Miklos Gyori

Reduced fronto-cortical brain connectivity during NREM sleep in Asperger syndrome: An EEG spectral and phase coherence study

OBJECTIVE To investigate whether sleep macrostructure and EEG power spectral density and coherence during NREM sleep are different in Asperger syndrome (AS) compared to typically developing children and adolescents. METHODS Standard all night EEG sleep parameters were obtained from 18 un-medicated subjects with AS and 14 controls (age range: 7.5-21.5years) after one adaptation night. Spectral, and phase coherence measures were computed for multiple frequency bands during NREM sleep. RESULTS Sleep latency and wake after sleep onset were increased in AS. Absolute power spectrum density (PSD) was significantly reduced in AS in the alpha, sigma, beta and gamma bands and in all 10 EEG derivations. Relative PSD showed a significant increase in delta and a decrease in the sigma band for frontal, and in beta for centro-temporal derivations. Intrahemispheric coherence measures were markedly lower in AS in the frontal areas, and the right hemisphere over all EEG channels. The most prominent reduction in intrahemispheric coherence was observed over the fronto-central areas in delta, theta, alpha and sigma EEG frequency bands. CONCLUSION EEG power spectra and coherence during NREM sleep, in particular in fronto-cortical derivations are different in AS compared to typically developing children and adolescents. SIGNIFICANCE Quantitative analysis of the EEG during NREM sleep supports the hypothesis of frontal dysfunction in AS.

Data Quality as a Bottleneck in Developing a Social-Serious-Game-Based Multi-modal System for Early Screening for ‘High Functioning’ Cases of Autism Spectrum Condition

Our aim is to explore raw data quality in the first evaluation of the first fully playable prototype of a social-serious-game-based, multi-modal, interactive software system for screening for high functioning cases of autism spectrum condition at kindergarten age. Data were collected from 10 high functioning children with autism spectrum condition and 10 typically developing children. Mouse and eye-tracking data, and data from automated emotional facial expression recognition were analyzed quantitatively. Results show a sub-optimal level of raw data quality and suggest that it is a bottleneck in developing screening/diagnostic/assessment tools based on multi-mode behavioral data.

Touching the Future Technology for Autism

Periodontal disease has been recently linked to a variety of systemic conditions such as diabetes, cardiovascular disease, preterm delivery, and oral cancer. The most common bacteria associated with periodontal disease, Porphyromonas gingivalis (P. gingivalis) has not yet been studied in the malignant gingival tissues. The objective of this study was to investigate the presence of P. gingivalis in specimens from squamous cell carcinoma patients. We have performed immunohistochemical staining to investigate the presence of P. gingivalis and Streptococcus gordonii (S. gordonii), a non invasive oral bacteria, in paraffin embedded samples of gingival squamous cell carcinoma (n=10) and normal gingiva (n=5). Staining for P. gingivalis revealed the presence of the bacteria in normal gingival tissues and gingival carcinoma, with higher levels (more than 33%, P<0.05) detected in the carcinoma samples. The staining intensity was also significantly enhanced in the malignant tissue by 2 folds (P<0.023) compared to specimens stained for the non-invasive S. gordonii. P. gingivalis is abundantly present in malignant oral epithelium suggesting a potential association of the bacteria with gingival squamous cell carcinoma.

Assessing Support Needs for Developing an App-Based Assistive System for Enhancing Independence in the Autism Spectrum: Results from a Mixed-Mode Study

The DATA project is intended to develop a compound digital assistive system, including a mobile app set, for increasing independence in people with autism. The objective of this study was to explore this target group’s support needs, as a basis for the system designing process. A mixed-mode study was implemented, with an on-line survey (N = 147), and with 3 focus groups. The support need profiles were different across levels of functioning, with only minor differences between profiles provided by parents vs professionals. One’s awareness of support methods emerged as an important factor.

Automated vs Human Recognition of Emotional Facial Expressions of High-Functioning Children with Autism in a Diagnostic-Technological Context: Explorations via a Bottom-Up Approach

Early detection of autism spectrum conditions (ASC) is an important goal. Automated facial expression recognition is a promising approach and has implications for assistive and educational technologies, too. This study was an initial exploration of (1) the inter-rater reliability of human recognition of facial emotions of high functioning (HF) children with ASC; (2) the relationship between human and automated recognition of facial emotions; and (3) a ‘bottom-up’ approach on identifying ASC/typical development (TD) differences, from a screening serious game context. Thirteen HF, kindergarten-age children with ASC and 13 children with TD, matched along age and IQ, participated. Emotion recognition was administered on video-recordings from sessions of their playing with the serious game. Results showed lack of inter-rater reliability in human coding, confirming some advantages of machine coding. The simple bottom-up cross-sectional exploratory analysis did not reveal any ASC/TD difference. This is in contrast with our and others’ previous results, indicating such differences when aggregating emotion data from wider time-windows in machine-coded data-sets. This suggests that this second approach may be a more promising one to identify autism-specific emotion expression patterns.