Brain Modes & Graph Signal Processing to investigate brain structure-function coupling

The relation between functional activity and the underlying structure in the brain has revealed to be complex and has gained increasing attention. In this context, graph signal processing (GSP) represents a novel framework allowing to link dynamic functional activity signals, e.g., from functional magnetic resonance imaging (fMRI), but also other modalities such as electro- and magneto-encephalography (EEG, MEG), with the brain structural architecture in a non-trivial way. Structural brain modes are extracted from a structural connectivity graph using Laplacian eigendecomposition, and functional activity patterns are represented as graph signals, defined on top of the graph. The Graph Fourier Transform allows then to decompose functional signals into structural bases and graph spectral filtering can be used to distinguish portions of functional activity that are more or less smooth on the graph; i.e., coupled or decoupled from brain structure, respectively. The application of this framework to healthy and pathological brain data has revealed key features of brain structure-function coupling, such as: (i) a cortical distribution along a gradient opposing lower to higher level cognitive regions; (ii) its individual specificity; (iii) its changes in resting-state and different tasks; (iv) a dynamic behavior; (v) the capability of highlighting abnormal brain features in diverse clinical contexts.
In this talk, I will give a detailed outline of the methodological framework for brain GSP, guiding the audience through the main conceptual bases on which GSP is founded; i.e.: (i) Graph and Graph signal definition, in the context of brain imaging; (ii) Graph Laplacian eigendecomposition to obtain structural brain modes or harmonics, constituting the graph spectral domain; (iii) projection of the functional signals into the graph spectral domain through the application of the Graph Fourier Transform; (iv) filtering of the functional signals in the spectral domain, e.g., with high/low pass filters allowing to reconstruct portions of the signals including only a partial number of harmonics; (v) extrapolation of node-wise and edge-wise structure-function coupling metrics; i.e., structural decoupling index and coupled / decoupled functional connectivity.
A thorough review of the existent literature on the topic will be included, where limitations and future challenges of the GSP framework will be highlighted and discussed.
A practical demonstration of the brain GSP pipeline by use of NiGSP, open source Python-based toolbox that we developed for this purpose. A real dataset of functional and diffusion MRI will be used to demonstrate applied examples within the toolbox.