Name: an electrophysiology protocol to measure reward anticipation and processing in children
Uploaded: 2018-10-04T14:30:00.000+00:00
Duration: 5 min 4 s
Description: Watch this Scientific Journal Video about An Electrophysiology Protocol to Measure Reward Anticipation and Processing in Children at JoVE.com

We thank all the children and families who participated in the protocols described. Publishing fees were paid by Brain Products.

Procedures involving human participants were approved by the Human Subject Research Ethics Committee/Institutional Review Board at University of California, Riverside and University of California, San Diego.
1. Stimuli Preparation
Note: The procedures described below are specific to a commercially available editing suite (see Table of Materials); however, other photo editing software can certainly be used.
<ol>
	<li>Prepare a set of emotional face photographs25 categorized into two facial expressions (happy and sad). Using previously collected behavioral ratings, choose photographs with highly accurate emotion ratings (in which over 80% of participants identified the emotion correctly)25. 
	NOTE: In our protocol, there were 33 chosen adult faces (18 female, 15 male). Of the 18 females, 9 are Caucasian, 4 are African American, and 5 are Asian American. Of the 15 males, 9 are Caucasian, 6 are African American, and none are Asian American. In this case, photos have already been cropped and placed on a white background. It is important to note that the NimStim set of facial expressions was rated and normed by adults25. Thus, it is possible that children or adolescents may perceive facial expressions differently than the adults who normed the images.</li>
	<li>Standardizing the emotional expression stimuli
	<ol>
		<li>Using a photo editing suite, edit the photographs to be in grayscale, then save them (in the software used here, this is done by selecting Image | Mode | Greyscale).</li>
		<li>Make sure that the resolution is 72 pixels/inch and that the file width is 8.5 inches and height is 11 inches.</li>
		<li>Using a ruler, change the width of the photograph (in pixels) until the face measures 11 cm from the outside of the hair on one side to the outside of hair on the other side, and 14 cm from the start of hairline to the bottom of the chin.</li>
	</ol>
	</li>
	<li>Creating the scrambled face arrow stimuli
	<ol>
		<li>Download the &#34;scramble&#34; plugin (http://telegraphics.com.au/sw/product/Scramble).</li>
		<li>Extract the ZIP and move it into &#34;plug-ins&#34; inside the editing suite&#39;s application folder.</li>
		<li>In the photo editing program, create a &#34;custom shape&#34; in the shape options. Make sure that the shape is an arrow.</li>
		<li>Open the emotional face images one at a time. Use the selection tool to select only the face and not the background.</li>
		<li>Select Filter | Telegraphics | Scramble | OK.</li>
		<li>Select Window | Layers (this should produce a &#34;layers&#34; window to the side).</li>
		<li>Double click background, then click ok and rename it to layer 0 (any name is fine).</li>
		<li>Right click on the shape tool in the left-hand menu bar and choose the custom arrow shape.</li>
		<li>Drag the arrow shape into the middle of the scrambled image.</li>
		<li>Use Ctrl + T to drag the arrow to the middle of the image and adjust the size so it is the same as the face image (e.g., 11 x 14 cm).</li>
		<li>Use the angle tool to ensure that for happy faces the arrow faces upwards and for sad faces the arrow faces downwards.</li>
		<li>Click layer 0 or the name given in step 1.3.7.</li>
		<li>Select Layers | Group with previous.</li>
		<li>Click Select-all | Edit | Copy merged.</li>
		<li>Create a new file with the arrow on a white background by selecting File | New. The new file should be 8.5 x 11 inches at a resolution of 72 pixels/inch.</li>
		<li>Save the files. 
		NOTE: After these are saved, there should be photographs of individuals with sad expressions, happy expressions, and the scrambled versions. The scrambled photograph with the upwards pointing arrow is the nonsocial image for happy, and the scrambled photograph with the downwards pointing arrow is the nonsocial image for sad.</li>
	</ol>
	</li>
	<li>Creating the reward images
	<ol>
		<li>Find an image of a goldfish cracker (without anything else in the frame) and download it to the computer.</li>
		<li>Open the goldfish cracker image in a photo editing suite and edit it to be in grayscale.</li>
		<li>Create two goldfish cracker images: 1) one in grayscale and 2) one that is crossed out (e.g., has a surrounding circle/oval with a line through the middle).</li>
		<li>Using Copy | Paste, place images of the intact goldfish cracker around the happy images (e.g., both the upwards pointing arrow and smiling people).</li>
		<li>Using Copy | Paste, place images of the crossed-out goldfish cracker around the sad images (e.g., both the downwards pointing arrow and frowning people).</li>
	</ol>
	</li>
	<li>Preparing the stimuli in electrophysiology (EEG) presentation software
	<ol>
		<li>Create two blocks of stimuli in an EEG presentation software package. One block will be social (e.g., pictures of smiling and frowning faces) and the other will be nonsocial (e.g., pictures of upwards and downwards facing arrows).</li>
		<li>Pseudo-randomize the order of stimuli in each block such that no image occurs more than once in a row, and such that the participant does not see more than three sad/downwards facing arrows or happy/upwards facing arrows in a row.</li>
		<li>Set up each trial to contain the following: a fixation cross; 2 boxes with question marks (the participant will use a button press to indicate a choice between the left and right box); an arrow pointing to the box the participant choses via the button press; and feedback (the stimuli created above). 
		NOTE: Although participants are choosing the left or right box, whether correct (e.g., happy face or upwards facing arrow surrounded by intact goldfish) or incorrect (e.g., sad face or downwards facing arrow surrounded by crossed out goldfish) feedback shown is pre-programmed by the randomization described in step 1.5.2. Thus, participants may feel that they are guessing correctly or incorrectly, but in reality the choice does not affect which feedback image is shown.</li>
		<li>Display each trial based on the following durations: 1) fixation cross for 500 ms, 2) two boxes with question marks inside for 3000 ms, 3) two boxes with question marks inside, with an arrow pointing towards the box chosen by the participant for 2000 ms, and 4) feedback (e.g., face or scrambled face images) for 1000 ms. See Figure 1. 
		NOTE: If participants fail to respond (via the button press) within 3000 ms, the trial ends and the next trial begins. The anticipated visual angle is a horizontal visual angle of 14.5&#176; and vertical visual angle of 10.67&#176;.</li>
	</ol>
	</li>
</ol>
2. Behavioral Procedures
<ol>
	<li>Recruit participants with and without ASD diagnoses based on the Institutional Review Board guidelines.</li>
	<li>Administer cognitive tests (e.g., Wechsler Abbreviated Scale of Intelligence, WASI26) to all participants to confirm that children have cognitive scores within the low-average to average range (e.g., a full-scale IQ of at least 70). 
	NOTE: It was determined that children with full-scale IQs below 70 would likely have difficulty understanding and remembering task directions. Therefore, an IQ cut-off of 70 was chosen as an exclusionary criterion for participation.</li>
	<li>For participants with a previous diagnosis of ASD, administer the autism diagnostic observation schedule (second edition, ADOS-2)27 to confirm their eligibility.</li>
</ol>
3. EEG Recording
<ol>
	<li>Set up the participants.
	<ol>
		<li>Ensure that each participant sits in a comfortable chair in a dimly lit room and adjust the chair so individuals are 72 cm away from the computer screen. Provide a brief tutorial about the procedure. 
		NOTE: In this study, participants were told the following: &#34;You will be playing a guessing game - just like pick-a-hand, except on the computer. You will see 2 boxes with question marks in them, and then you will use this button box to choose whether you think the right or left box is the right one. If you think the left box is the right one, press the left button. If you think the right box is the right one, press the right button. Once you pick, you&#39;ll see the boxes with question marks and an arrow in the middle showing which one you picked. Then you&#39;ll see if you got it right. For each one you get right, you&#39;ll get 1 goldfish cracker. If you don&#39;t like goldfish, you can trade for fruit snacks. When you get it right, you&#39;ll see a ring of goldfish crackers. That means you get a goldfish cracker! When you get it wrong, you&#39;ll see a ring of crossed out goldfish crackers. When you get it wrong, you won&#39;t lose any goldfish, you just won&#39;t gain any that time. The computer will keep track of how many goldfish you get, and then I&#39;ll give you that many after we are all done.&#34; After the tutorial, ask participants, &#34;Ok, so what are you going to do?&#34; followed by, &#34;What will you see when you get it right?&#34; and &#34;What will you see when you get it wrong?&#34; to confirm that they understand the task. If they do not seem to understand, explain it again until they are able to answer those questions correctly.</li>
	</ol>
	</li>
	<li>Use an EEG cap with 32 Ag/AgCl electrodes in the international 10-20 system, with additional vertical (VEOG) and horizontal (HEOG) electrodes to capture eye movement.</li>
	<li>Measure the participant&#39;s head to determine which size cap is appropriate for the head size.</li>
	<li>Using a blunt needle, pre-gel the cap by injecting conductive gel into the electrodes.</li>
	<li>Connect the EEG cap to the amplifier with a low-pass filter at 70 Hz, a directly coupled high pass (DC) filter, a 60Hz notch filter, and 500 Hz sampling rate.</li>
	<li>Fit the EEG cap to the participant&#39;s head such that the &#34;Cz&#34; electrode is placed at the middle of the scalp (e.g., middle of nasion to inion) as per the 10-20 system.</li>
	<li>Using a blunted needle or sterile wooden stick, swirl inside the electrode to move any hair and allow the gel to contact the scalp.</li>
	<li>Use an impedance meter (or the EEG computer) to ensure that impedance is below 10 K&#937;&#160;for a low impedance system and below 50 K&#937; for a high impedance system.</li>
	<li>Once all the electrodes on the cap show acceptable impedance levels, place the HEOG and VEOG electrodes. Place the HEOG electrodes at the canthus of each eye, and VEOG electrodes above and below the eye.</li>
	<li>If any electrodes have impedance levels above the acceptable thresholds noted above, record them in a notebook or on the computer.</li>
	<li>Begin the experimental blocks, counterbalancing the order of blocks (e.g., social and nonsocial) between participants. Ensure that the EEG computer is recording, the EEG computer and recording computer are synchronized, and the events are being sent correctly.</li>
	<li>Give the participants 30-second breaks after every 15 trials (approximately every 2-3 minutes) to allow them to move around if needed. 
	NOTE: Although no explicit directions were given to children regarding movement, participants were told to use breaks to &#34;get the wiggles out&#34;, &#34;get some energy out&#34;, or to perform any other movements they wanted to.</li>
	<li>Between blocks, give participants a longer break (about 5 minutes). After each block, have participants fill out a 4-question Likert scale about how much they enjoyed the guessing game and how often they felt they could get correct answers.</li>
	<li>After both blocks are completed and participants have filled out both Likert questionnaires, take off the EEG cap and allow them to wash their hair.
	<ol>
		<li>Provide payment (or an equivalent &#34;prize&#34;) to the participants and their families.</li>
		<li>Clean and sterilize the EEG cap.</li>
	</ol>
	</li>
</ol>
4. Processing EEG Data
Note: The procedures and commands described in this section are specific to EEGLAB and ERPlab toolboxes28.
<ol>
	<li>In ERPlab, filter the EEG data using a high-pass filter of .01 Hz and low-pass filter of 30 Hz.</li>
	<li>Discard (in ERPlab) or interpolate (in EEGLAB) bad channels that appear to have high impedance and/or were problematic during recording (e.g., lost contact with the skin during recording).</li>
	<li>Using the EEG channel operations GUI (in ERPlab), re-reference the EEG data using the average of the left and right mastoid electrodes (mastoid electrodes were chosen as the reference due to not having a dense electrode array; in which case, experts sometimes suggest using the average reference29, and because using the average of both mastoid electrodes is less problematic for laterality effects versus a single mastoid30).</li>
	<li>If using the average of mastoid electrodes for re-referencing, those two electrodes must provide a clear signal. If either mastoid electrode has a poor-quality signal (e.g., with high impedence or lost contact with skin during recording), do not use the participant&#39;s data for analysis.</li>
	<li>Use the EVENTLIST dropdown menu in ERPlab to create an EEG Eventlist, and assign codes from the stimulus computer to bins using BINLISTER.</li>
	<li>Using the extract bin-based epochs dropdown menu in ERPlab, segment the continuous EEG data into stimulus-locked epochs and baseline correct. To measure the stimulus-preceding negativity (SPN), use epoch from -2200 to 100 ms (baseline of -2200 to -2000 ms). To measure reward processing or post-stimulus brain activity, use epoch from -200 to 800 ms (baseline of -200 to 0 ms).</li>
	<li>Plot the data in EEGlab, and mark and discard epochs that appear to contain non-eye blink artifacts (e.g., excessive noise or motor movement).</li>
	<li>Choose Run ICA on the epoched data.</li>
	<li>Plot the independent components (select Plot | Component activations) and identify any artifacts from eye movement and blinks.</li>
	<li>Remove the components identified as eye movements or eye blink artifacts. To check whether the components marked for removal are responsible for eye movement, choose plot single trial data to visualize the data with the identified components removed. Once the blinks and eye movements are removed, accept the removal of the identified components.</li>
	<li>In ERPlab tools, select Artifact rejection in epoched data | Moving window peak-to-peak procedure. In the current study, a 200 ms moving window, 100 ms window step, and 150 mV voltage threshold were utilized.</li>
	<li>Compute the average ERPs. Make sure to use the default settings, which state that all epochs marked for removal will be discarded from the averaged ERP.</li>
	<li>To analyze average brain activity occurring prior to the onset of the stimuli, extraction (in step 4.11) should occur during the last 200 ms prior to stimulus onset (e.g., -210 to -10 ms) from the electrodes of interest.
	<ol>
		<li>In the case of stimulus-preceding negativity (SPN), electrodes of interest include: F3/F4, C3/C4, P3/P4, and T5/T6 (note that in some systems, temporal electrodes in this region are labeled T6/T7 or T3/T4). 
		NOTE: -210 to -10 ms was chosen in this protocol rather than -200 to 0 ms to avoid contamination by brain activity not related to the SPN (e.g., the beginning of neural activity when the feedback stimuli is shown at 0 ms).</li>
	</ol>
	</li>
	<li>To export numeric data for analysis, use the ERP measurement tool. 
	NOTE: This tool allows researchers to specify latency or amplitude information, the time windows of interest, and the electrodes of interest. Amplitude can be calculated either as the local peak in a given time window, or as the mean amplitude of a given time window.</li>
	<li>Download the numeric data as a .txt file. As needed, export the data into excel or copy and paste it into a statistical analysis program (e.g., SPSS or JMP).</li>
</ol>
5. Processing Differences for ERSP Analysis
<ol>
	<li>Begin with the file created in step 4.11 (e.g., the epoched file with all artifact rejection procedures complete).</li>
	<li>Utilize the EEGlab &#34;newtimef&#34; plug-in to obtain a time x frequency transform with values for each time point, frequency, and trial.</li>
	<li>To measure anticipatory alpha band activity, calculate the average values from 8 to 12 Hz.</li>
	<li>Calculate the average activation prior to feedback onset (e.g., -2200 to -100 ms with baseline of -2200 to -2000 ms) in the same electrodes used for the SPN.</li>
	<li>To calculate alpha asymmetry prior to feedback, subtract the log power in the left hemisphere from the right hemisphere. 
	NOTE: To calculate ERSP activation after feedback onset, data would need to be re-analyzed and re-grouped into epochs using a different time window (e.g., -200 to 800 ms with baseline of -200 to 0 ms).</li>
</ol>
6. Statistical Analysis
<ol>
	<li>Paste the numeric data extracted in section 4 (steps 4.11 and 4.12) into a statistical program (e.g., SPSS or JMP).</li>
	<li>Conduct repeated measures ANOVA in the statistical software to the mean values of ERPs to compare brain activity between hemispheres (left, right), electrode positions (frontal, central, temporal, parietal), conditions (face, arrow) and groups (autism spectrum disorder, typically developing). 
	NOTE: Hemisphere, electrode position, and condition are within-subjects factors, and group is a between-subjects factor.
	<ol>
		<li>If hemisphere or electrode position is not statistically significant, collapse across for future analysis.</li>
		<li>If the relationship between behavioral measures (e.g., ADOS severity score) and ERPs is of interest, correlation analyses can be run.</li>
	</ol>
	</li>
</ol>

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E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cortical+Measures+of+Anticipation.">Cortical Measures of Anticipation.</a> Journal of Psychophysiology. 18 (2-3), 61-76 (2004).</li><li>Ohgami, Y., Kotani, Y., Hiraku, S., Aihara, Y., Ishii, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effects+of+reward+and+stimulus+modality+on+stimulus-preceding+negativity.">Effects of reward and stimulus modality on stimulus-preceding negativity.</a> Psychophysiology. 41 (5), 729-738 (2004).</li><li>Takeuchi, S., Mochizuki, Y., Masaki, H., Takasawa, N., Yamazaki, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stimulus+preceding+negativity+represents+arousal+induced+by+affective+picture.">Stimulus preceding negativity represents arousal induced by affective picture.</a> International Congress Series. 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G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brain+activity+during+anticipation+of+smoking-related+and+emotionally+positive+pictures+in+smokers+and+nonsmokers:+a+new+measure+of+cue+reactivity.">Brain activity during anticipation of smoking-related and emotionally positive pictures in smokers and nonsmokers: a new measure of cue reactivity.</a> Nicotine &amp;amp; Tobacco Research. 10 (11), 1627-1631 (2008).</li><li>Poli, S., Sarlo, M., Bortoletto, M., Buodo, G., Palomba, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stimulus-preceding+negativity+and+heart+rate+changes+in+anticipation+of+affective+pictures.">Stimulus-preceding negativity and heart rate changes in anticipation of affective pictures.</a> International Journal of Psychophysiology. 65 (1), 32-39 (2007).</li><li>Tottenham, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+NimStim+set+of+facial+expressions:+judgments+from+untrained+research+participants.">The NimStim set of facial expressions: judgments from untrained research participants.</a> Psychiatry Research. 168 (3), 242-249 (2009).</li><li>Wechsler, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Wechlser+Abbreviated+Scale+of+Intelligence+(WASI).">Wechlser Abbreviated Scale of Intelligence (WASI).</a> The Psychological Corporation. , (1999).</li><li>Lord, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Autism+Diagnostic+Observation+Schedule:+ADOS-2.">Autism Diagnostic Observation Schedule: ADOS-2.</a> Western Psychological Services. , (2012).</li><li>Lopez-Calderon, J., Luck, S. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ERPLAB:+an+open-source+toolbox+for+the+analysis+of+event-related+potentials.">ERPLAB: an open-source toolbox for the analysis of event-related potentials.</a> Frontiers in Human Neuroscience. 8, 213 (2014).</li><li>Luck, S. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> An introduction to the event-related potential technique. , (2005).</li><li>Bruder, G. E., Kayser, J., Tenke, C. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Oxford handbook of event-related potential components. , (2012).</li><li>Yau, S. H., McArthur, G., Badcock, N. A., Brock, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Case+study:+auditory+brain+responses+in+a+minimally+verbal+child+with+autism+and+cerebral+palsy.">Case study: auditory brain responses in a minimally verbal child with autism and cerebral palsy.</a> Frontiers in Neuroscience. 9, 208 (2015).</li></ol>

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The current article describes the stimuli, data collection process, and analysis of ERP data in a reward paradigm for children. In this paradigm, children play a guessing game similar to pick-a-hand on the computer and see feedback about whether their guess is correct or incorrect. ERP results for reward anticipation (brain activity prior to the onset of feedback) were consistent with the stimulus-preceding negativity (SPN). Between conditions, the results suggest that TD children anticipate reward stimuli accompanied by faces more strongly than reward stimuli accompanied by non-face images6. Between groups of children, the results suggest that TD children anticipate face stimuli significantly more than children with autism do. These results are exciting, as they provide important information about how social and nonsocial information are anticipated in children with autism. This is particularly important for furthering the understanding of neural mechanisms of autism and providing support for the social motivation hypothesis. These findings provide useful information for the creation and refinement of interventions, as it underscores the importance of social motivation for children with ASD; for example, it may be important for interventions to explicitly attempt to increase the reward value of social partners to directly affect social motivation in this population. 
 This protocol is useful for measuring anticipatory brain activity in children with and without ASD, and the data provides evidence that this type of brain activity can be reliably and successfully elicited in children over 6 years old. Furthermore, this method allows social and nonsocial conditions to be directly compared without the presence of confounds related to reward properties (since the reward for correct responses was goldfish in both conditions). In the current protocol, faces were scrambled and an arrow shape was created. This procedure preserves the physical stimulus properties of faces in the nonsocial (non-face) condition. This protocol may be useful for future investigations into sub-groups of ASD (e.g., some children with ASD are more socially motivated than others), and could be utilized to better understand why some children respond more effectively than others to certain interventions. 
There are limitations to the current approach that must be taken into consideration. First, the paradigm described above is useful for children between 6 and 11 years old with and without ASD who have cognitive abilities in the average range. Pilot data of typically developing children younger than 6 was not successful, as children were confused by the directions and did not understand the game's instructions. In the current protocol, exclusionary criteria included a full-scale IQ score below 70. Therefore, the current paradigm may not be appropriate for children with a mental or chronological age below 6. However, it may be possible to modify the current protocol so it is appropriate for individuals with lower IQs and younger children. Some modifications to make it more appropriate for young children such as toddlers are currently being investigated. Such modifications include changing the task to be passive (e.g., having children watch stimuli that appear at predicable intervals in a block design) and using an S1/S2 paradigm24. In such a design, the content of S1 reliably provides information about the content of S2 (e.g., if S1 is a square, then S2 will be a face; if S1 is a circle, then S2 will be an arrow). Alternatively, the timing structure of the current paradigm could be used to create an anticipatory auditory protocol. 
In ASD, it would be of interest to use speech versus non-speech groups and measure brain activity in children with ASD who are non-verbal and have difficulty responding to instructions or attending to visual stimuli31. Related to the first limitation, it should be noted that results from children with ASD who have cognitive abilities in the average range are likely not representative of the entire autism spectrum - which, by definition, captures a broad range of functioning levels. Therefore, these representative results cannot be extrapolated to all children with ASD. Finally, it is important to note that the stimuli used in the current protocol were normed by adults rather than children. Therefore, future studies should consider using a stimulus set of facial expressions normed by children.

Autism spectrum disorder (ASD) is a developmental disability characterized by impairments in social communication (verbal and non-verbal) and the presence of restricted interest and/or repetitive behaviors1. Given that ASD is hypothesized to be neurologically-based2,3, it is unsurprising that neuroscience research involving children with ASD has become highly prevalent over the past decade. Though many theories about the brain basis of ASD have been proposed, one in particular that has garnered considerable research attention is the social motivation hypothesis4. Briefly, the social motivation hypothesis states that children with ASD engage in less social interaction than their typically developing (TD) peers because social interaction is not as rewarding for them. Chevallier et al. provide a review of the social motivation hypothesis5. Because this hypothesis directly relates to the reward system, specifically whether or not the system in ASD is responsive to social information, multiple studies have investigated the social reward system in ASD6,7,8,9,10,11,12. Results from these studies have differed, with some providing evidence that the reward system in ASD is hypoactive to both social and nonsocial information, and others suggesting that the reward system functions typically for nonsocial information but is hypoactive to social stimuli. One potential reason for these inconsistent results relates to the stimuli and methodology used in the protocols. It is difficult to match social and nonsocial rewards in an experimental context; for example, multiple studies have used a picture of a smiling face as the social reward, and the nonsocial reward is monetary (e.g., getting money after the experiment is complete7,8,11). Though these studies provide an important foundation for future research, it is difficult to determine whether or not the findings relate to differences in social versus nonsocial reward responsiveness in ASD or if they are due to differences between reward conditions.
The current protocol is designed to investigate the reward system in high-functioning children with ASD using electrophysiology. To explore differences between children with and without ASD based on reward anticipation, the stimulus-preceding negativity (SPN) was measured. The SPN is a slow-wave component that reflects an expectation of a reward stimulus13. The significance of the SPN is typically conceptualized as emotional anticipation14,15,16 and is thought to be reflected by activity in the insula17,18. The SPN is often measured after participants perform a motor response and before feedback onset during a decision-making task19,20. The SPN is sensitive to reward magnitude and is consistently larger in reward versus no-reward conditions15,16,21. Though the SPN is typically measured during decision-making tasks, researchers have reported that the SPN can be observed when anticipating affective upcoming stimuli without any task22,23,24. One critical aim of the current protocol is to perform an experimental task in which the rewards between social and nonsocial conditions are matched to eliminate potential confounds. Another goal is to test young children between 6 and 11 years old. Therefore, the protocol can serve as an age-appropriate reward task that children may find engaging without becoming frustrated.

<table><tbody><tr><td>EEG cap</td><td>ElectroCap International</td><td>E1-SM/ E1-S/XSM</td><td>Electro-Cap - Small (50-54 cm)/Electro-Cap -Small/Extra Small</td></tr><tr><td>NeuroScan 4.5</td><td>Neuro Scan</td><td>https://compumedicsneuroscan.com/tag/scan/</td><td>EEG Recording Software</td></tr><tr><td>Stim2</td><td>Neuro Scan</td><td>https://compumedicsneuroscan.com/product/stim2-precise-stimulus-presentation/</td><td>Stimulus Presentation Software</td></tr><tr><td>JMP Pro 11&amp;nbsp;</td><td>SAS</td><td>https://www.jmp.com/en_us/software/buy-jmp.html</td><td>Statistical analysis software</td></tr><tr><td>NimStim Face Stimulus Set&amp;nbsp;</td><td>N/A, open source images&amp;nbsp;</td><td>Open source, Available at https://www.macbrain.org/resources.htm&amp;nbsp;</td><td>Face Images&amp;nbsp;</td></tr><tr><td>EEGlab</td><td>N/A, free software</td><td>N/A, free software</td><td>EEG analysis software (free download)</td></tr><tr><td>ERPlab</td><td>N/A, free software</td><td>N/A, free software</td><td>EEG analysis software (free download)</td></tr><tr><td>Photoshop</td><td>Photoshop</td><td>https://www.photoshop.com</td><td>Adobe Photoshop, image editing software</td></tr><tr><td>Photoshop &#039;scramble&#039; plug-in</td><td>Telegraphics</td><td>http://telegraphics.com.au/sw/product/Scramble</td><td>photoshop plug-in to scramble images&amp;nbsp;</td></tr><tr><td>NUAMPS EEG AMPLIFIERd</td><td>Neuro Scan</td><td>http://compumedicsneuroscan.com/wp-content/uploads/NuAmps-Brochure.pdf</td><td>EEG amplifier&amp;nbsp;</td></tr></tbody></table>

an electrophysiology protocol to measure reward anticipation and processing in children

We present a protocol designed to measure the neural correlates of reward in children. The protocol allows researchers to measure both reward anticipation and processing. Its purpose is to create a reward task that is appropriate for young children with and without autism while controlling reward properties between two conditions: social and nonsocial. The current protocol allows for comparisons of brain activity between social and nonsocial reward conditions while keeping the reward itself identical between conditions. Using this protocol, we found evidence that neurotypical children demonstrate enhanced anticipatory brain activity during the social condition. Furthermore, we found that neurotypical children anticipate social reward more robustly than children with autism diagnoses. As the task uses snacks as a reward, it is most appropriate for young children. However, the protocol may be adapted for use with adolescent or adult populations if snacks are replaced by monetary incentives. The protocol is designed to measure electrophysiological events (event-related potentials), but it may be customized for use with eye-tracking or fMRI.

Designing experiments to systematically compare brain activity with social versus non-social reward stimuli is complex, due to the inherent difficulty in equating social and non-social rewards. Figure 1 represents stimuli from an experimental protocol designed for investigating neural responses to reward while controlling for reward properties. Specifically, this paradigm was designed to (i) keep rewards consistent between social and nonsocial trials, (ii) control for physical stimulus properties between social and nonsocial trials, and (iii) be age-appropriate for 6 to 11-year-old children with and without autism.
Figure 2&#160;depicts ERP responses as participants anticipate social and non-social stimuli. It should be noted that because the current protocol was designed to measure reward anticipation (the SPN), the displayed epochs are largely prior to feedback onset (which occur at 0 ms in the figures). These results suggest that typically developing (TD) children anticipate reward stimuli accompanied by faces more robustly than children with ASD. Furthermore, though TD children anticipate face stimuli significantly more than non-face stimuli, children with ASD do not evidently show significant differences in brain activity between conditions.&#160;

<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58348/58348fig1.jpg" /> 
Figure 1: Schematic of stimulus presentation and timing. Feedback for the social (face) condition is shown in the left column. Feedback for the nonsocial (non-face) condition is shown in the right column. Feedback for &#34;correct&#34; answers is shown on top, and feedback for &#34;incorrect&#34; answers is shown below. This figure is re-printed with permission12. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/58348/58348fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58348/58348fig2.jpg" /> 
Figure 2:&#160;Grand averaged waveforms for TD children and those with ASD from the SPN in response to social/faces (left) and nonsocial/arrows (right). TD children are represented by a solid line and children with ASD by a dashed line. The area between&#160;-210 and -10 ms, used for statistical analysis, is highlighted with a gray box. This figure is modified from a previous publication6.&#160;<a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/58348/58348fig2large.jpg">Please click here to view a larger version of this figure.</a>

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An Electrophysiology Protocol to Measure Reward Anticipation and Processing in Children

This protocol is designed to measure reward anticipation and processing in young children with and without autism. Specifically, the protocol is designed to study the neural correlates of reward during social and nonsocial conditions while controlling for reward between conditions.

We present a protocol designed to measure the neural correlates of reward in children. The protocol allows researchers to measure both reward anticipation ...

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6.9K Views.  University of California, Riverside. This protocol is designed to measure reward anticipation and processing in young children with and without autism. Specifically, the protocol is designed to study the neural correlates of reward during social and nonsocial conditions while controlling for reward between conditions. 

Video: An Electrophysiology Protocol to Measure Reward Anticipation and Processing in Children

Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia

Adolescence is a critical developmental period where the early symptoms of schizophrenia frequently emerge. First-degree relatives of people with schizophrenia who are at familial high risk (FHR) may show similar cognitive and emotional changes. However, the neurological changes underlying the emergence of these symptoms remain unclear. This study sought to identify differences in frontal, striatal, and limbic regions in children and adolescents with FHR using functional magnetic resonance imaging. Groups of 21 children and adolescents at FHR and 21 healthy controls completed an emotional oddball task that relied on selective attention and the suppression of task-irrelevant emotional information. The standard oddball task was modified to include aversive and neutral distractors in order to examine potential group differences in both emotional and executive processing. This task was designed specifically to allow for children and adolescents to complete by keeping the difficulty and emotional image content age-appropriate. Furthermore, we demonstrate a technique for suitable fMRI registration for children and adolescent participants. This paradigm may also be applied in future studies to measure changes in neural activity in other populations with hypothesized developmental changes in executive and emotional processing.

This paper describes how to use the emotional oddball task and fMRI to measure brain activation in children and adolescents at familial high risk for schizophrenia (FHR).&#160;FMRI was used to measure differences in fronto-striato-limbic regions during an emotional oddball task. Children with FHR exhibited abnormal functional activation during adolescence.

Adolescence is a critical developmental period where the early symptoms of schizophrenia frequently emerge. First-degree relatives of people with ...

Cortical Source Analysis of High-Density EEG Recordings in Children

EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and signal processing make it possible to exploit information from other imaging modalities like structural MRI that provide high spatial resolution to overcome this constraint1. This is especially useful for investigations that require high resolution in the temporal as well as spatial domain. In addition, due to the easy application and low cost of EEG recordings, EEG is often the method of choice when working with populations, such as young children, that do not tolerate functional MRI scans well. However, in order to investigate which neural substrates are involved, anatomical information from structural MRI is still needed. Most EEG analysis packages work with standard head models that are based on adult anatomy. The accuracy of these models when used for children is limited2, because the composition and spatial configuration of head tissues changes dramatically over development3.&#160;
In the present paper, we provide an overview of our recent work in utilizing head models based on individual structural MRI scans or age specific head models to reconstruct the cortical generators of high density EEG. This article describes how&#160;EEG recordings are acquired, processed, and analyzed with pediatric populations at the London Baby Lab, including laboratory setup, task design, EEG preprocessing, MRI processing, and EEG channel level and source analysis.&#160;

In recent years, there has been increasing interest in estimating the cortical sources of scalp measured electrical activity for cognitive neuroscience experiments. This article describes how high density EEG is acquired and how recordings are processed for cortical source estimation in children from the age of 2 years at the London Baby Lab.

EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and ...

Event-related Potentials During Target-response Tasks to Study Cognitive Processes of Upper Limb Use in Children with Unilateral Cerebral Palsy

Unilateral Cerebral Palsy (CP) is a neurodevelopmental disorder that is a very common cause of disability in childhood. It is characterized by unilateral motor impairments that are frequently dominated in the upper limb. In addition to a reduced movement capacity of the affected upper limb, several children with unilateral CP show a reduced awareness of the remaining movement capacity of that limb. This phenomenon of disregarding the preserved capacity of the affected upper limb is regularly referred to as Developmental Disregard (DD). Different theories have been postulated to explain DD, each suggesting slightly different guidelines for therapy. Still, cognitive processes that might additionally contribute to DD in children with unilateral CP have never been directly studied. The current protocol was developed to study cognitive aspects involved in upper limb control in children with unilateral CP with and without DD. This was done by recording event-related potentials (ERPs) extracted from the ongoing EEG during target-response tasks asking for a hand-movement response. ERPs consist of several components, each of them associated with a well-defined cognitive process (e.g., the N1 with early attention processes, the N2 with cognitive control and the P3 with cognitive load and mental effort). Due to its excellent temporal resolution, the ERP technique enables to study several covert cognitive processes preceding overt motor responses and thus allows insight into the cognitive processes that might contribute to the phenomenon of DD. Using this protocol adds a new level of explanation to existing behavioral studies and opens new avenues to the broader implementation of research on cognitive aspects of developmental movement restrictions in children.

Several children with unilateral Cerebral Palsy seem to disregard the preserved capacity of their affected upper limb. This Developmental Disregard is extensively described in the literature but the involved cognitive processes have not been studied. To study underlying cognitive factors of upper limb control, an event-related potential protocol was developed.

Unilateral Cerebral Palsy (CP) is a neurodevelopmental disorder that is a very common cause of disability in childhood. It is characterized by unilateral ...

Psychophysiological Assessment of the Effectiveness of Emotion Regulation Strategies in Childhood

Effective regulation of emotion is one of the most important skills that develops in childhood. Research interest in this area is expanding, but empirical work has been limited by predominantly correlational investigations of children's skills. Relatedly, a key conceptual challenge for emotion scientists is to distinguish between emotion responding and emotion regulatory processes. This paper presents a novel method to address these conceptual and methodological issues in child samples. An experimental paradigm that assesses the effectiveness with which children regulate emotion is described. Children are randomly assigned to use specific emotion regulation strategies, negative emotions are elicited with film clips, and changes in subsequent psychophysiology index the extent to which emotion regulation is effective. Children are instructed to simply watch the emotion-eliciting film (control), distract themselves from negative emotions (cognitive distraction), or reframe the situation in a way that downplays the importance of the emotional event (cognitive reappraisal). Cardiac physiology, continuously acquired before and during the emotional task, serves as an objective measure of children's unfolding emotional responding while viewing evocative films. Key comparisons in patterns of obtained physiological reactivity are between the control and emotion regulation strategy conditions. Representative results from this approach are described, and discussion focuses on the contribution of this methodological approach to developmental science.

A detailed protocol for an experimental paradigm to assess the effectiveness with which children regulate emotion is described. Children are randomly assigned to use specific emotion regulation strategies, negative emotions are elicited with film clips, and changes in subsequent psychophysiology index the extent to which emotion regulation is effective.

Effective regulation of emotion is one of the most important skills that develops in childhood. Research interest in this area is expanding, but empirical ...

A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents

We have developed a fully automated system for operant behavior testing and neuronal activity recording by which multiple cognitive brain functions can be investigated in a single task sequence. The unique feature of this system is a custom-made, acoustically transparent chamber that eliminates many of the issues associated with auditory cue control in most commercially available chambers. The ease with which operant devices can be added or replaced makes this system quite versatile, allowing for the implementation of a variety of auditory, visual, and olfactory behavioral tasks. Automation of the system allows fine temporal (10 ms) control and precise time-stamping of each event in a predesigned behavioral sequence. When combined with a multi-channel electrophysiology recording system, multiple cognitive brain functions, such as motivation, attention, decision-making, patience, and rewards, can be examined sequentially or independently.

In this report, we present a fully automated and highly versatile system capable of simultaneously testing multi-cognitive behaviors and recording neuronal activities for rodents.

We have developed a fully automated system for operant behavior testing and neuronal activity recording by which multiple cognitive brain functions can be ...

Neuroscience

Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats

The present article provides a guide for the conduction and analysis of three conditioning-based protocols to evaluate impulsivity in rats. Impulsivity is a meaningful concept because it is associated with psychiatric conditions in humans and with maladaptive behavior in non-human animals. It is believed that impulsivity is composed of separate factors. There are laboratory protocols devised to assess each of these factors using standardized automated equipment. Delay discounting is associated with the incapacity to be motivated by delayed outcomes. This factor is evaluated through intertemporal choice protocols, which consist&#160;of presenting the individual with a choice situation involving an immediate reward and a larger but delayed reward. Response inhibition deficit is associated with the incapacity to withhold prepotent responses. Differential reinforcement of low rates (DLR) and feature-negative discrimination protocols assess the response inhibition deficit factor of impulsivity. The former imposes a condition to a motivated individual in which most wait a minimum period of time for a response to be rewarded. The latter evaluates the capacity of individuals to refrain from food seeking responses when a signal of the absence of food is presented. The purpose of these protocols is to construct an objective quantitative measure of impulsivity, which serves to make cross-species comparisons, allowing the possibility of translational research. The advantages of these particular protocols include their easy set-up and application, which stems from the relatively small amount of equipment needed and the automated nature of these protocols.

We present three protocols that assess different forms of impulsivity in rats and other small mammals. Intertemporal choice procedures evaluate the tendency to discount the value of delayed outcomes. Differential reinforcement of low rates and feature-negative discrimination evaluate response inhibition capacity with and without punishment for inappropriate responses, respectively.

The present article provides a guide for the conduction and analysis of three conditioning-based protocols to evaluate impulsivity in rats. Impulsivity is ...

Measuring Cardiac Autonomic Nervous System (ANS) Activity in Toddlers - Resting and Developmental Challenges

The autonomic nervous system (ANS) consists of two branches, the parasympathetic and sympathetic nervous systems, and controls the function of internal organs (e.g., heart rate, respiration, digestion) and responds to everyday and adverse experiences 1. ANS measures in children have been found to be related to behavior problems, emotion regulation, and health 2-7. Therefore, understanding the factors that affect ANS development during early childhood is important. Both branches of the ANS affect young children&#8217;s cardiovascular responses to stimuli and have been measured noninvasively, via external monitoring equipment, using valid and reliable measures of physiological change 8-11. However, there are few studies of very young children with simultaneous measures of the parasympathetic and sympathetic nervous systems, which limits understanding of the integrated functioning of the two systems. In addition, the majority of existing studies of young children report on infants&#8217; resting ANS measures or their reactivity to commonly used mother-child interaction paradigms, and less is known about ANS reactivity to other challenging conditions. We present a study design and standardized protocol for a non-invasive and rapid assessment of cardiac autonomic control in 18 month old children. We describe methods for continuous monitoring of the parasympathetic and sympathetic branches of the ANS under resting and challenge conditions during a home or laboratory visit and provide descriptive findings from our sample of 140 ethnically diverse toddlers using validated equipment and scoring software. Results revealed that this protocol can produce a range of&#160;physiological responses to both resting and developmentally challenging conditions, as indicated by changes in heart rate and indices of parasympathetic and sympathetic activity. Individuals demonstrated&#160;variability in resting levels, responses to challenges, and challenge reactivity, which provides additional evidence that this protocol is useful for the examination of ANS individual differences for toddlers.

Measuring Cardiac Autonomic Nervous System ANS Activity in Toddlers - Resting and Developmental Challenges

We describe the methods for continuous monitoring of the autonomic nervous system under resting and challenge conditions with 18 month old children. Results revealed that this protocol can produce meaningful physiological responses in both branches of the autonomic nervous system and elicit significant individual variability in patterns of responses.

The autonomic nervous system (ANS) consists of two branches, the parasympathetic and sympathetic nervous systems, and controls the function of internal ...

Medicine

A Familiarization Protocol Facilitates the Participation of Children with ASD in Electrophysiological Research

This paper includes a detailed description of a familiarization protocol, which is used as an integral component of a larger research protocol to collect electroencephalography (EEG) data and Event-Related Potentials (ERPs). At present, the systems available for the collection of high-quality EEG/ERP data make significant demands on children with developmental disabilities, such as those with an Autism Spectrum Disorder (ASD). Children with ASD may have difficulty adapting to novel situations, tolerating uncomfortable sensory stimuli, and sitting quietly. This familiarization protocol uses Evidence-Based Practices (EBPs) to increase research participants&#39; knowledge and understanding of the specific activities and steps of the research protocol. The tools in this familiarization protocol are a social narrative, a visual schedule, the Premack principle, role-playing, and modeling. The goal of this familiarization protocol is to increase understanding and agency and to potentially reduce anxiety for child participants, resulting in a greater likelihood of the successful completion of the research protocol for the collection of EEG/ERP data.

Electrophysiological research is an important tool for identifying biomarkers of developmental disorders, including Autism Spectrum Disorders (ASD), but data collection in these populations remains challenging. This work presents a familiarization protocol to accompany research that includes electroencephalography (EEG) to improve the likelihood of collecting EEG data from children with ASD.

This paper includes a detailed description of a familiarization protocol, which is used as an integral component of a larger research protocol to collect ...

Using Brain Activation (nir-HEG/Q-EEG) and Execution Measures (CPTs) in a ADHD Assessment Protocol

Attention Deficit Hyperactivity Disorder (ADHD) is a problem that impacts academic performance and has serious consequences that result in difficulties in scholastic, social and familial contexts. One of the most common problems in the identification of this disorder relates to the apparent over diagnosis of the disorder due to the absence of global protocols for assessment. The research group of School Learning, Difficulties and Academic Performance (ADIR) from the University of Oviedo, has developed a complete protocol that suggests the existence of certain patterns of cortical activation and executive control for identifying ADHD more objectively. This protocol takes into consideration some of the hypothetical determinants of ADHD, including the relationship between activation of selected areas of the brain, and differences in performance on various aspects of executive functioning such as omissions, commissions or response times, using innovative tools of Continuous Performance Testing (based on Virtual Reality CPT and Traditional CPT) and brain activation measures (two different tools, based on Hemoencephalography- nirHEG; and Quantified Electroencephalography --Q-EEG, respectively). This model of assessment aims to provide an effective assessment of ADHD symptomatology in order to design an accurate intervention and make appropriate recommendations for parents and teachers.

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol

This work presents a new protocol for the assessment of Attention Deficit Hyperactivity Disorder (ADHD) by providing a more objective diagnostic procedure for this developmental disorder based on the use of innovative tools. It also analyzes the relationship between activation measures and executive function measures.

Attention Deficit Hyperactivity Disorder (ADHD) is a problem that impacts academic performance and has serious consequences that result in difficulties in ...

Event Related Potentials (ERPs) and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder (ADHD)

Neuropsychiatric diagnoses like ADHD are based on subjective methods like interviews, rating scales and observations. There is a need for more brain-based supplements. Stimulant medication is the most common treatment for ADHD. Clinically useful predictors of response have so far not been reported. The aim of this paper is to describe the EEG based methods we apply to extract potential biomarkers for brain dysfunction. Examples relate to biomarkers for pediatric ADHD, and prediction of medication response. The main emphasis is on Event Related Potentials (ERPs).
A nineteen channel EEG is recorded during a 3 min eyes-opened task, a 3 min eyes-closed task, and a 20 min cued visual GO/NOGO task (VCPT). ERPs are recorded during this task. The goal of the ERP protocol is to extract biomarkers of assumed brain dysfunctions that significantly differentiate between a patient group and healthy controls. The protocol includes recording during standard conditions and artifact correction. ERP waves can be used or transformed into latent components. The components of the patient group are compared with controls, empathizing components that, when compared, show relatively high effect sizes. Sub-groups of the patients are selected on the basis of the cluster analysis in the space of the components. Treatment procedure (such as medication, tDCS or neurofeedback protocol) can be applied and the changes in components related to treatment in the subgroups are observed, forming the basis for clinical recommendations.
The methods described were applied in a study of 87 pediatric ADHD patients. The index of medication response discriminated significantly between responders and non-responders with a large, and clinically meaningful effect size (d = 1.84). In an ongoing study comparing ADHD children with matched controls, several variables discriminate significantly between patients and controls. The global index will exceed d = .8. The EEG based methods described here could be clinically meaningful.

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

EEG-methods are applied for extracting biomarkers of brain dysfunctions. The focus is on multi-channel event-related potentials (ERPs) recorded in a cued GO/NOGO task. Non-brain artifacts are corrected and ERPs are compared with the normative data. Examples relate to biomarkers for ADHD diagnosis and prediction of medication response.

Neuropsychiatric diagnoses like ADHD are based on subjective methods like interviews, rating scales and observations. There is a need for more brain-based ...