Dual-tasking is extremely prominent nowadays, despite ample evidence that it comes with a performance cost: the Dual-Task (DT) cost. Neuroimaging studies have established that tasks are more likely to interfere if they rely on common brain regions, but the precise neural origin of the DT cost has proven elusive so far, mostly because fMRI does not record neural activity directly and cannot reveal the key effect of timing, and how the spatio-temporal neural dynamics of the tasks coincide. Recently, DT electrophysiological studies in monkeys have recorded neural populations shared by the two tasks with millisecond precision to provide a much finer understanding of the origin of the DT cost. We used a similar approach in humans, with intracranial EEG, to assess the neural origin of the DT cost in a particularly challenging naturalistic paradigm which required accurate motor responses to frequent visual stimuli (task T1) and the retrieval of information from long-term memory (task T2), as when answering passengers’ questions while driving. We found that T2 elicited neuroelectric interferences in the gamma-band (>40 Hz), in key regions of the T1 network including the Multiple Demand Network. They reproduced the effect of disruptive electrocortical stimulations to create a situation of dynamical incompatibility, which might explain the DT cost. Yet, participants were able to flexibly adapt their strategy to minimize interference, and most surprisingly, reduce the reliance of T1 on key regions of the executive control network-the anterior insula and the dorsal anterior cingulate cortex-with no performance decrement.

Confluent recent evidence indicates that the spectral slope of 1 /f neurophysiological recordings is correspondent to cortical excitation/inhibition (E/I) ratio. In this framework, a steeper power spectrum (i.e., one with a larger spectral exponent β ) is indicative of stronger inhibitory tone and thus lower E/I ratio, and vice versa. While the tools commonly utilized for estimating β are mostly consistent, there appears to be a lack of standardization among data processing protocols for slope analysis. In this work our goal is to draw attention to a fundamental consequence of this issue, namely that even in a confined, comparative research environment, applying different pre-processing steps to electroencephalography (EEG) data can lead to conflicting conclusions in terms of the E/I ratio. To this end, we analyzed resting-state EEG recordings in two independent datasets, containing data collected with eyes open (EO) and eyes closed (EC), with the latter considered as a physiological state with stronger inhibitory tone. Our analyses confirmed consistently in both cohorts that applying different spatial filtering schemes in an otherwise identical analytical pipeline indicated a decrease in E/I ratio over the prefrontal cortex in one case, but not the other when transitioning from EO to EC. In contrast, this same pattern was apparent over the occipital cortex regardless of the pre-processing scheme. This empirical evidence calls for the development of a standardized data processing protocol for EEG-based analyses of the E/I ratio. Highlights: Two EEG spatial filtering schemes - CAR and SL - are compared regarding E/I ratio CAR and SL are shown to substantially affect aperiodic spectral slope estimates CAR and SL yield opposite conclusions about frontal E/I ratio on the same data SL reverses the common β _lo < β _hi bimodal pattern over frontal cortex Spatial filtering effects are confirmed on two independent datasets

This article provides an exhaustive description of a new short computerized test to assess on a second-to-second basis the ability of individuals to stay on task, that is, to apply selectively and repeatedly task-relevant cognitive processes. The task (Bron/Lyon Attention Stability Test, or BLAST) lasts around one minute, and measures repeatedly the time to find a target letter in a two-by-two letter array, with an update of all letters every new trial across thirty trials. Several innovative psychometric measures of attention stability are proposed based on the instantaneous fluctuations of reaction times throughout the task, and normative data stratified over a wide range of age are provided by a large (>6000) dataset of participants aged 8 to 70. We also detail the large-scale brain dynamics supporting the task from an in-depth study of 32 participants with direct electrophysiological cortical recordings (intracranial EEG) to prove that BLAST involves critically large-scale executive attention networks, with a marked activation of the dorsal attention network and a deactivation of the default-mode network. Accordingly, we show that BLAST performance correlates with scores established by ADHD-questionnaires.

Motor recovery after stroke, spinal cord injury, or traumatic brain injury reflects relearning rather than simple restitution, as surviving circuits retain plastic potential that can be re-engaged through temporally precise stimulation. This review synthesizes convergent findings demonstrating that Hebbian and spike-timing-dependent mechanisms govern reorganization across cortical, striatal, and spinal levels. Leveraging these timing rules to shape excitability during receptive network states enables durable changes in connectivity and behavior. This effect depends on temporal precision, physiological state, and reinforcement—not stimulus intensity alone—within plasticity windows regulated by metaplastic mechanisms that determine whether Hebbian processes are expressed. Together, these principles define a translational framework for neurorehabilitation, emphasizing biomarker-guided, adaptive, and scalable strategies aligned with intrinsic rules of experience-dependent reorganization.

Mécanismes neuronaux sous-jacents à la distraction externe par des stimuli environnementaux inattendus ou par une tâche secondaire : une investigation EEG intracrânienne: Dans nos vies quotidiennes, l'efficacité et la facilité avec laquelle nous agissons dépendent de manière cruciale de notre capacité à nous concentrer sur ce que nous sommes en train de faire. D'un point de vue neuropsychologique, cet état particulier exige que notre cerveau engage à chaque instant les ressources cognitives strictement nécessaires et suffisantes pour la tâche à accomplir, ce qui se manifeste d'un point de vue neuronal par un équilibre subtil entre un ensemble d'activations et d'inhibitions pour garantir que seuls les processus perceptifs, cognitifs et moteurs pertinents pour la tâche soient actifs, à l'exclusion de tout autre. Dans la pratique, pourtant, ces conditions ne sont quasiment jamais réunies, car nous sommes presque toujours distraits d'une façon ou d'une autre, que ce soit par des événements de notre environnement immédiats ou par des pensées spontanées ou bien encore par des tentatives infructueuses de réaliser plusieurs tâches en même temps. Cette thèse cherche à dévoiler les raisons profondes pour lesquelles la distraction a un effet aussi néfaste sur l'attention et sur la performance. Pourquoi est-il si difficile de se concentrer dans un environnement bruyant ? Pourquoi la performance baisse-t-elle dès que nous essayons d'accomplir en même temps deux tâches difficiles qui demandent de l'attention ? Ces questions font l'objet de vifs débats depuis des années, et les mécanismes sous-jacents ont déjà été modélisés de nombreuses manières, mais jusqu'à présent, aucune étude n'avait encore mené l'enquête au niveau d'étude de la dynamique neuronale le plus fin qu'on puisse envisager pratiquement chez un être humain, combinant la précision spatiale de l'IRM fonctionnelle et la précision temporelle de l'électroencéphalographie. Nous avons utilisé des enregistrements EEG intracrâniens - avec une résolution millimétrique toutes les millisecondes - pour examiner dans deux expériences comment les distractions externes et le multitâche interfèrent avec la dynamique optimale d'une tâche d'attention continue et exigeante, et nous avons abordé ces questions dans le contexte naturaliste d'une interaction sociale directe pour garantir que nos conclusions puissent être étendues à des situations communes de la vie quotidienne. Nous avons constaté que l'équilibre très fin entre les activations et les inhibitions neuronales locales nécessaires à la concentration est rompu pendant de courtes périodes - de l'ordre de la seconde - dans les régions clés pour l'attention et le contrôle exécutif, suffisamment pour déséquilibrer l'activité de réseau et nuire à la performance. Ces données apportent un éclairage nouveau pour interpréter bon nombre des défaillances de l'attention que nous rencontrons tous dans nos vies modernes et ouvrent maintenant la voie à de nouvelles techniques pour tenter d'y remédier

Dual-tasking is extremely prominent nowadays, despite ample evidence that it comes with a performance cost: the Dual-Task (DT) cost. Neuroimaging studies have established that tasks are more likely to interfere if they rely on common brain regions, but the precise neural origin of the DT cost has proven elusive so far, mostly because fMRI does not record neural activity directly and cannot reveal the key effect of timing, and how the spatio-temporal neural dynamics of the tasks coincide. Recently, DT electrophysiological studies in monkeys have recorded neural populations shared by the two tasks with millisecond precision to provide a much finer understanding of the origin of the DT cost. We used a similar approach in humans, with intracranial EEG, to assess the neural origin of the DT cost in a particularly challenging naturalistic paradigm which required accurate motor responses to frequent visual stimuli (task T1) and the retrieval of information from long-term memory (task T2), as when answering passengers’ questions while driving. We found that T2 elicited neuroelectric interferences in the gamma-band (>40 Hz), in key regions of the T1 network including the Multiple Demand Network. They reproduced the effect of disruptive electrocortical stimulations to create a situation of dynamical incompatibility, which might explain the DT cost. Yet, participants were able to flexibly adapt their strategy to minimize interference, and most surprisingly, reduce the reliance of T1 on key regions of the executive control network – the anterior insula and the dorsal anterior cingulate cortex – with no performance decrement. HIGHLIGHTS - First direct evidence in humans of neural interferences between two tasks. - First explanation of the Dual-Task cost at the neural level in humans. - First Dual-Tasking study with intracranial EEG in naturalistic conditions.