Schizophrenia is a debilitating mental disorder with a lifetime prevalence of 1%, characterized by hallucinations and delusions as well as negative symptoms and cognitive deficits. Currently, the causes is still unclear and the existing treatments are largely ineffective in targeting the cognitive and physiological dysfunctions. While a range of neurobiological abnormalities have been identified, a mechanistic understanding of the origins of neuronal and cognitive deficits has remained elusive.
The project aimed to obtain novel insights into the neurotransmitter-systems involved in schizophrenia and their relationship to changes in brain activity as measured with Magnetoencephalography (MEG) and functional magentic resonance imaging (fMRI) through the use of computational modelling. Through this approach, we aimed to gain important new insights into the underlying causes of schizophrenia that could eventually lead to more effective treatments.
In the first project, we investigated MEG resting-state activity in patients with a first-episode psychosis patients (FEP, n=27) and healthy controls (HC, n=49). We analyzed global brain connectivity and, the organization of brain networks. We found a significant reduction of global brain connectivity in the alpha band for the FEP group, which was most pronounced in left frontal regions. These results further support the notion that the onset of psychosis is characterized by impairments of interregional cortical communication which depends on precisely timed neuronal communication.
In the second project, we examined resting-state fMRI data in chronic patients with schizophrenia together with computational modelling to identify the contribution of GABAergic and glutamatergic neurotransmission towards alterations in functional connectivity. We found that reduced brain connectivity in patients with schizophrenia was a prominent feature of the disorder. Perturbations of the computational model revealed that decreased global coupling and increased background noise levels both explained the experimentally found deficits better than local changes to the GABAergic or glutamatergic system.
Thirdly, we studied the effects of Ketamine, an NMDA receptor antagonist, on MEG-data to identify the underlying mechanisms at the circuit level. Ketamine is important for the understanding of schizophrenia as administration of sub-anesthetic dosages can induce transient, psychotic states in healthy volunteers. To this end, we analyzed resting-state MEG data from healthy participants participants who were administered Ketamine to assess changes in gamma-band (30-90Hz) power and compared the Ketamine-induced spectral changes to the effects in a computational cortical layer-2/3 model. Our data revealed that dysfunctional NMDA receptors in parvalbumin or somatostatin interneurons could reproduce increased gamma-band power by increasing pyramidal neuron firing rate but did not account for changes in the aperiodic slope.
Finally, we implemented a detailed computational model of auditory circuits, including primary auditory cortex (A1), medial geniculate body (MGB), and thalamic reticular nucleus, to identify circuit deficits of MEG-activity in patients with schizophrenia. The computional model simulates over 25 million synapses, incorporating data on cell-type-specific neuron densities, morphology, and connectivity across six cortical layers. We then simulated MEG-data to capture alterations in gamma-band oscillations in early-stage psyhosis using a 40 Hz Auditory Steady State (ASS) paradigm. This model will enable the detailed search for the contribution of GABAergic and glutamatergic neurotransmission abnormalities towards altered MEG-activity in patients with schizophrenia.