The Sinergia Summer School 2020 will take place in Lausanne at the University of Lausanne (UNIL) over five days, from September Monday 7th to Friday 11th 2020.

Download the program!
Introductory words by Patric Hagmann
Coffee break
Coffee break
Coffee break
Coffee break
Coffee break
Coffee break
Coffee break
Coffee break
Coffee break
Coffee break
Lunch with blitz presentations of participants
Lunch brainstorm
Lunch brainstorm
Lunch brainstorm
Lunch brainstorm
Coffee break
Coffee break
Coffee break
Coffee break
Coffee break
Brain Recovery and Refuel Time
Brain Recovery and Refuel Time
Brain Recovery and Refuel Time
Brain Recovery and Refuel Time
Brain Recovery and Refuel Time
Apero with blitz presentations of participants
Farewell Party
Farewell Party


Application details (limited to 30 young researchers, selection)...

Please fill out the form from this link by June 20th 2020.



Lecture 1.1 Overview by Patric Hagmann and Serge Vuillemoz


Lecture 1.2 Reproducible neuroscience by Sebastien Tourbier

This lecture will introduce you three topics, which are in the heart for establishing and efficiently using common resources: a common standard for brain imaging data organization, version control systems (for code and data), and software containers. This will help you to become more efficient in your day-to-day neuroimaging computing research tasks as you will learn about the keys to make your work more portable, inter-operable and reproducible.

Lecture 1.3 Brain parcellation by Yasser Aleman-Gomez and Katharina Glomb


Lecture 2.1 The Connectome by Patric Hagmann


Lecture 2.2 Diffusion and Tractography by Marco Pizzolato and J.-P. Thiran

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.

Lecture 2.3 Brain Networks Analysis by Patric Hagmann and Katharina Glomb


Lecture 3.1 EEG Neuroimaging: Advanced Applications and Challenges by Serge Vuillemoz and David Pascucci


Lecture 3.2 EEG Preprocessing by Pieter van Mierlo and Margherita Carboni

In this tutorial we will perform a live EEG recording and show were to pay attention to when setting up the EEG system. We will show the most common artefacts in the EEG and how to avoid and remove them from the data. This will include filtering (what type of filters are best to use), data decomposition (independent component analysis to remove eye blinks and cardiac artefacts), baseline correction, event related potential generation and spectral analysis.

Lecture 3.3 From Sensor Space to Regions Of Interest by Maria Rubega and Margherita Carboni

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.

Lecture 4.1 Static Functional Connectivity in EEG and fMRI by Jonathan Wirsich

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

Lecture 4.2 Dynamic Functional Connectivity by Gijs Plomp

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.

Lecture 4.3 Combining Structural and Functional Connectivity by David Pascucci and Maria Rubega

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. (2019). Modeling time-varying brain networks with a self-tuning optimized Kalman filter. bioRxiv, 856179.

Lecture 5.1 Connectome-based Graph Signal Processing in EEG by Katharina Glomb


Lecture 5.2 Computational Modelling 1 by Gustavo Deco


Lecture 5.3 Computational Modelling 2 by Gustavo Deco



Tutorial 1 Making data BIDS-compliant and parcellating the brain with a BIDS App by Sébastien Tourbier


Tutorial 2 Computing structural connectivity matrices from diffusion MRI with a BIDS App by Sébastien Tourbier


Tutorial 3 Computing EEG time-series in brain regions by Margherita Carboni

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

Tutorial 4 Combining structural and functional connectivity by Jolan Heyse and Joan Rué Queralt

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. (2019). Modeling time-varying brain networks with a self-tuning optimized Kalman filter. bioRxiv). This tutorial will require some Python programming skills and a good understanding of the theoretic principles that were introduced during the lectures.

Tutorial 5 Computational modelling by Elhum Shamshiri, Manel Vila-Vidal and Ane Lopez

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,


Here is the team, members of the Sinergia consortium on brain communication pathways. The project consists in exploring brain communication pathways by combining diffusion based quantitative structural connectivity and EEG source imaging with application to physiological and epileptic networks. See project website for more details.

Patric Hagmann

Patric's expertise: MRI processing, brain connectivity

Serge Vuillemoz

Serge's expertise: epilepsy

Gijs Plomp

Gijs' expertise: dynamic functional connectivity

Jean-Philippe Thiran

Jean-Philippe's expertise: diffusion signal processing

Gustavo Deco

Gustavo's expertise: Computational modelling in brain dynamics

Pieter van Mierlo

Pieter's expertise: dynamic causal functional connectivity

Katharina Glomb

Katharina's expertise: signal processing on connectome-based graph, combination of structural and functional connectivity

Maria Rubega

Maria's expertise: electrical source imaging

Elhum Shamshiri

Elhum's expertise: functional connectivity in epilepsy

Marco's expertise: diffusion MRI modelling

David Pascucci

David's expertise: dynamic causal functional connectivity, combination of structural and functional connectivity

Sébastien Tourbier

Seb's expertise: medical image analysis, BIDS and BIDS App standards, open and reproducible neuroscience

Margherita Carboni

Margherita's expertise: electrical source imaging applied to epilepsy

Joan Rué Queralt

Joan's expertise: combination of structural and functional connectivity

Jolan Heyse

Jolan's expertise: computational modelling and dynamic causal functional connectivity

Ane Lopez

Ane's expertise: computational modelling of brain dynamics

Manel Vila-Vidal

Manel's expertise: computational modelling of brain dynamics

Yasser Aleman-Gomez

Yasser's expertise: structural and diffusion MRI analysis, brain parcellation

Jonathan Wirsich

Jonatha's expertise: functional MRI and EEG analysis, brain connectivity


We are here to help. Don't hesitate to ask us any question.

Joan Rué Queralt

Connectomics Lab, University Hospital of Lausanne (CHUV), Switzerland

Ulrike Toepel

University of Lausanne, Switzerland

Sebastien Tourbier

Connectomics Lab, University Hospital of Lausanne (CHUV), Switzerland INCF/ReproNim Training Fellow 2019-2020

Contact & Venue


University of Lausanne
Biophore Building
UNIL Campus Map


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