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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

Future perspectives toward the early definition of a multivariate decision-support scheme employed in clinical decision making for senior citizens

Recent neuroscientific studies focused on the identification of pathological neurophysiological patterns (emotions, geriatric depression, memory impairment and sleep disturbances) through computerised clinical decision-support systems. Almost all these research attempts employed either resting-state condition (e.g. eyes-closed) or event-related potentials extracted during a cognitive task known to be affected by the disease under consideration. This Letter reviews existing data mining techniques and aims to enhance their robustness by proposing a holistic decision framework dealing with comorbidities and early symptoms' identification, while it could be applied in realistic occasions. Multivariate features are elicited and fused in order to be compared with average activities characteristic of each neuropathology group. A proposed model of the specific cognitive function which may be based on previous findings (a priori information) and/or validated by current experimental data should be then formed. So, the proposed scheme facilitates the early identification and prevention of neurodegenerative phenomena. Neurophysiological semantic annotation is hypothesised to enhance the importance of the proposed framework in facilitating the personalised healthcare of the information society and medical informatics research community.

Beta-Band Functional Connectivity is Reorganized in Mild Cognitive Impairment after Combined Computerized Physical and Cognitive Training

Physical and cognitive idleness constitute significant risk factors for the clinical manifestation of age-related neurodegenerative diseases. In contrast, a physically and cognitively active lifestyle may restructure age-declined neuronal networks enhancing neuroplasticity. The present study, investigated the changes of brain's functional network in a group of elderly individuals at risk for dementia that were induced by a combined cognitive and physical intervention scheme. Fifty seniors meeting Petersen's criteria of Mild Cognitive Impairment were equally divided into an experimental (LLM), and an active control (AC) group. Resting state electroencephalogram (EEG) was measured before and after the intervention. Functional networks were estimated by computing the magnitude square coherence between the time series of all available cortical sources as computed by standardized low resolution brain electromagnetic tomography (sLORETA). A statistical model was used to form groups' characteristic weighted graphs. The introduced modulation was assessed by networks' density and nodes' strength. Results focused on the beta band (12-30 Hz) in which the difference of the two networks' density is maximum, indicating that the structure of the LLM cortical network changes significantly due to the intervention, in contrast to the network of AC. The node strength of LLM participants in the beta band presents a higher number of bilateral connections in the occipital, parietal, temporal and prefrontal regions after the intervention. Our results show that the combined training scheme reorganizes the beta-band functional connectivity of MCI patients. ClinicalTrials.gov Identifier: NCT02313935 https://clinicaltrials.gov/ct2/show/NCT02313935.

Functional disorganization of small-world brain networks in mild Alzheimer's Disease and amnestic Mild Cognitive Impairment: an EEG study using Relative Wavelet Entropy (RWE)

Previous neuroscientific findings have linked Alzheimer's Disease (AD) with less efficient information processing and brain network disorganization. However, pathological alterations of the brain networks during the preclinical phase of amnestic Mild Cognitive Impairment (aMCI) remain largely unknown. The present study aimed at comparing patterns of the detection of functional disorganization in MCI relative to Mild Dementia (MD). Participants consisted of 23 cognitively healthy adults, 17 aMCI and 24 mild AD patients who underwent electroencephalographic (EEG) data acquisition during a resting-state condition. Synchronization analysis through the Orthogonal Discrete Wavelet Transform (ODWT), and directional brain network analysis were applied on the EEG data. This computational model was performed for networks that have the same number of edges (N = 500, 600, 700, 800 edges) across all participants and groups (fixed density values). All groups exhibited a small-world (SW) brain architecture. However, we found a significant reduction in the SW brain architecture in both aMCI and MD patients relative to the group of Healthy controls. This functional disorganization was also correlated with the participant's generic cognitive status. The deterioration of the network's organization was caused mainly by deficient local information processing as quantified by the mean cluster coefficient value. Functional hubs were identified through the normalized betweenness centrality metric. Analysis of the local characteristics showed relative hub preservation even with statistically significant reduced strength. Compensatory phenomena were also evident through the formation of additional hubs on left frontal and parietal regions. Our results indicate a declined functional network organization even during the prodromal phase. Degeneration is evident even in the preclinical phase and coexists with transient network reorganization due to compensation.

A decision-support framework for promoting independent living and ageing well

Artificial intelligence and decision support systems offer a plethora of health monitoring capabilities in ambient assisted living environment. Continuous assessment of health indicators for elderly people living on their own is of utmost importance, so as to prolong their independence and quality of life. Slow varying, long-term deteriorating health trends are not easily identifiable in seniors. Thus, early sign detection of a specific condition, as well as, any likely transition from a healthy state to a pathological one are key problems that the herein proposed framework aims at resolving. Statistical process control concepts offer a personalized approach toward identification of trends that are away from the atypical behavior or state of the seniors, while fuzzy cognitive maps knowledge representation and inference schema have proved to be efficient in terms of disease classification. Geriatric depression is used as a case study throughout the paper, so to prove the validity of the framework, which is planned to be pilot tested with a series of lone-living seniors in their own homes.

Cognitive and physical training for the elderly: evaluating outcome efficacy by means of neurophysiological synchronization

Recent neuroscientific research has demonstrated that both healthy and pathological aging induces alterations in the co-operative capacity of neuronal populations in the brain. Both compensatory and neurodegenerative mechanisms contribute to neurophysiological synchronization patterns, which provide a valuable marker for age-related cognitive decline. In this study, we propose that neuroplasticity-based training may facilitate coherent interaction of distant brain regions and consequently enhance cognitive performance in elderly people. If this is true, this would make neurophysiological synchronization a valid outcome measure to assess the efficacy of non-pharmacological interventions to prevent or delay age-related cognitive decline. The present study aims at providing an objective, synchronization-based tool to assess cognitive and/or physical interventions, adopting the notion of Relative Wavelet Entropy. This mathematical model employs a robust and parameter-free synchronization metric. By using data mining techniques, a distance value was computed for all participants so as to quantify the proximity of their individual profile to the mean group synchronization increase. In support of our hypothesis, results showed a significant increase in synchronization, for four electrode pairs, in the intervention group as compared to the active control group. It is concluded that the novel introduction of neurophysiological synchronization features could be used as a valid and reliable outcome measure; while the distance-based analysis could provide a reliable means of evaluating individual benefits.

Real time emotion aware applications: a case study employing emotion evocative pictures and neuro-physiological sensing enhanced by Graphic Processor Units

In this paper the feasibility of adopting Graphic Processor Units towards real-time emotion aware computing is investigated for boosting the time consuming computations employed in such applications. The proposed methodology was employed in analysis of encephalographic and electrodermal data gathered when participants passively viewed emotional evocative stimuli. The GPU effectiveness when processing electroencephalographic and electrodermal recordings is demonstrated by comparing the execution time of chaos/complexity analysis through nonlinear dynamics (multi-channel correlation dimension/D2) and signal processing algorithms (computation of skin conductance level/SCL) into various popular programming environments. Apart from the beneficial role of parallel programming, the adoption of special design techniques regarding memory management may further enhance the time minimization which approximates a factor of 30 in comparison with ANSI C language (single-core sequential execution). Therefore, the use of GPU parallel capabilities offers a reliable and robust solution for real-time sensing the user's affective state.

Toward emotion aware computing: an integrated approach using multichannel neurophysiological recordings and affective visual stimuli

This paper proposes a methodology for the robust classification of neurophysiological data into four emotional states collected during passive viewing of emotional evocative pictures selected from the International Affective Picture System. The proposed classification model is formed according to the current neuroscience trends, since it adopts the independency of two emotional dimensions, namely arousal and valence, as dictated by the bidirectional emotion theory, whereas it is gender-specific. A two-step classification procedure is proposed for the discrimination of emotional states between EEG signals evoked by pleasant and unpleasant stimuli, which also vary in their arousal/intensity levels. The first classification level involves the arousal discrimination. The valence discrimination is then performed. The Mahalanobis (MD) distance-based classifier and support vector machines (SVMs) were used for the discrimination of emotions. The achieved overall classification rates were 79.5% and 81.3% for the MD and SVM, respectively, significantly higher than in previous studies. The robust classification of objective emotional measures is the first step toward numerous applications within the sphere of human-computer interaction.

On the classification of emotional biosignals evoked while viewing affective pictures: an integrated data-mining-based approach for healthcare applications

Recent neuroscience findings demonstrate the fundamental role of emotion in the maintenance of physical and mental health. In the present study, a novel architecture is proposed for the robust discrimination of emotional physiological signals evoked upon viewing pictures selected from the International Affective Picture System (IAPS). Biosignals are multichannel recordings from both the central and the autonomic nervous systems. Following the bidirectional emotion theory model, IAPS pictures are rated along two dimensions, namely, their valence and arousal. Following this model, biosignals in this paper are initially differentiated according to their valence dimension by means of a data mining approach, which is the C4.5 decision tree algorithm. Then, the valence and the gender information serve as an input to a Mahalanobis distance classifier, which dissects the data into high and low arousing. Results are described in Extensible Markup Language (XML) format, thereby accounting for platform independency, easy interconnectivity, and information exchange. The average recognition (success) rate was 77.68% for the discrimination of four emotional states, differing both in their arousal and valence dimension. It is, therefore, envisaged that the proposed approach holds promise for the efficient discrimination of negative and positive emotions, and it is hereby discussed how future developments may be steered to serve for affective healthcare applications, such as the monitoring of the elderly or chronically ill people.

Towards a semantic framework for an integrative description of neuroscience patterns and studies: a case for emotion-related data

The continuously increasing number of neuroscience studies and the difficulties associated with searching for related information and properly tracking neuroscience findings makes it imperative that one may be lead to isolated theories and findings which may be incompatible to each other or partially occluded. Semantically describing several aspects of studies in this field, such as, research groups attributes, aims of studies, experimental procedures followed, hardware and software tools utilised, acquisition systems used, as well as, the emerging neuro-physiological patterns found, may facilitate an integrative view of neuroscience theories. To this end, the current piece of work aims to provide a global theoretical framework using ontologies and semantic rules to describe neuroscience studies. Implementation details and applicability of the proof of concept are illustrated by means of an example targeting the semantic description of an emotion related study. The importance of the proposed framework in facilitating the envisaged personalised healthcare of the information society is discussed.

An automated system for processing electrodermal activity

A new approach is presented in this paper for the display and processing of electrodermal activity. It offers a fully automated interface for the pre-processing and scoring individual skin conductance responses (SCRs). The application supports parallel processing by means of multiple threads. Batch processing is also available. The XML format is used to describe the derived features. The system is employed to analyze emotion-related data.