The project is focused on the development of computational models of brain activity that capture the dynamics of the brain at different time scales. The goal of the project is to develop research in the field of applied computational neuroscience in the Czech Republic, especially through the implementation of top research, strengthening ties to top foreign workplaces, developing the personnel capacities of the involved research teams and strengthening their experimental and computational capacities.
The project has a distinctly interdisciplinary character both in terms of the methods used and the experts involved. It mainly uses neuropsychological experiments, including complex multimodal sensory stimulations, measurements using advanced neuroimaging methods, experimental or therapeutic intervention using non-invasive electrical/magnetic stimulation or drug administration. On the other hand, it develops and uses computational model of brain dynamics and advanced methods of data analysis, including machine learning.
The application potential of the first research work package lies in the development of efficient and flexible computational models of brain dynamics, enabling adaptation to specific application tasks. The second research work package focuses on further development in the application area of monitoring and predicting brain states, for example monitoring tissue excitability in epilepsy and predicting seizures. The third research work package focuses on the use of computational models of intervention in brain dynamics with potential applications in the design and optimization of therapeutic brain stimulation and a personalised approach to pharmacotherapy.
The team includes experts in neuropsychiatry, neuroscience, epileptology, cognitive neuroscience, mathematical and computational neuroscience, as well as modelling and analysis of complex systems. Research goals include the highly topical issue of the development of computational models of brain dynamics, which lies at the intersection of basic medical sciences, especially neuroscience, natural sciences, especially mathematics, and clinical medical sciences, especially applications in psychiatry and neurology. Research in this area has reached maturity: solid theoretical foundations and developed methodological procedures bring the first application results and thus open the field to further flourishing.
At the same time, the project is designed to fulfil its application potential already during the project phase. This application potential is represented by the use of the results of modelling and data analyses in testing experimental therapies based on brain dynamics modulation in selected diseases.
Whole-brain spontaneous dynamics
Characteristics: The aim is to create a family of generative models of varying complexity that capture brain dynamics. These models should approximate the spatial patterns observed in neuroimaging at rest, including the topology and topography of functional connectivity, as well as the temporal dynamics of the brain, including fluctuations in activity, connectivity dynamics, and information flow. Furthermore, they should provide an internally consistent, unified description of electrophysiological and fMRI data, linking them to biologically interpretable latent variables and structural connectivity. The models should enable the prediction of the effect of external stimulation and capture key axes of variability between subjects, linking them to psychometric data. Ultimately, they should enable the characterization and prediction of deviations from healthy brain dynamics in a range of psychiatric and neurological disorders.
Observation of (dys)functional dynamics
Characteristics: The aim is to develop and utilize advanced multi-scale computational models incorporating system dynamics and network science principles to link structural, biophysically inspired models with functional and morphological brain data. The work package is characterized by its focus on investigating brain dynamics across various temporal and spatial scales, aiming to understand complex neural processes, including those related to perception and cognition, and specifically applying these models to study and potentially improve therapeutic interventions for major brain dynamic disorders like epilepsy and schizophrenia. This involves calibrating a detailed model of the visual cortex with experimental data (intracortical and scalp recordings) to describe the effects of both direct and non-invasive brain stimulation, ultimately seeking to stabilize brain dynamics and increase resilience to pathological states.
Interventions in Brain Dynamics
Characteristics: The main aim is to leverage state-of-the-art brain imaging (fMRI, hdEEG) and advanced analytical procedures to develop and test novel diagnostic and therapeutic interventions for major brain disorders, primarily Major Depressive Disorder (MDD), schizophrenia (SCH), and Parkinson's Disease (PD). The work package is characterized by its strong translational and clinical focus on optimizing non-invasive brain stimulation techniques, such as rTMS/deep rTMS and tACS, and deep brain stimulation (DBS), by using concurrent EEG-fMRI to characterize disrupted mesoscale brain network dynamics. This involves developing new algorithms for network excitability assessment, comparing different therapeutic interventions (including pharmacological models using ketamine and psilocybin), and conducting proof-of-concept randomized controlled trials (RCTs) informed by oscillatory network dynamics to personalize and refine stimulation protocols.