Atypical hemispheric specialization for faces in infants at risk for autism spectrum disorder

Among the many experimental findings that tend to distinguish those with and without autism spectrum disorder (ASD) are face processing deficits, reduced hemispheric specialization, and atypical neurostructural and functional connectivity. To investigate the earliest manifestations of these features, we examined lateralization of event-related gamma-band coherence to faces during the first year of life in infants at high risk for autism (HRA; defined as having an older sibling with ASD) who were compared with low-risk comparison (LRC) infants, defined as having no family history of ASD. Participants included 49 HRA and 46 LRC infants who contributed a total of 127 data sets at 6 and 12 months. Electroencephalography was recorded while infants viewed images of familiar/unfamiliar faces. Event-related gamma-band (30-50 Hz) phase coherence between anterior-posterior electrode pairs for left and right hemispheres was computed. Developmental trajectories for lateralization of intra-hemispheric coherence were significantly different in HRA and LRC infants: by 12 months, HRA infants showed significantly greater leftward lateralization compared with LRC infants who showed rightward lateralization. Preliminary results indicate that infants who later met criteria for ASD were those that showed the greatest leftward lateralization. HRA infants demonstrate an aberrant pattern of leftward lateralization of intra-hemispheric coherence by the end of the first year of life, suggesting that the network specialized for face processing may develop atypically. Further, infants with the greatest leftward asymmetry at 12 months where those that later met criteria for ASD, providing support to the growing body of evidence that atypical hemispheric specialization may be an early neurobiological marker for ASD.R01 DC010290 - NIDCD NIH HHS; R01-DC010290 - NIDCD NIH HH

Atypical eye contact in autism: Models, mechanisms and development

An atypical pattern of eye contact behaviour is one of the most significant symptoms of Autism Spectrum Disorder (ASD). Recent empirical advances have revealed the developmental, cognitive and neural basis of atypical eye contact behaviour in ASD. We review different models and advance a new ‘fast-track modulator model’. Specifically, we propose that atypical eye contact processing in ASD originates in the lack of influence from a subcortical face and eye contact detection route, which is hypothesized to modulate eye contact processing and guide its emergent specialization during development

Driving steady-state visual evoked potentials at arbitrary frequencies using temporal interpolation of stimulus presentation

Date of Acceptance: 29/10/2015 We thank Renate Zahn for help with data collection. This work was supported by Deutsche Forschungsgemeinschaft (AN 841/1-1, MU 972/20-1). We would like to thank A. Trujillo-Ortiz, R. Hernandez-Walls, A. Castro-Perez and K. BarbaRojo (Universidad Autonoma de Baja California) for making Matlab code for non-sphericity corrections freely available.Peer reviewedPublisher PD

Steady-state evoked potentials possibilities for mental-state estimation

The use of the human steady-state evoked potential (SSEP) as a possible measure of mental-state estimation is explored. A method for evoking a visual response to a sum-of-ten sine waves is presented. This approach provides simultaneous multiple frequency measurements of the human EEG to the evoking stimulus in terms of describing functions (gain and phase) and remnant spectra. Ways in which these quantities vary with the addition of performance tasks (manual tracking, grammatical reasoning, and decision making) are presented. Models of the describing function measures can be formulated using systems engineering technology. Relationships between model parameters and performance scores during manual tracking are discussed. Problems of unresponsiveness and lack of repeatability of subject responses are addressed in terms of a need for loop closure of the SSEP. A technique to achieve loop closure using a lock-in amplifier approach is presented. Results of a study designed to test the effectiveness of using feedback to consciously connect humans to their evoked response are presented. Findings indicate that conscious control of EEG is possible. Implications of these results in terms of secondary tasks for mental-state estimation and brain actuated control are addressed

EEG analytics for early detection of autism spectrum disorder: a data-driven approach

Autism spectrum disorder (ASD) is a complex and heterogeneous disorder, diagnosed on the basis of behavioral symptoms during the second year of life or later. Finding scalable biomarkers for early detection is challenging because of the variability in presentation of the disorder and the need for simple measurements that could be implemented routinely during well-baby checkups. EEG is a relatively easy-to-use, low cost brain measurement tool that is being increasingly explored as a potential clinical tool for monitoring atypical brain development. EEG measurements were collected from 99 infants with an older sibling diagnosed with ASD, and 89 low risk controls, beginning at 3 months of age and continuing until 36 months of age. Nonlinear features were computed from EEG signals and used as input to statistical learning methods. Prediction of the clinical diagnostic outcome of ASD or not ASD was highly accurate when using EEG measurements from as early as 3 months of age. Specificity, sensitivity and PPV were high, exceeding 95% at some ages. Prediction of ADOS calibrated severity scores for all infants in the study using only EEG data taken as early as 3 months of age was strongly correlated with the actual measured scores. This suggests that useful digital biomarkers might be extracted from EEG measurements.This research was supported by National Institute of Mental Health (NIMH) grant R21 MH 093753 (to WJB), National Institute on Deafness and Other Communication Disorders (NIDCD) grant R21 DC08647 (to HTF), NIDCD grant R01 DC 10290 (to HTF and CAN) and a grant from the Simons Foundation (to CAN, HTF, and WJB). We are especially grateful to the staff and students who worked on the study and to the families who participated. (R21 MH 093753 - National Institute of Mental Health (NIMH); R21 DC08647 - National Institute on Deafness and Other Communication Disorders (NIDCD); R01 DC 10290 - NIDCD; Simons Foundation)Published versio

Analyzing the Impact of Cognitive Load in Evaluating Gaze-based Typing

Gaze-based virtual keyboards provide an effective interface for text entry by eye movements. The efficiency and usability of these keyboards have traditionally been evaluated with conventional text entry performance measures such as words per minute, keystrokes per character, backspace usage, etc. However, in comparison to the traditional text entry approaches, gaze-based typing involves natural eye movements that are highly correlated with human brain cognition. Employing eye gaze as an input could lead to excessive mental demand, and in this work we argue the need to include cognitive load as an eye typing evaluation measure. We evaluate three variations of gaze-based virtual keyboards, which implement variable designs in terms of word suggestion positioning. The conventional text entry metrics indicate no significant difference in the performance of the different keyboard designs. However, STFT (Short-time Fourier Transform) based analysis of EEG signals indicate variances in the mental workload of participants while interacting with these designs. Moreover, the EEG analysis provides insights into the user's cognition variation for different typing phases and intervals, which should be considered in order to improve eye typing usability.Comment: 6 pages, 4 figures, IEEE CBMS 201

Analytical methods and experimental approaches for electrophysiological studies of brain oscillations

Brain oscillations are increasingly the subject of electrophysiological studies probing their role in the functioning and dysfunction of the human brain. In recent years this research area has seen rapid and significant changes in the experimental approaches and analysis methods. This article reviews these developments and provides a structured overview of experimental approaches, spectral analysis techniques and methods to establish relationships between brain oscillations and behaviour