T-BraiC: a new Taxonomy of Brain rhythms in Cognition

• Applicant: Professor Dr. Joachim Gross
• Subject Area: Human Cognitive and Systems Neuroscience
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 491157081

About 100 hundred years ago the first non-invasive recording of human brain activity revealed strong rhythmic patterns. We now know that these rhythms are ubiquitous in the human brain and are sensitive markers of brain states in health and disease. Still, fundamental questions remain unanswered: What proportion of brain activity is rhythmic? How stable are brain rhythms in time and frequency? How important is the shape of rhythmic brain waves and how does this relate to behaviour? What are the critical structural and functional 'neural rules' that shape these properties of brain dynamics and how are they related to individual traits and states? T-BraiC is an ambitious programme to address these long-standing questions. T-BraiC will implement and use new analysis methods to overcome three current challenges in the field. It will then apply these methods to a unique data set that is acquired during this project. It consists of high-resolution measurements of brain activity from 200 participants (magnetoencephalograpy (MEG) and electroencephalography (EEG)) during rest and different cognitive tasks. Importantly, participants will be recruited from an existing cohort in Muenster where detailed phenotyping has been carried out (questionnaires, neuropsychological assessments, anthropometry, body impedance, vascular status, blood samples and structural (T1, DTI) and functional magnetic resonance imaging (MRI). T-BraiC will combine this large-scale data with state-of-the-art computational modelling. Computational models will be constructed based on individual anatomical information to simulate realistic MEG data. Systematic comparison between simulated and measured data provides information about the physiological underpinnings of measured brain rhythms. This approach will be complemented with predictive models that will be used to predict individuals traits and states from brain activity using state-of-the-art machine learning tools. Using this integrated approach, T-BraiC will provide a comprehensive account of the structure and organisation of rhythmic human brain activity, its individual variability, relationship to behaviour and dependence on phenotype.

• DFG Programme: Reinhart Koselleck Projects