Come to a two-day event on multi-modal neuroimaging! Lectures and hands-on on different neuroimaging types and their combination. Anyone from engineers to doctors are welcome to join!
Lectures and theory will be given in the morning of each day. You'll then be able to apply the learned concepts during the afternoon hands-on sessions. Data and code will be provided so that you can keep learning and using the tools afterwards!
The main idea is to bring together participants from different backgrounds to learn about latest techniques on multi-modal neuroimaging integration, not only to possibly make new scientific advances but also to expand the horizon and perspectives of anyone involved!
Introductory words by Patric Hagmann
Get to know each other
Social event (TBD)
Wrap up by Serge Vulliemoz
Please fill out the form from this link by August 1st 2021.Apply!
In this talk I will explain what is meant by «connectome» and how one can measure it with MRI.
The lecture will cover the fundamentals of the diffusion MRI signal such as its interpretation, physical meaning, and directional dependence. These concepts will be exploited to formulate the tractography problem, and various popular solutions to it will be presented.
In this lecture, we will review the neurophysiological principles of EEG recording, together with their applications in clinical practice and neuroscience research. Normal and pathological activities on scalp EEG in wakefulness and sleep, as well as common artefacts will be presented. Principles of intracranial EEG will be discussed.
The lecture will introduce you to the open problem of how to represent the time- and frequency-content carried by hundreds of dipoles with diverging orientation in each brain region of interest with one unique representative time-series. This will help you to understand how much ad-hoc assumptions and constraints can influence the accuracy of the results in manipulating brain signals.
Ref: Rubega, Maria, et al. "Estimating EEG source dipole orientation based on singular-value decomposition for connectivity analysis." Brain topography 32.4 (2019): 704-719.
The discovery of a stable, whole-brain functional connectivity organization that is largely independent of external events has drastically extended our view of human brain function. However, this discovery has been primarily based on functional magnetic resonance imaging (fMRI). The role of this whole-brain organization in fast oscillation-based connectivity as measured, for example, by electroencephalography (EEG) and magnetoencephalography (MEG) is only beginning to emerge. This lecture will put into context studies of intrinsic connectivity and its whole-brain organization in EEG, MEG, and intracranial electrophysiology with a particular focus on direct comparisons to connectome studies in fMRI. Irrespective of temporal scale over four orders of magnitude, intrinsic neurophysiological connectivity shows spatial similarity to the connectivity organization commonly observed in fMRI. A shared structural connectivity basis and cross-frequency coupling are possible mechanisms contributing to this similarity. Acknowledging that a stable whole-brain organization governs long-range coupling across all timescales of neural processing motivates researchers to take “baseline” intrinsic connectivity into account when investigating brain-behavior associations, and further encourages more widespread exploration of functional connectomics approaches beyond fMRI by using EEG and MEG modalities.
Ref:Intrinsic connectome organization across temporal scales: New insights from cross-modal approaches, Sepideh Sadaghiani and Jonathan Wirsich, Network Neuroscience 2020 4:1, 1-29
Perception, cognition and behavior critically depend on how multiple brain areas flexibly interact and form functional networks. In task situations, stimulus-evoked responses recorded with M/EEG reflect coordinated activity among multiple brain areas within 100 ms that show complex evolutions. This seminar introduces time- and frequency-resolved functional connectivity analyses of stimulus-evoked responses using multivariate autoregressive modeling. It will present the key concepts and challenges for deriving dynamic networks from ERP data at the brain’s native time scale, and will highlight recent findings obtained with fast dynamic network modeling of large-scale sensory and cognitive processes.
In the last decade, the emerging field of network neuroscience has opened a new frontier of research into the structural and functional organization of human brain networks. Despite the inherent link between the two, structural and functional connectivity have been mostly investigated separately or compared against each other, revealing a rather complex relationship. On the one hand, indeed, structural properties like the topology, length and myelination of axonal pathways provide a static backbone for neuronal communication. On the other hand, functional interactions are highly dynamic and exploit multiple configurations of structural links at the sub-second time scale of sensory, motor and cognitive processes. In the present lecture, we will discuss the concordant and discordant attributes of structural and functional brain networks and we will introduce a new algorithm for combining the two in the context of dynamic connectivity of event-related M/EEG signals. We will demonstrate how different structural properties can be incorporated as priors to inform time-varying directed connectivity analysis of M/EEG data in source space. This will help you to familiarize with advanced techniques for high-temporal resolution and multimodal connectivity analysis.
Ref: Pascucci, D., Rubega, M., & Plomp, G. (2020). Modeling time-varying brain networks with a self-tuning optimized Kalman filter. Plos Biology.
It is an intuitively understandable idea to view brain activity as a signal that spreads through a network of interconnected brain regions. Mathematically, such a network is also known as a graph, and apart from the brain, many things outside of neuroscience can be considered graphs: traffic networks, social networks, the irregular surfaces of 3-dimensional objects, and many more. Importantly, in this context, there is a distinction to be made between the signal - in the brain, this is neural activity - and the graph on top of which this signal plays out. This point of view makes it possible to apply tools from a well-established framework to neural data, i.e., graph signal processing (GSP). In this lecture, we will take a look at how GSP can be used to decompose, interpret and statistically analyze neural signals on a graph.
by Sebastien Tourbier
The tutorial will allow you to become more familiar with the Brain Imaging Data Structure and the BIDS Apps standards, keys to make your work more shareable, portable, inter-operable and reproducible. We will guide you in all the steps required for the creation of a BIDS dataset and the processing of the anatomical T1w image with a BIDS App to parcellate the brain using the Lausanne2018 multi-scale hierarchical scheme (Ref), that we will be used and extended with new derivatives during most of the curse of the week. Heudiconv will be used to create the BIDS dataset from DICOM files and Connectome Mapper 3 to compute the brain parcellations.
The tutorial will extend the BIDS dataset created in Tutorial 1 with strutural connectome derivatives. It will guide you in all the steps involved in the computation of structural connectivity matrices derived from diffusion MRI using the Connectome Mapper 3. In particular, this includes: pre-processing of the diffusion MRI, T1w/parcellation/diffusion space co-registration, diffusion signal modeling, tractography and creation of structural connectivity matrices for each parcellation scale.
The tutorial will allow you to have hands-on starting from raw EEG data to representative time-series in each brain region of interest. This will allow you to have a general overview about different inverse methods for EEG source reconstruction. Ref: Rubega, Maria, et al. "Estimating EEG source dipole orientation based on singular-value decomposition for connectivity analysis." Brain topography 32.4 (2019): 704-719
The combination of structural and functional connectivity can shed new light upon the operational principles of brain networks. In this tutorial session we will get some hands-on experience in multimodal integration from the structural and functional networks. Starting from the structural connectome and source-reconstructed activity signals that were obtained in the previous tutorials, the students will create a structurally constrained dynamic model of the brain networks. This will be done by introducing the information from tractography (diffusion MRI) into a state-of-the-art measure for dynamic functional connectivity (Pascucci, D., Rubega, M., & Plomp, G. (2020). Modeling time-varying brain networks with a self-tuning optimized Kalman filter. Plos Bio). This tutorial will require some Python programming skills and a good understanding of the theoretic principles that were introduced during the lectures.
In this tutorial we take one step further on the integration between EEG and dMRI by means of Connectome Spectral Analysis. The students will apply tools from graph signal processing to discover statistical functional and statistical properties of the brain electrical signal that are revealed by representing the signal in terms of structural connectivity modes.
Ref: Glomb, Katharina, et al. "Connectome spectral analysis to track EEG task dynamics on a subsecond scale." (2020) NeuroImage .
Ref: Queralt, Joan Rue, et al. "The connectome spectrum as a canonical basis for a sparse representation of fast brain activity." (2021) bioRxiv .
Computational brain network models have emerged as a powerful tool to investigate the dynamics of the human brain. This tutorial will introduce students to the basics of whole-brain computational modelling, with the aim of understanding its functionality and applicability. Specifically, we will introduce a whole-brain network model based on a very general neural mass model known as the normal form of a Hopf bifurcation. In the first part of the tutorial we will focus on understanding fundamental properties of the Hopf oscillator. In particular, we will investigate the effect of the bifurcation parameter in the local node dynamics (describing either noisy or oscillatory behaviour) and the role of the global coupling parameter in the emerging patterns of global connectivity. In the second part of the tutorial, we will learn how to use this model to gain insight into global brain dynamics. In particular, we will construct a whole-brain model using structural and functional neuroimaging data and we will then use this model to reveal fundamental network principles of large-scale brain activity observable by noninvasive neuroimaging.
Ref: Deco, G., Kringelbach, M. L., Jirsa, V. K., & Ritter, P. (2017). The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core. Scientific reports, 7(1), 1-14,
Saenger, V. M., Kahan, J., Foltynie, T., Friston, K., Aziz, T. Z., Green, A. L., ... & Mancini, L. (2017). Uncovering the underlying mechanisms and whole-brain dynamics of deep brain stimulation for Parkinson’s disease. Scientific reports, 7(1), 1-14,
Patric's expertise: MRI processing, brain connectivity
Serge's expertise: epilepsy, EEG source imaging and connectivity
Gijs' expertise: dynamic functional connectivity
Gustavo's expertise: Computational modelling in brain dynamics
Pieter's expertise: dynamic causal functional connectivity
Katharina's expertise: signal processing on connectome-based graph, combination of structural and functional connectivity
Maria's expertise: electrical source imaging
Marco's expertise: diffusion MRI modelling
David's expertise: dynamic causal functional connectivity, combination of structural and functional connectivity
Seb's expertise: medical image analysis, reproducible workflows, open-science, BIDS and BIDS App standards
Joan's expertise: combination of structural and functional connectivity
Jolan's expertise: computational modelling and dynamic causal functional connectivity
Ane's expertise: computational modelling of brain dynamics
Manel's expertise: computational modelling of brain dynamics
Jonatha's expertise: functional MRI and EEG analysis, brain connectivity
Nicolas' expertise: EEG and SEEG analysis, wavelet transform, epilepsy research
Isotta's expertise: EEG analyses, functional connectivity
We are here to help. Don't hesitate to ask us any question.