Introducing HiBoP: a Unity-based visualization software for large iEEG datasets.

BACKGROUND: Intracranial EEG data offer a unique spatio-temporal precision to investigate human brain functions. Large datasets have become recently accessible thanks to new iEEG data-sharing practices and tighter collaboration with clinicians. Yet, the complexity of such datasets poses new challenges, especially regarding the visualization and anatomical display of iEEG. NEW METHOD: We introduce HiBoP, a multi-modal visualization software specifically designed for large groups of patients and multiple experiments. Its main features include the dynamic display of iEEG responses induced by tasks/stimulations, the definition of Regions and electrodes Of Interest, and the shift between group-level and individual-level 3D anatomo-functional data. RESULTS: We provide a use-case with data from 36 patients to reveal the global cortical dynamics following tactile stimulation. We used HiBoP to visualize high-gamma responses [50-150 Hz], and define three major response components in primary somatosensory and premotor cortices and parietal operculum. COMPARISON WITH EXISTING METHODS(S): Several iEEG softwares are now publicly available with outstanding analysis features. Yet, most were developed in languages (Python/Matlab) chosen to facilitate the inclusion of new analysis by users, rather than the quality of the visualization. HiBoP represents a visualization tool developed with videogame standards (Unity/C#), and performs detailed anatomical analysis rapidly, across multiple conditions, patients, and modalities with an easy export toward third-party softwares. CONCLUSION: HiBoP provides a user-friendly environment that greatly facilitates the exploration of large iEEG datasets, and helps users decipher subtle structure/function relationships.

Human local field potentials in motor and non-motor brain areas encode upcoming movement direction.

Limb movement direction can be inferred from local field potentials in motor cortex during movement execution. Yet, it remains unclear to what extent intended hand movements can be predicted from brain activity recorded during movement planning. Here, we set out to probe the directional-tuning of oscillatory features during motor planning and execution, using a machine learning framework on multi-site local field potentials (LFPs) in humans. We recorded intracranial EEG data from implanted epilepsy patients as they performed a four-direction delayed center-out motor task. Fronto-parietal LFP low-frequency power predicted hand-movement direction during planning while execution was largely mediated by higher frequency power and low-frequency phase in motor areas. By contrast, Phase-Amplitude Coupling showed uniform modulations across directions. Finally, multivariate classification led to an increase in overall decoding accuracy (>80%). The novel insights revealed here extend our understanding of the role of neural oscillations in encoding motor plans.

Cross-frequency coupling in cortico-hippocampal networks supports the maintenance of sequential auditory information in short-term memory.

It has been suggested that cross-frequency coupling in cortico-hippocampal networks enables the maintenance of multiple visuo-spatial items in working memory. However, whether this mechanism acts as a global neural code for memory retention across sensory modalities remains to be demonstrated. Intracranial EEG data were recorded while drug-resistant patients with epilepsy performed a delayed matched-to-sample task with tone sequences. We manipulated task difficulty by varying the memory load and the duration of the silent retention period between the to-be-compared sequences. We show that the strength of theta-gamma phase amplitude coupling in the superior temporal sulcus, the inferior frontal gyrus, the inferior temporal gyrus, and the hippocampus (i) supports the short-term retention of auditory sequences; (ii) decodes correct and incorrect memory trials as revealed by machine learning analysis; and (iii) is positively correlated with individual short-term memory performance. Specifically, we show that successful task performance is associated with consistent phase coupling in these regions across participants, with gamma bursts restricted to specific theta phase ranges corresponding to higher levels of neural excitability. These findings highlight the role of cortico-hippocampal activity in auditory short-term memory and expand our knowledge about the role of cross-frequency coupling as a global biological mechanism for information processing, integration, and memory in the human brain.

The Reflective Mind of the Anxious in Action: Metacognitive Beliefs and Maladaptive Emotional Regulation Strategies Constrain Working Memory Efficiency.

The Attentional Control Theory (ACT) posits that, while trait anxiety may not directly impact performance, it can influence processing efficiency by prompting the use of compensatory mechanisms. The specific nature of these mechanisms, which might be reflective, is not detailed by the ACT. In a study involving 110 students (M = 20.12; SD = 2.10), surveys were administered to assess the students' metacognitive beliefs, trait anxiety, and emotion regulation strategies (ERSs). The participants engaged in two working memory exercises: the digit span task from the WAIS-IV and an emotional n-back task. The findings indicated that anxiety, metacognitive beliefs, and maladaptive ERSs did not affect task performance but were correlated with increased response times. Several regression analyses demonstrated that a lack of confidence in one's cognitive abilities and maladaptive ERSs predict higher reaction times (RT) in the n-back task. Additionally, maladaptive ERSs also predict an increased use of strategies in the digit span task. Finally, two mediation analyses revealed that anxiety increases processing efficiency, and this relation is mediated by the use of maladaptive ERSs. These results underscore the importance of the reflective level in mediating the effects of trait anxiety on efficiency. They highlight the necessity of incorporating metacognitive beliefs and maladaptive emotion regulation strategies for a thorough comprehension of the Attentional Control Theory. Recognizing these factors offers valuable perspectives for enhancing cognitive capabilities and fostering academic achievement.

Trait Anxiety, Emotion Regulation, and Metacognitive Beliefs: An Observational Study Incorporating Separate Network and Correlation Analyses to Examine Associations with Executive Functions and Academic Achievement.

Trait anxiety, emotion regulation strategies, and metacognitive beliefs influence executive functions (EFs) and academic achievement. This study examines their interplay and impact on academic success. In total, 275 adolescents (10-17 years) and parents completed an online questionnaire assessing trait anxiety, emotion regulation strategies, metacognition, parent-reported behaviors related to executive functioning, and overall school average. Preliminary analyses confirmed consistency with the existing literature for each variable and their interaction. Furthermore, we conducted a network analysis among the main variables. This analysis supports the need to pay more attention to reflective variables-maladaptive emotion regulation strategies and metacognitive beliefs about worry-when studying trait anxiety. These variables were linked to problematic executive functioning in adolescents, and the latter was negatively linked to academic achievement. This study offers innovative insights by investigating relationships less explored in the scientific literature. It reveals high and significant correlations between metacognitive beliefs, maladaptive emotion regulation strategies, and trait anxiety (r > 0.500, p < 0.001) but also between these variables and both executive functioning and academic achievement. These findings offer new perspectives for research and underscore the importance of holistically examining the psychological factors related to academic success.

Interest of the BLAST paradigm and salivary markers for the evaluation of sleepiness in drivers.

Objectives: Sleepiness is associated with decreased cognitive abilities and remains one of the main causes of fatal road accidents. The tools currently available to assess sleepiness, such as questionnaires, are subject to intra- and inter-individual variability, while multiple sleep latency tests are only feasible in few sleep laboratories. The main objective of this study was to explore new potential markers (neurocognitive, biological) to objectively assess sleepiness in drivers. Methods: A total of 186 drivers (median age 44 years, range 20-74 years, 73% men, 14% obese) were included during a break at a highway service area, in the morning, while on the road for vacation. Questionnaires on sleepiness and sleep characteristics (habitual and on the night before travel), the Bron-Lyon Attention Stability Test (BLAST), and two salivary samples (α-amylase and oxalate) were collected. Associations between measures of sleepiness [Epworth Sleepiness Scale (ESS), and Stanford Sleepiness Scale (SSS)], sleep characteristics, neurocognitive, and biological markers were tested using regression models adjusted for confounding factors. Results: The night before travel, 83% of the drivers reduced their sleep time and 30% slept 5 h or less. The higher the number of miles to be traveled, the higher the decrease, and the shorter the sleep time. The night before travel, 18 and 24% of the drivers complained of poor sleep quality and difficulty falling asleep. The sleep characteristics on the night before travel were associated with the habitual sleep characteristics. At the time of the test, 47% of the drivers scored pathologically on the SSS. Poor sleep quality and difficulty falling asleep the night before travel were associated with increased sleepiness as assessed by the SSS and decreased attentional ability as assessed by the BLAST. No association between salivary markers and acute sleepiness was observed. Conclusions: The sleep characteristics of the night before travel were associated with sleepiness and attentional performance. The SSS and the BLAST could be used by individual drivers in a self-evaluation context. Biological markers showed a high variability and limited association with sleep parameters across subjects, emphasizing the need for within-subject designs to assess their usefulness.

Advances in human intracranial electroencephalography research, guidelines and good practices.

Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.

Neural dynamics of mindfulness meditation and hypnosis explored with intracranial EEG: A feasibility study.

PURPOSE: Intracranial electroencephalography (iEEG) offers a unique window on brain dynamics with excellent temporal and spatial resolution and is less prone to recording artefacts than surface EEG. This study used a within-subject design to explore the feasibility to compare iEEG data during mind wandering, mindfulness meditation and hypnosis. RESULTS: Three patients who had iEEG for clinical monitoring and who were new to mindfulness meditation and hypnosis were able to enter these states. We found non-specific and wide-spread amplitude modulations. Data-driven connectivity analysis revealed widespread connectivity patterns that were common across the three conditions. These were predominant in the low frequencies (delta, theta and alpha) and characterised by positively correlated activity. Connectivity patterns that were unique to the three conditions predominated in the gamma band, one third of the correlations in these patterns were negative. CONCLUSIONS: This study is the first to support the feasibility of a direct comparison of the neural correlates of mindfulness meditation and hypnosis using iEEG. These modulations may reflect the complex interplay between different known brain networks, and warrant further functional investigations in particular in the gamma band.

The Dual-Task Cost Is Due to Neural Interferences Disrupting the Optimal Spatio-Temporal Dynamics of the Competing Tasks.

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.

Anatomical dissociation of intracerebral signals for reward and punishment prediction errors in humans.

Whether maximizing rewards and minimizing punishments rely on distinct brain systems remains debated, given inconsistent results coming from human neuroimaging and animal electrophysiology studies. Bridging the gap across techniques, we recorded intracerebral activity from twenty participants while they performed an instrumental learning task. We found that both reward and punishment prediction errors (PE), estimated from computational modeling of choice behavior, correlate positively with broadband gamma activity (BGA) in several brain regions. In all cases, BGA scaled positively with the outcome (reward or punishment versus nothing) and negatively with the expectation (predictability of reward or punishment). However, reward PE were better signaled in some regions (such as the ventromedial prefrontal and lateral orbitofrontal cortex), and punishment PE in other regions (such as the anterior insula and dorsolateral prefrontal cortex). These regions might therefore belong to brain systems that differentially contribute to the repetition of rewarded choices and the avoidance of punished choices.

Decoding the neural dynamics of free choice in humans.

How do we choose a particular action among equally valid alternatives? Nonhuman primate findings have shown that decision-making implicates modulations in unit firing rates and local field potentials (LFPs) across frontal and parietal cortices. Yet the electrophysiological brain mechanisms that underlie free choice in humans remain ill defined. Here, we address this question using rare intracerebral electroencephalography (EEG) recordings in surgical epilepsy patients performing a delayed oculomotor decision task. We find that the temporal dynamics of high-gamma (HG, 60-140 Hz) neural activity in distinct frontal and parietal brain areas robustly discriminate free choice from instructed saccade planning at the level of single trials. Classification analysis was applied to the LFP signals to isolate decision-related activity from sensory and motor planning processes. Compared with instructed saccades, free-choice trials exhibited delayed and longer-lasting HG activity during the delay period. The temporal dynamics of the decision-specific sustained HG activity indexed the unfolding of a deliberation process, rather than memory maintenance. Taken together, these findings provide the first direct electrophysiological evidence in humans for the role of sustained high-frequency neural activation in frontoparietal cortex in mediating the intrinsically driven process of freely choosing among competing behavioral alternatives.

Neural correlates of verbal working memory in children with epilepsy with centro-temporal spikes.

BACKGROUND: Previous functional magnetic resonance imaging (fMRI) studies have identified brain systems underlying different components of working memory (WM) in healthy subjects. The aim of this study was to compare the functional integrity of these neural networks in children with self-limited childhood epilepsy with centro-temporal spikes (ECTS) as compared to healthy controls, using a verbal working memory task (WMT). METHODS: Functional MRI of WM in seventeen 6-to-13 year-old children, diagnosed with ECTS, and 17 sex- and age-matched healthy controls were conducted at 3 T. To estimate BOLD responses during the maintenance of low, medium, and high WMT loads, we used a Sternberg verbal WMT. Neuropsychological testing prior to scanning and behavioral data during scanning were also acquired. RESULTS: Behavioral performances during WMT, in particular accuracy and response time, were poorer in children with ECTS than in controls. Increased WM load was associated with increased BOLD signal in all subjects, with significant clusters detected in frontal and parietal regions, predominantly in the left hemisphere. However, under the high load condition, patients showed reduced activation in the frontal, temporal and parietal regions as compared to controls. In brain regions where WM-triggered BOLD activation differed between groups, this activation correlated with neuropsychological performances in healthy controls but not in patients with ECTS, further suggesting WM network dysfunction in the latter. CONCLUSION: Children with ECTS differ from healthy controls in how they control WM processes during tasks with increasing difficulty level, notably for high WM load where patients demonstrate both reduced BOLD activation and behavioral performances.

Identifying task-relevant spectral signatures of perceptual categorization in the human cortex.

Human brain has developed mechanisms to efficiently decode sensory information according to perceptual categories of high prevalence in the environment, such as faces, symbols, objects. Neural activity produced within localized brain networks has been associated with the process that integrates both sensory bottom-up and cognitive top-down information processing. Yet, how specifically the different types and components of neural responses reflect the local networks' selectivity for categorical information processing is still unknown. In this work we train Random Forest classification models to decode eight perceptual categories from broad spectrum of human intracranial signals (4-150 Hz, 100 subjects) obtained during a visual perception task. We then analyze which of the spectral features the algorithm deemed relevant to the perceptual decoding and gain the insights into which parts of the recorded activity are actually characteristic of the visual categorization process in the human brain. We show that network selectivity for a single or multiple categories in sensory and non-sensory cortices is related to specific patterns of power increases and decreases in both low (4-50 Hz) and high (50-150 Hz) frequency bands. By focusing on task-relevant neural activity and separating it into dissociated anatomical and spectrotemporal groups we uncover spectral signatures that characterize neural mechanisms of visual category perception in human brain that have not yet been reported in the literature.

Relationship between direct cortical stimulation and induced high-frequency activity for language mapping during SEEG recording.

OBJECTIVE: The authors assessed the clinical relevance of preoperative task-induced high-frequency activity (HFA) for language mapping in patients with refractory epilepsy during stereoelectroencephalography recording. Although HFA evaluation was described as a putative biomarker of cognition, its clinical relevance for mapping language networks was assessed predominantly by studies using electrocorticography (ECOG). METHODS: Forty-two patients with epilepsy who underwent intracranial electrode implantation during both task-induced HFA and direct cortical stimulation (DCS) language mapping were evaluated. The spatial and functional relevance of each method in terms of specificity and sensitivity were evaluated. RESULTS: The results showed that the two methods were able to map classic language regions, and a large and bilateral language network was obtained with induced HFA. At a regional level, differences were observed between methods for parietal and temporal lobes: HFA recruited a larger number of cortical parietal sites, while DCS involved more cortical temporal sites. Importantly, the results showed that HFA predicts language interference induced by DCS with high specificity (92.4%; negative predictive value 95.9%) and very low sensitivity (8.9%; positive predictive value 4.8%). CONCLUSIONS: DCS language mapping appears to be more appropriate for an extensive temporal mapping than induced HFA mapping. Furthermore, induced HFA should be used as a complement to DCS to preselect the number of stimulated sites during DCS, by omitting those reported as HFA-. This may be a considerable advantage because it allows a reduction in the duration of the stimulation procedure. Several parameters to be used for each method are discussed and the results are interpreted in relation to previous results reported in ECOG studies.

Human Anterior Insula Encodes Performance Feedback and Relays Prediction Error to the Medial Prefrontal Cortex.

Adaptive behavior requires the comparison of outcome predictions with actual outcomes (e.g., performance feedback). This process of performance monitoring is computed by a distributed brain network comprising the medial prefrontal cortex (mPFC) and the anterior insular cortex (AIC). Despite being consistently co-activated during different tasks, the precise neuronal computations of each region and their interactions remain elusive. In order to assess the neural mechanism by which the AIC processes performance feedback, we recorded AIC electrophysiological activity in humans. We found that the AIC beta oscillations amplitude is modulated by the probability of performance feedback valence (positive or negative) given the context (task and condition difficulty). Furthermore, the valence of feedback was encoded by delta waves phase-modulating the power of beta oscillations. Finally, connectivity and causal analysis showed that beta oscillations relay feedback information signals to the mPFC. These results reveal that structured oscillatory activity in the anterior insula encodes performance feedback information, thus coordinating brain circuits related to reward-based learning.

The ConDialInt Model: Condensation, Dialogality, and Intentionality Dimensions of Inner Speech Within a Hierarchical Predictive Control Framework.

Inner speech has been shown to vary in form along several dimensions. Along condensation, condensed inner speech forms have been described, that are supposed to be deprived of acoustic, phonological and even syntactic qualities. Expanded forms, on the other extreme, display articulatory and auditory properties. Along dialogality, inner speech can be monologal, when we engage in internal soliloquy, or dialogal, when we recall past conversations or imagine future dialogs involving our own voice as well as that of others addressing us. Along intentionality, it can be intentional (when we deliberately rehearse material in short-term memory) or it can arise unintentionally (during mind wandering). We introduce the ConDialInt model, a neurocognitive predictive control model of inner speech that accounts for its varieties along these three dimensions. ConDialInt spells out the condensation dimension by including inhibitory control at the conceptualization, formulation or articulatory planning stage. It accounts for dialogality, by assuming internal model adaptations and by speculating on neural processes underlying perspective switching. It explains the differences between intentional and spontaneous varieties in terms of monitoring. We present an fMRI study in which we probed varieties of inner speech along dialogality and intentionality, to examine the validity of the neuroanatomical correlates posited in ConDialInt. Condensation was also informally tackled. Our data support the hypothesis that expanded inner speech recruits speech production processes down to articulatory planning, resulting in a predicted signal, the inner voice, with auditory qualities. Along dialogality, covertly using an avatar's voice resulted in the activation of right hemisphere homologs of the regions involved in internal own-voice soliloquy and in reduced cerebellar activation, consistent with internal model adaptation. Switching from first-person to third-person perspective resulted in activations in precuneus and parietal lobules. Along intentionality, compared with intentional inner speech, mind wandering with inner speech episodes was associated with greater bilateral inferior frontal activation and decreased activation in left temporal regions. This is consistent with the reported subjective evanescence and presumably reflects condensation processes. Our results provide neuroanatomical evidence compatible with predictive control and in favor of the assumptions made in the ConDialInt model.

BLAST: A short computerized test to measure the ability to stay on task. Normative behavioral data and detailed cortical dynamics.

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 «â€¯stay on task ¼, that is, to apply selectively and repeatedly task-relevant cognitive processes. The task (Bron/Lyon Attention Stability Test, or BLAST) lasts around 1 min, 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.

BLAST paradigm: A new test to assess brief attentional fluctuations in children with epilepsy, ADHD, and normally developing children.

BACKGROUND: Pure attentional deficits are still underdiagnosed in children with epilepsy. While attention-deficit hyperactivity disorder (ADHD) is historically the most studied cause of attentional disorders, an important number of children with epilepsy and attentional complaints do not fully meet the DSM-V (Diagnostic and Statistical Manual of Mental Disorders - Fifth Edition) criteria for ADHD and may be excluded from specific care. Clinical tools currently available are insufficient to detect more subtle but clinically relevant attentional fluctuations. OBJECTIVE/METHODS: The recently developed Bron-Lyon Attention Stability Test (BLAST) was used to evaluate brief attentional fluctuations with a high temporal precision. Drawing on two new attentional indices, we evaluated spontaneous fluctuations of response accuracy and timing, underlying attentional stability. The main objective was to assess attentional stability in children with i) epilepsy with comorbid ADHD, ii) epilepsy without comorbid ADHD, iii) ADHD not medicated and without epilepsy, and iv) normal development. Further objectives were to assess the main determinants of attentional stability in those groups, including the effect of factors related to the epileptic condition. RESULTS: In 122 children with epilepsy (67 with comorbid ADHD), 52 children with ADHD, and 53 healthy controls, we demonstrated lower attentional stability in both the groups with epilepsy and ADHD compared with healthy children. In children with epilepsy, BLAST scores were negatively associated with earlier seizure onset and AED (antiepileptic drug) polytherapy, while the seizure frequency, epilepsy duration, or type did not influence BLAST scores. CONCLUSIONS: This study demonstrates that attentional stability is impaired in children with epilepsy and/or ADHD. Bron-Lyon Attention Stability Test seems to be a sensitive test to detect attentional stability deficit in children with epilepsy and with attentional complaints who did not meet all criteria of ADHD. We propose that BLAST could be a useful clinical neuropsychological tool to assess attentional disorders in children.

Mind-wandering, or the allocation of attentional resources, is sleep-driven across childhood.

Mind-wandering or the spontaneous, uncontrolled changes in the allocation of attention resources (lapses) may cause variability in performance. In childhood, the relationship between the activation state of the brain, such as in attentional performance, and sleep has not been explored in detail. We investigated the role of sleep in attentional performance, and explored the most important parameters of their relationship. We objectively measured momentary lapses of attention of 522 children and correlated them with sleep schedules. In the subgroup of young children (age 7.1 ± 0.6 years; 60.8% girls), increasing age, long sleep duration and assessment closer to the previous night's sleep period was associated with impaired performance speed and consistency. From pre-adolescence (age 9.4 ± 0.8 years; 50.5% girls) onwards somno-typologies may develop. As a result, in adolescence (age 13.4 ± 1.2 years; 51.3% girls) not only sleep duration but also sleep midpoint and sleep regularity influence the individual speed and stability of attention. Across development, regularity of sleep, individual sleep midpoint and bedtime become increasingly important for optimal performance throughout the day. Attentional performance and sleep shared almost half of their variance, and performance was sleep-driven across childhood. Future studies should focus on intra- and inter-individual differences in sleep-wake behavior to improve performance or decrease mind-wandering in youth by targeting sleep habits.