Sunday, Jun 23: 9:00 AM - 5:30 PM

Educational Course - Full Day (8 hours)

Educational Course - Full Day (8 hours)

COEX

Room: Grand Ballroom 102

Mathematical models of human brain activity have been central in gaining insights into the hidden mechanisms of the underlying neural processes at multiple scales. In this context, Whole-Brain Modelling (WBM) is a sub-field of computational neuroscience concerned with building comprehensive theoretical and computational models that represent and simulate the neural activity across the entire brain. The common objective of this approach is to investigate the mechanisms through which macroscopic spatiotemporal patterns of neural activity can be explained by studying the interplay among anatomical connectivity structure, intrinsic neural dynamics, and external perturbations (sensory, cognitive, pharmacological, electromagnetic, etc). Such macroscopic phenomena (i.e brain oscillations), and models thereof, are of particular scientific interest because a) large scale neural activity can be most readily obtained from the brains of healthy human subjects, using noninvasive neuroimaging and related methods b) they represent neural systems in a holistic and relatively intact state, . Simulations of human brain activity, in both health and disease, are therefore a principal focus of current WBM research.

The overarching idea is to model the brain at the macroscale as a network of interconnected regions, which are defined by (principally) neuroimaging-based brain parcellations The presence and weights of the network edges interconnecting the nodes are then derived from neuroimaging- or chemical tract tracing-based anatomical connectivity measurements. The nodes can be described using neural mass models (NMM) which represent the coarse grained activity of large populations of neurons and synapses using a small number of equations to express their mean firing rates and mean membrane potentials. NMM are capable of describing the change in firing rate of neural populations without spatial information and spatiotemporal time delays providing a succinct yet biophysically meaningful description of brain activity at the mesoscopic scale to reflect phenomena observed empirically at the macroscale. The main advantage of NMM is that the simplification of the dynamics reduces the number of dimensions or differential equations that need to be integrated enabling us to hone in on the behavior of a large number of ensembles and understand more clearly their dynamics. The aim of those models is to propose a balanced model between mathematical tractability and biological plausibility while still reproducing a wide range of empirical data features across multiple measurement modalities.

These features include: fast oscillations in local field potential (LFP) and extracranial electromagnetic (MEG, EEG) signals; slow quasi-periodic activity fluctuations in haemodynamic (BOLD fMRI, fNIRS) signals; inter-regional synchrony/covariance (‘functional connectivity’) and causal interactions (‘effective connectivity’) in both fast and slow activity patterns; sensory- or electromagnetic stimulation-evoked response waveforms; graph-theoretic properties large-scale network activity; and many others.

The overarching idea is to model the brain at the macroscale as a network of interconnected regions, which are defined by (principally) neuroimaging-based brain parcellations The presence and weights of the network edges interconnecting the nodes are then derived from neuroimaging- or chemical tract tracing-based anatomical connectivity measurements. The nodes can be described using neural mass models (NMM) which represent the coarse grained activity of large populations of neurons and synapses using a small number of equations to express their mean firing rates and mean membrane potentials. NMM are capable of describing the change in firing rate of neural populations without spatial information and spatiotemporal time delays providing a succinct yet biophysically meaningful description of brain activity at the mesoscopic scale to reflect phenomena observed empirically at the macroscale. The main advantage of NMM is that the simplification of the dynamics reduces the number of dimensions or differential equations that need to be integrated enabling us to hone in on the behavior of a large number of ensembles and understand more clearly their dynamics. The aim of those models is to propose a balanced model between mathematical tractability and biological plausibility while still reproducing a wide range of empirical data features across multiple measurement modalities.

These features include: fast oscillations in local field potential (LFP) and extracranial electromagnetic (MEG, EEG) signals; slow quasi-periodic activity fluctuations in haemodynamic (BOLD fMRI, fNIRS) signals; inter-regional synchrony/covariance (‘functional connectivity’) and causal interactions (‘effective connectivity’) in both fast and slow activity patterns; sensory- or electromagnetic stimulation-evoked response waveforms; graph-theoretic properties large-scale network activity; and many others.