Date on Master's Thesis/Doctoral Dissertation

4-2012

Document Type

Master's Thesis

Degree Name

M. Eng.

Department

Bioengineering

Committee Chair

El-Baz, Ayman Sabry

Author's Keywords

Induced gamma; ADHD; Autism; Data alignment; Emotion recognition

Subject

Brain--Electric properties; Oscillations; Autism spectrum disorders; Attention-deficit hyperactivity disorder

Abstract

Introduction: Children diagnosed with an Autism Spectrum Disorder (ASD) often lack the ability to recognize and properly respond to emotional stimuli. These emotional deficits are also observed in children with Attention-Deficit Hyperactivity Disorder (ADHD), but are often overshadowed by the focus on limited attention span. A growing body of research suggests that there may be links between ASD and ADHD, which requires further study. Investigation of this hypothesis often relies on the Theory of Mind (ToM) construct to frame experiments that explore the relationship between these two conditions. Many experiments utilize electroencephalographic (EEG) data to quantitatively assess brain activity. The emotional deficits in ASD and ADHD may cause a difference within the induced EEG gamma wave burst phenomenon (35-45 Hz) produced approximately 300-400 milliseconds following an emotional stimulus. Because induced gamma oscillations are not fixed at a definite point in time post-stimulus, analysis of averaged EEG data with traditional methods may result in an attenuated gamma burst power. Two hypotheses were proposed in this study. First, a software based data alignment technique could be employed to reduce the attenuation observed in the analysis of these phenomena. Second, improvement of the attenuation would better elucidate similarities and differences to stimuli in an experimental study comparing ASD, ADHD, and control subjects. Methods: A study was designed to test the response of a subject to emotional stimuli, presented in the form of expressive facial images. In a four part experiment, the subjects were instructed to identify gender in the first two blocks of the test, followed by differentiating between basic emotions in the final two blocks (i.e. anger vs. disgust). EEG data was collected from ASD (n=10), ADHD (n=9), and control (n=11) subjects via a 128 channel EGI system, and processed through a continuous wavelet transform and bandpass filter to isolate the gamma frequencies. Data alignment was then employed by using a custom MATLAB code to align the individual trials between 200-600 ms post-stimulus for each subject, EEG site, and condition by maximizing the Pearson product-moment correlation coefficient between trials within these groups. The gamma power for the 400 ms window of maximum induced gamma burst was then calculated and compared between subject groups. Results: Significant main effects for the alignment condition were present across all subject groups, experiment conditions, and EEG channels. Significant main effects also existed for the experimental condition and subject groups. Condition (anger/disgust recognition, gender recognition) x Alignment x Group (ADHD, ASD, Controls) interaction was significant across the parietal topographies. These interactions were better manifested in the aligned data set. Conclusions: Both hypotheses were supported by the obtained results. The employed data alignment technique significantly reduced the amount of attenuation observed in the averaged signals. Additionally, further analysis showed that significant interactions were more easily observed in the aligned dataset, which suggests that this technique may be beneficial for furthering the comparison of the emotional deficits in ASD and ADHD.

Download

Share

COinS