Conference Agenda

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.

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).

References

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

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

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

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.

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.

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

Introduction: Neurexan®, a medicinal product sold over the counter (OTC), contains passionflower, oat, coffee and zinc valerianate. Neurexan® has been investigated in patients with symptoms related to acute stress, nervousness and insomnia. Amygdala is involved in the development of fear and emotional behavior. Acute stress sensitizes the amygdala, thereby increasing vigilance/anxiety, which in turn promotes the stress response. Amygdala reactivity to negative stimuli is a reliable phenotype that closely associates with stress regulation and can be assessed with Hariri paradigm. Furthermore, a linkage between an increased level of stress hormones and increased emotional response to angry faces has been shown in patients with social phobia. Previous investigation suggested an attenuated neuroendocrine stress response in healthy volunteers induced by Neurexan®. Thus, our aim was to further explore the effect of Neurexan® on the emotional brain response in the amygdala.

Methods: In a randomized, placebo-controlled, double-blind, two-period crossover trial brain response to the Hariri task, an emotional paradigm, of 39 healthy, moderate stressed males was measured after intake of a single dose of Neurexan® and placebo control via 3 Tesla fMRI. Data were preprocessed and analyzed in SPM12.

Results&Conclusion: Hariri task was firstly validated for negative emotional faces response. Significant (peak level FWE corrected) bilateral activations of fusiform gyri, amygdalae and prefrontal cortex as well as unilateral activation in right thalamus were confirmed as previously reported. Paired t-test showed a significant reduction of BOLD response to negative faces in the left amygdala (p<0.05) during the Neurexan® session as compared to placebo.