Neural activity underlying human brain produces electrical signals that we can measure and observe at different frequencies. Specific frequencies encode unique information about the cognitive mechanisms. Thus, classical investigations devoted to understanding brain functioning have focused the analysis on limited frequency bands in which the brain is supposed to work during the task under exam. However, this choice might not paint the whole picture, having a serious impact on the results, as far as it prevents to see brain activity as a whole. Moreover, many recent studies support the hypothesis that brain functioning is based on mechanisms of integration and segregation within and across different frequency domains. All these evidences require the development of a pipeline in which there is no need for aggregation or selection of information, but in which considering brain signals at different frequencies simultaneously. Our project addresses this issue by aiming to provide a novel framework for the analysis of the interaction between brain regions across frequencies. We will ground our framework on the latest advances in graph theory, building a multilayer network model in which each layer embodies the brain functional connectivity information carried by a single frequency band. Such multilayer network will be estimated from electroencephalographic signals collected from both healthy subjects and stroke patients during resting state and a tasks of motor imagination/execution. These networks will be then analyzed through indices based on node centrality and community detection. With this model we aim to provide a more complete and accurate description of brain functional connectivity to gain insights on brain functioning. Furthermore, we want to test if this description varies between healthy subjects and patients, as this difference could provide clinical relevant information.
This project aims to provide a new model and interpretation of the brain functioning at different frequencies. So far most of the works on brain have been conducted collapsing all the frequency information or only considering one band supposed to be the most relevant in the specific context. However, the choice of the frequency band in which to focus the analysis could have a great impact on the representation of the brain functioning, and recent studies have shown how conventionally excluded frequency bands can actually yield to additional insights on it. Basically, all the information carried by specific frequencies can be thought as pieces of a puzzle, which is brain functioning and which make sense to see only as an entire. The main objective of this project then is to try to find all relationship among all the pieces to reconstruct the puzzle, namely find important relations among structures, cognition and health of the human brain, in order to gain a more thorough knowledge of its organizational principles.
This objective will be pursued exploiting the recent advances in the complex network theory and using datasets based on EEG recordings. More than all the neuroimaging instruments used before, EEG will guarantee both signals with a wide range of frequencies to analyze and portability, that will allow the analysis on several subjects. The latest progress in the graph theory will furnish a representation of the brain connectivity through a frequency-based multilayer network. Compared to previous study, a complete analysis (based on centrality measures and community detection) will be made on this enriched representation, and the results will be useful to test the hypothesis that the functional layers (i.e. frequencies) don¿t act independently, but according to mechanisms of segregation and integration typical of brain activity.
Furthermore, the obtained results might be exploited in practical application where the choice of frequency bands plays a crucial role. For example, this could be the case of Brain Computer Interface (BCI) applications, in which the features selection is made at one specific frequency band.
The methodology that we aim to propose will also, hopefully, lay the groundwork for further analysis in which the connectivity between brain regions will be considered at different frequencies. For instance, future studies might take advantage of the proposed pipeline to examine, through complex networks theory, the interplay between the information underlying specific frequency band in various brain states or in subject with different pathologies.