Objective: To interpret the current evidence on the prevalence of neurodevelopmental disorders (NDDs) through a systematic review based on both DSM-5 (2013) and PRISMA criteria. Method: Empirical studies complying with the PRISMA guidelines were identified from four databases (PubMed, Scopus, Science Direct, and ProQuest) and systematically reviewed. In total, 17 articles were selected for the study. Results: In the scientific literature, there have been only a few studies measuring the prevalence of NDDs according to the DSM-5 (2013) criteria in people under 18 years old. The reported prevalence rates were as follows: intellectual disability (ID), 0.63%; attention-deficit/hyperactivity disorder (ADHD), 5–11%; autism spectrum disorder (ASD), 0.70–3%; specific learning disorder (SLD), 3–10%; communication disorders (CDs), 1–3.42%; and motor disorders (MDs), 0.76–17%. Although there is extensive literature on specific disorders, NDDs have rarely been assessed as a whole. All of the reviewed studies support the idea that such disorders can be considered chronic, heterogeneous, underdiagnosed conditions and that comorbidity of multiple NDDs is the norm. Likewise, it is estimated that the prevalence of the most studied disorders, such as ADHD, ASD and SLD, remains stable over time and is consistent in different cultures, ages, ethnicities and sexes. Conclusion: The studies reviewed lead us to conclude that the prevalence rate of NDDs fluctuates globally between 4.70 and 88.50%; these variations depend on methodological aspects such as estimation procedures, as well as on sociocontextual phenomena. It is also important to consider that the prevalence found is probably highly influenced by the activity of the countries in the diagnosis and training of professionals who care for children and adolescents. Hence, there is a need for a secondary intervention in the fields of public health and education to minimize socioemotional consequences, prevent academic failure, and reduce the economic cost to society.

Visual aesthetic perception (“aesthetics”) or the capacity to visually perceive a particular attribute added to other features of objects, such as form, color, and movement, was fixed during human evolutionary lineage as a trait not shared with any great ape. Although prefrontal brain expansion is mentioned as responsible for the appearance of such human trait, no current knowledge exists on the role of prefrontal areas in the aesthetic perception. The visual brain consists of “several parallel multistage processing systems, each specialized in a given task such as, color or motion” [Bartels, A. & Zeki, S. (1999) Proc. R. Soc. London Ser. B 265, 2327–2332]. Here we report the results of an experiment carried out with magnetoencephalography which shows that the prefrontal area is selectively activated in humans during the perception of objects qualified as “beautiful” by the participants. Therefore, aesthetics can be hypothetically considered as an attribute perceived by means of a particular brain processing system, in which the prefrontal cortex seems to play a key role.

Alzheimer's disease (AD) is an irreversible brain disorder of unknown aetiology that gradually destroys brain cells and represents the most prevalent form of dementia in western countries. The main aim of this study was to analyse the magnetoencephalogram (MEG) background activity from 20 AD patients and 21 elderly control subjects using Higuchi's fractal dimension (HFD). This non-linear measure can be used to estimate the dimensional complexity of biomedical time series. Before the analysis with HFD, the stationarity and the non-linear structure of the signals were proved. Our results showed that MEG signals from AD patients had lower HFD values than control subjects' recordings. We found significant differences between both groups at 71 of the 148 MEG channels (p<0.01; Student's t-test with Bonferroni's correction). Additionally, five brain regions (anterior, central, left lateral, posterior and right lateral) were analysed by means of receiver operating characteristic curves, using a leave-one-out cross-validation procedure. The highest accuracy (87.8%) was achieved when the mean HFD over all channels was analysed. To sum up, our results suggest that spontaneous MEG rhythms are less complex in AD patients than in healthy control subjects, hence indicating an abnormal type of dynamics in AD.

Multiscale entropy (MSE) has been a prevalent algorithm to quantify the complexity of biomedical time series. Recent developments in the field have tried to alleviate the problem of undefined MSE values for short signals. Moreover, there has been a recent interest in using other statistical moments than the mean, i.e., variance, in the coarse-graining step of the MSE. Building on these trends, here we introduce the so-called refined composite multiscale fuzzy entropy based on the standard deviation (RCMFE_σ) and mean (RCMFE_μ) to quantify the dynamical properties of spread and mean, respectively, over multiple time scales. We demonstrate the dependency of the RCMFE_σ and RCMFE_μ, in comparison with other multiscale approaches, on several straightforward signal processing concepts using a set of synthetic signals. The results evidenced that the RCMFE_σ and RCMFE_μ values are more stable and reliable than the classical multiscale entropy ones. We also inspect the ability of using the standard deviation as well as the mean in the coarse-graining process using magnetoencephalograms in Alzheimer’s disease and publicly available electroencephalograms recorded from focal and non-focal areas in epilepsy. Our results indicated that when the RCMFE_μ cannot distinguish different types of dynamics of a particular time series at some scale factors, the RCMFE_σ may do so, and vice versa. The results showed that RCMFE_σ-based features lead to higher classification accuracies in comparison with the RCMFE_μ-based ones. We also made freely available all the Matlab codes used in this study at http://dx.doi.org/10.7488/ds/1477.

The neurophysiological changes associated with Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI) include an increase in low frequency activity, as measured with electroencephalography or magnetoencephalography (MEG). A relevant property of spectral measures is the alpha peak, which corresponds to the dominant alpha rhythm. Here we studied the spatial distribution of MEG resting state alpha peak frequency and amplitude values in a sample of 27 MCI patients and 24 age-matched healthy controls. Power spectra were reconstructed in source space with linearly constrained minimum variance beamformer. Then, 88 Regions of Interest (ROIs) were defined and an alpha peak per ROI and subject was identified. Statistical analyses were performed at every ROI, accounting for age, sex and educational level. Peak frequency was significantly decreased (p< 0.05) in MCIs in many posterior ROIs. The average peak frequency over all ROIs was 9.68±0.71 Hz for controls and 9.05±0.90 Hz for MCIs and the average normalized amplitude was (2.57±0.59)•10-2 for controls and (2.70±0.49)•10-2 for MCIs. Age and gender were also found to play a role in the alpha peak, since its frequency was higher in females than in males in posterior ROIs and correlated negatively with age in frontal ROIs. Furthermore, we examined the dependence of peak parameters with hippocampal volume, which is a commonly used marker of early structural AD-related damage. Peak frequency was positively correlated with hippocampal volume in many posterior ROIs. Overall, these findings indicate a pathological alpha slowing in MCI.