Tensorpac: An open-source Python toolbox for tensor-based phase-amplitude coupling measurement in electrophysiological brain signals

Despite being the focus of a thriving field of research, the biological mechanisms that underlie information integration in the brain are not yet fully understood. A theory that has gained a lot of traction in recent years suggests that multi-scale integration is regulated by a hierarchy of mutually interacting neural oscillations. In particular, there is accumulating evidence that phase-amplitude coupling (PAC), a specific form of cross-frequency interaction, plays a key role in numerous cognitive processes. Current research in the field is not only hampered by the absence of a gold standard for PAC analysis, but also by the computational costs of running exhaustive computations on large and high-dimensional electrophysiological brain signals. In addition, various signal properties and analyses parameters can lead to spurious PAC. Here, we present Tensorpac, an open-source Python toolbox dedicated to PAC analysis of neurophysiological data. The advantages of Tensorpac include (1) higher computational efficiency thanks to software design that combines tensor computations and parallel computing, (2) the implementation of all most widely used PAC methods in one package, (3) the statistical analysis of PAC measures, and (4) extended PAC visualization capabilities. Tensorpac is distributed under a BSD-3-Clause license and can be launched on any operating system (Linux, OSX and Windows). It can be installed directly via pip or downloaded from Github (https://github.com/EtienneCmb/tensorpac). By making Tensorpac available, we aim to enhance the reproducibility and quality of PAC research, and provide open tools that will accelerate future method development in neuroscience

Good scientific practice in MEEG Research: Progress and Perspectives

Good Scientific Practice (GSP) refers to both explicit and implicit rules, recommendations, and guidelines that help scientists to produce work that is of the highest quality at any given time, and to efficiently share that work with the community for further scrutiny or utilization.For experimental research using magneto- and electroencephalography (MEEG), GSP includes specific standards and guidelines for technical competence, which are periodically updated and adapted to new findings. However, GSP also needs to be periodically revisited in a broader light. At the LiveMEEG 2020 conference, a reflection on GSP was fostered that included explicitly documented guidelines and technical advances, but also emphasized intangible GSP: a general awareness of personal, organizational, and societal realities and how they can influence MEEG research.This article provides an extensive report on most of the LiveMEEG contributions and new literature, with the additional aim to synthesize ongoing cultural changes in GSP. It first covers GSP with respect to cognitive biases and logical fallacies, pre-registration as a tool to avoid those and other early pitfalls, and a number of resources to enable collaborative and reproducible research as a general approach to minimize misconceptions. Second, it covers GSP with respect to data acquisition, analysis, reporting, and sharing, including new tools and frameworks to support collaborative work. Finally, GSP is considered in light of ethical implications of MEEG research and the resulting responsibility that scientists have to engage with societal challenges.Considering among other things the benefits of peer review and open access at all stages, the need to coordinate larger international projects, the complexity of MEEG subject matter, and today's prioritization of fairness, privacy, and the environment, we find that current GSP tends to favor collective and cooperative work, for both scientific and for societal reasons

Decoding motor intentions and movement execution : investigating the role of cerebral oscillations using machine learning and development of open-source tools

L'exécution d'un simple mouvement est associée à des modulations complexes de l'activité oscillatoire du cerveau. Toutefois, notre compréhension du rôle spécifique des composantes de phase, d'amplitude ou de couplage phase-amplitude (PAC) durant la préparation et l'exécution motrice est encore partielle. La première partie de cette thèse traite de cette question en analysant des données d'EEG intracrânien obtenues chez des sujets épileptiques effectuant une tâche center out différée. Les outils d'apprentissage machine ont permis d'identifier des marqueurs neuronaux propres aux états moteur ou aux directions de mouvement. En plus du rôle déjà bien connu de la puissance spectrale, cette approche dictée par les données (data-driven) a identifié une implication importante de la composante de phase basse fréquence ainsi que du PAC dans les processus neuronaux de la préparation et de l'exécution motrice. En plus de cet apport empirique, une importante partie de ce travail de thèse a consisté à implémenter des outils d'analyse et de visualisation de données électrophysiologiques. Plusieurs utilitaires ont été conçus spécifiquement : une toolbox dédiée à l'extraction et à la classification de marqueurs neuronaux (Brainpipe), des outils de calcul de PAC modulaire basé sur des tenseurs (Tensorpac) ainsi qu'un ensemble d'interfaces graphiques dédiées à la visualisation de données cérébrales (Visbrain). Ces recherches auront permis de mieux comprendre le rôle des oscillations neuronales lors de comportements dirigés et met également à disposition un ensemble d'outils efficaces et libres permettant à la communauté scientifique de répliquer et d'étendre ces recherchesThe execution of a motor task is associated with complex patterns of oscillatory modulations in the brain. However, the specific role of oscillatory phase, amplitude and phase-amplitude coupling (PAC) across the planning and execution stages of goal-directed motor behavior is still not yet fully understood. The aim of the first part of this PhD thesis was to address this question by analyzing intracranial EEG data recorded in epilepsy patients during the performance of a delayed center-out task. Using machine learning, we identified functionally relevant oscillatory features via their accuracy in predicting motor states and movement directions. In addition to the established role of oscillatory power, our data-driven approach revealed the prominent role of low-frequency phase as well as significant involvement of PAC in the neuronal underpinnings of motor planning and execution. In parallel to this empirical research, an important portion of this PhD work was dedicated to the development of efficient tools to analyze and visualize electrophysiological brain data. These packages include a feature extraction and classification toolbox (Brainpipe), modular and tensor-based PAC computation tools (Tensorpac) and a versatile brain data visualization GUI (Visbrain). Taken together, this body of research advances our understanding of the role of brain oscillations in goal-directed behavior, and provides efficient open-source packages for the scientific community to replicate and extend this researc

Décodage des intentions et des exécutions motrices : étude du rôle des oscillations cérébrales via l’apprentissage machine et développement d’outils open-source

The execution of a motor task is associated with complex patterns of oscillatory modulations in the brain. However, the specific role of oscillatory phase, amplitude and phase-amplitude coupling (PAC) across the planning and execution stages of goal-directed motor behavior is still not yet fully understood. The aim of the first part of this PhD thesis was to address this question by analyzing intracranial EEG data recorded in epilepsy patients during the performance of a delayed center-out task. Using machine learning, we identified functionally relevant oscillatory features via their accuracy in predicting motor states and movement directions. In addition to the established role of oscillatory power, our data-driven approach revealed the prominent role of low-frequency phase as well as significant involvement of PAC in the neuronal underpinnings of motor planning and execution. In parallel to this empirical research, an important portion of this PhD work was dedicated to the development of efficient tools to analyze and visualize electrophysiological brain data. These packages include a feature extraction and classification toolbox (Brainpipe), modular and tensor-based PAC computation tools (Tensorpac) and a versatile brain data visualization GUI (Visbrain). Taken together, this body of research advances our understanding of the role of brain oscillations in goal-directed behavior, and provides efficient open-source packages for the scientific community to replicate and extend this researchL'exécution d'un simple mouvement est associée à des modulations complexes de l'activité oscillatoire du cerveau. Toutefois, notre compréhension du rôle spécifique des composantes de phase, d'amplitude ou de couplage phase-amplitude (PAC) durant la préparation et l'exécution motrice est encore partielle. La première partie de cette thèse traite de cette question en analysant des données d'EEG intracrânien obtenues chez des sujets épileptiques effectuant une tâche center out différée. Les outils d'apprentissage machine ont permis d'identifier des marqueurs neuronaux propres aux états moteur ou aux directions de mouvement. En plus du rôle déjà bien connu de la puissance spectrale, cette approche dictée par les données (data-driven) a identifié une implication importante de la composante de phase basse fréquence ainsi que du PAC dans les processus neuronaux de la préparation et de l'exécution motrice. En plus de cet apport empirique, une importante partie de ce travail de thèse a consisté à implémenter des outils d'analyse et de visualisation de données électrophysiologiques. Plusieurs utilitaires ont été conçus spécifiquement : une toolbox dédiée à l'extraction et à la classification de marqueurs neuronaux (Brainpipe), des outils de calcul de PAC modulaire basé sur des tenseurs (Tensorpac) ainsi qu'un ensemble d'interfaces graphiques dédiées à la visualisation de données cérébrales (Visbrain). Ces recherches auront permis de mieux comprendre le rôle des oscillations neuronales lors de comportements dirigés et met également à disposition un ensemble d'outils efficaces et libres permettant à la communauté scientifique de répliquer et d'étendre ces recherche

Sleep: An Open-Source Python Software for Visualization, Analysis, and Staging of Sleep Data

International audienc

Beta Oscillations in Monkey Striatum Encode Reward Prediction Error Signals

International audienceReward prediction error (RPE) signals are crucial for reinforcement learning and decision-making as they quantify the mismatch between predicted and obtained rewards. RPE signals are encoded in the neural activity of multiple brain areas, such as midbrain dopaminergic neurons, prefrontal cortex, and striatum. However, it remains unclear how these signals are expressed through anatomically and functionally distinct subregions of the striatum. In the current study, we examined to which extent RPE signals are represented across different striatal regions. To do so, we recorded local field potentials (LFPs) in sensorimotor, associative, and limbic striatal territories of two male rhesus monkeys performing a free-choice probabilistic learning task. The trial-by-trial evolution of RPE during task performance was estimated using a reinforcement learning model fitted on monkeys' choice behavior. Overall, we found that changes in beta band oscillations (15–35 Hz), after the outcome of the animal's choice, are consistent with RPE encoding. Moreover, we provide evidence that the signals related to RPE are more strongly represented in the ventral (limbic) than dorsal (sensorimotor and associative) part of the striatum. To conclude, our results suggest a relationship between striatal beta oscillations and the evaluation of outcomes based on RPE signals and highlight a major contribution of the ventral striatum to the updating of learning processes

Subthalamic low-frequency oscillations predict vulnerability to cocaine addiction

International audienceIdentifying vulnerable individuals before they transition to a compulsive pattern of drug seeking and taking is a key challenge in addiction to develop efficient prevention strategies. Oscillatory activity within the subthalamic nucleus (STN) has been associated with compulsive-related disorders. To study compulsive cocaine-seeking behavior, a core component of drug addiction, we have used a rat model in which cocaine seeking despite a foot-shock contingency only emerges in some vulnerable individuals having escalated their cocaine intake. We show that abnormal oscillatory activity within the alpha/theta and low-beta bands during the escalation of cocaine intake phase predicts the subsequent emergence of compulsive-like seeking behavior. In fact, mimicking STN pathological activity in noncompulsive rats during cocaine escalation turns them into compulsive ones. We also find that 30 Hz, but not 130 Hz, STN deep brain stimulation (DBS) reduces pathological cocaine seeking in compulsive individuals. Our results identify an early electrical signature of future compulsive-like cocaine-seeking behavior and further advocates the use of frequency-dependent STN DBS for the treatment of addiction

Neural shape mediation analysis

Abstract Neural signal shapes convey significant information about their generating processes. In this study, we introduce a data-driven methodology to identify sensory and behaviourally-relevant traces within neural responses. We present a phenomenological model that characterises temporal variations in intracranial EEG using eight interpretable parameters: peak time, peak intensity, initial and final baselines, accumulation and depletion period, and their respective concavities. This model effectively captures subtle signal variations, especially in sensory decision-making tasks. By decomposing the signals in this manner, we then conduct a comprehensive brain mediation analysis on iEEG data’s shape, pinpointing regions that mediate behavioural processes. Importantly, we can determine which signal dynamics specifically reflect underlying behavioural processes, enhancing the depth of analysis and critique of their role in behaviour. Preliminary applications on a cohort of epileptic patients reveal that our model explains over a third of the signal variance at the trial level across all brain regions. We identified four key regions—encompassing sensory, associative, frontal, and premotor areas—that mediate the impact of task difficulty on reaction time. Notably, in these regions, it was the depletion period, rather than signal amplitude, that correlated with behavioural speed. This study highlights the potential of our approach in providing detailed insights into the neural mechanisms linking stimuli to behaviour

Neural interactions in the human frontal cortex dissociate reward and punishment learning

How human prefrontal and insular regions interact while maximizing rewards and minimizing punishments is unknown. Capitalizing on human intracranial recordings, we demonstrate that the functional specificity toward reward or punishment learning is better disentangled by interactions compared to local representations. Prefrontal and insular cortices display non-selective neural populations to reward and punishment. The non-selective responses, however, give rise to context-specific interareal interactions. We identify a reward subsystem with redundant interactions between the orbitofrontal and ventromedial prefrontal cortices, with a driving role of the latter. In addition, we find a punishment subsystem with redundant interactions between the insular and dorsolateral cortices, with a driving role of the insula. Finally, switching between reward and punishment learning is mediated by synergistic interactions between the two subsystems. These results provide a unifying explanation of distributed cortical representations and interactions supporting reward and punishment learning