Topic: neural mass models

Modeling non-invasive brain stimulation, particularly transcranial magnetic stimulation (TMS) of the primary motor cortex (M1), has been previously explored through simulation methods at different scales. However, the coupling of TMS induced electric fields to neural mass models is still largely unexplored, while previously approached as an arbitrary current pulse [1, 2]. Via multi-scale simulations at the subcellular and neural mass level we study the underlying mechanisms of electromagnetic activation of cortical tissue by TMS. Validation of the coupling model is aided by measurements like E

Neural mass models (NMM) seek to unveil the fundamental principles governing macroscopic neural activity, drawing insights from the brain's intricate and multi-scale structure. The advent of an exact mean-field theory for networks of quadratic integrate-and-fire (QIF) neurons represents a significant leap forward in our comprehension of the link between microscale neural mechanisms and mesoscopic brain activity [1]. This model establishes a robust coupling between two biophysically relevant macroscopic quantities: the firing rate and the mean membrane potential, which collectively drive the ev

In the awake human brain, alpha-band activity (8–13 Hz) is the most prominent electrophysiological signature. Several models exist for alpha-rhythm generation using neural mass models, including the Jansen-Rit, Moran-David-Friston, Robinson-Rennie-Wright, and Liley-Wright models [1]. Fitting these models traditionally relies on manual parameter tuning or computationally expensive parameter searches, and they rarely account for inter-individual variation. However, there is substantial experimental variability in the propensity for robust oscillatory activity in EEG, e.g., influenced by structur