AimThe last systematic review of research on the behavior of children with neurofibromatosis type 1 (NF1) was in 2012. Since then, several important findings have been published. Therefore, the study aim was to synthesize recent relevant work related to this issue.MethodWe conducted a systematic review of the literature. Relevant articles were identified using the electronic databases PubMed, PsycINFO, and Scopus and a manual search of references lists. Thirty of 156 articles identified met the inclusion criteria. A quality evaluation of the articles was performed and the information was synthesized using a narrative approach.ResultsCompared with controls, children and adolescents with NF1 present significant alterations in language, reading, visuospatial skills, motor function, executive function, attention, behavior, emotion, and social skills. The prevalence of attention-deficit/hyperactivity disorder (ADHD) is important and can affect cognition and executive function variables. A high prevalence of autistic traits and autistic spectrum disorder were reported. The benefits of using statins to treat cognitive deficits are unclear. However, children with NF1 and ADHD seem to benefit from methylphenidate treatment. The presence of hyperintensities in brain magnetic resonance imaging data seem to be related to poor cognitive performance. Analysis of these lesions could help to predict cognitive alterations in children with NF1.InterpretationThere has been important progress to evaluate cognitive characteristics of children with NF1 and to determine the physiological mechanisms of the concomitant disorders. However, discrepancies in relation to intelligence, learning disabilities, attention deficits, and treatment remain. Further investigations on this topic are recommended.

Neural oscillations are present in the brain at different spatial and temporal scales, and they are linked to several cognitive functions. Furthermore, the information carried by their phases is fundamental for the coordination of anatomically distributed processing in the brain. The concept of phase transfer entropy refers to an information theory-based measure of directed connectivity among neural oscillations that allows studying such distributed processes. Phase TE is commonly obtained from probability estimations carried out over data from multiple trials, which bars its use as a characterization strategy in brain–computer interfaces. In this work, we propose a novel methodology to estimate TE between single pairs of instantaneous phase time series. Our approach combines a kernel-based TE estimator defined in terms of Renyi’s α entropy, which sidesteps the need for probability distribution computation with phase time series obtained by complex filtering the neural signals. Besides, a kernel-alignment-based relevance analysis is added to highlight relevant features from effective connectivity-based representation supporting further classification stages in EEG-based brain–computer interface systems. Our proposal is tested on simulated coupled data and two publicly available databases containing EEG signals recorded under motor imagery and visual working memory paradigms. Attained results demonstrate how the introduced effective connectivity succeeds in detecting the interactions present in the data for the former, with statistically significant results around the frequencies of interest. It also reflects differences in coupling strength, is robust to realistic noise and signal mixing levels, and captures bidirectional interactions of localized frequency content. Obtained results for the motor imagery and working memory databases show that our approach, combined with the relevance analysis strategy, codes discriminant spatial and frequency-dependent patterns for the different conditions in each experimental paradigm, with classification performances that do well in comparison with those of alternative methods of similar nature.

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