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A Semantic Priming Event-related Potential (ERP) Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder
Chapters
Summary April 12th, 2018
This paper describes a semantic priming ERP task using within-modality pairs of pictures and words to investigate semantic processing in individuals with autism spectrum disorder (ASD).
Transcript
The overall goal of this semantic priming ERP methodology is to compare semantic processing of linguistic and non-linguistic stimuli in the form of words and pictures, in individuals with autism spectrum disorders, compared to typically-developing individuals. This method can help answer key questions in the autism field, such as whether individuals with autism spectrum disorders have a global deficit in semantic processing that extends beyond language. The main advantage of this technique is that it uses electroencephalography, a technique of measuring brain activity that can pick up on very subtle differences in cognitive processing between groups.
Demonstrating the procedure will be Emme O'Rourke, a research assistant in my lab. Measure the circumference of the participant's head, passing through the inion and nasion in order to select the appropriate sized EEG net. Begin by immersing the electroencephalography, or EEG electrodes, in electrolyte solution for five minutes.
Instruct the participant to close their eyes, then place the net so it fits snugly against their head. Once all electrodes have been seated against the scalp, check impedances. Re-seat or re-wet any electrodes with impedances above 50 kilo-Ohms.
Finally, instruct the participant to judge whether the stimuli on the screen are related or unrelated, by pressing a button on the keyboard. Tell them to wait to make their response until the second stimulus has disappeared from the screen, and the black cross has disappeared. Finally, have the participant complete the task.
Begin by opening the EEG processing software. To filter the data, create a new filtering tool by selecting filtering in the create drop-down menu, and rename the tool. Then set the highpass filter to 0.1 Hertz, and the lowpass filter to 50 Hertz.
Save the tool, then drag the original EEG recording file into the input files box at the top left in the window, and hit run. Next, to segment the data into trials, create a new segmentation tool, and name it appropriately. Under categories to create, hit the plus sign to create a new category, and rename it picture related.
Drag the code icon into the create category based on criteria box, and set it as code is PIC1, to time lock to the presentation of stimulus 1. Drag the key code icon into the create category box, and set it as key code cell number is 1. Note, to include only correct trials, drag another key code icon into the create category box, and set it as key code eval is 1.
Then, at the bottom of the window, set the segment length to extend segment 100 milliseconds before, and 2, 300 milliseconds after. Clone the category by hitting the clone button, and rename it picture unrelated. Set the code to PIC1, and the key code cell number to 2.
Clone the category and rename it word related. Set the code to WRD1, and the key code cell number to 3. Then clone the category again, and rename it word unrelated.
Set the code to WRD1 and the key code cell number to 4. Save the tool, then drag the most recent preprocessed file into the input files box at the top left of the window, and hit run. To perform artifact detection, create a new artifact detection tool, and name it.
Under artifact detection settings, hit the plus sign at the bottom of the window to add a new setting. Select bad channels from the drop-down menu under operation. Leave all of the default settings.
Add a new setting and add select eye blink from the operation drop-down menu, and leave all of the default settings. Then, add a new setting, and select eye movement from the operation drop-down menu, and leave all of the settings. Save the tool, then drag the most recent preprocessed file into the input files box at the top left of the window, and hit run.
Open the resulting file in the review panel and scroll through every trial by hitting the arrow buttons under the categories menu on the right sidebar. Mark trials as good or bad by hitting the green or red circles, respectively. When done, close the file to save the results.
Next, create a new bad bad channel replacement tool, and name it. Save the tool, then drag the most recent preprocessed file into the input files box at the top left of the window, and hit run. Perform single subject averaging to collapse over trials.
Create a new averaging tool and name it appropriately. Under averaging settings, select handle source files together, and handle subjects separately. Then, save the tool, then drag the most recent preprocessed file into the input files box at the top left of the window, and hit run.
Next, create a new montage operations tool, and name it appropriately. Select the appropriate net from the drop-down menu under list montages for sensor layout. Select average reference, and make sure the exclude bad channels from reference box is selected.
Save the tool, then drag the most recent preprocessed file into the input files box at the top left of the window, and hit run. Finally, perform baseline correction using the first 100 milliseconds of the segment. Create a new baseline correction tool, and name it.
Under baseline correction settings, click select baseline from portion of segment. Select baseline with respect to segment time equals zero, and baseline begins 100 milliseconds before time zero, and is 100 milliseconds long. Lastly, save the tool, then drag the most recent preprocessed file into the input files box at the top left of the window, and hit run.
Here, representative ERP waveforms at electrode CZ show a larger amplitude to unrelated conditions compared to related conditions, at approximately 400 milliseconds. Further, these topographic plots show the unrelated/related difference, averaged over a window from 400 to 500 milliseconds. Clinical groups, such as individuals with ASD, may show a smaller N400 effect in response to words, which suggests difficulties with lexicosemantic processing.
Once the stimuli are created and the task is ready, EEG testing and pre-processing takes approximately one and a half hours per participant. This technique provides a unique way for researchers in the field of autism to explore semantic processing across different modalities in individuals with autism spectrum disorder. Following this procedure, other methods, like functional magnetic resonance imaging, can be performed to answer additional questions, like the neural circuitry underlying lexicosemantic and visual-semantic processing in individuals with autism spectrum disorder.
Don't forget that working with individuals with autism can sometimes be challenging, and that certain adaptations or amendments to testing may need to be done while performing the EEG procedure.
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