizophrenia

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.

Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizo...

Vitamin D Deficiency in Obsessive-Compulsive Disorder Patients with Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcal Infections: A Case Control Study.

INTRODUCTION: Previous studies have indicated that vitamin D deficiency is common in psychiatric patients, particularly in those with neuropsychiatric disorders such as autism and schizophrenia. Vitamin D is an important neurosteroid hormone and immunomodulatory agent that also has bone metabolic effects. There has been an increasing interest in immune-related neuropsychiatric symptoms that are tr...

Accelerated Brain Aging in Schizophrenia: A Longitudinal Pattern Recognition Study.

OBJECTIVE: Despite the multitude of longitudinal neuroimaging studies that have been published, a basic question on the progressive brain loss in schi...

A multi-layer network approach to MEG connectivity analysis.

Recent years have shown the critical importance of inter-regional neural network connectivity in supporting healthy brain function. Such connectivity ...

Classification of first-episode psychosis in a large cohort of patients using support vector machine and multiple kernel learning techniques.

First episode psychosis (FEP) patients are of particular interest for neuroimaging investigations because of the absence of confounding effects due to...

Does lower urine-specific gravity predict decline in renal function and hypernatremia in older adults exposed to psychotropic medications? An exploratory analysis.

BACKGROUND: Exposure to psychotropic agents, including lithium, antipsychotics and antidepressants, has been associated with nephrogenic diabetes insi...

Prediction of psychosis using neural oscillations and machine learning in neuroleptic-naïve at-risk patients.

OBJECTIVES: This study investigates whether abnormal neural oscillations, which have been shown to precede the onset of frank psychosis, could be used...

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsych...

Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM.

Resting state functional Magnetic Resonance Imaging (rs-fMRI) is increasingly used for the identification of image biomarkers of brain diseases or psy...

Abstract computation in schizophrenia detection through artificial neural network based systems.

Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that i...

Discrimination of schizophrenia auditory hallucinators by machine learning of resting-state functional MRI.

Auditory hallucinations (AH) are a symptom that is most often associated with schizophrenia, but patients with other neuropsychiatric conditions, and ...

A machine learning approach using auditory odd-ball responses to investigate the effect of Clozapine therapy.

OBJECTIVE: To develop a machine learning (ML) methodology based on features extracted from odd-ball auditory evoked potentials to identify neurophysio...

Predictors of schizophrenia spectrum disorders in early-onset first episodes of psychosis: a support vector machine model.

Identifying early-onset schizophrenia spectrum disorders (SSD) at a very early stage remains challenging. To assess the diagnostic predictive value of...

Large Language Models (LLMs) for Telecom Root Cause Analysis (RCA): A Structured Reasoning Framework for Evidence-Grounded Diagnosis

Root cause analysis (RCA) is a critical task in telecom network operations, but diagnosing performance degradations in modern 5G and emerging 6G netwo...

Benchmarking Vision-Language Models for Automated Pathology Diagnosis and Report Generation

The rapid advancement of vision-language models (VLMs) has accelerated progress in computational pathology; however, whole-slide image (WSI)-based pat...

Beyond Language Priors: Diagnosing and Fixing Visual-Origin Hallucinations in Multimodal LLM

Existing research on object hallucination in multimodal large language models (MLLMs) predominantly attributes the problem to language priors such as ...

VisER: Visual Evidence and Reliance for Object Hallucination Detection in LVLMs

Object hallucination remains a persistent reliability issue in large vision-language models, where generated object mentions may sound plausible but l...

Fine-Grained Multi Image Object Hallucination Benchmark

Multimodal Large Language Models (MLLMs) are increasingly deployed in multi-image scenarios requiring complex reasoning across visual contexts. Howeve...

SingProbe Technical Report

Runtime guardrails are essential for reliable large language model (LLM) deployment, yet existing approaches typically rely on independent, external m...

Hallucination Mitigation for Large Vision-Language Models via Implicit Feature Stabilization

Large Vision-Language Models (LVLMs) are prone to hallucinations: they fluently describe objects, attributes, and scenes that are not in the image. We...