Stable and dynamic EEG and fMRI functional connectivity patterns and their relation to individual differences

Status: Completed project
Primary investigator: Grega Repovš
Duration: 1.5.2017 - 30.04.2020
Basic information on Sicris.

Abstract

Since human brain imaging research has shifted its focus from efforts to localise individual cognitive processes and related brain regions to the study of integration of brain function, fMRI functional connectivity research has provided a wealth of information on how multiple brain regions across the brain connect to form complex networks and systems. Numerous studies have explored how functional brain networks relate to cognitive processes, intellectual abilities, brain development and maturation, and addressed the question, what patterns of functional brain networks dysconnectivity underlie psychiatric diseases and reflect the progress of neuropathology in neurodegenerative diseases.

Exploration of fMRI functional brain connectivity is though facing a number of obstacles that will have to be resolved to allow substantive further progress. One is a lack of understanding of the neuronal mechanisms and dynamic that give rise to functional connectivity as estimated by observing slowly evolving BOLD signal. A promising approach is multimodal imaging, in which information of different modalities can be combined to provide novel insights. One such possibility is the integration of fMRI and EEG signals. Whereas fMRI enables impressive spatial resolution it is impaired by its low temporal resolution. Integration with the EEG signal, directly tracking neuronal activity with high temporal precision, could enable the much needed understanding of the mechanisms giving rise to fMRI functional connectivity, and jointly provide spatially well specified estimates of functional connectivity with high temporal resolution.

Another significant challenge is the extent to which transient changes in mood and psycho-physical state impact individual’s functional connectivity results. Whereas in group level analyses these effect—if random—can average out, at the individual level, though, they can mask the information relevant for the assessment of individual’s cognition or presence of neuropathology, reducing the reliability and validity of use of functional connectivity as a diagnostic tool.

The aim of the proposed project is to address these two questions through completion of four goals. First, to gain detailed understanding of spatiotemporal properties of large scale brain networks through integration of EEG and fMRI signals acquired during rest and task. Second, to explore the longitudinal variability in spatiotemporal network properties as reflected in EEG and fMRI signals at both rest and task, and test the stability of identified brain networks and their properties. Third, to examine, how both inter- and intra-individual differences relate to the stability of brain networks as assessed by EEG and fMRI at both rest and task. Fourth, to explore to what extent patterns of functional dysconnectivity overlap with stable and variable properties of brain networks, and apply these findings to the use of functional connectivity in diagnosis and monitoring of neuropathology of psychiatric and neurodegenerative diseases.

To realise the first three goals we plan to collect concurrent EEG and fMRI data at multiple sessions in a smaller homogenous sample of 30-40 young adults and a larger heterogenous sample of 60-70 participants. For the completion of the last goal, we will use a large sample (over 2400 participants) of previously collected MR data of individuals with different psychiatric diseases and their healthy controls, and a sample of over 1000 participants with different levels of neurodegenerative impairment, and their controls.

The project is planned to progress over three stages, each addressing specific set of research questions and combining development of preprocessing and analysis tools, data collection, and data analysis efforts. The proposed research team consists of experts in the relevant fields and has access to all the necessary resources for data collection and analysis, ensuring efficient and successful completion of the