We thank Erin Douglas, Anna Evans, and Carolyn Bellion for their contributions to participant recruitment and clinical assessment. We also thank Michael Casp, Zoe Englander, Justin Woodlief, and James Carter for their contributions to data collection and analysis, and Robert M. Hamer for consultation on statistical analysis and editing of the manuscript. Finally, we thank the individuals and their families who participated in this study. 
This study was supported by Conte center grant P50 MH064065 from the National Institute of Mental Health. Dr. Hart was supported by T32 HD040127 from the National Institute of Child Health and Human Development.

The research techniques used during this study were approved by the institutional review boards (IRB) of Duke University and the University of North Carolina &#8211; Chapel Hill.
1. Imaging Task Design
<ol>
	<li>Generate an event-based behavioral task that presents infrequent target stimuli (a circle) within a sequence of more-frequent standard stimuli (scrambled images). A schematic of the task is shown in Figure 1. Present the task using CIGAL software10.</li>
</ol>
<img alt="Figure 1" src="/files/ftp_upload/51484/51484fig1.jpg" /> 
Figure 1.&#160;Schematic of Task Design. This figure has been modified from Hart et al.20, with permission. <a href="https://www.jove.com/files/ftp_upload/51484/51484fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol style="margin-left: 40px;">
	<li>Select a set of aversive stimuli and a set of neutral stimuli from the International Affective Picture System database (IAPS). IAPS images are rated on a scale of 1-9 in order to reflect levels of arousal and valence11. High numbers indicate higher positive valence and arousal. Select a set of images that are age-appropriate to the study group such as pictures of snakes, spiders, or other animals. 
	Note: The task-irrelevant aversive stimuli images used for this study had an average valence rating of 3.38 (S.D. = 1.78) and an average arousal rating of 6.14 (S.D. = 2.08). The neutral stimuli images had an average valence of 6.21 (S.D. = 0.26) and an average arousal rating of 3.72 (S.D. = 2.15).</li>
	<li>Program the task script such that images are presented in a pseudo-randomized order for 1,500 msec with a 500 msec mean inter-stimulus interval. Present target stimuli and task-irrelevant neutral images no more frequently than every 15 sec and make each about 4% of the stimuli. Jitter event onset times in order to provide better resolution of the hemodynamic response function.</li>
</ol>
<ol start="2">
	<li>Create 8 sets of images, one for each of 8 functional runs such that the participants are presented with a total of 40 targets and 40 task-irrelevant neutral images over the course of all 8 runs.</li>
</ol>
2. Participant Setup and Scanning
<ol>
	<li>Recruit children and adolescents between the ages of 9 and 18 who are either healthy control individuals or who are at familial high risk for psychosis.
	<ol>
		<li>Ensure that healthy individuals have no psychiatric illnesses or any first-degree family members with a psychiatric illness. Ensure that familial risk participants have at least one first-degree relative (parent or sibling) with schizophrenia. Do not exclude them for the presence of other psychiatric illnesses in first-degree relatives.</li>
		<li>Age and gender match healthy participants with familial risk group participants.</li>
	</ol>
	</li>
	<li>Acquire informed consent from participants over the age of 18. For minors, acquire informed consent from parents/legal guardians. Additionally, acquire written assent from minors who are taking part in the study.</li>
	<li>Place the participants in a mock MRI scanner in order to familiarize them with the environment. Play an audio recording of scanner noise and have them complete a practice run of the behavioral task in order to ensure that they understand the task instructions.</li>
	<li>Place the participant in the MRI Scanner and acquire any necessary brain localization scans and/or anatomical images.</li>
	<li>Using an MRI-safe input box, tell participants to press one button with their index finger in response to all target stimuli and another button with their middle finger for all other stimuli.</li>
	<li>Following fMRI scanning, collect subjective ratings of arousal and valence for the images used in the study from a subset of participants. The current study obtained ratings from 15 controls and 13 with familial high risk.</li>
</ol>
3. Image Acquisition
<ol>
	<li>Place participants into a 3.0 Tesla MRI scanner. First, acquire a set of structural images including a 3D coplanar anatomical T1-contrast image using a spoiled gradient-recalled acquisition pulse sequence (TR: 5.16 msec; TE: 2.04 msec; FOV: 24 cm; image matrix: 256 &#215; 256; Flip Angle: 20; voxel size: 0.94 mm &#215; 0.94 mm &#215; 1.9 mm; 68 axial slices).</li>
	<li>Acquire functional imaging data using a gradient echo echo-planar imaging sequence with full-brain coverage (TR: 2,000 msec; TE: 27 msec; FOV: 24 cm; image matrix: 64 &#215; 64; Flip Angle: 60; voxel size: 3.75 mm &#215; 3.75 mm &#215; 3.8 mm; 34 axial slices) so that brain activity can be measured during the performance of the behavioral task. Run this imaging sequence for each run of the behavioral task. Each run should consist of 120 imaging time points.</li>
	<li>Present the task in 8 functional runs, each lasting approximately 4 min.</li>
</ol>
4. Analyses
<ol>
	<li>Image Preprocessing: Open fMRI Expert Analysis Tool (FEAT) in FSL12. Select First-level analysis and Pre-stats.
	<ol>
		<li>On the &#8220;Data&#8221; tab, select the number of input images and enter the path to each of the MR images you are going to process. Set the Output directory. Enter the Total volumes, number of discarded acquisitions, and the TR.</li>
		<li>On the &#34;Pre-stats&#34; tab, set motion correction to MCFLIRT, Spatial smoothing FWHM to 5 mms, and &#34;Slice timing correction&#34;. Select &#34;BET brain extraction&#34; and &#34;Highpass&#34; temporal filtering but do not select B0 unwarping (uness you have a gradient field map) or &#34;Intensitiy normaization&#34;. 12,14.</li>
		<li>On the &#34;Registration&#34; tab, select &#34;Main structural image&#34;. Enter the path to the subject&#8217;s skull-stripped T1-weighted image. Use a linear normal search with at least 6 DOF. Select the Standard space check box. Enter the path to the MNI atlas image. Use a normal, linear search with 12 DOF. Press Go.</li>
		<li>Exclude participants with greater than 3 mm head motion in the X, Y, or Z directions.</li>
	</ol>
	</li>
	<li>Level 1: Compare data between task conditions within a single run. Open FEAT. Select &#34;First-level analysis&#34; and &#34;Stats + Post-stats&#34;.
	<ol>
		<li>On the Data tab, set the number of inputs and enter the path to each of the MR images. Enter a path for the &#34;Output directory&#34;. Enter the &#34;Total volumes&#34;, number of discarded acquisitions, and the TR.</li>
		<li>On the &#8220;Stats&#8221; tab, select the &#8220;Use FILM prewhitening&#8221; check box16. Press the &#8220;Full model setup&#8221; button. Set the &#8220;Number of original EVs&#8221; to the number of task conditions. For each condition, select &#8220;Custom (3 column format)&#8221; from the basic shape drop-down menu and &#8220;Double-Gama HRF from the &#8220;Convolution&#8221; drop-down menu17,18 and select a text file containing the task timing.
		<ol>
			<li>Format this text file in 3 columns with one entry for each &#8220;event&#8221; of the given type. The first column should contain the onset time (in seconds), the second should contain the duration (in seconds), and the third should contain the event weight. On the Contrasts &#38; F-tests tab, create one contrast for each task condition and one for each comparison.</li>
		</ol>
		</li>
		<li>On the &#34;Post-stats&#34; tab, select &#34;Cluster&#34; on the &#34;Thresholding&#34; drop-down menu and set the &#34;Z threshold&#34; and Cluster P threshold to 2.3 and 0.05 respectively8,19.</li>
		<li>On the &#34;Registration&#34; tab, select &#34;Main structural image&#34;. Enter the path to the subject&#8217;s skull-stripped T1-weighted image. Use a linear normal search with at least 6 DOF. Select the &#34;Standard space&#34; check box. Enter the path to the MNI atlas image. Use a normal, linear search with 12 DOF. Press &#34;Go&#34;.</li>
	</ol>
	</li>
	<li>Level 2: Compare data between runs for each task condition. Open FEAT. Select &#8220;Higher-level analysis&#8221; and &#8220;Stats + Post-stats&#8221; from the drop down menu.
	<ol>
		<li>On the Data tab, select &#34;Inputs are lower-level FEAT directories&#34;. Set the number of inputs and enter the path to each of the MR images. Enter a path for the &#34;Output directory&#34;.</li>
		<li>On the &#34;Stats&#34; tab, change the &#34;Mixed Effects: FLAME1&#34; selection box to &#34;Fixed Effects&#34;. Press the &#34;Model setup wizard&#34; button. Select &#34;single group average&#34; and click the &#34;Process&#34; button.</li>
		<li>On the &#34;Post-stats&#34; tab, select &#34;Cluster&#34; on the &#34;Thresholding&#34; drop-down menu and set the &#34;Z threshold&#34; and &#34;Cluster P&#34; threshold to 2.3 and 0.05 respectively8,19. Press &#34;Go&#34;.</li>
	</ol>
	</li>
	<li>Level 3: Compare data between subjects for each task condition across all runs. Open FEAT. Select &#8220;Higher-level analysis&#8221; and &#8220;Stats + Post-stats&#8221; from the drop down menu.
	<ol>
		<li>On the Data tab, select &#8220;Inputs are 3D cope images from FEAT directories.&#8221; Set the number of inputs and enter the path to each of the MR images. Enter a path for the &#34;Output directory&#34;.</li>
		<li>On the &#34;Stats&#34; tab, Press the &#34;Full model setup&#34;. Set the number of EVs equal to the number of group variables and covariates such as diagnostic group, age, sex, etc. Enter the values for each subject (Input 1 &#8211; Input n) for each EV. You can use the &#34;Paste&#34; window in order to copy a spreadsheet of these values.
		<ol>
			<li>On the &#34;Contrasts &#38; F-tests&#34; tab, add a contrast for each test variable and for each contrast (e.g., diagnostic group). For each test variable, set the contrast by selecting the value 1 in the column under the appropriate EV. For each contrast, set the first value to 1 and the second to -1. Select &#34;Done&#34;.</li>
		</ol>
		</li>
		<li>On the &#34;Post-stats&#34; tab, select &#34;Cluster&#34; on the &#34;Thresholding&#34; drop-down menu and set the &#34;Z threshold&#34; and &#34;Cluster P&#34; threshold to 2.3 and 0.05 respectively8,19. Press &#34;Go&#34;.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Kety, S. S., Rosenthal, D., Wender, P. H., Schulsinger, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mental+illness+in+the+biological+and+adoptive+families+of+adpoted+schizophrenics.">Mental illness in the biological and adoptive families of adpoted schizophrenics.</a> Am J Psyc. 128, 302-306 (1971).</li><li>Weinberger, D. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Implications+of+normal+brain+development+for+the+pathogenesis+of+schizophrenia.">Implications of normal brain development for the pathogenesis of schizophrenia.</a> Arch Gen Psychia. 44, 660-669 (1987).</li><li>Nuechterlein, K. H., Dawson, M. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Information+processing+and+attentional+functioning+in+the+developmental+course+of+schizophrenic+disorders.">Information processing and attentional functioning in the developmental course of schizophrenic disorders.</a> Schizophr Bul. 10, 160-203 (1984).</li><li>Nuechterlein, K. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+vulnerability/stress+model+of+schizophrenic+relapse:+a+longitudinal+study.">The vulnerability/stress model of schizophrenic relapse: a longitudinal study.</a> Acta Psychiatr Scand, Supp. 382, 58-64 .</li><li>Keshavan, M. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Premorbid+cognitive+deficits+in+young+relatives+of+schizophrenia+patients.">Premorbid cognitive deficits in young relatives of schizophrenia patients.</a> Front Hum Neurosc. 3 (62), (2010).</li><li>Kiehl, K. A., Liddle, P. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+event-related+functional+magnetic+resonance+imaging+study+of+an+auditory+oddball+task+in+schizophrenia.">An event-related functional magnetic resonance imaging study of an auditory oddball task in schizophrenia.</a> Schizophr Re. 48, 159-171 (2001).</li><li>Bramon, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Is+the+P300+wave+an+endophenotype+for+schizophrenia?+A+meta-analysis+and+a+family+study.">Is the P300 wave an endophenotype for schizophrenia? A meta-analysis and a family study.</a> Neuroimag. 27, 960-968 (2005).</li><li>Dichter, G. S., Bellion, C., Casp, M., Belger, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impaired+modulation+of+attention+and+emotion+in+schizophrenia.">Impaired modulation of attention and emotion in schizophrenia.</a> Schizophr Bul. 36, 595-606 (2010).</li><li>Fichtenholtz, H. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Emotion-attention+network+interactions+during+a+visual+oddball+task.">Emotion-attention network interactions during a visual oddball task.</a> Brain Res Cogn Brain Re. 20, 67-80 (2004).</li><li>Voyvodic, J. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Real-time+fMRI+paradigm+control,+physiology,+and+behavior+combined+with+near+real-time+statistical+analysis.">Real-time fMRI paradigm control, physiology, and behavior combined with near real-time statistical analysis.</a> Neuroimag. 10, 91-106 (1999).</li><li>Lang, P. J., Bradley, M. M., Cuthbert, B. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> International affective picture system (IAPS): Digitized photographs, instruction manual and affective ratings. Technical Report A-6. , (2005).</li><li>Smith, S. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advances+in+functional+and+structural+MR+image+analysis+and+implementation+as+FSL.">Advances in functional and structural MR image analysis and implementation as FSL.</a> Neuroimag. 23, 208-219 (2004).</li><li>Smith, S. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+robust+automated+brain+extraction.">Fast robust automated brain extraction.</a> Hum Brain Map. 17, 143-155 (2002).</li><li>Jenkinson, M., Bannister, P., Brady, M., Smith, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+optimization+for+the+robust+and+accurate+linear+registration+and+motion+correction+of+brain+images.">Improved optimization for the robust and accurate linear registration and motion correction of brain images.</a> Neuroimag. 17, 825-841 (2002).</li><li>Jenkinson, M., Smith, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+global+optimisation+method+for+robust+affine+registration+of+brain+images.">A global optimisation method for robust affine registration of brain images.</a> Med Image Ana. 5, 143-156 (2001).</li><li>Woolrich, M. W., Ripley, B. D., Brady, M., Smith, S. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Temporal+autocorrelation+in+univariate+linear+modeling+of+FMRI+data.">Temporal autocorrelation in univariate linear modeling of FMRI data.</a> Neuroimag. 14, 1370-1386 (2001).</li><li>Beckmann, C. F., Jenkinson, M., Smith, S. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=General+multilevel+linear+modeling+for+group+analysis+in+FMRI.">General multilevel linear modeling for group analysis in FMRI.</a> Neuroimag. 20, 1052-1063 (2003).</li><li>Woolrich, M. W., Behrens, T. E., Beckmann, C. F., Jenkinson, M., Smith, S. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multilevel+linear+modelling+for+FMRI+group+analysis+using+Bayesian+inference.">Multilevel linear modelling for FMRI group analysis using Bayesian inference.</a> Neuroimag. 21, 1732-1747 (2004).</li><li>Genovese, C. R., Lazar, N. A., Nichols, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Thresholding+of+statistical+maps+in+functional+neuroimaging+using+the+false+discovery+rate.">Thresholding of statistical maps in functional neuroimaging using the false discovery rate.</a> Neuroimag. 15, 870-878 (2002).</li><li>Hart, 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=Altered+fronto-limbic+activity+in+children+and+adolescents+with+familial+high+risk+for+schizophrenia..">Altered fronto-limbic activity in children and adolescents with familial high risk for schizophrenia..</a> Psychiatry Re. 212, 19-27 (2013).</li><li>Hariri, A. R., Bookheimer, S. Y., Mazziotta, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modulating+emotional+responses:+effects+of+a+neocortical+network+on+the+limbic+system.">Modulating emotional responses: effects of a neocortical network on the limbic system.</a> Neurorepor. 11, 43-48 (2000).</li><li>Gottesman, I. I., Gould, T. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+endophenotype+concept+in+psychiatry:+etymology+and+strategic+intentions.">The endophenotype concept in psychiatry: etymology and strategic intentions.</a> Am J Psyc. 160, 636-645 (2003).</li><li>Glahn, D. C., Thompson, P. M., Blangero, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neuroimaging+endophenotypes:+strategies+for+finding+genes+influencing+brain+structure+and+function.">Neuroimaging endophenotypes: strategies for finding genes influencing brain structure and function.</a> Hum Brain Map. 28, 488-501 (2007).</li></ol>

Dr. Perkins is currently receiving grant support from Janssen, and is a consultant to Dainippon. In the past, Dr. Perkins is or has been on speaker's bureau for Eli Lilly and AstraZeneca. Dr. Perkins has previously received grants from AstraZeneca, Bristol-Myers Squibb, Otsuka, Eli Lilly, Janssen and Pfizer; and consulting and educational fees from AstraZeneca, Bristol-Myers Squibb, Eli Lilly, Janssen, Glaxo Smith Kline, Forest Labs, Pfizer and Shire. All other authors declare no biomedical financial interests or potential conflict of interest.

The modified emotional oddball paradigm in the current study has been shown to elicit differences in neural recruitment during selective attention and emotional processing in children and adolescents at risk for schizophrenia. While existing paradigms using the emotional oddball task have been used to investigate neural changes in adult populations with psychiatric illness8, the current paradigm may be particularly useful for measurement of vulnerability markers in younger age groups. 
<p class='jove_conte...

Schizophrenia is a neurodevelopmental disorder with a known genetic component1,2 and with symptoms including deficits in both executive and emotional processing3,4. First-degree relatives are thought to be at an increased risk of developing schizophrenia, and have been shown to share some of these same neurocognitive deficits in both cognitive and social-emotional domains5. We therefore expect that brain activity in regions associated with executive and emotional processing may be altered in at-risk family members preceding the onset of clinical symptoms. 
Previous studies have indicated that both adults with schizophrenia and adults at familial high risk show aberrant activity within executive and emotional processing networks; however it remains unclear how these changes come about during development. Demonstrating that these changes occur early in life will be a critical first step in understanding the pathophysiology of the disorder. Therefore, this study utilizes an emotional oddball paradigm during functional MRI (fMRI) scanning in order to measure brain activity during the completion of a task that requires both executive and emotional processing in adolescents who are at risk for developing schizophrenia. Oddball paradigms are frequently used to examine the function of fronto-striate circuitry in schizophrenia6 and in individuals with familial high risk7 by measuring selective attention processes allocated to task-relevant target stimuli. Here, a standard oddball task has been modified to include task-irrelevant aversive and neutral stimuli that have been shown to elicit changes in brain activity in patients with schizophrenia8. 
This paper measures functional differences between healthy adolescents and adolescents at high familial risk for schizophrenia using an emotional oddball task. The task design is similar to that used by Fichtenholtz and colleagues9, but the selection of aversive emotional images has been modified to be appropriate for children between the ages of 9-18. The use of this task during functional MRI allowed for the identification of specific brain regions that showed patterns of hyperactivation and hypoactivation in children and adolescents with FHR for schizophrenia, in addition to age-related changes in neural activity during adolescent development.

<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td class="xl63">3T MRI scanner</td>
			<td class="xl63">GE</td>
			<td class="xl63"></td>
			<td class="xl63">BIAC 3T scanner (replaced)</td>
		</tr>
	</tbody>
</table>

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.

There were no differences between groups based on demographic characteristics20. Behavioral data indicated that the target detection task is at an appropriate level of difficulty for children and adolescents between the ages of 9-18. In the current study, controls correctly identified 82.36% of targets (S.D. = 0.14), and the familial risk group correctly identified 76.8% of targets (S.D. = 0.17). Both groups showed decreased accuracy when identifying emotional pictures compared to neutral pic...

Watch this Scientific Journal Video about Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia at JoVE.com

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

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 ...

measurement-fronto-limbic-activity-using-an-emotional-oddball-task

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Behavior

8.8K Görüntüleme.  University of North Carolina at Chapel Hill School of Medicine. 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). 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.

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

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

There has been a long-standing interest in the assessment of the neurobehavioral integrity of the newborn infant. The NICU Network Neurobehavioral Scale (NNNS) was developed as an assessment for the at-risk infant. These are infants who are at increased risk for poor developmental outcome because of insults during prenatal development, such as substance exposure or prematurity or factors such as poverty, poor nutrition or lack of prenatal care that can have adverse effects on the intrauterine environment and affect the developing fetus. The NNNS assesses the full range of infant neurobehavioral performance including neurological integrity, behavioral functioning, and signs of stress/abstinence. The NNNS is a noninvasive neonatal assessment tool with demonstrated validity as a predictor, not only of medical outcomes such as cerebral palsy diagnosis, neurological abnormalities, and diseases with risks to the brain, but also of developmental outcomes such as mental and motor functioning, behavior problems, school readiness, and IQ. The NNNS can identify infants at high risk for abnormal developmental outcome and is an important clinical tool that enables medical researchers and health practitioners to identify these infants and develop intervention programs to optimize the development of these infants as early as possible. The video shows the NNNS procedures, shows examples of normal and abnormal performance and the various clinical populations in which the exam can be used.

The NICU Network Neurobehavioral Scale (NNNS) was developed as an assessment for the at-risk infant. The purpose of this article is to describe the NNNS, provide video examples of the NNNS procedures and discuss the ways in which the exam has been used.

There has been a long-standing interest in the assessment of the neurobehavioral integrity of the newborn infant.  The NICU Network Neurobehavioral Scale ...

Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Na&#239;ve Adolescents at High Risk for Psychosis

Growing evidence suggests that movement abnormalities are a core feature of psychosis. One marker of movement abnormality, dyskinesia, is a result of impaired neuromodulation of dopamine in fronto-striatal pathways. The traditional methods for identifying movement abnormalities include observer-based reports and force stability gauges. The drawbacks of these methods are long training times for raters, experimenter bias, large site differences in instrumental apparatus, and suboptimal reliability. Taking these drawbacks into account has guided the development of better&#160;standardized and more efficient procedures to examine movement abnormalities through handwriting analysis software and tablet. Individuals at&#160;risk for psychosis showed significantly more dysfluent pen movements (a proximal measure for dyskinesia) in a handwriting task. Handwriting kinematics offers a great advance over previous methods of assessing dyskinesia, which could clearly be beneficial for understanding the etiology of psychosis.

Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Na&amp;#239;ve Adolescents at High Risk for Psychosis

Handwriting analysis software significantly improves upon existing instruments measuring movement disorders. Individuals at&#160;risk for psychosis and healthy controls completed handwriting tasks to test for dyskinesia. Results suggest that youth at&#160;risk for psychosis exhibit dyskinesia and that handwriting analysis could significantly contribute to wider dissemination of early identification efforts

Growing evidence suggests that movement abnormalities are a core feature of psychosis. One marker of movement abnormality, dyskinesia, is a result of ...

Quantitative Assessment of Cortical Auditory-tactile Processing in Children with Disabilities

Objective and easy measurement of sensory processing is extremely difficult in nonverbal or vulnerable pediatric patients. We developed a new methodology to quantitatively assess children&#39;s cortical processing of light touch, speech sounds and the multisensory processing of the 2 stimuli, without requiring active subject participation or causing children discomfort. To accomplish this we developed a dual channel, time and strength calibrated air puff stimulator that allows both tactile stimulation and sham control. We combined this with the use of event-related potential methodology to allow for high temporal resolution of signals from the primary and secondary somatosensory cortices as well as higher order processing. This methodology also allowed us to measure a multisensory response to auditory-tactile stimulation.

Objective and easy measurement of sensory processing is extremely difficult in nonverbal or vulnerable pediatric patients. We developed a new methodology to quantitatively assess infants and children&#39;s cortical processing of light touch, speech sounds, and the multisensory processing of the 2 stimuli, without requiring active subject participation or causing discomfort in vulnerable patients.

Objective and easy measurement of sensory processing is extremely difficult in nonverbal or vulnerable pediatric patients. We developed a new methodology ...

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 ...

Design and Implementation of an fMRI Study Examining Thought Suppression in Young Women with, and At-risk, for Depression

Ruminative brooding is associated with increased vulnerability to major depression. Individuals who regularly ruminate will often try to reduce the frequency of their negative thoughts by actively suppressing them. We aim to identify the neural correlates underlying thought suppression in at-risk and depressed individuals. Three groups of women were studied; a major depressive disorder group, an at-risk group (having a first degree relative with depression) and controls. Participants performed a mixed block-event fMRI paradigm involving thought suppression, free thought and motor control periods. Participants identified the re-emergence of &#8220;to-be-suppressed&#8221; thoughts (&#8220;popping&#8221; back into conscious awareness) with a button press. During thought suppression the control group showed the greatest activation of the dorsolateral prefrontal cortex, followed by the at-risk, then depressed group. During the re-emergence of intrusive thoughts compared to successful re-suppression of those thoughts, the control group showed the greatest activation of the anterior cingulate cortices, followed by the at-risk, then depressed group. At-risk participants displayed anomalies in the neural regulation of thought suppression resembling the dysregulation found in depressed individuals. The predictive value of these changes in the onset of depression remains to be determined.

We aim to identify the neural correlates underlying sustained and transient thought suppression, and thought re-emergence in controls, at-risk and depressed individuals. Activation was greatest for controls compared to the at-risk and the depressed group in the dorsolateral prefrontal cortex during thought suppression and anterior cingulate cortex during thought re-emergence.

Ruminative brooding is associated with increased vulnerability to major depression. Individuals who regularly ruminate will often try to reduce the ...

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Delay discounting refers to a decline in the value of a reward when it is delayed relative to when it is immediately available. Delay discounting tasks are used to identify indifference points, which reflect equal preference for two dichotomous reward alternatives differing in both delay and magnitude. Indifference points are key to assessing the shape of a delay-discounting gradient because they allow us to isolate the effect of delay on value. For example, if at a 1 week delay and a maximum of $1,000, the indifference point is at $700 we know that, for that participant, a 1-week delay corresponds to a 30% reduction in value. This video outlines an adjusting amount delay-discounting task that identifies indifference points relatively quickly and is inexpensive and easy to administer. Once data have been collected, non-linear regression techniques are typically used to generate discounting curves. The steepness of the discounting curve reflects the degree of impulsive choice of a group or individual. These techniques have been used with a wide range of commodities and have identified populations that are relatively impulsive. For example, people with substance abuse problems discount delayed rewards more steeply than control participants. Although degree of discounting varies as a function of the commodity examined, discounting of one commodity correlates with discounting of other commodities, which suggests that discounting may be a persistent pattern of behavior1.

Delay discounting refers to a decline in the value of a reward when it is delayed relative to when it is immediately available. We outline a computer-based delay discounting task that is easy to implement and allows for the quantification of the degree of delay discounting in human participants.

Delay discounting refers to a decline in the value of a reward when it is delayed relative to when it is immediately available. Delay discounting tasks ...

The Emotional Stroop Task: Assessing Cognitive Performance under Exposure to Emotional Content

The emotional Stroop effect (ESE) is the result of longer naming latencies to ink colors of emotion words than to ink colors of neutral words. The difference shows that people are affected by the emotional content conveyed by the carrier words even though they are irrelevant to the color-naming task at hand. The ESE has been widely deployed with patient populations, as well as with non-selected populations, because the emotion words can be selected to match the tested pathology. The ESE is a powerful tool, yet it is vulnerable to various threats to its validity. This report refers to potential sources of confounding and includes a modal experiment that provides the means to control for them. The most prevalent threat to the validity of existing ESE studies is sustained effects and habituation wrought about by repeated exposure to emotion stimuli. Consequently, the order of exposure to emotion and neutral stimuli is of utmost importance. We show that in the standard design, only one specific order produces the ESE.

The emotional Stroop effect (ESE) is the result of longer naming latencies to ink colors of emotion words than those of neutral words. This report refers to potential sources of confounding and includes a modal experiment that provides the means to control for them.

The emotional Stroop effect (ESE) is the result of longer naming latencies to ink colors of emotion words than to ink colors of neutral words. The ...

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

Human pain models are useful in the assessing the analgesic effect of drugs, providing information about a drug&#39;s pharmacology and identify potentially suitable therapeutic populations. The need to use a comprehensive battery of pain models is highlighted by studies whereby only a single pain model, thought to relate to the clinical situation, demonstrates lack of efficacy. No single experimental model can mimic the complex nature of clinical pain. The integrated, multi-modal pain task&#160;battery presented here encompasses the electrical stimulation task, pressure stimulation task, cold pressor task, the UVB inflammatory model which includes a thermal task and a paradigm for inhibitory conditioned pain modulation. These human pain models have been tested for predicative validity and reliability both in their own right and in combination, and can be used repeatedly, quickly, in short succession, with minimum burden for the subject and with a modest quantity of equipment. This allows a drug to be fully characterized and profiled for analgesic effect which is especially useful for drugs with a novel or untested mechanism of action.

Human pain models are valuable tools used to assess the analgesic potential of novel compounds and predict their clinical efficacy, especially when used in an integrated manner. Although implementation of these models is complex, with proper execution, the pain models described in this protocol can provide predictive and reliable results.

Human pain models are useful in the assessing the analgesic effect of drugs, providing information about a drug&#39;s pharmacology and identify ...

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Sentence comprehension relies on the ability to rapidly integrate different types of linguistic and non-linguistic information. However, there is currently a paucity of research exploring how preschool children with autism understand sentences using different types of cues. The mechanisms underlying sentence comprehension remains largely unclear. The present study presents a protocol to examine the sentence comprehension abilities of preschool children with autism. More specifically, a visual world paradigm of eye-tracking is used to explore the moment-to-moment sentence comprehension in the children. The paradigm has multiple advantages. First, it is sensitive to the time course of sentence comprehension and thus can provide rich information about how sentence comprehension unfolds over time. Second, it requires minimal task and communication demands, so it is ideal for testing children with autism. To further minimize the computational burden of children, the present study measures eye movements that arise as automatic responses to linguistic input rather than measuring eye movements that accompany conscious responses to spoken instructions.

We present a protocol to examine the use of morphological cues during real-time sentence comprehension by children with autism.

Sentence comprehension relies on the ability to rapidly integrate different types of linguistic and non-linguistic information. However, there is ...

Impact of High-intensity Interval Exercise and Moderate-Intensity Continuous Exercise on the Cardiac Troponin T Level at an Early Stage of Training

An elevation in cardiac troponin T (cTnT), as a highly specific biomarker of cardiomyocyte damage, after moderate-intensity continuous exercise (MCE) has been described. The exercise-induced cTnT response distorts the diagnostic role of the cTnT assay. Although high-intensity interval exercise (HIE) is growing in popularity and concerns remain about its safety, available data related to cTnT release after HIE is limited, which hampers the use of HIE as a health intervention. Here, we present three representative HIE protocols [traditional HIE (repeated 4 min cycling at 90% V&#775;O2max interspersed with 3 min rest, 200 kJ/session); sprint interval exercise (SIE, repeated 1 min cycling at 120% V&#775;O2max interspersed with 1.5 min rest, 200 kJ/session); and repeated sprint exercise (RSE, 40 x 6 s all-out sprints interspersed with 9 s rest)] and one representative MCE protocol (continuous cycling exercise at an intensity of 60% V&#775;O2max, 200 kJ/session). Forty-seven sedentary, overweight young women were randomly assigned to one of four groups (HIE, SIE, RSE, and MCE). Six bouts of respective exercise were performed by every single group, with each being 48 h apart. Meanwhile, for four groups, the duration of the entire testing period was identical, being 10 days. Before and after the first and final exercise bouts, an assessment was conducted of cTnT. The current study provides a frame of reference giving a clear picture of how a specific exercise session affects the circulating cTnT concentration at the early stage of training. The information may assist with clinical interpretations of post-exercise cTnT elevation and guide the prescription of exercise, especially for HIE.

Here, we present protocols of high-intensity interval and moderate-intensity continuous exercise to observe the response of circulating cardiac troponin T (cTnT) concentration to acute exercise over 10 days. The information may assist with clinical interpretations of post-exercise cTnT elevation and guide the prescription of exercise.

An elevation in cardiac troponin T (cTnT), as a highly specific biomarker of cardiomyocyte damage, after moderate-intensity continuous exercise (MCE) has ...