Reading comprehension differences between children with Autism Spectrum Disorder and low cognitive abilities and children with Autism Spectrum Disorder and intact cognitive skills: the roles of decoding, fluency and morphosyntax.

Introduction: Reading comprehension is one of the most important skills learned in school and it has an important contribution to the academic success of children with Autism Spectrum Disorder (ASD). Though previous studies have investigated reading comprehension difficulties in ASD and highlighted factors that contribute to these difficulties, this evidence has mainly stemmed from children with ASD and intact cognitive skills. Also, much emphasis has been placed on the relation between reading comprehension and word recognition skills, while the role of other skills, including fluency and morphosyntax, remains underexplored. This study addresses these gaps by investigating reading comprehension in two groups of school-aged children with ASD, one with intact and one with low cognitive abilities, also exploring the roles of word decoding, fluency and morphosyntax in each group's reading comprehension performance. Methods: The study recruited 16 children with ASD and low cognitive abilities, and 22 age-matched children with ASD and intact cognitive skills. The children were assessed on four reading subdomains, namely, decoding, fluency, morphosyntax, and reading comprehension. Results: The children with ASD and low cognitive abilities scored significantly lower than their peers with intact cognitive abilities in all reading subdomains, except for decoding, verb production and compound word formation. Regression analyses showed that reading comprehension in the group with ASD and intact cognitive abilities was independently driven by their decoding and fluency skills, and to a lesser extent, by morphosyntax. On the other hand, the children with ASD and low cognitive abilities mainly drew on their decoding, and to a lesser extent, their morphosyntactic skills to perform in reading comprehension. Discussion: The results suggest that reading comprehension was more strongly affected in the children with ASD and low cognitive abilities as compared to those with intact cognitive skills. About half of the children with ASD and intact cognitive skills also exhibited mild-to-moderate reading comprehension difficulties, further implying that ASD may influence reading comprehension regardless of cognitive functioning. Finally, strengths in decoding seemed to predominantly drive cognitively-impaired children's reading performance, while the group with ASD and intact cognitive skills mainly recruited fluency and metalinguistic lexical skills to cope with reading comprehension demands, further suggesting that metalinguistic awareness may be a viable way to enhance reading comprehension in ASD.

Animacy Processing in Autism: Event-Related Potentials Reflect Social Functioning Skills.

Though previous studies with autistic individuals have provided behavioral evidence of animacy perception difficulties, the spatio-temporal dynamics of animacy processing in autism remain underexplored. This study investigated how animacy is neurally encoded in autistic adults, and whether potential deficits in animacy processing have cascading deleterious effects on their social functioning skills. We employed a picture naming paradigm that recorded accuracy and response latencies to animate and inanimate pictures in young autistic adults and age- and IQ-matched healthy individuals, while also employing high-density EEG analysis to map the spatio-temporal dynamics of animacy processing. Participants' social skills were also assessed through a social comprehension task. The autistic adults exhibited lower accuracy than controls on the animate pictures of the task and also exhibited altered brain responses, including larger and smaller N100 amplitudes than controls on inanimate and animate stimuli, respectively. At late stages of processing, there were shorter slow negative wave latencies for the autistic group as compared to controls for the animate trials only. The autistic individuals' altered brain responses negatively correlated with their social difficulties. The results suggest deficits in brain responses to animacy in the autistic group, which were related to the individuals' social functioning skills.

Towards a hybrid approach to unveil the Chimaira of neurosciences: philosophy, aperiodic activity and the neural correlates of consciousness.

Contemporary theories of consciousness, although very efficient in postulating testable hypotheses, seem to either neglect its relational aspect or to have a profound difficulty in operationalizing this aspect in a measurable manner. We further argue that the analysis of periodic brain activity is inadequate to reveal consciousness's subjective facet. This creates an important epistemic gap in the quest for the neural correlates of consciousness. We suggest a possible solution to bridge this gap, by analysing aperiodic brain activity. We further argue for the imperative need to inform neuroscientific theories of consciousness with relevant philosophical endeavours, in an effort to define, and therefore operationalise, consciousness thoroughly.

Investigation of Sleep Quality and Mental Health of Greek Physicians During the COVID-19 Pandemic.

The global outbreak of COVID-19 has had an impact on physicians, not only as a time of great concern and responsibility, but also as a human performance factor influencing their sleep quality and mental health. However, studies have not yet defined the frequency and the interplay of sleep and mental issues. The purpose of this study was to explore the anxiety and sleep disturbances prevalence in Greek physicians, as well as their relationship with sociodemographic and profession-related traits, aiming to raise awareness for changes in healthcare management and policy making.

Arterial Stiffness Alterations in Simulated Microgravity and Reactive Sledge as a Countermeasure.

INTRODUCTION: Experiments during spaceflight and simulated microgravity as head-down tilt bedrest, demonstrated the role of arterial stiffness among others, in microgravity induced cardiovascular pathologies and emphasized the need for a robust countermeasure. AIM: The purpose of the present study was to evaluate the use of a new countermeasure, consisting of a high intensity Reactive Sledge (RSL) jumps training protocol, to counteract changes in arterial stiffness during long term head down tilt bedrest (LTBR). METHODS: The participants enrolled in the study were 23 male, healthy volunteers, aged between 20 and 45 years, subjected to LTBR for 60 days and randomly assigned either to a control (11) or to a training sledge (12) group using RSL 3-4 times per week, as a countermeasure. Recorded values were systolic and diastolic blood pressure, heart rate and the user's arterial stiffness index. RESULTS: Compared to baseline measurements, there was a deterioration in the values of arterial stiffness, systolic and diastolic blood pressure and heart rate, in both groups until day 35 of LTBR, interpreted as adaptation to the microgravity environment. From this day until the end of the experiment, arterial stiffness of the control group was constantly fluctuating, while constantly improving for the training group. During the recovery period, arterial stiffness values returned to the pre-experimental levels in both groups. CONCLUSIONS: Overall, arterial stiffness increased the longer the time spent in LTBR and the countermeasure was partially effective in preventing the observed phenomenon. German Clinical Trials Register (DRKS), DRKS00012946, September 18, 2017, retrospectively registered.

Gravity Threshold and Dose Response Relationships: Health Benefits Using a Short Arm Human Centrifuge.

PURPOSE: Increasing the level of gravity passively on a centrifuge, should be equal to or even more beneficial not only to astronauts living in a microgravity environment but also to patients confined to bed. Gravity therapy (GT) may have beneficial effects on numerous conditions, such as immobility due to neuromuscular disorders, balance disorders, stroke, sports injuries. However, the appropriate configuration for administering the Gz load remains to be determined. METHODS: To address these issues, we studied graded G-loads from 0.5 to 2.0g in 24 young healthy, male and female participants, trained on a short arm human centrifuge (SAHC) combined with mild activity exercise within 40-59% MHR, provided by an onboard bicycle ergometer. Hemodynamic parameters, as cardiac output (CO), stroke volume (SV), mean arterial pressure (MAP), systolic blood pressure (SBP), diastolic blood pressure (DBP), and heart rate (HR) were analyzed, as well as blood gas analysis. A one-way repeated measures ANOVA and pairwise comparisons were conducted with a level of significance p < 0.05. RESULTS: Significant changes in heart rate variability (HRV) and its spectral components (Class, Fmax, and VHF) were found in all g loads when compared to standing (p < 0.001), except in 1.7 and 2.0g. There were significant changes in CO, cardiac index (CI), and cardiac power (CP) (p < 0.001), and in MAP (p = 0.003) at different artificial gravity (AG) levels. Dose-response curves were determined based on statistically significant changes in cardiovascular parameters, as well as in identifying the optimal G level for training, as well as the optimal G level for training. There were statistically significant gender differences in Cardiac Output/CO (p = 0.002) and Cardiac Power/CP (p = 0.016) during the AG training as compared to standing. More specifically, these cardiovascular parameters were significantly higher for male than female participants. Also, there was a statistically significant (p = 0.022) gender by experimental condition interaction, since the high-frequency parameter of the heart rate variability was attenuated during AG training as compared to standing but only for the female participants (p = 0.004). CONCLUSION: The comprehensive cardiovascular evaluation of the response to a range of graded AG loads, as compared to standing, in male and female subjects provides the dose-response framework that enables us to explore and validate the usefulness of the centrifuge as a medical device. It further allows its use in precisely selecting personalized gravity therapy (GT) as needed for treatment or rehabilitation of individuals confined to bed.

Human blood adenosine biomarkers and non-rapid eye movement sleep stage 3 (NREM3) cortical functional connectivity associations during a 30-day head-down-tilt bed rest analogue: Potential effectiveness of a reactive sledge jump as a countermeasure.

We investigated the alterations of sleep regulation and promotion biomarkers as adenosine through its enzymes total adenosine deaminase (tADA)/adenosine deaminase (ADA2) in a microgravity analogue environment of head-down-tilt bed rest and their association with brain connectivity networks during non-rapid eye movement sleep stage 3 (NREM3), as well as the effectiveness of the reactive sledge (RSL) jump countermeasure to promote sleep. A total of 23 healthy male volunteers were maintained in 6° head-down-tilt position for 30 days and assigned either to a control or to a RSL group. Blood collection and polysomnographic recordings were performed on data acquisition day 1, 14, 30 and -14, 21, respectively. Immunochemical techniques and network-based statistics were employed for adenosine enzymes and cortical connectivity estimation. Our findings indicate that human blood adenosine biomarkers as well as NREM3 cortical functional connectivity are impaired in simulated microgravity. RSL physical activity intervened in sleep quality via tADA/ADA2 fluctuations lack, minor cortical connectivity increases, and limited degree of node and resting-state networks. Statistically significant decreases in adenosine biomarkers and NREM3 functional connectivity involving regions (left superior temporal gyrus, right postcentral gyrus, precuneus, left middle frontal gyrus, left postcentral gyrus, left angular gyrus and precuneus) of the auditory, sensorimotor default-mode and executive networks highlight the sleep disturbances due to simulated microgravity and the sleep-promoting role of RSL countermeasure. The head-down-tilt environment led to sleep deterioration projected through NREM3 cortical brain connectivity or/and adenosine biomarkers shift. This decline was more pronounced in the absence of the RSL countermeasure, thereby highlighting its likely exploitation during space missions.

Therapeutic Benefits of Short-Arm Human Centrifugation in Multiple Sclerosis-A New Approach.

Short-arm human centrifugation (SAHC) is proposed as a robust countermeasure to treat deconditioning and prevent progressive disability in a case of secondary progressive multiple sclerosis. Based on long-term physiological knowledge derived from space medicine and missions, artificial gravity training seems to be a promising physical rehabilitation approach toward the prevention of musculoskeletal decrement due to confinement and inactivity. So, the present study proposes a novel infrastructure based on SAHC to investigate the hypothesis that artificial gravity ameliorates the degree of disability. The patient was submitted to a 4-week training programme including three weekly sessions of 30 min of intermittent centrifugation at 1.5-2 g. During sessions, cardiovascular, muscle oxygen saturation (SmO2) and electroencephalographic (EEG) responses were monitored, whereas neurological and physical performance tests were carried out before and after the intervention. Cardiovascular parameters improved in a way reminiscent of adaptations to aerobic exercise. SmO2 decreased during sessions concomitant with increased g load, and, as training progressed, SmO2 of the suffering limb dropped, both effects suggesting increased oxygen use, similar to that seen during hard exercise. EEG showed increased slow and decreased fast brain waves, with brain reorganization/plasticity evidenced through functional connectivity alterations. Multiple-sclerosis-related disability and balance capacity also improved. Overall, this study provides novel evidence supporting SAHC as a promising therapeutic strategy in multiple sclerosis, based on mechanical loading, thereby setting the basis for future randomized controlled trials.

A review on current trends in automatic sleep staging through bio-signal recordings and future challenges.

Sleep staging is a vital process conducted in order to analyze polysomnographic data. To facilitate prompt interpretation of these recordings, many automatic sleep staging methods have been proposed. These methods rely on bio-signal recordings, which include electroencephalography, electrocardiography, electromyography, electrooculography, respiratory, pulse oximetry and others. However, advanced, uncomplicated and swift sleep-staging-evaluation is still needed in order to improve the existing polysomnographic data interpretation. The present review focuses on automatic sleep staging methods through bio-signal recording including current and future challenges.

Applications of Convolutional Neural Networks in neurodegeneration and physiological aging.

The process of aging is linked with significant changes in a human's physiological organization and structure. This is more evident in the case of the brain whose functions generally vary between young and old individuals. Detecting such patterns can be of significant importance especially during the Mild Cognitive Impairment (MCI) stage which is a transition state before the clinical onset of dementia. Intervening in that stage may delay or eventually prevent dementia onset. In this paper we propose a new methodology based in electroencephalographic (EEG) recordings, aiming to classify individuals into healthy, pathological (patients diagnosed with MCI or Mild Dementia) and young, old groups (healthy individuals over and under 50 years of age) through functional connectivity and macro-architecture features. These features are calculated on the estimated brain region activations through the inverse problem solution, enabling us to transform the sensor level EEG recordings through an appropriate transformation matrix. Afterwards, Synchronization Likelihood and Relative Wavelet Entropy values were calculated along with the graph metrics corresponding to the functional connectivity values, as well as the relative energy contributions of five EEG bands (delta, theta, alpha, beta and gamma). These features were organized in Red, Green, Blue (RGB) image-like data structures. Therefore, it was possible to classify each individual into one of the two groups per experiment employing Convolutional Neural Networks. From the maximum classification accuracy achieved on the test set, 90.48% for the pathological aging group and 91.19% for the physiological aging, it is evident that the proposed approach is capable of providing adequate health and age group classification.

Automatic Sleep Staging Employing Convolutional Neural Networks and Cortical Connectivity Images.

Understanding of the neuroscientific sleep mechanisms is associated with mental/cognitive and physical well-being and pathological conditions. A prerequisite for further analysis is the identification of the sleep macroarchitecture through manual sleep staging. Several computer-based approaches have been proposed to extract time and/or frequency-domain features with accuracy ranging from 80% to 95% compared with the golden standard of manual staging. However, their acceptability by the medical community is still suboptimal. Recently, utilizing deep learning methodologies increased the research interest in computer-assisted recognition of sleep stages. Aiming to enhance the arsenal of automatic sleep staging, we propose a novel classification framework based on convolutional neural networks. These receive as input synchronizations features derived from cortical interactions within various electroencephalographic rhythms (delta, theta, alpha, and beta) for specific cortical regions which are critical for the sleep deepening. These functional connectivity metrics are then processed as multidimensional images. We also propose to augment the small portion of sleep onset (N1 stage) through the Synthetic Minority Oversampling Technique in order to deal with the great difference in its duration when compared with the remaining sleep stages. Our results (99.85%) indicate the flexibility of deep learning techniques to learn sleep-related neurophysiological patterns.

The Pattern of Mu Rhythm Modulation During Emotional Destination Memory: Comparison Between Mild Cognitive Impairment Patients and Healthy Controls.

Leading theories of affect development and empirical studies suggest that emotion can enhance memory in older adults. Destination memory which is defined as the ability to remember to whom we told a piece of information is being found to be compromised in aging. In the present study, we sought to assess destination memory using emotional stimuli (Emotional Destination Memory, EDM) in 16 older adults with mild cognitive impairment (MCI) and 16 healthy controls and shed light onto its potential neurophysiological aspects. We measured Mu suppression in frontal and temporal regions via EEG in real time while participants performed the task of EDM. Results showed no group differences in task performance but significant differences in fronto-temporal activations, specifically in electrodes F7 and F8. Differential Mu rhythm pattern was observed between healthy controls and MCI with the first exhibiting Mu suppression and the last Mu enhancement. Furthermore, Mu enhancement in temporal electrodes within the MCI group was associated with lower scores on EDM. The absence of group differences in the task can be explained by the fact that even if there are underlying structural or functional deficits in the MCI group, these deficits are manifested only at neurophysiological level and not at a behavioral level, which is a common pattern in the process of cognitive decline in its initial phases. The overall findings reveal that, even if there are not any behavioral decrements in MCI patients, they show reduced activations in fronto-temporal regions and this can be attributed to general impairment in emotional destination memory due to possible mirror neuron deficiency.

Current trends and future perspectives of space neuroscience towards preparation for interplanetary missions.

This review addresses central nervous system (CNS) physiological changes during inter-planetary missions, specifically sensorimotor processing and sleep disorders. Isolation, confinement and induced stress factors also have a detrimental effect on cognitive and mental well-being, which could jeopardize mission accomplishment. Although countermeasures have been proposed, they mostly focus on cardiovascular and/or musculoskeletal systems. Long-term space flights require optimal cognitive performance of crew members during weightlessness for longer time periods independent of ground support. The present study describes various countermeasures trends in neuroscientific data acquisition and future perspectives of advanced analysis through functional connectivity and graph theory. These could be used to identify early deterioration patterns and evaluate the robustness of countermeasures employed.

SmartHypnos: Developing a Toolbox for Polysomnographic Data Visualization and Analysis.

In this paper we present the first steps in developing SmartHypnos, an easy to use and user friendly graphical user interface, which aims to provide polysomngographic data visualization and the detection and classification of sleep related events. Currently SmartHypnos supports the visualization of EEG, ECG, EOG and EMG signals, and respiratory signals such as nasal pressure, thermistor, oxygen saturation, thoracic and abdominal belt recordings. All these are incorporated into an interface that provides quick and effortless access to the signals mentioned above. The interface displays automatic sleep staging capabilities as well as the detection of apnea events with accuracy rates surpassing 80%. It is expected that SmartHypnos will reduce the time required to analyze sleep data and also reduce possible human errors.

Advanced network neuroscience approaches in sleep neurobiology on extreme environments.

In this paper we propose a novel methodology for investigating pathological sleep patterns through network neuroscience approaches. It consists of initial identification of statistically significant alterations in cortical functional connectivity patterns. The resulting sub-network is then analyzed by employing graph theory for estimating both global performance metrics (integration and specialization) as well as the significance of specific network nodes and their hierarchical organization. So, nodes with important role in network structure are recognized and their functionality is correlated with adenosine biomarker which is important in sleep regulation and promotion. The aforementioned pipeline is applied in a dataset of sleep data gathered during a microgravity simulation experiment. The analysis was performed on cortical resting-state networks involved in sleep physiology. It demonstrated the detrimental effects of microgravity which were more prominent for the group which did not perform reactive sledge jumps as a countermeasure.

Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics.

Sleep staging, the process of assigning labels to epochs of sleep, depending on the stage of sleep they belong, is an arduous, time consuming and error prone process as the initial recordings are quite often polluted by noise from different sources. To properly analyze such data and extract clinical knowledge, noise components must be removed or alleviated. In this paper a pre-processing and subsequent sleep staging pipeline for the sleep analysis of electroencephalographic signals is described. Two novel methods of functional connectivity estimation (Synchronization Likelihood/SL and Relative Wavelet Entropy/RWE) are comparatively investigated for automatic sleep staging through manually pre-processed electroencephalographic recordings. A multi-step process that renders signals suitable for further analysis is initially described. Then, two methods that rely on extracting synchronization features from electroencephalographic recordings to achieve computerized sleep staging are proposed, based on bivariate features which provide a functional overview of the brain network, contrary to most proposed methods that rely on extracting univariate time and frequency features. Annotation of sleep epochs is achieved through the presented feature extraction methods by training classifiers, which are in turn able to accurately classify new epochs. Analysis of data from sleep experiments on a randomized, controlled bed-rest study, which was organized by the European Space Agency and was conducted in the "ENVIHAB" facility of the Institute of Aerospace Medicine at the German Aerospace Center (DLR) in Cologne, Germany attains high accuracy rates, over 90% based on ground truth that resulted from manual sleep staging by two experienced sleep experts. Therefore, it can be concluded that the above feature extraction methods are suitable for semi-automatic sleep staging.

Functional Re-organization of Cortical Networks of Senior Citizens After a 24-Week Traditional Dance Program.

Neuroscience is developing rapidly by providing a variety of modern tools for analyzing the functional interactions of the brain and detection of pathological deviations due to neurodegeneration. The present study argues that the induction of neuroplasticity of the mature human brain leads to the prevention of dementia. Promising solution seems to be the dance programs because they combine cognitive and physical activity in a pleasant way. So, we investigated whether the traditional Greek dances can improve the cognitive, physical and functional status of the elderly always aiming at promoting active and healthy aging. Forty-four participants were randomly assigned equally to the training group and an active control group. The duration of the program was 6 months. Also, the participants were evaluated for their physical status and through an electroencephalographic (EEG) examination at rest (eyes-closed condition). The EEG testing was performed 1-14 days before (pre) and after (post) the training. Cortical network analysis was applied by modeling the cortex through a generic anatomical model of 20,000 fixed dipoles. These were grouped into 512 cortical regions of interest (ROIs). High quality, artifact-free data resulting from an elaborate pre-processing pipeline were segmented into multiple, 30 s of continuous epochs. Then, functional connectivity among those ROIs was performed for each epoch through the relative wavelet entropy (RWE). Synchronization matrices were computed and then thresholded in order to provide binary, directed cortical networks of various density ranges. The results showed that the dance training improved optimal network performance as estimated by the small-world property. Further analysis demonstrated that there were also local network changes resulting in better information flow and functional re-organization of the network nodes. These results indicate the application of the dance training as a possible non-pharmacological intervention for promoting mental and physical well-being of senior citizens. Our results were also compared with a combination of computerized cognitive and physical training, which has already been demonstrated to induce neuroplasticity (LLM Care).