Theories of human neurobehavioral development suggest executive functions mature from childhood through adolescence, underlying adolescent risk-taking and the emergence of psychopathology. Investigations with relatively small datasets or narrow subsets of measures have identified general executive function development, but the specific maturational timing and independence of potential executive function subcomponents remain unknown. Integrating four independent datasets  with twenty-three measures from seventeen tasks, we provide a precise charting, multi-assessment investigation, and replication of executive function development from adolescence to adulthood. Across assessments and datasets, executive functions follow a canonical non-linear trajectory, with rapid and statistically significant development in late childhood to mid-adolescence (10–15 years old), before stabilizing to adult-levels in late adolescence (18–20 years old). Age effects are well captured by domain-general processes that generate reproducible developmental templates across assessments and datasets. Results provide a canonical trajectory of executive function maturation that demarcates the boundaries of adolescence and can be integrated into future studies. Goal-directed cognition (executive function) is thought to develop through adolescence. Here, the authors find evidence across multiple datasets and measures that executive function develops until 18–20 years old. Among maturational processes, theories from neuroscience and psychology have placed a particular emphasis on the development of goal-directed cognitive abilities  that are hypothesized to index a common process of executive function or cognitive control6. In parallel to socioemotional development and environmental influences, a protracted maturation8 and/or stabilization of executive function9 into adulthood has been suggested to contribute to lifespan peaks in risk-taking behaviors  and increased vulnerability to psychiatric disorders13 during adolescence. Ongoing executive function changes during adolescence have been used in colloquial, legal14, and scientific (see4 for review) contexts to differentiate adolescents from adults and clarify adolescence as a period of continued development.Adolescence is a unique period of the lifespan, initiated by puberty and characterized by the maturation of cognitive, affective, and social processes that culminate in a transition to independence and adulthood16) independent investigations using a broad range of tasks or in relatively large studies (N’s ~ 1000)18 using very narrow assessments of executive function. No large-scale, multi-assessment, multi-dataset reproducibility investigations of adolescent executive function development have been performed. Further, common analytic approaches do not quantitatively define maturational timing and/or plateaus toward adult-levels of performance. The magnitude of executive function changes during adolescence, the precise timing of when adolescents reach adult-levels, and the potential diversity of processes assessed by varying executive function tasks, thus remains widely debated.Adolescent executive function development has been studied in relatively small (N’s ~20024. Age-related increases in correct response rates (accuracy) and decreases in the speed of responses  have been observed for a broad range of laboratory-based and neuropsychological executive function tasks  during adolescence (see refs. 25 for reviews). Theoretical models built from these observations and related observations in animal studies26, as well as broader27 and historical perspectives of psychological development28, have led to hypotheses suggesting that cognitive development continues through adolescence and may reach maturity in the second decade of life  or later  in humans. A range of methodological, analytic, and data availability challenges, however, have thus far prevented direct and comprehensive testing of the maturational timing of adolescent executive function development and the specific age when executive functions reach adult-levels. Nevertheless, understanding not just whether behaviors are changing with age, but also their shape and form, is fundamental to developmental science32 and corresponding health policies and intervention/prevention strategies for adolescents. Defining the shape and form of cognitive development likewise has key implications for research on mechanisms of ongoing  plasticity and factors influencing the opening and closing of the adolescent period3.Empirical research suggests that while adolescents can perform complex, goal-directed behaviors that rely on executive functions, their performance is not as accurate or as fast as adults33, and potential variability among the many tasks designed to assess executive function34. The first of these challenges is beginning to be addressed through larger study designs  and data aggregation techniques, as increasing dataset sizes and the inclusion of multiple datasets can better overcome sampling variability39 to estimate generalizable normative developmental trajectories. Addressing the latter challenges, however, requires conceptual and methodological advancements.There are unique challenges to defining the normative maturational timing of adolescent executive function development that arises from multiple sources, including inter-individual differences among participants and across datasets, difficulties in designing analytic frameworks that directly assess maturational timing33. While essential to establish that age-related differences in executive function generally occur during adolescence, such fixed-developmental, parametric comparisons prevent the systematic investigation of the relative rate and timing of maturation that is essential for precise developmental science. Such approaches likewise have prevented resolution of foundational theories of adolescent neurobehavioral development, where distinct linear and non-linear shapes have been proposed4. Therefore, while prominent theories suggest adolescents may reach adult-levels of executive function between 20- and 25 years old, such plateaus in developmental change have not been investigated in most empirical research and are not testable within commonly used analytic frameworks. This lack of resolution on the maturational timing of adolescent executive function also poses challenges for related lifespan research, where a potentially distinct developmental concept of emerging adulthood (~18–25 years old40) has likewise been justified, in part, by potential ongoing cognitive changes. New methods  that can quantitatively define the potentially non-linear developmental trajectory of executive function during adolescence, as well as multiple large publicly available datasets, now allow for precise estimation of the maturational timing of executive function.Initial investigations in adolescent research have often relied on a fixed-developmental shape  or categorical comparisons  to identify age-related differences42). Fewer studies have therefore investigated the developmental similarity among potential subprocesses indexed by the dozens of laboratory-based and neuropsychological executive function measures used in the broader literature. While there is a growing use of standardized tasks , systematic comparisons across these instruments are similarly limited. Many neurodevelopmental and psychological theories9 emphasize a broad unitary process of executive function development, suggesting the maturation of performance on any one of these tasks may generalize to broader executive function development. However, alternative perspectives have also been proposed. Prior work in adults , for example, has suggested that executive function tasks support a unity/diversity framework, where commonality and correlation are observed amongst all executive function measures (unity), but individual aspects of executive function maintain a degree of separability (diversity). Owing to the focus on individual functions and tasks or narrow subsets in most adolescent research, it nevertheless remains unclear whether adolescent executive function development is driven by multiple, independent processes (diversity) and/or the maturation of a more common domain-general process (unity).A further challenge to defining the maturational timing of executive function development arises from the potential variability among the many tasks designed to assess executive function. Empirical studies have often focused on an individual, or a relatively narrow subset, of tasks  that includes 23 executive function measures from 17 distinct tasks. In addition to large-scale replication, we directly address prior challenges in defining the maturational timing and domain-generality of adolescent executive function development with multiple large independent cohorts , non-linear modeling approaches that directly define maturational timing, and the inclusion of a broad executive function battery that permit the investigation of both potential unitary and diversity processes. Taken together, this work identifies a canonical non-linear developmental trajectory of executive function maturation that generalizes across datasets and assessments, with rapid age-related change from late childhood to early adolescence (10–15 years old), small but significant changes in mid-adolescence (15–18 years old), before stabilizing to adult-levels in late adolescence (18–20 years old). The similarity in developmental trajectories is well accounted for by domain-general processes consistent with theories of unitary executive function and fluid cognition. The insights and data developed here can inform neuroscientific and psychological theories of the adolescent period and guide future translational research in health and disease.Here we aggregate four large-scale, independent datasets to construct a comprehensive set of executive function data spanning the entire adolescent period as well as the relative transitional periods of late childhood and early adulthood  and data collected as part of the National Consortium on Alcohol and Neurodevelopment , as well as two large cross-sectional studies, including data collected as part of the Nathan Kline Institute-Rockland Sample , and data from the Philadelphia Neurodevelopmental Cohort . Studies relied on community-based samples from across the United States (see Methods) that were balanced for biological sex at birth and in the aggregate, were consistent with national patterns of race and ethnicity  that include tasks designed to measure executive function  and a neuropsychological executive function battery . Response types included button presses, eye movements, and experimenter-administered neuropsychological performance . For most tasks, both latency (speed of responses) and accuracy  measures were available .Participants ranging from 8–35 years old  for longitudinal data; general additive models (GAM) for cross-sectional data: see Methods). Unlike the fixed-developmental shape approaches that are typically used in adolescent research, this allowed us to estimate flexible, data-driven trajectories and explore the shape of development  for each executive function measure. These analyses revealed that nearly all executive function measures (20/23 measures) had corrected significant  was used to construct confidence intervals for the first derivative of the fitted models to assess statistically significant age-related differences at each age . Age-ranges in which the simultaneous 95% confidence interval of the first derivative of the GAM/GAMM fits did not include zero  were classified as statistically significant. We note that a thresholded 95% confidence interval  age-related changes in executive function accuracy (increases) and latency (decreases) were observed during early to middle adolescent periods (10–15 years old) for nearly all measures . From middle to late adolescent periods (15–18 years old), smaller but still statistically significant  changes were observed for several measures  age-related change  of age-related differences across all ages in 1/10th of year intervals for all non-linear GAMM/GAM models. As in prior developmental research in other domainsres Fig. . Effect res Fig. , local eres Fig. . After lnge Fig. .Fig. 2Dep < 0.05 [two-sided] via simultaneous confidence intervals) age-related differences detected throughout early to late adolescent periods (10–18 years old) for both accuracy and latency measures  age-related differences could also be observed in this highly powered aggregate analysis until 20 years old for accuracy measures ; Fig. p’s < 0.004, [two-sided]; see also Fig. Aggregate analysis across measures and tasks  support the inference from individual measures and datasets Fig. , with stres Fig. . While sres Fig. , the absr| = 0.261; Supplementary Table Building from the observation that nearly all executive function measures showed the same developmental trajectory and relative maturational timing, we next examined the potential shared information across measures at the per-participant level using between-person  and within-person  correlations and factor analysis (see Methods). Composite metrics were not used here, as they are by construction  correlated with multiple measures. Consistent with a domain general, unity process of executive function, per-participant scores across nearly all measures were moderately correlated (see Methods) in all datasets in both between-person  and within-person  analyses  where multiple measures had the same putative, primary executive function subdomain , we determined the percentage of age-related differences on each specific executive function measure explained by a single domain general composite metric of the accuracy and latency metrics from the remaining tasks in the dataset  often explained more than half of age-related information  for a single assessment with a single scale of measurement . Executive function, however, is assessed with dozens of different measurements5 and owing to the potential range of participant ages included in any one developmental dataset, the total extent of observed individual variability may substantially differ across datasets, even if developmental change proceeds according to the shape of the canonical executive function trajectory. In the current datasets, scaling to adult performance  to approximate a common scale provides a further robust demonstration of the shape of the canonical executive function trajectory for domain general accuracy  is therefore the same as what occurs with standard parametric functional forms of age , but would have the added benefit of its shape/functional form being directly informed by prior developmental data. We tested this procedure to directly assess whether the insights generated in the current work regarding a canonical executive function trajectory could quantitatively guide future research allowing for simplified modeling approaches that are developmentally informed and computationally efficient. To mirror the use of this approach in future developmental research with new datasets and new measures, we tested the generalizability of this procedure through cross-validation (“leave one dataset out”) and compared performance to standard functional forms of age used in developmental research  that may otherwise be used to understand age-related executive function change and deviations from normative development.We sought to establish a procedure for constructing normative maturational templates applicable to all relevant ages (8–35 years old) that utilizes a linear scaling of the canonical executive function trajectory to a specific measure . Unlike the GAM/GAMMs used to initially derive the canonical executive function trajectory or alternative, multiparameter non-linear models of age, we tested a procedure that only requires a simple linear transformation of the age variable in each dataset  and is then fit as a single parameter in a general linear model/general linear mixed effects model. This data-driven basis function process  during model comparison testing that aggregated multiple metrics of model fit and complexity , which was significantly higher than all other age models  other than the quadratic model (vs. age + age2 (best model 37.3%), χ2 = 2.29, p [two-sided] = 0.130). Results were further unchanged when specifically looking at generalizability between Luna and NKI datasets that do not share any measures . Consistent with the strength of the basis function being derived from its developmentally precise shape, offsetting the basis function with respect to age led to lower and more variable model performance  and used as a single parameter basis function . However, the tools and insights from the current work can be used for future studies focused on relationships between these factors and executive function in more detail (see Discussion). As in previous longitudinal investigations of computerized and neuropsychological performance58, age-independent visit effects (e.g. practice effects) on cognitive testing were observed for many executive function tasks in longitudinal samples  independent investigations using a broad range of tasks or in relatively large studies ) with few, very narrow assessments of executive function in intelligence testing. Collectively, prior work demonstrates significant improvements from childhood through adolescence24, but the precise magnitude, maturational timing, and significant periods of development in executive function during the transition from adolescence to adulthood has not been defined. With four, large independent datasets, and non-linear modeling techniques to identify specific periods of significant development, we provide reproducible and direct evidence that executive functions continue to develop into late adolescence, which has been widely suggested by theory9 but has rarely been directly tested in empirical research. Building from prominent neurodevelopmental9 and psychological28 theories, these results highlight adolescence as an essential period of transition during which individuals reach maturity in goal-directed cognition. This suggests that while adolescents clearly possess complex cognitive abilities, including the ability to inhibit prepotent responses, maintain and update information in memory, and abstractly plan for future events, such abilities do not reach their full potential until 18–20 years old (late adolescence). Adolescent periods prior to this age-range  are therefore likely critical final stages of this type of cognitive development, where deviations from normative development may lead to poorer outcomes in adulthood. Identifying these sensitive, or even critical3, periods of cognitive development is essential for advancing neurocognitive growth-charting to determine normative development and deviations from this normative development in health and disease45, in designing developmentally informed interventions/preventions for youth62, and policy concerning adolescents14.The development of executive function has been studied in relatively small  for the canonical executive function trajectory, with the goal that subsequent work may utilize and continue to refine empirically defined normative maturational templates in executive function research. While such refinement should include ongoing model comparison of other candidate functional forms of age . As in these and related fields65, large-scale reproducible normative templates of change can be leveraged to better understand risk factors or consequences of mental and physical health conditions related to executive function during adolescence and across a range of experimental conditions.Given the reproducible and converging evidence for adolescence as a distinct period of the lifespan, and one now better conceptualized as a period of normative closure in goal-directed cognitive development prior to the establishment of adult-level trajectories, the current results support a broader understanding of the neurobehavioral basis for the adolescent period. Together with essential additional historical and sociocultural frameworks67) did not bias our results that focused on aggregate and average executive function changes during adolescence. The tools and insights developed here can support future studies of executive function differences in psychopathology both in new datasets, as well as targeted investigations within the current datasets. Normative templates of age-related differences in executive function derived here may also be useful for future research to disambiguate developmental effects and non-developmental visit effects  that, consistent with prior reports58, we observed in longitudinal executive function data. Future work may also use these insights towards optimizing developmental study designs with respect to the number of participants, construct breadth of assessments, and the number of longitudinal time points.Three of the four datasets used here , as community samples, did not exclude participants on the basis of mental health presentations. However, our sensitivity analyses demonstrated that our approach .The results of the current work provide support for prior theoretical and quantitative work suggesting non-linear developmental trajectories of cognition during adolescence68 and younger children71 has provided evidence for a potential unity/diversity framework of executive function, the relative domain-generality versus specificity of executive function has largely not been examined with respect to changes during adolescence. The strongest evidence across the large-scale data aggregated here suggests that age-related differences and longitudinal changes across executive function tasks are driven predominantly by a domain-general process. This indicates that across executive processes  there is a common system of goal-directed cognition that may lead to developmental improvements across multiple contexts. Such domain-general executive function development may help explain, for example, wide-spread differences across executive function tasks in clinical73 and/or population research , as well as the tendency for many executive function tasks to engage common neural circuitry77. Domain-general executive function development during adolescence also provides support for general heuristic perspectives of adolescence that emphasize a core set of cognitive development9. The current work that focused on multi-assessment and multi-dataset reproducibility of trajectories of adolescent executive function across large-scale cross-sectional and longitudinal data further sets priorities for additional within-person modeling  in future targeted investigations.While prior work in adults42, current measures and methods do not rule out additional executive function variance relevant to development . Our analyses were generally well accounted for by a domain general perspective of executive function, and further exploring this allowed us to examine multi-assessment multi-dataset estimates towards reproducibility and generalizability. However, as in other reports71, executive function variance was not entirely captured by a single factor. Future work, including using the tools and insights developed here, may address these questions in multi-dataset reproducibility and generalizability investigations. With respect to potential distinction among other cognitive processes, our sensitivity analyses did however demonstrate that the canonical executive functional trajectory was robust to individual differences in measures of culturally acquired knowledge . Future empirical and theoretical work, to add to existing frameworks, will be required to rectify these related but often historically distinct accounts. From either account, we suggest that commonality across measures, while essential for basic and translational research and practical demarcations of adolescent development, be expanded to consider broader sociocultural and historical perspectives as well. The increasing availability of future large-scale population-level cohorts , together with the methods used and developed in the current work, can facilitate future empirical investigations into these areas.Although we found a considerable degree of commonality in adolescent executive function development, as in related work from adults68) and amongst the largest developmental effects, it is possible a global inhibitory control process provides the most parsimonious explanation for domain-general/unitary executive function. If, as has been suggested, executive function tasks often fail to solely isolate a specific cognitive process , global inhibitory control processes may give rise to broad executive function changes through adolescence across diverse tasks, each of which requires some level of global, goal-directed inhibition. Nevertheless, we suggest that future work determining the common driver of executive function changes will benefit most from novel dense longitudinal study designs  and/or further multi-method investigations  that provide a means to understand temporal processes and/or correlated neurobiology, respectively. This would help protect against the possible circularity of descriptions of a common driver of executive function that are limited to functions assessed contemporaneously and/or with the same methodology. As demonstrated in the current work, however, even without a clear narrative description of the origins of domain-general executive function, the maturation of domain-general executive function provides a means to qualitatively understand the adolescent period and quantitatively guide future work. In pursuit of these goals, the current results emphasize the utility of research designs that include not just large sample sizes and/or longitudinal data, but also multiple measures within a broader construct (executive function). Our results suggest the utility of shared information and/or the potential utility of convergent validity from multiple executive function indicators in outcome research when such construct depth is available. Even when more domain-specific effects are of interest, our results suggest that the estimation of domain-general executive function via a broad battery is optimal, as developmental differences on nearly all measures had sizeable influences from a more general process.Conceptually, the potential cognitive and psychological mechanisms of such domain general executive function development remain somewhat of an open question. As inhibitory control tasks  often had both the highest loadings on domain-general factors observed here  may serve to increase reproducibility by facilitating overlap and replication efforts across instruments and datasets.The identification of common adolescent executive function development may guide future translational and multidisciplinary research. For example, our results suggest that neuroimaging research of adolescent executive function may be well-suited by leveraging multiple executive function tasks to examine shared information in association with brain structure/function or to better isolate domain-specific effects. Likewise, as has become increasingly common85. Furthermore, the breadth of executive functions indexed by these tasks was not exhaustive, and other domains of individual differences in cognition were not explored. For example, by design, this study, and many of the original datasets, did not examine executive function tasks in the context of affective stimuli. That is, the included measures focused on what have been considered affectively neutral cognitive measures. This allowed us to specifically isolate fundamental properties of executive function development as typically understood, but future work with more diverse cognitive batteries should examine whether affective manipulations likewise follow the canonical executive function trajectory established here. Another potential limitation is that the current work did not try to disambiguate age-related changes from pubertal development, given challenges in independently estimating these effects in the presence of large cross-sectional age effects . However, it will be important for future work, particularly when focusing on early periods of adolescence to likewise seek large-scale multi-assessment, multi-dataset reproducibility for the specific role of pubertal status in driving executive function development. A further potential limitation arises from our general focus on the average executive function trajectory during adolescence. While we determined that our results were generally robust to multiple participant-level factors, the results of the current work should be interpreted as a normative template and individual and dataset-level variability is expected. Relatedly, while the aggregated datasets and inferences drawn here appear to approximate population patterns from the United States, further work with multinational and multicultural samples is required to determine the generalizability of these results to other countries and cultures. The tools and data developed here can nevertheless provide resources for additional research on deviations from this normative trajectory, promote improved estimates of uncertainty, and ultimately support potential translational efforts seeking to identify clinically relevant executive function-related processes during adolescence.The current project leveraged multiple large independent datasets, developed methodological improvements permitting the identification of maturational timing of executive function, and investigated both common and specific components of executive function processes, but nevertheless potential limitations and explicit suggestions for future work should be considered. First, although this investigation used a comprehensive approach to characterizing executive function, these analyses focused on the most prominent outcome measures from these tests. This approach had the advantage of aligning the current analyses with predominant practices in the literature and the level of granularity supported by large-scale, public datasets, but future work would benefit from alternative and/or model-based, computational parameterizations of behavioral performance36 [NCANDA], Nathan Kline Institute-Rockland Sample35 [NKI], Philadelphia Neurodevelopmental Cohort37 [PNC]) were included based on (1) their inclusion of executive function tasks performed in a developmental or lifespan dataset spanning the entirety of the adolescent period and (2) to aggregate the largest possible dataset to explore the aims of this project. The primary focus of the current work was on the adolescent period. To explicitly capture transitions into and out of adolescence as well as the entire adolescent period33, we included participants ranging from late childhood to adulthood (8–35 years old). Lower (8 years old) and upper (35 years old) age ranges were selected to be as inclusive as possible, given the overarching goal of capturing non-linear developmental trajectories, while also ensuring that at least two separate datasets had participants in each age range. This meant that only participants from 8–35 years old from the NKI lifespan dataset were included (Full NKI Rockland Sample Range: 6–85). No participants were excluded based on age from the other datasets , which were designed to assess childhood to adolescence/adulthood and fully fell within this age range. In order to maximize generalizability and representation within the datasets , no other participant-level demographic exclusion criteria were applied to the datasets. Instead, we thoroughly examined the potential impact of such factors in a series of sensitivity analyses . One internal dataset (Luna Dataset) and three external datasets . From this dataset, the current project included 196 participants  dataset in an accelerated longitudinal/cohort sequential design, with participants completing a range of follow-up visits . The current project used data from 831 participants  in the first five visits of the accelerated longitudinal design . A central goal of the NCANDA study was to examine the transition to significant substance use during adolescence and as a result, approximately 50% of the dataset was recruited based on subclinical factors thought to increase the likelihood of alcohol use disorder . The inclusion of participants with psychiatric conditions however was shown to not substantively influence the current projects’ analyses through sensitivity analyses  (see ref. 35 for detailed sampling strategy and recruitment information). The current project used data from 588 participants . The NKI-Rockland Sample includes longitudinal follow-up data (up to two visits) on the included tasks here for a very small number of participants (n = 10) within our specified age range. However, given this represented such a small percentage of participants (<2% of dataset) and only included two visits, the current analyses only included the first visit from these participants and thus this dataset was utilized as cross-sectional  met criteria (DSM-IV TR) for at least one lifetime diagnosis of a psychiatric disorder. These factors were shown to not substantively influence the current projects’ analyses that focused on average and aggregate developmental changes in executive function through sensitivity analyses (see ref. 37 for detailed sampling strategy and recruitment information). The current project utilized data from 9151 participants in the cross-sectional, PNC dataset . Given the large community-based sampling procedure of the PNC, this dataset included participants with psychiatric disorders that may be associated with neurocognitive performance. The current project followed previous work with this dataset87 regarding data inclusion (see below) and sensitivity analyses examined the influence of these participants on the current project’s analyses   and participants over 18 provided informed consent, while participants younger than 18 provided written assent and parental consent. To our knowledge, no participant was involved in more than one of the studies. For the current analyses, no statistical method was used to predetermine the included sample size. All four datasets were included in their entirety, apart from analysis-specific exclusions detailed below (Data Processing). As observational studies, the included experiments were not randomized. Likewise, no blinding procedures were employed.5 and empirical work88, with a general operationalization of goal-directed cognitive behaviors that encompassed processes of inhibition, attention, working memory, switching, or planning. Where possible, prior work with the included tasks and datasets and/or test authors34 was used to define whether specific tasks indexed executive function. To avoid potential influences of verbal skills potentially related to educational attainment, measures relying heavily on reading and language skills were not included  as primary executive function assessments, but the influence of culturally acquired knowledge was shown to not influence primary results in a sensitivity analysis . Scoring procedures and outcome measures were based on previous work from our group and general use in the literature. Briefly, the Antisaccade task required participants to inhibit a proponent response (saccade) to a peripheral stimulus  and saccade towards the opposite hemifield. Both accuracy  and latency  of the Antisaccade task were examined. A second mixed version of the Antisaccade task was also performed, where participants performed an antisaccade but trials with different task demands were also interleaved. Specifically, in 1/3rd of trials, participants were required to saccade towards the peripheral stimulus  or in 1/3rd number of trials, simply maintain fixation. Both accuracy and latency of this mixed version were examined, but only calculated for the antisaccade trials (with the same scoring procedure as above), given the visually guided saccade is not thought to rely on executive function (see ref. 15) and the number of fixation errors was included in a different measure that captured this performance in a goal-oriented context (see below). The Memory Guided Saccade task required participants to saccade towards a peripheral stimulus , remember its location during a subsequent fixation period, and then saccade towards the remembered location when no stimulus was presented. Both accuracy  and latency  of the Memory Guided Saccade task were examined. We also calculated the number of fixation breaks (FIX) during the middle phase of the memory guided saccade task as a putative measure of inhibition. In addition to the three eye movement tasks, the Luna dataset also included the Delayed Matching to Sample, Spatial Span, and Stockings of Cambridge tasks from the CANTAB Battery, each of which have been broadly used and whose stimuli can be found online (see www.cambridgecognition.com/cantab/). Standard accuracy  and latency  measures from each of the three CANTAB tasks were examined. For interpretive consistency across measures in the Luna dataset, the direction of the scoring of two accuracy measures  was multiplied by −1 to ensure that higher scores indexed better performance on all accuracy measures.Based on the above criteria, the Luna dataset included twelve measures from six executive function tasks that were completed at each visit: Antisaccade (ANTI), Memory Guided Saccade (MGS), a mixed (MIX) Antisaccade/Visually Guided Saccade/Fixation task, Cambridge Neuropsychological Test Automated Battery [CANTAB] Delayed Matching to Sample (DMS), CANTAB Spatial Span (SSP), CANTAB Stockings of Cambridge (SOC). Each of these tasks have been described in detail elsewhere . The current project utilized data from three CNB tasks that met our operationalization of executive function and have been classified as executive by the CNB authors34, the Penn Conditional Exclusion Test (PCET), a Penn N-Back Test , and the Penn Continuous Performance Test: Number and Letter version (PCPT). Standard outcome measures for each task were included for accuracy  and latency . The NCANDA dataset also included a standard Stroop Test (STRP), where the primary measure of average latency over all correct trials was included. The NKI dataset also included four executive function tasks from the Delis-Kaplan Executive Function System43 (D-KEFS) that were included in the current study: color-word interference (CWI), design fluency (DFL), tower (TOW), and the trail-making test (TMT). Again, standard outcome measures were used for these tasks . The DKEFS Sort Task was also available for a small percentage of participant visits within our analytic age range (8–35) for the NKI dataset but was not used because over two-thirds of the visits did not have this measure (66.82%), whereas all other NKI measures included had at maximum <4% missingness.The NCANDA, PNC, and NKI datasets used versions of the University of Pennsylvania Computerized Neurocognitive Battery  and/or included in official data releases .All data processing and statistical analyses were performed in R version 4.1.2 (2021)91) were also removed from all analyses. Next, data inclusion criteria were used to maximize the included dataset sizes and result generalizability, while also ensuring no considerable outlier  biased results. Within these procedures, individual executive function measures were first screened for potential univariate leverage points in the association between age and each specific measure within general additive models (GAM: see below) or general additive mixed models (GAMM: see below). Leverage points were defined as those observations (measure for participant at testing session) with a residual from this model that was four standard deviations above the mean and removed from all subsequent analyses. Second, data were examined for potential multivariate outliers among all included executive function measures within each dataset using Mahalanobis distance within the psych package in R92. Sessions  with a Mahalanobis distance four standard deviations above the mean were removed from all subsequent analyses.Aggregated data, either from distributed data releases  or our in-house database (Luna dataset) were first screened to ensure each visit (participant at testing session) had a valid age, anonymous id variable, and if longitudinal data, visit  and included expected data. Data that did not meet these minimum criteria were removed from all analyses. As in our prior work, eye-tracking tasks in the Luna dataset (specific task at specific visit) with more than 30% of trials dropped due to poor eye-tracking or missing  utilized a simple bivariate model examining the smoothed association between age (the independent variable) and executive function (the outcome measure). Primary longitudinal analyses  additionally included a smoothed term for visit number to account for potential non-developmental visit effects  to 5 . Age-related fits from these primary GAM/GAMM models are presented in Fig. mgcv GAM/GAMM predict function by 2 and summing this with the predicted fit estimate. Sensitivity analyses  and general additive mixed models  with penalized smooth plate regression splines via the 87, periods of significant age-related change (age ranges) were defined by estimating the first derivative (finite differences method) in 1/10th of a year intervals of GAM fits and performing a posterior simulation based on the GAM/GAMM model coefficients. Simultaneous (used given the multiple testing) confidence intervals (CI) were generated with the gratia package51 with 10,000 simulations. Age ranges in which the simultaneous 95% CI did not include zero (p < 0.05) were classified as significant. Using this method, raster plots in Fig. metafor package93 with effects nested in measure and study. A cross-dataset label was used to nest measures from the same tasks  across datasets. As in prior methodological work on point-wise meta-analysis with GAMs94, meta-analytic estimates were computed across a common span of the independent variable: here, 1/10th year age bins, following linear interpolation of GAM/GAMM first derivatives. The same pointwise, three-level meta-analytic approach was used in aggregate analysis of GAM/GAMM fits in Fig. As in previous developmental research in different domainsstatsBy function in the psych package in R. This approach was chosen to balance interpretability with model complexity for the accelerated longitudinal designs of Luna and NCANDA datasets. Aggregate analysis  in Fig. metafor with correlation pairs nested in task pairs and datasets. Exploratory factor analysis  and within-person correlation matrices . Multiple data-driven thresholds for the number of extracted factors .Cross-sectional and longitudinal correlations  were computed among executive function measures in each dataset Fig. . For lonsis Fig.  via maxiors Fig.  were exaContributions from domain-general versus specific processes to age-related differences in executive function were determined via model comparison that is also presented with the same description as well as additional visualization in Supplementary Fig. x_i): model A, a composite metric created from all measures not in the same putative subdomain as measurex_i: composite metric M ∌ x, where M ∌ x represents the set (M) of executive function measures that do not contain measures from subdomain x: model B, and a model where age is estimated from both measurex_i and composite metric M ∌ x: model C. As in primary analyses, the relationship between age and each measure was modeled with penalized splines. For each model (A–C), the percent of deviance explained in age was extracted (following standard estimation in mgcv GAM model). Next, the incremental deviance of age explained by measurex_i over composite metric M ∌ x was computed. Finally, the resulting measure specific age-related deviance was scaled to the original deviance estimate for the specific measure (model A) to create a percent of the original measure’s age effect. The remaining percentage of model A’s deviance was assigned as the domain-general percentage. To ensure consistent interpretability of the directionality of composite metric M ∌ x, measures from the opposing response type were sign flipped . Sensitivity analysis examined the influence of the composite measure’s precision in the estimation of domain-general accounts of age-related differences in executive function  as well as an intercept only (no age) model. Potential age models were evaluated with multiple metrics of model fit and complexity via the performance package in R96 ; cross-sectional models : R2, adjusted R2, RMSE, Sigma, AIC, BIC). An additional sensitivity analysis explored the influence of the exact developmental timing of the developmental function with a similar procedure that offset in years (earlier or later) the canonical executive function trajectory  to determine whether normative maturational templates of executive function could improve developmental inferences in new datasets and measures. A diagram of this procedure is likewise presented in Supplementary Fig. Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummarySource Data


To investigate the impact of frontal macro-structural lesions on intrinsic network measures, we examined brain network function during resting-state fMRI in patients with frontal lesions in the subacute phase after mild to moderate traumatic brain injury. Additionally, network function was related to neuropsychological performances. 17 patients with frontal lesions, identified on admission CT after mild to moderate trauma, were compared to 30 traumatic brain injury patients without frontal lesions and 20 healthy controls. Three months post-injury, we acquired fMRI scans and neuropsychological assessments . Using independent component analysis, the activity of and connectivity between network components  and relations with neuropsychological measures were examined and compared across groups. The analysis yielded five predominantly frontal components: anterior and posterior part of the default mode network, left and right frontoparietal network and salience network. No significant differences concerning fMRI measures were found across groups. However, the frontal lesions group performed significantly worse on neuropsychological tests than the other two groups. Additionally, the frontal lesions group showed a significant positive association of stronger default mode network–salience network connectivity with better executive performances. Our findings suggest that, on fMRI level, frontal network measures are not largely affected by frontal lesions following a mild to moderate traumatic brain injury. Yet, patients with damage to the frontal structures did show poorer executive abilities which might to some degree be related to altered frontal network connectivity between the default mode network and salience network. Traumatic brain injury (TBI) is a major global health problem . The sevIn general, the presence of diffuse injury following TBI may cause a less efficient connection between functionally related brain areas  resultinExistent literature shows that executive abilities depend on flexible and effective switching between relevant networks with opposing functions , 15. BetConsidering that frontal lesions are the most prevailing focal lesions and are important for executive abilities in daily life, it is important to investigate the specific effects of frontal lesions on brain networks in brain trauma patients. Independent components analysis (ICA) commonly is used to detect distinct networks involved in various mental processes. It is assumed that frontal components that are functionally coupled at rest, are also co-activated during active cognitive processes .The main objective of the present study was to investigate the impact of macro-structural lesions on frontal intrinsic network measures (resting-state fMRI) and the effect on neuropsychological measures (information processing speed and executive functions) in patients with mild to moderate TBI in the early chronic phase post-injury. Results were compared with mild TBI patients without frontal lesions and healthy controls.This fMRI study was part of a larger prospective follow-up study on recovery after mild TBI (UPFRONT study) . Data weFor the present study, we included patients with mild or moderate TBI with frontal lesions on admission CT-scans (CT+ group). Additionally, we included patients with mild TBI without lesions on admission CT-scans (CT- group) and a group of healthy controls (HC group). Patients were scanned at 3 months post-injury with a 3.0 T MRI scanner and concurrently completed the neuropsychological assessment on the same day. Exclusion criteria for patients and healthy controls were: age < 18 years, history of drug or alcohol abuse, major psychiatric or neurological disorders, no permanent home address, insufficient comprehension of the Dutch language and contraindications for MRI . Additionally, for healthy controls, previous traumatic brain injury was an exclusion criteria. Data were obtained in compliance with the ethical regulations of the UMCG. All participants gave written informed consent and procedures were executed according to the declaration of Helsinki.A 3.0 T Philips Achieva MRI scanner  equipped with a 32-channel SENSE head coil was used for image acquisition. A high resolution transversal T1-weighted sequence image was made for anatomical reference . For resting-state imaging, three-hundred volumes were acquired with slices aligned in the anterior commissure (AC)-posterior commissure (PC) plane and recorded in descending order . Participants were instructed to close their eyes and stay awake.The Trail Making Test (TMT)  consistsTMT part B measures mental flexibility, an aspect of executive functioning . In TMT The Controlled Oral Word Association Test (COWAT)  is a meaStatistical Parametric Mapping  implemented in Matlab  was used for pre-processing, which consisted of slice timing correction, image realignment to the first functional image, co-registration of functional data with individual participants’ T1-weighted images, normalization using diffeomorphic nonlinear registration tool (DARTEL) (isotropic voxels of 3x3x3 mm) to the MNI template and smoothing  Gaussian kernel).Group ICA of fMRI Toolbox (GIFT) version 4.0a, implemented in Matlab, was used for spatial independent component analysis (ICA) . The meaThe statistical analyses on behavioral data were performed in Statistical Package and Service Solutions . Assumptions were checked. Differences in demographic and clinical variables between the CT+, CT- and HC groups were examined using chi-square tests for categorical data and Mann-Whitney U tests for ordinal and nonparametric data. Differences in neuropsychological measures  across groups were assessed using ANOVA with post hoc t-tests.For the fMRI data analysis concerning component activity, first, we tested in SPM whether there was a significant difference across groups in the spatial maps of the selected components using an ANOVA . Secondly, activation of the spatial maps was voxel wise related to frontal executive measures (TMTB and COWAT). Thirdly, by modelling an interaction between regressors of neuropsychological performance per group, we investigated whether there was a significant difference across groups in the relation between components activity and executive measures.Concerning component connectivity, first, we investigated connectivity strength between components using the time courses of the selected components. Subjects’ between-component correlations were extracted from the correlation matrix of post-processed time courses created by GIFT and imported in SPSS. Secondly, ANOVA analyses were conducted to compare the Z-scores across groups. Thirdly, by modelling an interaction between regressors of neuropsychological performance per group, we investigated whether there was a significant difference across groups in the relation between components connectivity and executive measures. Finally, to investigate whether correlations between groups significantly differed, we transformed the correlations to Fisher Z scores and calculated the standard error of the difference of the Z-scores and the associated ratio of the difference to the standard error which was subsequently compared to the normal distribution. The alpha level was set at .05 for all analyses.We included 17 mild to moderate TBI patients with frontal lesions (CT+ group), 30 mild TBI patients without frontal lesions (CT- group) and 20 healthy controls (HC group). Information about the clinical characteristics of the CT-abnormalities are presented in For all measures, there were no significant differences between CT- patients and healthy controls.Across groups, the neuropsychological measure for information processing speed significantly differed . Across groups, the frontal executive measure for mental flexibility (TMTB) significantly differed . The frontal executive measure for executive control (COWAT) did not significantly differ across groups . Yet, ICA resulted in 29 components from which we selected six components of interest. Components included were the anterior and posterior part of the default mode network (DMN), the left and right frontoparietal network (FPN), dorsal attention network (DAN) and salience network (SN). For an overview, see Across the CT+, CT- and HC groups, no significant differences were found regarding spatial maps of the components, indicating no significant differences in component activity. Furthermore, spatial maps of these frontal components were not significantly correlated with the frontal executive measures (TMTB and COWAT), nor did these relations between spatial maps and the frontal executive measures significantly differ across the groups.The present study investigated whether frontal lesions following mild to moderate TBI would negatively affect intrinsic network measures by examining frontal network activity and connectivity and frontal executive measures. When solely looking at the fMRI measures of the brain networks, no apparent differences across the groups could be detected. However, patients with frontal lesions showed significantly poorer executive abilities when compared to the control groups . The control groups did not significantly differ from each other. Remarkably, only for the frontal lesions group, stronger resting-state connectivity between the DMN and SN appeared related to better executive performances.The finding that patients with frontal lesions exhibited poorer frontal executive functions regarding mental flexibility and executive control is in line with our expectation that damage to frontal structures as noted on the CT-scans can compromise executive functioning. Furthermore, we found that the frontal lesions group also exhibited a slower information processing speed in comparison to the patient group without CT-abnormalities. Presumably, this is a more subtle consequence of the fact that the frontal lesions group is on average more severely injured, even though, based on the PTA duration, we included patients at the rather mild end of the PTA spectrum and the GCS-scores did not significantly differ.When considering the fMRI measures of the frontal components, we found no evidence that either component activity or connectivity between frontal components significantly differed across groups. Apparently, the present methodology does not detect evident alterations as shown by frontal CT-abnormalities on frontal network functioning on fMRI level. Yet, when correlating the resting-state connectivity between frontal components with frontal executive measures, it is striking that only for the frontal lesions group, a stronger DMN ‐ SN connectivity was found to be related to better frontal executive performances.The finding that stronger connectivity between the DMN and SN is associated with better executive abilities may indicate altered functional network connectivity in this group. Another argument for this assumption is that this stronger anterior DMN ‐ SN connectivity significantly differed from other non-frontal groups. Based on prior research, we expected an opposed relationship with weaker connectivity between the DMN and the executive/attention networks, reflecting efficient switching between the brain networks, in relation to better executive abilities , 15. ThiThe present study is subject to several limitations. The available data allow us to investigate several differences and correlations but we are unfortunately unable to make any causal inferences. Furthermore, CT-scans have a limited sensitivity when compared to for example MRI-scans, which may also in part explain the lack of significant differences between groups. Furthermore, although no differences were present regarding GCS scores between the frontal and nonfrontal groups, the duration of PTA was slightly higher and therefore the CT group should be regarded as representing the more severe end of the mild TBI spectrum. Also, the poorer information processing speed found in the frontal lesions group may in part have negatively affected the executive measures. Finally, other networks or brain areas may be involved in changes caused by the lesions that are not included in the present study.To conclude, present findings indicate that frontal lesions following mild to moderate TBI do not evidently affect frontal network functions on a fMRI level. However, patients with frontal lesions did show poorer executive abilities on neuropsychological tests that may partly be related to an imbalance in connectivity between the DMN and SN. This finding deserves further exploration in future studies.S1 Table(DOCX)Click here for additional data file.


Aortic regurgitation is a major concern following transcatheter aortic valve implantation (TAVI), as even low-grade regurgitation is associated with increased mortality. This is of particular concern to patients with pre-existing aortic disease who are at increased risk of TAVI valve slippage. Furthermore, conduction system disturbances after TAVI, namely left bundle branch block (LBBB), may have an additional detrimental effect on cardiac function.This report documents a successful treatment strategy in a frail patient with a bicuspid aortic valve and aortic disease after valve-sparing surgical repair in 1998, who subsequently developed aortic stenosis and underwent TAVI with an Evolut R self-expanding aortic valve. The progression of aortic disease, aortic root dilatation, and leaflet degeneration over the following years caused aortic regurgitation of the self-expanding aortic valve, resulting in left ventricular dilatation and heart failure along with LBBB and left ventricular (LV) mechanical dyssynchrony. Diagnostic workup of the patient showed persistence of the aneurysm distal to the graft with a dissection spanning the ascending aorta, arch, and terminating proximal to the aortic isthmus. After consideration by the cardiac team, a balloon-expandable valve was chosen for a valve-in-valve (ViV) procedure to provide sufficient radial force to expand the existing valve and correct the regurgitation. Due to the anatomy, a J-wire and pigtail catheter were successfully used for a safe approach and placement of the valve. Following the procedure, intermittent complete atrioventricular block was observed in addition to the pre-existing left bundle branch block, necessitating resynchronization pacing. Due to anatomical considerations, ease of placement, and the expected good level of resynchronization due to the proximal block, we opted for left bundle branch pacing, which showed improvement in left ventricular dyssynchrony and LV function at follow-up.Valve-in-valve implantation of a balloon-expandable Myval TAVI device to treat aortic regurgitation caused by slippage and right leaflet disfunction of slef valve is feasible in challenging anatomical scenarios. Left bundle branch pacing is a viable alternative to correct mechanical dyssynchrony in complex patients with LBBB and anatomical challenges necessitating resynchronization. Aortic regurgitation following transcatheter aortic valve implantation (TAVI) is an important concern, as even low-grade paravalvular leak (PVL) is associated with increased mortality . This isA 79-year-old female patient was referred to our center due to severe aortic insufficiency after a previously implanted transcatheter aortic valve (TAVI). The patient's complex medical history included a bicuspid aortic valve with an aneurysm of the ascending aorta, which resulted in an acute type A aortic dissection in 1998. Following the dissection, emergency aortic valve reconstruction and a valve-sparing aortic root procedure (David procedure) were performed. In 2013, aortic stenosis and heart failure (HF) were diagnosed, both of which deteriorated during subsequent visits to the outpatient clinic. A Bentall procedure was a suboptimal therapeutic option due to the high procedural risk and the possibility of complications. TAVI was chosen as the intervention of choice in 2016, with planned oversizing due to the expected dilatation of the aortic root. An Evolut R 29 mm self-expanding valve was successfully inserted via a transfemoral approach with good expansion but low placement and minimal paravalvular leak seen after the procedure. Left bundle branch block (LBBB) developed after the first TAVI procedure. The patient was admitted again to a regional hospital shortly before referral to our center in 2021 due to worsening HF symptoms. Echocardiography confirmed a severely reduced ejection fraction (EF 20%) with concomitant severe aortic valve insufficiency resulting from a severe paravalvular leak. The patient was transferred to our center for further evaluation of possible treatment options  with mild wall hypertrophy  with severely reduced ejection fraction, a normal stroke volume , and severe valvular and paravalvular insufficiency of the Evolut R biological TAVI valve with mild mitral regurgitation. The central valvular insufficiency was caused by the degeneration of the right leaflet, which led to a diagnostic work-up and exclusion of infective endocarditis of the artificial valve—this was ruled out with transesophageal ultrasound (TEE), cardiac magnetic resonance imaging (MRI), and positron emission tomography (PET-CT) scans, in addition to serial blood cultures, which remained sterile. Echocardiographic signs of LV mechanical dyssynchrony were present. These findings were also confirmed by MRI. A CT of the thoracic and abdominal aorta revealed persistence of the ascending aortic aneurysm distal to the graft placement with dissection spanning the ascending aorta and aortic arch and terminating proximal to the aortic isthmus with involvement of the brachiocephalic trunk and progression of the aortic root dilatation compared to previous CT scans  valve due to the appropriate size, the radial force achieved by balloon-expandable valves, and the shorter stent frame height of the valve, reducing the neo-skirt. Careful procedural planning was performed in order to assess the anatomical considerations of the slipped Evolut R valve and its relationship to the aortic root. A bifemoral approach was obtained by positioning a 16F transcatheter aortic valve introducer in the right femoral artery and a 6F introducer for a pigtail catheter in the left femoral artery. Due to the severe iliac artery tortuosity, a nitinol hydrophilic guide wire covered with polyurethane Radifocus ™guidewire M  with RJ 4.0 6F support was used. A distal part “S-reshaped” J soft wire-mounted pigtail catheter was used to safely cross the dissected segment . The aorFollowing valve-in-valve TAVI, intermittent complete AV block was recorded. As LBBB was already present after the first TAVI procedure in conjunction with severely reduced ejection fraction, resynchronization therapy was indicated. As an alternative to biventricular pacing, we opted for the LBBAP technique. A right atrial lead  was temporarily inserted into the right ventricle (RV) to prevent potential asystole due to mechanical injury of the right bundle branch during LBBP lead positioning. As previously described –18, a 3,2), severely reduced ejection fraction (EF 17%) with a normal stroke volume (SV 74 ml), elevated LV filling pressures, and moderate post-capillary pulmonary hypertension. The TAVI valve was well expanded with minimal trace paravalvular leakage. A Doppler ultrasound of the bifemoral puncture sites was performed before discharge due to a murmur, which did not reveal signs of fistulas or pseudoaneurysms.A post-procedural echocardiography did not show immediate improvement in left ventricular function with a residual dilatation of the left ventricle . Follow-up echocardiography was also performed and showed improvement in LV function with an increase in ejection fraction (3D EF 27% as compared to 17% before discharge, Structural valve degeneration and paravalvular leaks are not uncommon after TAVI. According to the literature, redo TAVI for the treatment of acute post-procedural or late paravalvular regurgitation is associated with favorable clinical and echocardiographic outcomes . In our This complex case report highlights the feasibility of using a Myval transcatheter prosthetic valve for redo TAVI to correct paravalvular regurgitation and valve degeneration in a patient with pre-existing aortic disease. In addition, LBBP can be used as an alternative resynchronization pacing modality following TAVI in frail patients with a low BMI and concerns about device size.