Conference Agenda

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Session Overview
Free communications 4: Multimodal imaging and Neurology
Friday, 01/Sep/2017:
2:30pm - 4:00pm

Session Chair: Christoph Michel
Location: Room A-003
Uni-S Schanzeneckstrasse 1 3012 Bern

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2:30pm - 2:45pm

BCG artefact removal in simultaneous EEG-fMRI: an Adaptive Optimal Basis Set method

Marco Marino1,2,3, Quanying Liu1,3, Vlastimil Koudelka4, Jaroslav Hlinka4,5, Nicole Wenderoth1,3, Dante Mantini1,2,3

1Neural Control of Movement Laboratory, ETH Zurich, Zurich, Switzerland; 2Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom; 3Laboratory of Movement Control and Neuroplasticity, KU Leuven, Leuven, Belgium; 4National Institute of Mental Health, Klecany, Czech Republic; 5Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic

Introduction: EEG signals recorded during simultaneous fMRI are contaminated by strong artifacts, among which the ballistocardiographic (BCG), induced by subject’s cardiac activity, is the most challenging to be removed due to its complex non-stationary nature.

The presence of BCG residuals in EEG data may hide true, or generate spurious, correlations between EEG and fMRI time-courses. In this study, we propose an adaptive optimal basis set (AOBS) method for BCG removal, which uses artifact spatio-temporal features to firmly reduce BCG residuals.

Methods: Each EEG signal was epoched based on BCG rather than ECG events, to ensure more effective artifact characterization by Principal Component Analysis (PCA). Furthermore, the artifactual components to be removed were automatically identified from the data, based on signal features.

AOBS method performance was evaluated in terms of BCG removal and brain signal preservation, with respect to Average Artifact Subtraction (AAS), Independent Component Analysis (ICA) and Optimal Basis Set (OBS), using high-density EEG data acquired during simultaneous fMRI in 6 subjects.

Results: As compared to alternative methods, the application of AOBS led to a remarkable BCG artifact attenuation. Specifically, it yielded a percentage of BCG residuals equal to 7.51%, versus 19.16%, 13.81% and 13.21%, for AAS, ICA and OBS, respectively.

Conclusions: AOBS method enables reliable and effective reduction of BCG residuals. It is easy to use and does not require parameter tuning. Thus, it may find wide application in the field of simultaneous EEG-fMRI, especially for applications in which no data epoching and averaging is possible, e.g. resting-state studies.

2:45pm - 3:00pm

NeuroPycon: A Python-based package for advanced MEG, EEG and fMRI connectivity analyses

David Meunier1, Annalisa Pascarella2, Daphné Bertrand-Dubois3, Jordan Alves1, Fanny Barlaam1, Arthur Dehgan3, Tarek Lajnef3,4, Etienne Combrisson1,3,5, Dmitrii Altukhov6, Karim Jerbi3

1Lyon Neuroscience Research Center, INSERM U1028, CNRS UMR 5292, University Claude-Bernard Lyon 1, France; 2Institute for Applied Mathematics Mauro Picone, National Research Council, Roma, Italy; 3Psychology Department, University of Montreal, Quebec, Canada; 4Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Canada; 5Centre de Recherche et d’Innovation sur le Sport, Villeurbanne, University Lyon 1, France; 6Moscow State University of Psychology and Education, MEG Center, Moscow, Russia

NeuroPycon is an open-source multi-modal brain data analysis kit which provides Python-based pipelines for advanced multi-thread processing of fMRI, MEG and EEG data, with a focus on connectivity and graph analyses [1].

NeuroPycon is based on NiPype framework [2] which facilitates data analyses by wrapping many commonly-used neuroimaging software into a common python framework. Therefore, a major strength of NeuroPycon is that it relies on (and interfaces with) several freely available Python packages developed for efficient and fast parallel processing and that it seamlessly connects with existing open-science neuroimaging and signal processing toolboxes.

The flexible design allows users to configure analysis pipelines defined by connecting different nodes, where each node may be a user-defined function or a well-established tool or python-wrapped module (e.g. MNE-python for MEG analysis [3], etc.).

The current implementation of NeuroPycon contains three different packages:

- ephypype includes pipelines for electrophysiology analysis; current implementations allow for MEG/EEG data import, data pre-processing and cleaning by an automatic removal of eyes and heart related artefacts, sensor or source-level connectivity analyses

- graphpype allows to study functional connectivity exploiting graph-theoretical metrics including also modular partitions

- clipype is a command line interface for ephypype package.

NeuroPycon will shortly be available for download via github (installation via Docker) and is currently being documented. Future developments include fusion of multi-modal data (ex. MEG and fMRI or iEEG and fMRI).


1. Bullmore, Sporns (2009), Nat Rev Neurosci

2. Gorgolewski et al. (2011) Front. Neuroinform

3. Gramfort et al. (2013), Front. Neurosci

3:00pm - 3:15pm

The choice of the optimal model order in the estimation of EEG epileptic connectivity

Margherita Carboni1,2, Maria Rubega2, Pieter Van Mierlo1,3, Christoph M. Michel2, Serge Vulliemoz1

1EEG and Epilepsy Unit, University Hospital of Geneva, Geneva, Switzerland; 2Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland; 3Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University – iMinds Ghent, Belgium

Background: Epilepsy is a widespread brain networks disorder with a high risk of recurrent unprovoked epileptic seizures associated with impaired awareness or convulsions. The main feature in pathological epileptic EEG is the presence of spikes. Brain connectivity is an important tool to explore this pathological network aspects, but there are many open issues on the methods consistency. The choice of the parameters in the connectivity estimation, e.g., the selection of the p-order of the multivariate autoregressive model, is fundamental to avoid meaningless results such as spurious connections. In epileptic EEG during spikes, both Akaike Information Criterion (AIC) and Bayesian Information Criterion are often not effective in the determination of the optimal p order, i.e., AIC increased monotonically with increasing model order.

Methods: In the pre-operative EEG of 9 patients we applied the same analysis to the sources time-courses and their time-reversal versions in order to detect false-positive connections. In particular, we computed Partial Directed Coherence (PDC) in the source space varying p in the range [2-20] for both dipoles and reverse-dipoles. In the ideal case with infinitive signal-to-noise-ratio, the connectivity matrix computed from the reverse-dipoles should be the transpose of the original connectivity one. Therefore, we defined as optimal p-order the one which minimizes the absolute difference between the two connectivity matrices.

Results: The optimal p-order varied in the range [8-19] for each different type of spike-time series.

Conclusion: Future development will be to validate our methodology using concordance of connectivity with surgical resection in post-operatively seizure free patients.

3:15pm - 3:30pm

Neural Dynamics based on EEG and diffusion MRI: Potential in studying stroke

Pablo Maceira-Elvira1, Olena G. Filatova1, Yuan Yang1, Yusuke Takeda2, Julius P.A. Dewald3, Gert Kwakkel4, Okito Yamashita2, Frans C.T. Van der Helm1

1Delft University of Technology, Delft, The Netherlands; 2ATR Neural Information Analysis Laboratories, Kyoto, Japan; 3Northwestern University, Chicago, United States of America; 4VU University Medical Center, Amsterdam, Netherlands

After stroke, functional recovery may be promoted through rehabilitation. In such cases, a remapping of affected limbs to other regions of the cortex is often observed. High spatial resolution neuroimaging techniques, like magnetic resonance imaging (MRI), can be used to investigate the anatomical changes in the brain, but their low temporal resolution provides less insight of dynamic changes of brain function. In contrast, electroencephalography (EEG) has an excellent temporal resolution to measure such transient events, hindered in turn by its low spatial resolution. This study introduces a multimodal brain imaging technique to improve the spatial resolution of EEG to study stroke. The limitations of EEG are complemented by constraints derived from anatomical MRI and diffusion weighted imaging (DWI). EEG data was acquired from patients (N = 3) and healthy controls (N = 2) while electrical stimuli were delivered sequentially at index finger in left and right hand, respectively. A reasonably accurate estimation for active sources and inter-source connectivity was achieved in this study. Results indicate the changes of information flow in the brain after stroke, although the interpretation of these results in terms of neuroplasticity relearning is yet to be performed. This study provides evidence of this method being useful to track the information flow in the brain and may lead to a precise prognostic model of stroke.

3:30pm - 3:45pm

Neuroprostheses based on intracortical recordings of neural activity for restoration of movement and communication of people with paralysis

Tomislav Milekovic, Christoph Michel

University of Geneva, Switzerland

Paralysis has a severe impact on a patient’s quality of life and entails a high emotional burden and life-long social and financial costs. Restoring movement and independence for the paralyzed remains a challenging clinical problem, currently with no viable solution. Recent demonstrations of intracortical brain-computer interfaces, neuroprosthetic devices that create a link between a person and a computer based on a person’s brain activity, have brought hope for their potential to restore movement and communication. While the intracortical brain-computer interfaces have steadily improved over the last decade, our recent success in linking brain activity with the newly developed techniques for spinal cord stimulation look to revolutionize locomotor rehabilitation. Specifically, our brain-spine interface restored weight-bearing locomotion of the paralyzed leg as early as six days post-injury in macaques1. New approaches in identifying neural features and designing decoding algorithms, which transform neural signals into computer commands, aim to deliver both stable and accurate control over clinically relevant periods of several months. To this end, we developed signal processing and decoder calibration approaches that enabled a person with long-stable tetraplegia to control a communication brain-computer interface for 138 days with an unchanged decoder2. Preliminary clinical studies suggest that these concepts and technologies are directly translatable to therapeutic strategies for people with paralysis.

1. M. Capogrosso*, T. Milekovic*, D. Borton*, et al., A brain–spine interface alleviating gait deficits after spinal cord injury in primates. Nature 539, 284-288 (2016).

2. T. Milekovic, et al., Stable asynchronous BCIs based on field potentials for communication, BCCN Conference

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