Description
co-Chairs: Lorenzo Pini, Boris Bernhardt
The classical Jansen and Rit Neural Mass Model has been a cornerstone in computational neuroscience, offering valuable insights into brain activity and neural dynamics. However, traditional implementations of this model often fall short in capturing the complexities of real neural systems, particularly in terms of conduction delays and parameter sensitivity.
To address these limitations, we...
This project presents a novel Region-Specific Brain Network Model (RSBNM) framework that integrates high-resolution multimodal data, exemplified through a case study focusing on the hippocampus. The core elements include leveraging the high-density surface mesh of the hippocampus from BigBrain [1, 2] to simulate EEGs within The Virtual Brain (TVB) environment, employing MRI-derived structural...
Biophysical network modeling (BNM) of the brain is a promising technique for bridging macro- and microscale levels of investigation, enabling inferences about latent features of brain activity such as excitation-inhibition balance. This approach allows personalized models of the brain to be fitted to individual subjects' imaging data through parameter optimization. However, the process...
Abstract
Cobrawap (Collaborative Brain Wave Analysis Pipeline) [1,2] is an open-source, modular and customizable data analysis tool developed in the context of HBP/EBRAINS [3], with the aim of enabling standardized quantitative descriptions of cortical wave dynamics observed in heterogenous data sources, both experimental and simulated. The tool intercepts the increasing demand...
The human brain is intrinsically organized as (anti-)correlated regions as shown repeatedly by resting-state fMRI (Biswal et al. 2010), where these regions are thought to represent activity fluctuations of neuronal populations across large cortical swathes (Chen et al. 2020). Yet elucidating properties of these functional networks based on neuroanatomy has remained elusive. Prior attempts to...
