Exploring brain communication pathways by combining diffusion based quantitative structural connectivity and EEG source imaging: application to physiological and epileptic networks
Traditionally explorations of the structural connectivity patterns of the brain have been kept separate from the studies of brain functional dynamics and connectivity probably because the fields of expertise are quite different. However there are strong reasons to have those fields merge in a common framework. Indeed, neuronal assemblies are distributed networks of physically connected neurons transiently coupled by temporally coherent activity. They are of strong interest because they are believed to encode elementary cognitive acts or more generally produce temporal windows for neuronal communication. In epilepsy patients, the axonal scaffold sets the premises of seizure onset and shapes the patterns of seizure propagation. The critical and metastable state of brain functional activity is largely related to the underlying connectome architecture, as computational models show. Electrical Source Imaging (ESI), can map neuronal activity over the entire brain at appropriate temporal resolution but suffers from limited localization power and alone has strong limitations when studying neuronal assemblies or seizures onset and propagation since physical connectedness between sources is unknown. Furthermore functional coupling metrics derived from ESI are heavily influenced by assumptions on conduction delays. On the other hand diffusion MRI (dMRI) allows mapping the large-scale structural brain connectivity network, including length of fiber pathways and has the potential, in combination with additional MRI techniques, to inform on axonal diameter distribution and myelination. Those elements are essential to map propagation routs and conduction speed. Beyond the technical challenge of combining structural connectivity data derived from MRI with ESI, the analysis of the complex interplay between those two dimensions, namely structure and function, is not trivial and has only been partially addressed. Accordingly, in addition to the fusion of structural and functional data, there is a need for a new framework encompassing simultaneously the dynamic non-stationary nature of brain functional oscillations and the physical constraints on propagation through axonal connections to identify and follow across time and space information or seizure propagation.
AIM OF THE PROJECT
- Develop a reconstruction framework, which will provide, from ESI and dMRI data, a brain network representation. To each node, representing a cortical region, an electrical source will be associated (time series). Each edge will represent an existing fiber tract to which information of length, size, axonal diameter distribution and myelination will be associated.
- Develop new methods to constrain ESI sources by using structural connectivity information.
- Determine the connection specific propagation delays by combining microstructural information and phase lags inwell-defined visual stimulus paradigms and intracranial electrophysiology.
- Fit new computational models of spontaneous activity and epilepsy to study criticality in the brain.
- Develop a dedicated analysis framework to follow and characterize the propagation of spatio-temporal coupling witha dynamic network model.
- Explore with this framework mechanisms of connectome constrained functional connectivity in the visual system andassociated feed-forward and –backward mechanisms.
- Explore with this framework the mechanisms of seizure onset, propagation and inter-ictal resting activity in epileptic patients.
Providing a mapping and analysis framework enabling to follow at high spatial and temporal resolution the propagation of neuronal electrical activity through the connectome is of highest utility to a large community of systems, cognitive and clinical neuroscientists. In addition our expected contributions to the understanding of neuronal information flow during vision and characterization of seizure propagation through experimentation and modeling is expected to advance those specific fields significantly.
Patric Hagmann, MD, PhD
Christoph Michel, PhD
Jean-Philippe Thiran, PhD
Serge Vulliemoz, MD-PhD
Gijs Plomp, PhD
Gustavo Deco, PhD
Pieter van Mierlo, PhD
Funded by: Swiss National Science Foundation (SNSF)
Project reference: CRSII5_170873