Large-scale analysis of neuronal oscillations in the human brain during resting state

Functional connectivity studies allow the investigation of functional interactions that occur in the brain when perception, cognition and action are required. Nowadays these techniques are mainly applied on fMRI (functional Magnetic Resonance Imaging) and MEG (Magnetoencephalography) data to retrieve brain networks, represented as maps where neuronal activity is similar and correlated. However, both fMRI and MEG acquisition systems have some limitations that can be overcome using hdEEG (high-density Electroencephalography). In fact, EEG permits to measure direct neuronal activity while keeping both its time and frequency content. Previous works demonstrated that it is possible to identify resting state networks (RSNs) comparable to those obtained from fMRI and MEG using BLP (band limited power) metrics. In this project, we will reconstruct brain source activity from hdEEG resting state data and then apply a seed-based connectivity metric on the power envelopes of the source signals in order to investigate the functional interactions between the main regions of several RSNs at different frequencies. Using hdEEG resting-state data, we will first focus on the four nodes of the Default Mode Network, which is considered the hall mark of the resting brain. Then, we will extend the analysis to other five known RSNs and analyze the interactions taking place within and between the networks in each frequency band (delta, theta, alpha, beta and gamma) separately. Finally, we will compare the connectivity pattern of young individuals with the corresponding one retrieved from a group of older healthy adults. We aim at identifying frequency-specific age-related changes in the brain functional patterns. Moreover, we will investigate the brain-behavior relationship, in order to understand whether connectivity at rest, associated to specific cortical regions and frequencies, can be used as a predictor of the motor impairment occurring with aging.

Publication year:2021

Accessibility:Open