Cognitive network modelling employs mathematical models to infer hidden neural mechanisms from recordings of behaviour and brain activity–functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data–acquired during cognitive tasks. We utilize a suite of mathematical models, including hierarchical Bayesian, reinforcement learning, and machine learning algorithms, to infer underlying cognitive and neuronal mechanisms.On one axis, our research examines how basic learning signals, such as predictions and “correction” signals or prediction errors, are represented in the brain using probabilistic learning tasks and multimodal imaging (EEG and fMRI). This foundational knowledge serves as a substrate for our parallel line of inquiry that investigates how distortions in the representation of these learning signals contribute to maladaptive behaviours and pathological states. Specifically, we are interested in how these mechanisms could underpin the neurobiology of severe mental disorders, including schizophrenia and major depression.
First, we examine the role of neuromodulation in supporting learning and decision-making under uncertainty using advanced fMRI, to better understand how disruptions in neuromodulation across dopaminergic, cholinergic, serotonergic, and noradrenergic systems can lead to the emergence of core symptoms, such as paranoia in psychosis.
The second research pillar is focused on evaluating the efficacy of computational modelling when applied to electrophysiological or neuroimaging data to predict treatment response in individuals who have been diagnosed with either psychosis or major depressive disorder.
The third pillar focuses on the application of computational modelling for averting severe consequences of mental illness, such as the emergence of suicidal thoughts and behaviours or psychosis spectrum symptoms in help-seeking youth.
Email Dr. Diaconescu at email@example.com
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