Latest AI and machine learning research in psychiatry for healthcare professionals.
Functional MRI enables non-invasive mapping of brain connectivity, yet its clinical translation remains hindered by uncontrolled state-dependent variability that obscures individual-specific signatures during routine scanning. Here we introduce PI-FC — a deep learning framework leveraging state-invariant contrastive learning to extract stable individual brain signatures across diverse arousal leve...
Neuromodulation is a complex process in which chemical substances modulate brain activity, allowing its rich repertoire of behaviors. Among these substances, dopamine has a preponderant role, being involved in several mechanisms. Moreover, dysfunctions in the dopamine connections has been observed in pathology, such as Parkinson’s disease and schizophrenia. To investigate the mechanism of neuromod...
Neural circuits emerge during development through dynamic interactions between genetic instructions and environmental cues that shape cell fate, conne...
Microbiota transplant therapy (MTT) is a promising avenue for the substantial improvement of gastrointestinal and behavioral symptoms in children with...
Schizophrenia (ScZ) is a growing global health concern that affects millions of people and puts severe pressure on healthcare systems. Early detection...
This study critically reevaluates the utility of brain-age models within the context of detecting neurological and psychiatric disorders, challenging ...
Formal thought disorder (FTD) is a core symptom of schizophrenia, yet the neural network mechanisms underlying this phenotype remain poorly understood...
Attention-deficit/hyperactivity disorder and anxiety disorders are highly prevalent in youth and are characterized by substantial heterogeneity and fr...
Neuronal activity shapes brain development and refines synaptic connectivity in part through dynamic changes in gene expression. While activity-regula...
The flexible deployment of cognitive control is essential for adaptive functioning in dynamic environments given limited cognitive resources. That fle...
The diagnosis of neurological disorders requires comprehensive frameworks that incorporate multimodal neuroimaging data while ensuring clinical interp...
Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in the U.S., and the stimulant and nonstimulant medicat...
Major depressive disorder (MDD) exhibits significant heterogeneity whose neurobiological mechanisms remain elusive. Alterations in morphological-funct...
Major depressive disorder (MDD), bipolar disorder (BP), and schizophrenia (SCZ) involve learning impairments with poorly understood mechanisms. Unders...
Deep neural networks have revolutionized functional neuroimaging analysis but remain “black boxes,” concealing which brain mechanisms and regions driv...
Neuroscience-inspired neural networks bridge biology and technology, offering powerful tools to model brain function while enabling adaptive, efficien...
Advances in computational methods have accelerated the application of machine learning to analyze large complex biological data. By applying machine l...
The brain’s ability to detect behaviorally relevant stimuli from sensory inputs is fundamental to cognition, yet the neural mechanisms linking synapti...
Genomic technology advancements have facilitated associations between genetic variants and disease risk. Rare deleterious variants can independently i...
The basal ganglia (BG) are central to action selection and reinforcement learning, yet how the topological organization of the BG circuit with dopamin...