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¹ Sorbonne Université, Institut du Cerveau – Paris Brain Institute, ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
² Institut de Neurosciences des Systèmes, Marseille, France
³ University of Sassari, Department of Biomedical Sciences, Viale San Pietro, 07100, Sassari, Italy

⁴ Monash University, Melbourne, Victoria, Australia
⁵ Université Paris-Saclay, Saclay, France
⁶ University of Pennsylvania, Philadelphia, USA

Brain-Computer Interfaces (BCIs) constitute a promising tool for communication and control. However, mastering non-invasive BCI systems remains a learned skill difficult to develop for a non-negligible proportion of users. Even though similarities have been shown between MI-based BCI learning and motor sequence learning our understanding of the dynamical processes, and their reflection on brain signals during BCI performance is still incomplete. In particular, whole-brain functional imaging is dominated by a 'bursty' dynamics, "neuronal avalanches", with fast, fat-tailed distributed, aperiodic perturbations spreading across the whole brain. Neuronal avalanches evolve over a manifold during resting-state, generating a rich functional connectivity dynamics. In this work, we evaluated to which extent neuronal avalanches can be used as a tool to differentiate mental states in the context of BCI experiments.

## BCI experiment

The BCI task consisted of a standard 1D, two-target box task in which the subjects modulated their **a** and/or **β** activity. To hit the target-up, the subjects performed a sustained motor imagery of their right-hand grasping and to hit the target-down, they remained at rest. Twenty subjects (aged 27.45 ± 4.01

years, 12 men), all right-handed participated in the study.

Magnetoencephalography (MEG) and electroencephalography (EEG) signals were simultaneously recorded. M/EEG data were preprocessed using the Independent Component Analysis method, followed by the source reconstruction on the epoched data [1].

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## Neuronal avalanches computation

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The signal was z-scored and thresholded, and set to 1 when above threshold, and to zero otherwise (threshold = 131). An avalanche was defined as starting when at least one region is above threshold, and as finishing when no region is active. For each avalanche, we have estimated a transition matrix (TM) containing the probability that regions j would be active at time t+1, when region i was active at time t [2].

For each subject, we obtained an average transition matrix for the baseline condition (Rest), and an average transition matrix for the motor imagery task (MI).

We set out to test if neuronal avalanches would be suitable to track subject-specific changes induced by a task in the large-scale brain dynamics. Our approach might capture part of the processes that were typically overlooked in a more oscillatory perspective. Our work paves the way to use aperiodic activities to improve classification performance and tailor BCI training programs.

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pierpaolo.sorrentino@univ-amu.fr

marie-constance.corsi@inria.fr

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@PierpaSorre

@MConstanceCorsi

## Neuronal avalanches to inform mental states

After identifying at the individual level the edges and the nodes, that show a significant condition effect, we looked at the concordance of such differences across subjects. This way, we spotted the edges were engaged differently by perturbations in the two conditions in most subjects. The reliably different edges cluster on premotor regions bilaterally.

Then, we have summed the differences corresponding to all the edges which are incident upon each reliably different region obtaining an overall difference per each such node. In particular, the caudal middle frontal gyri bilaterally are the main regions upon which edges which differ between conditions cluster. These regions are involved in planning of motor actions, imagining of actions, as well as in executive attention [3] and in the selection between competing visual targets [4].

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To assess to which extent neuronal avalanches can predict BCI scores, we focused on the differences in the transition probabilities in the edges which are incident upon the "task-related" regions. For each of these edges, we correlated the difference in probability in each subject with the performance in the MI task, as measured using the BCI score. Our findings suggest that perturbations spread more often between premotor/motor areas and parietal areas when the subject is engaged in the motor-imagery task as compared to resting-state condition.

## Neuronal avalanches to perform classification

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To explore the performance of neuronal avalanches in the decoding of the tasks, we compared the ATMs to the common spatial patterns (CSPs) approach. Both were classified with a Support Vector Machine (SVM).

At the group level, the classification performance was greater for ATM+SVM than CSP+SVM. We observed greater inter-subject variability in the case of CSP+SVM (std = 0.15) than with ATM+SVM (std = 0.10). At the individual level, ATM+SVM yielded better classification accuracy than CSP+SVM for 12 subjects. In four subjects, CSPs yielded better accuracy than ATMs. In 5 subjects, there was not any statistically significant difference between the two approaches.

## References

[1] Corsi, et al (2020). Functional disconnection of associative cortical areas predicts performance during BCI training. *NeuroImage*

[2] Sorrentino, et al (2021). The structural connectome constrains fast brain dynamics. *Elife*

[3] Andersson, et al (2009). Correlations between measures of executive attention and cortical thickness of left posterior middle frontal gyrus-A dichotic listening study. *Behavioral and Brain Functions*

[4] Germann, et al (2020). Area 8A within the Posterior Middle Frontal Gyrus Underlies Cognitive Selection between Competing Visual Targets. *ENeuro*

## Acknowledgements

The authors acknowledge support from European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 864729), from the program "Investissements d'avenir" ANR-10-IAIHU-06, from European Union's Horizon 2020 research and innovation programme under grant agreement No. 945539 (SGA3) Human Brain Project, and from VirtualBrainCloud No.826421.

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Paris Brain Institute

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Inserm
La science pour la santé
Pratique, science to health

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SORBONNE
UNIVERSITÉ
CRÉateurs de futurs
DEPUIS 1287

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AixMarseille
université

Institut de
Neurosciences des
Systèmes