Psychiatry

Schizophrenia

Latest AI and machine learning research in schizophrenia for healthcare professionals.

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Mesocorticolimbic reinforcement learning of reward representation and value provides an integrated mechanistic account for schizophrenia

Mesocorticolimbic dopamine projections are crucial for value learning, motivational control, and cog...

A genetic algorithm for self-supervised models of oscillatory neurodynamics

Predictive processing theories propose that the brain supervises itself, to build an internal model ...

PRIME: A Multi-Agent Environment for Orchestrating Dynamic Computational Workflows in Protein Engineerings

Artificial intelligence (AI) is revolutionizing protein engineering, yet its practical application i...

Claustrum volume in humans – lifespan trajectory and effect of age, hemisphere, and sex

The human claustrum is a bilateral, thin, irregularly shaped gray matter structure located between t...

Automating Candidate Gene Prioritization with Large Language Models: From Naive Scoring to Literature-Grounded Validation

Identifying promising therapeutic targets from thousands of genes in transcriptomic studies remains ...

PI-FC: Pre-training Individual-specific Functional Connectome through State-invariant Contrastive Learning

Functional MRI enables non-invasive mapping of brain connectivity, yet its clinical translation rema...

Next generation neural mass model with dopamine modulation mediated by D1-type receptors

Neuromodulation is a complex process in which chemical substances modulate brain activity, allowing ...

Exploring brain lobe-specific insights in an explainable framework for EEG-based schizophrenia detection

Schizophrenia (ScZ) is a growing global health concern that affects millions of people and puts seve...

Dynamic Meta-Networking Identifies Distinct Network Correlates of Positive and Negative Formal Thought Disorder in Schizophrenia

Formal thought disorder (FTD) is a core symptom of schizophrenia, yet the neural network mechanisms ...

Decision Voting Based Multiscale Convolutional Learning of Brain Networks With Explainability

The diagnosis of neurological disorders requires comprehensive frameworks that incorporate multimoda...

HalluDesign: Protein Optimization and de novo Design via Iterative Structure Hallucination and Sequence design

Deep learning has revolutionized biomolecular modeling, enabling the prediction of diverse structure...

Model-based EEG phenotyping uncovers distinct neurocomputational mechanisms underlying learning impairments across psychopathologies

Major depressive disorder (MDD), bipolar disorder (BP), and schizophrenia (SCZ) involve learning imp...

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,” ...

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 mode...

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 co...

Flexible brain state engagement predicts cognitive control transdiagnostically

Cognitive control supports adaptive responses in an ever-changing world. While alterations in cognit...

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...

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 ...

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