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Tutorial 1: Brain Imaging Data Structure
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BIDS is a community-driven standard to organize your existing raw data, with well-accepted file formats, and consistent and complete documentation to facilitate re-use by your future self and others; BIDS is not a new file format or a search engine or a data sharing tool.
It exists a large ecosystem of tools around BIDS to support you in the tasks of dataset creation, validation, manipulation, and analysis and supported by great tutorials.
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Tutorial 2: Anatomical and Diffusion MRI Pipelines
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A BIDS App is a neuroimaging pipeline encapsulated in container image that handles BIDS datasets and adopts a specific core set of commandline arguments.
Connectome Mapper 3 provides a unique solution in the BIDS ecosystem to estimate from raw anatomical and diffusion MRI, brain parcellation at multiple scales and the corresponding structural connectivity matrices.
Inspection of the quality of the outputs of the different processing stages is still an important task to be conducted before any further analysis of the outputs of such complex pipelines.
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Tutorial 3: "ESI: Scalp to sources"
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The functional signal (EEG) needs to be artefact-free for ESI to be accurate
The source points are distributed in the grey matter, which needs to be coregistered with the MRI used to calculate the headmodel
The solution of inverse problem is ill-posed so no solution is optimal
Source timecourses need to be summarised in ROI-timecourses
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Tutorial 4: Dynamic Functional Connectivity
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Dynamic Functional Connectivity takes into account that EEG is a nonstationary signal (i.e., signal statistical characteristics change with time).
The proposed algorithms (STOK and siSTOK) to estimate Dynamic Functional Connectivity are powerful tools to integrate to effective connectivity brain structural information as a prior.
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Tutorial 5: Connectome Spectral Analysis
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Tutorial 6: Computational modelling
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Whole-brain modelling study complex non-linear systems, such as the brain, in order to investigate the interplay between known dynamical and structural features.
The model based on Hopf oscillators has been successfully applied to simulate and explain the mechanism underlying the network non-linear dynamics occurring at the ultra-slow scale of resting-state BOLD signals.
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