Schizophrenia

Biophysical validation of explainable AI for functional brain imaging: bridging cellular mechanisms and network dynamics

Deep neural networks have revolutionized functional neuroimaging analysis but remain “black boxes,” concealing which brain mechanisms and regions drive their predictions—a critical limitation for clinical neuroscience. Here we develop and validate an explainable AI (xAI) framework to test whether feature attribution techniques can reliably recover brain regions affected by excitation/inhibition (E...

Synaptic Synchronization-Based Learning of Pattern Separation in Self-Organizing Probabilistic Spiking Neural Networks

Neuroscience-inspired neural networks bridge biology and technology, offering powerful tools to model brain function while enabling adaptive, efficient control in robotics. In this work, we present a neuroscience-inspired synaptic learning rule based on the synchronization of synaptic inputs to single excitatory neurons within a feedforward spiking neural network. The model consists of three excit...

Recurrent neural network models reveal unified mechanisms generating event-related potentials from MMN to P300

The brain’s ability to detect behaviorally relevant stimuli from sensory inputs is fundamental to cognition, yet the neural mechanisms linking synapti...

Topological basal ganglia model with dopamine-modulated spike-timing-dependent plasticity reproduces reinforcement learning, discriminatory learning, and neuropsychiatric disorders

The basal ganglia (BG) are central to action selection and reinforcement learning, yet how the topological organization of the BG circuit with dopamin...

Flexible brain state engagement predicts cognitive control transdiagnostically

Cognitive control supports adaptive responses in an ever-changing world. While alterations in cognitive control have been consistently observed in a r...

The expression level of CACNA1C-encoded CaV1.2 is a tipping point between promotion and inhibition of dendritic growth in neurons

The CACNA1C gene encodes the CaV1.2 L-type voltage-gated calcium channel, which plays a crucial role in neuronal signaling. CACNA1C is a risk gene for...

Image-Based Profiling of Induced Trophoblast Stem Cells Identifies Signatures Associated with Sex, Schizophrenia Genomic Risk and Placental Stress

Schizophrenia (SCZ) is a neurodevelopmental disorder where both genetic and environmental risks converge during pregnancy. Recent studies have highlig...

Brain Age Gap Reduction Following Physical Exercise Mirrors Negative Symptom Improvement in Schizophrenia Spectrum Disorders

Schizophrenia spectrum disorders (SSD) are associated with accelerated brain aging, reflected in an increased brain age gap. This gap serves as a biom...

SLaM Image Bank – a real-world diverse London cohort linking brain MRI to electronic mental health and dementia records for the development of clinical decision support tools using artificial intelligence

Rapid developments are occurring in artificial intelligence (AI) and machine learning (ML) applied to neuroimaging. To date, advances in this space ha...

Schizophrenia versus Healthy Controls Classification based on fMRI 4D Spatiotemporal Data

A wide array of machine learning approaches have been employed for differentiating patients with mental health disorders from healthy controls using n...

Prognostic predictions in psychosis: exploring the complementary role of machine learning models

Predicting outcomes in schizophrenia spectrum disorders is challenging due to the variability of individual trajectories. While machine learning (ML) ...

Interrelations Between Dopaminergic-, GABAergic- and Glutamatergic Neurotransmitters in Antipsychotic-Naïve Psychosis Patients and the Association to Initial Treatment Response

Preclinical evidence points to disturbances in neural networks in psychosis involving interrelations between dopaminergic-, GABAergic- and glutamaterg...

Open-Source Retrieval Augmented Generation Framework for Retrieving Accurate Medication Insights from Formularies for African Healthcare Workers

Accessing accurate medication insights is vital for enhancing patient safety, minimizing errors, and supporting clinical decision-making. However, hea...

DUNE: a versatile neuroimaging encoder captures brain complexity across three major diseases: cancer, dementia and schizophrenia

Magnetic resonance images (MRI) of the brain exhibit high dimensionality that pose significant challenges for computational analysis. While models pro...

Characterizing multivariate regional hubs for schizophrenia classification, sex differences, and brain age estimation using explainable AI

To investigate multivariate regional patterns for schizophrenia (SZ) classification, sex differences, and brain age by utilizing structural MRI, demog...

A Framework to Assess Clinical Safety and Hallucination Rates of LLMs for Medical Text Summarisation

Integrating large language models (LLMs) into healthcare settings can improve workflow efficiency and patient care by automating tasks such as summari...

Deep learning approach for automatic assessment of schizophrenia and bipolar disorder in patients using R-R intervals

Schizophrenia and bipolar disorder are severe mental illnesses that significantly impact quality of life. These disorders are associated with autonomi...

Predicting agranulocytosis in patients treated with clozapine – development and validation of a machine learning algorithm based on 5,550 patients

To prevent clozapine-induced agranulocytosis (CIA), patients’ white blood cell counts are closely monitored, with treatment stopped if the absolute ne...

Leveraging Feature Transfer to Predict Medication Resistance and Secondary-Clinical Outcomes in Psychotic Disorders in Forensic Settings

Medication resistance in psychotic disorders represents a critical challenge in forensic psychiatry, where up to 50% of patients show poor treatment r...

Large Language Models Are Highly Vulnerable to Adversarial Hallucination Attacks in Clinical Decision Support: A Multi-Model Assurance Analysis

Large language models (LLMs) show promise in clinical contexts but can generate false facts (often referred to as “hallucinations”). One subset of the...