We thank Aldo Rustichini for fruitful discussions and comments on the design. Funded by NIA grant R01-AG033406 to IL and PWG.

1. Preparing the Experiment
<ol>
	<li>The first step is to design visual stimuli representing risky and ambiguous choices that will be presented on the screen in the scanner. We use images such as those presented in Figure 1 to represent bags filled with poker chips which we call "lottery bags". Graphically, these images can be thought of as stacks of poker chips before they are placed in a bag. Importantly, these images represent real containers, in our case envelopes, filled with 100 red and blue poker chips which the subject will see before starting the experiment. This insures that subjects both understand the lotteries they will face and believe that the computer display accurately presents those lotteries. For risky lotteries the winning probability, the ratio of red to blue chips in a given envelope, is precisely stated using both numbers and a graphic stimulus (Figure 1A). For ambiguous lotteries part of the information about the probability is missing (Figure 1B), such that the possible ratio of red to blue chips is bounded but not specified, rendering the winning probability partially ambiguous.</li>
	<li>For risky lotteries the red and blue areas of each image are proportional to the number of red and blue chips in the envelope. We recommend using a minimum of 3 outcome probabilities4 (Figure 1A). The exact probabilities used can vary according to the specific requirements of the experiment, but experimenters should be cautious about using very high and very low probabilities. It is known that human subjects typically misrepresent probabilities below 10% or above 90%5. Unless one intends to study this systematic misrepresentation, these extremes should be avoided.</li>
	<li>To convey ambiguity the central part of the stack of chips in the computer display is obscured with a gray occluder (Figure 1B). In the gray area the number of chips of each color will be unknown, and thus the probability of drawing a red or a blue chip will not be precisely known. For example, in the middle bag in Figure 1B the occluder covers 50% of the bag, and thus the number of red chips can be anywhere between 25 (if all the chips behind the occluder are blue) and 75 (if all the chips behind the occluder are red). Of course, the number of blue chips can also be anywhere between these two values.</li>
	<li>Increasing the occluder size increases the ambiguity level (the range of possible probabilities for drawing a red or blue chip). We recommend using at least 3 levels of occlusion, covering ~25, 50 or 75% of the bag (Figure 1B).</li>
	<li>When subjects perform the task on the computer, we present each winning probability/ambiguity level with a range of possible outcome amounts. We recommend using 5 reward levels4, spanning a wide range of amounts, for example: 5, 9.5, 18, 34 and 65 dollars. In the display, we present the outcome amount next to the winning color and display "0" next to the other color. For example, in Figure 2 drawing a red chip would result in winning $18 while drawing a blue chip would result in a zero outcome.</li>
	<li>Critically, on each trial subjects will choose between two lotteries. For simplicity we keep one of the options constant throughout the experiment (in this example a 50% chance of winning $5) and only vary the other option. This has two advantages. First, the constant option does not have to appear on the screen, simplifying the visual display (although a reminder every now and then may be helpful). Second, because one option never changes, regression-based analyses of the fMRI signal can effectively neglect this parameter. Note that in order to have a "common currency" for the subjective values of risky and ambiguous lotteries the reference option has to be the same for both types of trials.</li>
	<li>Each combination of probability or ambiguity level and amount should be presented a total of at least 4 times, and preferably more, to ensure sufficient statistical power in both the behavioral and fMRI analyses. In half of the repetitions red should be associated with a non-zero outcome and in the other half blue, to avoid color bias and to insure symmetry in the ambiguity.</li>
	<li>We chose to use a slow event-related design (Figure 3), in which the hemodynamic response to different trials is well separated in time. In such a design each lottery should be presented as a stimulus briefly, in our case for 2 s, followed by a delay period (in our case, 6 s), to allow time for the decision-related neural activation to build up. Responses should be made within a brief time window (1-2 s). Use an image such as the one shown in Figure 3 as a brief feedback, so that the subject knows their response has been recorded. Separate the trials by long rest periods (10 s or more) to allow for the hemodynamic response to go back to baseline. Shorter rest periods can be used with appropriate jitter. Group the trials in blocks of up to 30 trials each, but be sure not to let the blocks take longer than about 10 minutes. This allows for rest periods in the scanner which maximize performance and minimize subject fatigue. To allow for at least 4 repetitions of each combination of probability/ambiguity and amount the total number of trials will be at least 120, i.e. 4 blocks.</li>
	<li>Prepare the physical bags (in our case envelopes) so they can be shown to subjects before they perform the task. They will be used later to play the randomly chosen trial(s) for pay off. Prepare a bag for each lottery image used in the experiment. Fill each bag with a total of 100 poker/bingo red and blue chips, with proportions corresponding to the probability of drawing a chip of each color from that bag shown in the display. For ambiguous bags use a random number generator to decide on the actual numbers of red and blue chips, corresponding to each ambiguity level. Prepare the reference bag with 50 red and 50 blue chips. Preparing physical bags and showing them to the subjects is particularly important for subjects recruited in psychology departments. These subjects are likely to suspect some kind of deception and should they suspect deception their responses will be uninterpretable.</li>
</ol>
2. Preparing the Subject
<ol>
	<li>Each subject must fill out a consent form and a screening questionnaire. The screening form verifies that the subject does not have any metal in their body, that they are not pregnant or claustrophobic, and that they can be safely scanned. Subjects must also remove all metals from their body to insure safety in the scanner environment. This is critical.</li>
	<li>Provide the subject with detailed instructions about the experiment. Ask them a few simple questions to make sure they understand how probabilities and amounts are conveyed in each image, and to make sure they understand their task. Make sure not to reveal any information that could influence their choices. For example, do not frame the choice problem they face in such a way as to bias the subjects towards a particular risk attitude. Show them the physical bags and stress that each image in the experiment refers to a single specific physical bag that you cannot and will not tamper with. Also explain that in half the trials blue will be the winning color and in half red. Explain the payment mechanism, so that the subject understands that they will be paid according to their choices. Encourage subjects to ask questions about anything they do not understand. This is a critical period when the beliefs of the subjects about the experiment are being established. It is essential that subjects be confident that the experiment does not involve any type of deception or the behavioral and neural results will be uninterpretable.</li>
	<li>Seal the bags and have the subject sign their name across the seal. Explain that this will enable them to verify at the end of the experiment that you did not change the contents of the bags during the experiment. This helps reassure subjects that they are playing an entirely fair game. Explain also that after the lotteries are played at the end of the experiment they will be allowed to look into the contents of the bags to make sure they conform to the stated probability or ambiguity level.</li>
</ol>
3. Scanning
<ol>
	<li>We use a 3T MRI scanner with a head coil (4 channels or more) to get Blood Oxygenation Level-Dependent (BOLD) signals from the whole brain.</li>
	<li>Use a 2-button response box to record subjects choices.</li>
	<li>Anatomical scan: We use a T1-weighted MPRAGE sequence to get a clear high-resolution (1x1x1 mm) image of the subject's brain that can be used for 3D reconstruction. Any high-resolution sequence can be used for this purpose.</li>
	<li>Functional scans: We use a T2*-weighted EPI sequence, with a TR of 2 s, and 3x3x3 mm voxels. Make sure to position the slices such that they include the brain areas you are most interested in, typically the prefrontal cortex, parietal cortex and the basal ganglia. Scanning parameters should be optimized for the specific scanner, we used: TE 30ms, flip angle 75&deg;, 36 3 mm slices with no inter-slice gap, parallel to the AC-PC line, in-plane resolution 3x3 mm, FOV 192 mm. Other studies have suggested that positioning the slices at 30&deg; to the AC-PC line may reduce signal dropout in the orbitofrontal cortex6.</li>
</ol>
4. Payment Procedure
<ol>
	<li>After removing the subject from the scanner, retrieve the behavioral data from the computer that has recorded the subject's responses.</li>
	<li>Randomly select one or few trials for payment. It is best to let the subject do the selection, for example by drawing a numbered poker chip out of an opaque bag that contains chips with all trial numbers. This will ensure to the subject that the selection is indeed random.</li>
	<li>For each selected trial show the subject the presented option and the choice they made on that trial. Ask them to draw a chip from the bag chosen on that trial, and pay them according to the drawn color and the amount presented on the trial.</li>
</ol>
For example, if the selected trial presented the lottery depicted in Figure 2 (an ambiguous lottery, offering $18 if a red chip is drawn) and the subject chose this lottery (rather than the reference lottery), then the subject should draw a chip out of the physical bag corresponding to the lottery image. If a red chip is drawn the subject will receive $18, if a blue chip is drawn they will receive nothing.
5. Analyzing the Behavioral Data
<ol>
	<li>Using maximum likelihood we fit the choice data of each subject to a logistic function of the form:</li>
</ol>
<img alt="Equation 1" src="/files/ftp_upload/3724/3724eq1.jpg" /> 
Where Pv is the probability that the subject chose the variable lottery, SVF and SVV are the subjective values of the fixed and variable options respectively, and &gamma; is the slope of the logistic function, which is a subject-specific parameter. An alternative approach is to use a probit distribution.
<ol start="2">
	<li>To model the subjective value of each option for each subject you can use one of a number of models that take into account the amount, probability and ambiguity level of the option and the attitudes of the individual subject towards risk and ambiguity. We chose to use a power function5 that includes a linear effect of ambiguity on the perceived probability7:</li>
</ol>
<img alt="Equation 2" src="/files/ftp_upload/3724/3724eq2.jpg" /> 
Where p is the objective probability (by definition 0.5 for this class of ambiguous lotteries), A is the ambiguity level (the fraction of the total probability that is unknown, 0 for risky lotteries), V is the amount, and &alpha; and &beta;are subject-specific risk and ambiguity attitude parameters respectively. One of several alternative approaches is to include ambiguity as an exponential effect8:
<img alt="Equation 3" src="/files/ftp_upload/3724/3724eq3.jpg" /> 
Fitting the choice data with the choice function thus provides estimates for the risk attitude (&alpha;) and ambiguity attitude (&beta;) for each subject.
6. Analyzing the Neural Data
<ol>
	<li>Perform standard preprocessing of the data, including: 1) slice scan-time correction to account for the slight differences in scanning times of different slices; 2) motion correction to account for intra- and inter-run subject movement; and 3) removal of low frequencies that are typically related to physiological noise and scanner drifts.</li>
	<li>Register the functional data of each subject to their anatomical data.</li>
	<li>For analysis at the single subject level model the activity of each voxel as a sustained response during the entire trial (in our case 10 s), convolved with a standard hemodynamic response function9. Use a General Linear Model with the following predictors:</li>
</ol>
<ul>
	<li>Two predictors of subjective value (SV), one for risky trials and one for ambiguous trials. Use equation 2 and the individual subject specific parameters derived from the behavioral fit to calculate the SV of each lottery. Since the reference lottery is the same for all trials we can use the SV of the variable lottery alone in each trial. For the risky SV predictor insert the SV for each risky trial, and 0 for each ambiguous trial, and vice versa for the ambiguous predictor.</li>
	<li>Two dummy predictors, one for risky trials and one for ambiguous trials, to capture general activation, such as visual and motor activations.</li>
</ul>
<ol start="4">
	<li>Look for voxels in which the coefficients of SV under risk and/or under ambiguity are significant. The test for significance should take into account the multiple comparisons performed. The method we used was limiting the minimum cluster size to 6 contiguous functional voxels10. Alternatively, other methods, such as the False Discovery Rate (FDR)11, can be used to correct for multiple corrections.</li>
</ol>
7. Representative Results
Behavior
Figure 4 presents the behavioral results of three representative subjects. Each panel presents the choice data and model fit results for one subject under either risk (left) or ambiguity (right). The graphs depict the proportion of trials in which the subject chose the variable lottery as a function of amount, separately for each level of probability or ambiguity. As can be seen, subjects may vary a lot in their attitudes towards risk and ambiguity.
To examine the goodness of the fit, check the r2, which should ideally be over 0.5, and also inspect the curves visually. While all our three example subjects had lawful behavior that enabled reasonable fits, note that subject 2 hardly chose the variable option in the risk condition with the lowest probability (0.13). This suggests that expanding the range of amounts and/or using higher probabilities may provide better results, because it will ensure that subjects choose the variable options on at least some of the trials. Another option is to pre-test each subject on a wide range of amounts and choose those amounts that ensure a comparable number of reference and variable option choices for each individual.
fMRI
Figure 5 presents the imaging results in one representative subject. Highlighted voxels are ones in which the coefficient of the subjective value predictor under ambiguity (top) or risk (bottom) was significantly different from 0. In this typical subject, significant correlation was found in medial prefrontal cortex (MPFC) and the striatum under both conditions. These areas are the most consistent across subjects, but significant correlations may also be expected in areas in medial and lateral parietal cortex, as well as the amygdala. As activity in this type of tasks is usually weak and noisy you should expect high variability across subjects with many subjects exhibiting significant correlations only in a subset of areas.
<img alt="Figure 1" src="/files/ftp_upload/3724/3724fig1.jpg" /> 
Figure 1. Risky and ambiguous stimuli. A) In risky stimuli the red and blue areas of each image on the screen are proportional to the number of red and blue chips in the envelope. Three outcome probabilities were used here: 0.13, 0.25 and 0.38. B) In ambiguous stimuli the central part of the image is obscured with a gray occluder. In the gray area the number of chips of each color is unknown, and thus the probability of drawing a chip of a certain color is not precisely known. Three levels of ambiguity are used here, where 25, 50 or 75% of the image are occluded.
<img alt="Figure 2" src="/files/ftp_upload/3724/3724fig2.jpg" /> 
Figure 2. A lottery example. This is an ambiguous lottery, at a 50% ambiguity level. At least 25 of the chips in the envelope are red and at least 25 are blue. If a red chip is drawn the subject will win $18, while they will win nothing if a blue chip is drawn.
<img alt="Figure 3" src="/files/ftp_upload/3724/3724fig3.jpg" /> 
Figure 3. The trial structure. A lottery is briefly presented, followed by a delay period. A response cue then prompts subjects to indicate their choice between the lottery on the screen and the reference lottery (in this case a 50% chance of winning $5). Trials are interleaved with long rest periods.
<img alt="Figure 4" src="/files/ftp_upload/3724/3724fig4.jpg" /> 
Figure 4. Examples of single subject choice behavior. The graphs present the proportion of trials in which each subject chose the variable option over the reference, as a function of the offered amount, in risky (left) and ambiguous (right) trials. Different curves are for different risk or ambiguity levels. &alpha;, risk attitude parameter; &beta;, ambiguity attitude parameter; r2, McFadden's pseudo R-squared, a measure of the goodness of fit of the behavioral model, equivalent to the portion of the variance that is explained by the model; n, number of trials in which response was made (out of a total of 180).
<img alt="Figure 5" src="/files/ftp_upload/3724/3724fig5.jpg" /> 
Figure 5. Example of single subject activation maps. Activation maps are presented on a high resolution anatomical image. Highlighted areas are those whose activation was significantly correlated with subjective value under risk (top) or under ambiguity (bottom). In most subjects the medial prefrontal cortex (MPFC) and the striatum represent subjective value under both risk and ambiguity. Corrected p-values are based on a minimum cluster size of 6 functional voxels. <a href="https://www.jove.com/files/ftp_upload/3724/3724fig5large.jpg" target="_blank"> Click here to view larger figure</a>.

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	<li>Glimcher, P. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Understanding+risk:+a+guide+for+the+perplexed.">Understanding risk: a guide for the perplexed.</a> Cogn. Affect Behav. Neurosci. 8, 348-354 (2008).</li><li>Camerer, C., Weber, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Recent+Developments+in+Modeling+Preferences+-+Uncertainty+and+Ambiguity.">Recent Developments in Modeling Preferences - Uncertainty and Ambiguity.</a> Journal of Risk and Uncertainty. 5, 325-370 (1992).</li><li>Holt, C. A., Laury, S. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Risk+aversion+and+incentive+effects.">Risk aversion and incentive effects.</a> Am. Econ. Rev. 92, 1644-1655 (2002).</li><li>Levy, I., Snell, J., Nelson, A. J., Rustichini, A., Glimcher, P. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neural+representation+of+subjective+value+under+risk+and+ambiguity.">Neural representation of subjective value under risk and ambiguity.</a> J. Neurophysiol. 103, 1036-1047 (2010).</li><li>Kahneman, D., Tversky, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prospect+Theory+-+Analysis+of+Decision+under+Risk.">Prospect Theory - Analysis of Decision under Risk.</a> Econometrica. 47, 263-291 (1979).</li><li>Deichmann, R., Gottfried, J. A., Hutton, C., Turner, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimized+EPI+for+fMRI+studies+of+the+orbitofrontal+cortex.">Optimized EPI for fMRI studies of the orbitofrontal cortex.</a> Neuroimage. 19, 430-441 (2003).</li><li>Gilboa, I., Schmeidler, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Maxmin+Expected+Utility+with+Non-Unique+Prior.">Maxmin Expected Utility with Non-Unique Prior.</a> J. Math Econ. 18, 141-153 (1989).</li><li>Hsu, M., Bhatt, M., Adolphs, R., Tranel, D., Camerer, C. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neural+systems+responding+to+degrees+of+uncertainty+in+human+decision-making.">Neural systems responding to degrees of uncertainty in human decision-making.</a> Science. 310, 1680-1683 (2005).</li><li>Boynton, G. A., Engel, S. A., Glover, G., Heeger, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> J Neurosci. 16, 4207-4221 (1996).</li><li>Forman, S. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+Assessment+of+Significant+Activation+in+Functional+Magnetic-Resonance-Imaging+(Fmri)+-+Use+of+a+Cluster-Size+Threshold.">Improved Assessment of Significant Activation in Functional Magnetic-Resonance-Imaging (Fmri) - Use of a Cluster-Size Threshold.</a> Magnetic Resonance in Medicine. 33, 636-647 (1995).</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> NeuroImage. 15, 870-878 (2002).</li><li>Huettel, S. A., Stowe, C. J., Gordon, E. M., Warner, B. T., Platt, M. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neural+signatures+of+economic+preferences+for+risk+and+ambiguity.">Neural signatures of economic preferences for risk and ambiguity.</a> Neuron. 49, 765-775 (2006).</li><li>Smith, V. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Papers in experimental economics. , (1991).</li></ol>

No conflicts of interest declared.

We have used a method from experimental economics to characterize subjects' behavior and estimate individual attitudes towards risk and ambiguity. We then used these estimates to analyze neural data.
Other methods for examining fMRI activity while subjects make choices under risk and ambiguity have been used before8,12. Our approach, however, combines several important features. First, it uses a parametric design, in which different parameters (amount, probability and ambiguity leve...

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 <tbody>
 <tr>
 <td>Name</td>
 <td>Company</td>
 <td>Comments</td>
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 <td>Allegra Head Only 3.0 T MRI Scanner</td>
 <td>Siemens</td>
 <td>A whole body scanner can also be used</td>
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 <td>NM-011 transmit head coil</td>
 <td>Nova Medical</td>
 <td>&nbsp;</td>
 </tr>
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 <td>E-prime</td>
 <td>Psychology Software Tools</td>
 <td>Stimuli presentation software</td>
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 <td>Matlab</td>
 <td>MathWorks</td>
 <td>&nbsp;</td>
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</table>

measuring the subjective value of risky and ambiguous options using experimental economics and functional mri methods

Most of the choices we make have uncertain consequences. In some cases the probabilities for different possible outcomes are precisely known, a condition termed "risky". In other cases when probabilities cannot be estimated, this is a condition described as "ambiguous". While most people are averse to both risk and ambiguity1,2, the degree of those aversions vary substantially across individuals, such that the subjective value of the same risky or ambiguous option can be very different for different individuals. We combine functional MRI (fMRI) with an experimental economics-based method3 to assess the neural representation of the subjective values of risky and ambiguous options4. This technique can be now used to study these neural representations in different populations, such as different age groups and different patient populations.
In our experiment, subjects make consequential choices between two alternatives while their neural activation is tracked using fMRI. On each trial subjects choose between lotteries that vary in their monetary amount and in either the probability of winning that amount or the ambiguity level associated with winning. Our parametric design allows us to use each individual's choice behavior to estimate their attitudes towards risk and ambiguity, and thus to estimate the subjective values that each option held for them. Another important feature of the design is that the outcome of the chosen lottery is not revealed during the experiment, so that no learning can take place, and thus the ambiguous options remain ambiguous and risk attitudes are stable. Instead, at the end of the scanning session one or few trials are randomly selected and played for real money. Since subjects do not know beforehand which trials will be selected, they must treat each and every trial as if it and it alone was the one trial on which they will be paid. This design ensures that we can estimate the true subjective value of each option to each subject. We then look for areas in the brain whose activation is correlated with the subjective value of risky options and for areas whose activation is correlated with the subjective value of ambiguous options.

Watch this Scientific Journal Video about Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods at JoVE.com

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Using functional MRI and behavioral methods to determine the neural representation of the subjective value of risky and ambiguous options in the human brain.

Most of the choices we make have uncertain consequences. In some cases the probabilities for different possible outcomes are precisely known, a condition ...

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12.0K vues.  Yale School of Medicine. En utilisant des méthodes d'IRM fonctionnelles et comportementales afin de déterminer la représentation neuronale de la valeur subjective des options risquées et ambiguës dans le cerveau humain.

Vidéo: Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Combining Behavioral Endocrinology and Experimental Economics: Testosterone and Social Decision Making

Behavioral endocrinological research in humans as well as in animals suggests that testosterone plays a key role in social interactions. Studies in rodents have shown a direct link between testosterone and aggressive behavior1 and folk wisdom adapts these findings to humans, suggesting that testosterone induces antisocial, egoistic or even aggressive behavior2. However, many researchers doubt a direct testosterone-aggression link in humans, arguing instead that testosterone is primarily involved in status-related behavior3,4. As a high status can also be achieved by aggressive and antisocial means it can be difficult to distinguish between anti-social and status seeking behavior.
We therefore set up an experimental environment, in which status can only be achieved by prosocial means. In a double-blind and placebo-controlled experiment, we administered a single sublingual dose of 0.5 mg of testosterone (with a hydroxypropyl-&beta;-cyclodextrin carrier) to 121 women and investigated their social interaction behavior in an economic bargaining paradigm. Real monetary incentives are at stake in this paradigm; every player A receives a certain amount of money and has to make an offer to another player B on how to share the money. If B accepts, she gets what was offered and player A keeps the rest. If B refuses the offer, nobody gets anything. A status seeking player A is expected to avoid being rejected by behaving in a prosocial way, i.e. by making higher offers.
The results show that if expectations about the hormone are controlled for, testosterone administration leads to a significant increase in fair bargaining offers compared to placebo. The role of expectations is reflected in the fact that subjects who report that they believe to have received testosterone make lower offers than those who say they believe that they were treated with a placebo. These findings suggest that the experimental economics approach is sensitive for detecting neurobiological effects as subtle as those achieved by administration of hormones. Moreover, the findings point towards the importance of both psychosocial as well as neuroendocrine factors in determining the influence of testosterone on human social behavior.

The procedure described in this protocol shows that testosterone administration and folk beliefs about testosterone may be associated with directly opposed social behaviors.

Behavioral endocrinological research in humans as well as in animals suggests that testosterone plays a key role in social interactions. Studies in ...

Assessment of Cerebral Lateralization in Children using Functional Transcranial Doppler Ultrasound (fTCD)

There are many unanswered questions about cerebral lateralization. In particular, it remains unclear which aspects of language and nonverbal ability are lateralized, whether there are any disadvantages associated with atypical patterns of cerebral lateralization, and whether cerebral lateralization develops with age. In the past, researchers interested in these questions tended to use handedness as a proxy measure for cerebral lateralization, but this is unsatisfactory because handedness is only a weak and indirect indicator of laterality of cognitive functions1. Other methods, such as fMRI, are expensive for large-scale studies, and not always feasible with children2.
Here we will describe the use of functional transcranial Doppler ultrasound (fTCD) as a cost-effective, non-invasive and reliable method for assessing cerebral lateralization. The procedure involves measuring blood flow in the middle cerebral artery via an ultrasound probe placed just in front of the ear. Our work builds on work by Rune Aaslid, who co-introduced TCD in 1982, and Stefan Knecht, Michael Deppe and their colleagues at the University of M&uuml;nster, who pioneered the use of simultaneous measurements of left- and right middle cerebral artery blood flow, and devised a method of correcting for heart beat activity. This made it possible to see a clear increase in left-sided blood flow during language generation, with lateralization agreeing well with that obtained using other methods3.
The middle cerebral artery has a very wide vascular territory (see Figure 1) and the method does not provide useful information about localization within a hemisphere. Our experience suggests it is particularly sensitive to tasks that involve explicit or implicit speech production. The 'gold standard' task is a word generation task (e.g. think of as many words as you can that begin with the letter 'B') 4, but this is not suitable for young children and others with limited literacy skills. Compared with other brain imaging methods, fTCD is relatively unaffected by movement artefacts from speaking, and so we are able to get a reliable result from tasks that involve describing pictures aloud5,6. Accordingly, we have developed a child-friendly task that involves looking at video-clips that tell a story, and then describing what was seen.

Assessment of Cerebral Lateralization in Children using Functional Transcranial Doppler Ultrasound fTCD

Functional transcranial Doppler sonography (fTCD) is a simple and non-invasive ultrasound technique which can be used to assess the lateralization of cognitive functions, especially language, and is suitable for use with children.

There are many unanswered questions about cerebral lateralization.  In particular, it remains unclear which aspects of language and nonverbal ability are ...

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Functional MRI (fMRI) is a widely used tool for non-invasively measuring correlates of human brain activity. However, its use has mostly been focused upon measuring activity on the surface of cerebral cortex rather than in subcortical regions such as midbrain and brainstem. Subcortical fMRI must overcome two challenges: spatial resolution and physiological noise. Here we describe an optimized set of techniques developed to perform high-resolution fMRI in human SC, a structure on the dorsal surface of the midbrain; the methods can also be used to image other brainstem and subcortical structures.
High-resolution (1.2 mm voxels) fMRI of the SC requires a non-conventional approach. The desired spatial sampling is obtained using a multi-shot (interleaved) spiral acquisition1. Since, T2* of SC tissue is longer than in cortex, a correspondingly longer echo time (TE ~ 40 msec) is used to maximize functional contrast. To cover the full extent of the SC, 8-10 slices are obtained. For each session a structural anatomy with the same slice prescription as the fMRI is also obtained, which is used to align the functional data to a high-resolution reference volume.
In a separate session, for each subject, we create a high-resolution (0.7 mm sampling) reference volume using a T1-weighted sequence that gives good tissue contrast. In the reference volume, the midbrain region is segmented using the ITK-SNAP software application2. This segmentation is used to create a 3D surface representation of the midbrain that is both smooth and accurate3. The surface vertices and normals are used to create a map of depth from the midbrain surface within the tissue4.
Functional data is transformed into the coordinate system of the segmented reference volume. Depth associations of the voxels enable the averaging of fMRI time series data within specified depth ranges to improve signal quality. Data is rendered on the 3D surface for visualization.
In our lab we use this technique for measuring topographic maps of visual stimulation and covert and overt visual attention within the SC1. As an example, we demonstrate the topographic representation of polar angle to visual stimulation in SC.

This article describes techniques to perform high-resolution functional magnetic resonance imaging with 1.2 mm sampling in human midbrain and subcortical structures using a 3T scanner. Use of these techniques to resolve topographic maps of visual stimulation in the human superior colliculus (SC) is given as an example.

Functional MRI (fMRI) is a widely used tool for non-invasively measuring correlates of human brain activity. However, its use has mostly been focused upon ...

Methods to Assay Drosophila Behavior

Drosophila melanogaster, the fruit fly, has been used to study molecular mechanisms of a wide range of human diseases such as cancer, cardiovascular disease and various neurological diseases1. We have optimized simple and robust behavioral assays for determining larval locomotion, adult climbing ability (RING assay), and courtship behaviors of Drosophila. These behavioral assays are widely applicable for studying the role of genetic and environmental factors on fly behavior. Larval crawling ability can be reliably used for determining early stage changes in the crawling abilities of Drosophila larvae and also for examining effect of drugs or human disease genes (in transgenic flies) on their locomotion. The larval crawling assay becomes more applicable if expression or abolition of a gene causes lethality in pupal or adult stages, as these flies do not survive to adulthood where they otherwise could be assessed. This basic assay can also be used in conjunction with bright light or stress to examine additional behavioral responses in Drosophila larvae. Courtship behavior has been widely used to investigate genetic basis of sexual behavior, and can also be used to examine activity and coordination, as well as learning and memory. Drosophila courtship behavior involves the exchange of various sensory stimuli including visual, auditory, and chemosensory signals between males and females that lead to a complex series of well characterized motor behaviors culminating in successful copulation. Traditional adult climbing assays (negative geotaxis) are tedious, labor intensive, and time consuming, with significant variation between different trials2-4. The rapid iterative negative geotaxis (RING) assay5 has many advantages over more widely employed protocols, providing a reproducible, sensitive, and high throughput approach to quantify adult locomotor and negative geotaxis behaviors. In the RING assay, several genotypes or drug treatments can be tested simultaneously using large number of animals, with the high-throughput approach making it more amenable for screening experiments.

Methods to Assay Drosophila Behavior

Drosophila melanogaster is a genetically and behaviorally tractable model system that has been used to understand the molecular and cellular basis of many important biological processes for over a century 1. Drosophila has been well exploited to gain insights into the genetic basis of fly behavior.

Drosophila melanogaster, the fruit fly, has been used to study molecular mechanisms of a wide range of human diseases such as cancer, cardiovascular ...

Functional Neuroimaging Using Ultrasonic Blood-brain Barrier Disruption and Manganese-enhanced MRI

Although mice are the dominant model system for studying the genetic and molecular underpinnings of neuroscience, functional neuroimaging in mice remains technically challenging. One approach, Activation-Induced Manganese-enhanced MRI (AIM MRI), has been used successfully to map neuronal activity in rodents 1-5. In AIM MRI, Mn2+ acts a calcium analog and accumulates in depolarized neurons 6,7. Because Mn2+ shortens the T1 tissue property, regions of elevated neuronal activity will enhance in MRI. Furthermore, Mn2+ clears slowly from the activated regions; therefore, stimulation can be performed outside the magnet prior to imaging, enabling greater experimental flexibility. However, because Mn2+ does not readily cross the blood-brain barrier (BBB), the need to open the BBB has limited the use of AIM MRI, especially in mice.
One tool for opening the BBB is ultrasound. Though potentially damaging, if ultrasound is administered in combination with gas-filled microbubbles (i.e., ultrasound contrast agents), the acoustic pressure required for BBB opening is considerably lower. This combination of ultrasound and microbubbles can be used to reliably open the BBB without causing tissue damage 8-11.
Here, a method is presented for performing AIM MRI by using microbubbles and ultrasound to open the BBB. After an intravenous injection of perflutren microbubbles, an unfocused pulsed ultrasound beam is applied to the shaved mouse head for 3 minutes. For simplicity, we refer to this technique of BBB Opening with Microbubbles and UltraSound as BOMUS 12. Using BOMUS to open the BBB throughout both cerebral hemispheres, manganese is administered to the whole mouse brain. After experimental stimulation of the lightly sedated mice, AIM MRI is used to map the neuronal response.
To demonstrate this approach, herein BOMUS and AIM MRI are used to map unilateral mechanical stimulation of the vibrissae in lightly sedated mice 13. Because BOMUS can open the BBB throughout both hemispheres, the unstimulated side of the brain is used to control for nonspecific background stimulation. The resultant 3D activation map agrees well with published representations of the vibrissae regions of the barrel field cortex 14. The ultrasonic opening of the BBB is fast, noninvasive, and reversible; and thus this approach is suitable for high-throughput and/or longitudinal studies in awake mice.

A technique is described for broadly opening the blood-brain barrier in the mouse using microbubbles and ultrasound. Using this technique, manganese can be administered to the mouse brain. Because manganese is an MRI contrast agent that accumulates in depolarized neurons, this approach enables imaging of neuronal activity.

Although mice are the dominant model system for studying the genetic and molecular underpinnings of neuroscience, functional neuroimaging in mice remains ...

Optogenetic Functional MRI

The investigation of the functional connectivity of precise neural circuits across the entire intact brain can be achieved through optogenetic functional magnetic resonance imaging (ofMRI), which is a novel technique that combines the relatively high spatial resolution of high-field fMRI with the precision of optogenetic stimulation. Fiber optics that enable delivery of specific wavelengths of light deep into the brain in vivo are implanted into regions of interest in order to specifically stimulate targeted cell types that have been genetically induced to express light-sensitive trans-membrane conductance channels, called opsins. fMRI is used to provide a non-invasive method of determining the brain's global dynamic response to optogenetic stimulation of specific neural circuits through measurement of the blood-oxygen-level-dependent (BOLD) signal, which provides an indirect measurement of neuronal activity. This protocol describes the construction of fiber optic implants, the implantation surgeries, the imaging with photostimulation and the data analysis required to successfully perform ofMRI. In summary, the precise stimulation and whole-brain monitoring ability of ofMRI are crucial factors in making ofMRI a powerful tool for the study of the connectomics of the brain in both healthy and diseased states.

This protocol describes the steps and data analysis required to successfully perform optogenetic functional magnetic resonance imaging (ofMRI). ofMRI is a novel technique that combines high-field fMRI readout with optogenetic stimulation, allowing for cell type-specific mapping of functional neural circuits and their dynamics across the whole living brain.

The investigation of the functional connectivity of precise neural circuits across the entire intact brain can be achieved through optogenetic functional ...

Effects of Transcranial Alternating Current Stimulation on the Primary Motor Cortex by Online Combined Approach with Transcranial Magnetic Stimulation

Transcranial Alternating Current Stimulation (tACS) is a neuromodulatory technique able to act through sinusoidal electrical waveforms in a specific frequency and in turn modulate ongoing cortical oscillatory activity. This neurotool allows the establishment of a causal link between endogenous oscillatory activity and behavior. Most of the tACS studies have shown online effects of tACS. However, little is known about the underlying action mechanisms of this technique because of the AC-induced artifacts on Electroencephalography (EEG) signals. Here we show a unique approach to investigate online physiological frequency-specific effects of tACS of the primary motor cortex (M1) by using single pulse Transcranial Magnetic Stimulation (TMS) to probe cortical excitability changes. In our setup, the TMS coil is placed over the tACS electrode while Motor Evoked Potentials (MEPs) are collected to test the effects of the ongoing M1-tACS. So far, this approach has mainly been used to study the visual and motor systems. However, the current tACS-TMS setup can pave the way for future investigations of cognitive functions. Therefore, we provide a step-by-step manual and video guidelines for the procedure.

Transcranial Alternating Current Stimulation (tACS) allows the modulation of cortical excitability in a frequency-specific fashion. Here we show a unique approach which combines online tACS with single pulse Transcranial Magnetic Stimulation (TMS) in order to "probe" cortical excitability by means of Motor Evoked Potentials.

Transcranial Alternating Current Stimulation (tACS) is a neuromodulatory technique able to act through sinusoidal electrical waveforms in a specific ...

A Simple Approach to Induce Experimental Autoimmune Neuritis in C57BL/6 Mice for Functional and Neuropathological Assessments

Experimental autoimmune neuritis (EAN) is a well-appreciated experimental model of autoimmune peripheral demyelinating diseases. EAN disease is induced by immunizing mice with neurogenic peptides to direct an inflammatory attack toward components of the peripheral nervous system (PNS). Recent advances have enabled the induction of EAN in the relatively resistant C57BL/6 mouse line using myelin protein zero (P0)106-125 or P0180-199 peptides delivered in adjuvant combined with the injection of pertussis toxin. The ability to induce EAN in the C57BL/6 strain allows for the use of the numerous genetic tools that exist on this mouse background, and thus allows the sophisticated study of disease pathogenesis and interrogation of the mechanistic action of novel therapeutics in combination with transgenic approaches. In this study, we demonstrate a simple approach to successfully induce EAN using the P0180-199 peptide in C57BL/6 mice. We also outline a protocol for the assessment of functional deficits that occur in this model, accompanied by an array of neuropathological features. Thus, this model is a powerful experimental model to study the pathogenesis of human peripheral demyelinating neuropathies, and to determine the efficacy of potential therapies that aim to promote myelin repair and protect against nerve damage in autoimmune neuritis.

This report outlines a simple approach to successfully induce experimental autoimmune neuritis (EAN) using the myelin protein zero (P0)180-199 peptide in combination with Freund&#39;s complete adjuvant and pertussis toxin. We present a sophisticated paradigm capable of accurately assessing the extent of functional deficits and neuropathology that occur in this EAN.

Experimental autoimmune neuritis (EAN) is a well-appreciated experimental model of autoimmune peripheral demyelinating diseases. EAN disease is induced by ...

Role of Diffusion MRI Tractography in Endoscopic Endonasal Skull Base Surgery

Endoscopic endonasal surgery has gained a prominent role in the management of complex skull base tumors. It allows the resection of a large group of benign and malignant lesions through a natural anatomical extra-cranial pathway, represented by the nasal cavities, avoiding brain retraction and neurovascular manipulation. This is reflected by the patients' prompt clinical recovery and the low risk of permanent neurological sequelae, representing the main caveat of conventional skull base surgery. This surgery must be tailored to each specific case, considering its features and relationship with surrounding neural structures, mostly based on preoperative neuroimaging. Advanced MRI techniques, such as tractography, have been rarely adopted in skull base surgery due to technical issues: lengthy and complicated processes to generate reliable reconstructions for inclusion in the neuronavigation system. 
This paper aims to present the protocol implemented in the institution and highlights the synergistic collaboration and teamwork between neurosurgeons and the neuroimaging team (neurologists, neuroradiologists, neuropsychologists, physicists, and bioengineers) with the final goal of selecting the optimal treatment for each patient, improving the surgical results and pursuing the advancement of personalized medicine in this field.

We present a protocol to integrate diffusion MRI tractography in patient work-up to endoscopic endonasal surgery for a skull base tumor. The methods for adopting these neuroimaging studies in the pre- and intra-operative phases are described.

Endoscopic endonasal surgery has gained a prominent role in the management of complex skull base tumors. It allows the resection of a large group of ...

Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), EEG-fMRI, combines the complementary properties of scalp EEG (good temporal resolution) and fMRI (good spatial resolution) to measure neuronal activity during an electrographic event, through hemodynamic responses known as blood-oxygen-level-dependent (BOLD) changes. It is a non-invasive research tool that is utilized in neuroscience research and is highly beneficial to the clinical community, especially for the management of neurological diseases, provided that proper equipment and protocols are administered during data acquisition. Although recording EEG-fMRI is apparently straightforward, the correct preparation, especially in placing and securing the electrodes, is not only important for safety but is also critical in ensuring the reliability and analyzability of the EEG data obtained. This is also the most experience-demanding part of the preparation. To address these issues, a straightforward protocol that ensures data quality was developed. This article provides a step-by-step guide for acquiring reliable EEG data during EEG-fMRI using this protocol that utilizes readily available medical products. The presented protocol can be adapted to different applications of EEG-fMRI in research and clinical settings, and may be beneficial to both inexperienced and expert operators.

This article provides a straightforward protocol for acquiring good quality electroencephalography (EEG) data during simultaneous EEG and functional magnetic resonance imaging by utilizing readily available medical products.

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), EEG-fMRI, combines the complementary properties of scalp EEG ...