Exploiting brain critical dynamics to inform Brain-Computer Interfaces performance

International audienceThe reconfiguration of large-scale interactions among multiple brain regions is characterized by aperiodic perturbations, called “Neuronal Avalanches”, which can be tracked non-invasively as they expand across the brain. As such, learning a new task might affect the path of propagation of neuronal avalanches. Brain-Computer Interfaces (BCIs) constitute a promising tool for establishing direct communication and control from the brain over external effectors but mastering them remains a poorly-understood learned skill. Therefore, neuronal avalanche measures may constitute natural candidates to inform the underlying brain processes and their reflection on brain signals. To test this hypothesis, we used source-reconstructed magneto/electroencephalography, comparing resting-state to motor imagery conditions during a BCI protocol. For each experimental condition, we computed an individual avalanche transition matrix, to track the probability that an avalanche would spread across any two regions. We found a robust topography of the edges that were affected by the execution of the task, which mainly hinge upon the premotor regions. Finally, we related the individual differences to the task performance, showing that significant correlations are predominantly positive and involve edges connecting pre/motor regions to parietal ones. Our findings suggest that avalanches capture functionally-relevant processes crucial for alternative BCI designing

Exploiting brain critical dynamics to inform Brain-Computer Interfaces performance

International audienc

Exploiting brain critical dynamics to inform Brain-Computer Interfaces

International audienceBrain-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 functionalconnectivity 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

Neuronal avalanches differentiate resting-state and task conditions in Brain-Computer Interfaces

International audienc

Neuronal avalanches as alternative features for motor imagery-based brain-computer interface

International audienceBrain-Computer Interface (BCI) translates brain activity into commands for control and communication. However, the high inter-subject variability limits its efficiency. Most BCI studies use local measurements without considering the interconnected nature of brain functioning. As a result, mastering non-invasive BCI systems remains a learned skill that yields suboptimal performance in ~30% of users, referred to as the “BCI inefficiency” phenomenon [1]. In this study, we are therefore considering how communication between brain areas impacts BCI performance by studying neuronal avalanches that can be described as a non-linear dynamic biomarker where neurons fire together in a cascade-like pattern following an inverse power law [2]. To test our hypothesis, we are creating a BCI pipeline with neuronal avalanches as features and comparing this with state-of-the-art methods

Neuronal avalanches as alternative features for motor imagery-based brain-computer interface

International audienceBrain-Computer Interface (BCI) translates brain activity into commands for control and communication. However, the high inter-subject variability limits its efficiency. Most BCI studies use local measurements without considering the interconnected nature of brain functioning. As a result, mastering non-invasive BCI systems remains a learned skill that yields suboptimal performance in ~30% of users, referred to as the “BCI inefficiency” phenomenon [1]. In this study, we are therefore considering how communication between brain areas impacts BCI performance by studying neuronal avalanches that can be described as a non-linear dynamic biomarker where neurons fire together in a cascade-like pattern following an inverse power law [2]. To test our hypothesis, we are creating a BCI pipeline with neuronal avalanches as features and comparing this with state-of-the-art methods

Brain fingerprint is based on the aperiodic, scale-free, neuronal activity

Subject differentiation bears the possibility to individualize brain analyses. However, the nature of the processes generating subject-specific features remains unknown. Most of the current literature uses techniques that assume stationarity (e.g., Pearson's correlation), which might fail to capture the non-linear nature of brain activity. We hypothesize that non-linear perturbations (defined as neuronal avalanches in the context of critical dynamics) spread across the brain and carry subject-specific information, contributing the most to differentiability. To test this hypothesis, we compute the avalanche transition matrix (ATM) from source-reconstructed magnetoencephalographic data, as to characterize subject-specific fast dynamics. We perform differentiability analysis based on the ATMs, and compare the performance to that obtained using Pearson's correlation (which assumes stationarity). We demonstrate that selecting the moments and places where neuronal avalanches spread improves differentiation (P < 0.0001, permutation testing), despite the fact that most of the data (i.e., the linear part) are discarded. Our results show that the non-linear part of the brain signals carries most of the subject-specific information, thereby clarifying the nature of the processes that underlie individual differentiation. Borrowing from statistical mechanics, we provide a principled way to link emergent large-scale personalized activations to non-observable, microscopic processes