The current body of work is supported by the National Institute on Aging at the National Institute of Health (K01AG049813 to JRM). Supplementary funding was provided by the Resnick Gerontology Center of the Albert Einstein College of Medicine. Special thanks to all the volunteers and research staff for exceptional support with this project.

All participants provided written informed consent to the experimental procedures, which were approved by the institutional review board of the Albert Einstein College of Medicine.
1. Participant Recruitment, Inclusion Criteria, and Consent
<ol>
	<li>Recruit a relatively large cohort of English-speaking individuals who can ambulate independently and are free of significant sensory loss; active neurological or psychiatric disorders that interfere with experimental evaluations; and current/fut...

<ol>
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M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Grabbing+your+ear:+rapid+auditory-somatosensory+multisensory+interactions+in+low-level+sensory+cortices+are+not+constrained+by+stimulus+alignment.">Grabbing your ear: rapid auditory-somatosensory multisensory interactions in low-level sensory cortices are not constrained by stimulus alignment.</a> Cerebral Cortex. 15 (7), 963-974 (2005).</li><li>Molholm, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Audio-visual+multisensory+integration+in+superior+parietal+lobule+revealed+by+human+intracranial+recordings.">Audio-visual multisensory integration in superior parietal lobule revealed by human intracranial recordings.</a> Journal of Neurophysiology. 96 (2), 721-729 (2006).</li><li>Peiffer, A. M., Mozolic, J. L., Hugenschmidt, C. E., Laurienti, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Age-related+multisensory+enhancement+in+a+simple+audiovisual+detection+task.">Age-related multisensory enhancement in a simple audiovisual detection task.</a> Neuroreport. 18 (10), 1077-1081 (2007).</li><li>Brandwein, A. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+development+of+audiovisual+multisensory+integration+across+childhood+and+early+adolescence:+a+high-density+electrical+mapping+study.">The development of audiovisual multisensory integration across childhood and early adolescence: a high-density electrical mapping study.</a> Cerebral Cortex. 21 (5), 1042-1055 (2011).</li><li>Girard, S., Collignon, O., Lepore, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multisensory+gain+within+and+across+hemispaces+in+simple+and+choice+reaction+time+paradigms.">Multisensory gain within and across hemispaces in simple and choice reaction time paradigms.</a> Experimental Brain Research. 214 (1), 1-8 (2011).</li><li>Mahoney, J. R., Li, P. C., Oh-Park, M., Verghese, J., Holtzer, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multisensory+integration+across+the+senses+in+young+and+old+adults.">Multisensory integration across the senses in young and old adults.</a> Brain Research. 1426, 43-53 (2011).</li><li>Foxe, J. J., Ross, L. A., Molholm, S., Stein, B. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ch.+38.">Ch. 38.</a> The New Handbook of Multisensory Processing. , 691-706 (2012).</li><li>Kinchla, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detecting+target+elements+in+multielement+arrays:+A+confusability+model.">Detecting target elements in multielement arrays: A confusability model.</a> Perception and Psychophysics. 15, 149-158 (1974).</li><li>Miller, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Divided+attention:+Evidence+for+coactivation+with+redundant+signals.">Divided attention: Evidence for coactivation with redundant signals.</a> Cognitive Psychology. 14 (2), 247-279 (1982).</li><li>Eriksen, C. W., Goettl, B., St James, J. D., Fournier, L. 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T., Yantis, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+interactive+race+model+of+divided+attention.">An interactive race model of divided attention.</a> Journal of Experimental Psychology: Human Perception and Performance. 17 (2), 520-538 (1991).</li><li>Miller, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Timecourse+of+coactivation+in+bimodal+divided+attention.">Timecourse of coactivation in bimodal divided attention.</a> Perception and Psychophysics. 40 (5), 331-343 (1986).</li><li>Gondan, M., Lange, K., Rosler, F., Roder, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+redundant+target+effect+is+affected+by+modality+switch+costs.">The redundant target effect is affected by modality switch costs.</a> Psychonomic Bulletin Review. 11 (2), 307-313 (2004).</li><li>Colonius, H., Diederich, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+race+model+inequality:+interpreting+a+geometric+measure+of+the+amount+of+violation.">The race model inequality: interpreting a geometric measure of the amount of violation.</a> Psychological Review. 113 (1), 148-154 (2006).</li><li>Maris, G., Maris, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Testing+the+race+model+inequality:+A+nonparametric+approach.">Testing the race model inequality: A nonparametric approach.</a> Journal of Mathematical Psychology. 47 (5-6), 507-514 (2003).</li><li>Clark, J. J., Yuille, A. 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> Data Fusion for Sensory Information Processing Systems. , (1990).</li><li>Ernst, M. O., Banks, 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=Humans+integrate+visual+and+haptic+information+in+a+statistically+optimal+fashion.">Humans integrate visual and haptic information in a statistically optimal fashion.</a> Nature. 415 (6870), 429-433 (2002).</li><li>Mahoney, J. R., Verghese, J., Dumas, K., Wang, C., Holtzer, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+effect+of+multisensory+cues+on+attention+in+aging.">The effect of multisensory cues on attention in aging.</a> Brain Research. 1472, 63-73 (2012).</li><li>Mahoney, J. R., Holtzer, R., Verghese, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visual-somatosensory+integration+and+balance:+evidence+for+psychophysical+integrative+differences+in+aging.">Visual-somatosensory integration and balance: evidence for psychophysical integrative differences in aging.</a> Multisensory Research. 27 (1), 17-42 (2014).</li><li>Mahoney, J. R., Dumas, K., Holtzer, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visual-Somatosensory+Integration+is+linked+to+Physical+Activity+Level+in+Older+Adults.">Visual-Somatosensory Integration is linked to Physical Activity Level in Older Adults.</a> Multisensory Research. 28 (1-2), 11-29 (2015).</li><li>Dumas, K., Holtzer, R., Mahoney, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visual-Somatosensory+Integration+in+Older+Adults:+Links+to+Sensory+Functioning.">Visual-Somatosensory Integration in Older Adults: Links to Sensory Functioning.</a> Multisensory Research. 29 (4-5), 397-420 (2016).</li><li>Couth, S., Gowen, E., Poliakoff, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+race+model+violation+to+explore+multisensory+responses+in+older+adults:+Enhanced+multisensory+integration+or+slower+unisensory+processing.">Using race model violation to explore multisensory responses in older adults: Enhanced multisensory integration or slower unisensory processing.</a> Multisensory Research. 31 (3-4), 151-174 (2017).</li><li>Gondan, M., Minakata, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+tutorial+on+testing+the+race+model+inequality.">A tutorial on testing the race model inequality.</a> Attention, Perception &amp; Psychophysics. 78 (3), 723-735 (2016).</li><li>Gondan, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+permutation+test+for+the+race+model+inequality.">A permutation test for the race model inequality.</a> Behavior Research Methods. 42 (1), 23-28 (2010).</li><li>Kiesel, A., Miller, J., Ulrich, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+biases+and+Type+I+error+accumulation+in+tests+of+the+race+model+inequality.">Systematic biases and Type I error accumulation in tests of the race model inequality.</a> Behavior Research Methods. 39 (3), 539-551 (2007).</li><li>Mahoney, J., Cotton, K., Verghese, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multisensory+Integration+Predicts+Balance+and+Falls+in+Older+Adults.">Multisensory Integration Predicts Balance and Falls in Older Adults.</a> Journal of Gerontology: Medical Sciences. , (2018).</li><li>Mahoney, J. R., Verghese, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visual-Somatosensory+Integration+and+Quantitative+Gait+Performance+in+Aging.">Visual-Somatosensory Integration and Quantitative Gait Performance in Aging.</a> Frontiers in Aging Neuroscience. 10, 377 (2018).</li><li>Yueh, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Long-term+effectiveness+of+screening+for+hearing+loss:+the+screening+for+auditory+impairment--which+hearing+assessment+test+(SAI-WHAT)+randomized+trial.">Long-term effectiveness of screening for hearing loss: the screening for auditory impairment--which hearing assessment test (SAI-WHAT) randomized trial.</a> Journal of the American Geriatrics Society. 58 (3), 427-434 (2010).</li><li>Galvin, J. 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There are no conflicts of interest to report and the authors have nothing to disclose.

The goal of the current study was to detail the process behind the establishment of a robust multisensory integration phenotype. Here, we provide the necessary and critical steps required to acquire multisensory integration effects that can be utilized to predict important cognitive and motor outcomes relying on similar neural circuitry. Our overall objective was to provide a step-by-step tutorial for calculating the magnitude of multisensory integration in an effort to facilitate innovative and novel translational multi...

Interactions across sensory systems are essential for everyday functions. While multisensory integration effects are measured across a wide array of populations using assorted sensory combinations and different neuroscience approaches [including but not limited to the psychophysical, electrophysiological, and neuroimaging methodologies]1,2,3,4,5,6,7,8,9, currently a gold standard for quantifying multisensory integration is lacking. Given that multisensory experiments typically contain&#160;a behavioral component, reaction time (RT) data is often examined to determine the existence of a well-known phenomenon called the redundant signals effect10. As its name suggests, simultaneous sensory signals provide redundant information, which typically yield quicker RTs. Race and co-activation models are used to explain the above mentioned redundant signals effect11. Under race models, the unisensory signal that is processed the fastest is the winner of the race and is responsible for producing the behavioral response. However, evidence for co-activation occurs when responses to multisensory stimuli are quicker than what race models predict.
Earlier versions of the race model are inherently controversial12,13 as they are referred to by some as overly conservative14,15 and purportedly contain limitations regarding the independence between the constituent unisensory detection times inherent in the multisensory condition16. In an effort to address some of these limitations, Colonius &#38; Diederich16 developed a more conventional race model test:
<img alt="Equation 1" src="/files/ftp_upload/59575/59575eq1.jpg" />,
where the cumulative distribution frequencies (CDFs) of the unisensory conditions (e.g., A &#38; B; with an upper limit of one) are compared to the CDF of the simultaneous multisensory condition (e.g., AB) for any given latency (t)11,16,17. In general, a CDF determines how often an RT occurs, within a given range of RTs, divided by the total number of stimulus presentations (i.e., trials). If the CDF of the actual multisensory condition <img alt="Equation 2" src="/files/ftp_upload/59575/59575eq2.jpg" /> is less than or equal to the predicted CDF derived from the unisensory conditions
<img alt="Equation 3" src="/files/ftp_upload/59575/59575eq3.jpg" />,
then the race model is accepted and there is no evidence for sensory integration. However, when the multisensory CDF is greater than the predicted CDF derived from the unisensory conditions, the race model is rejected. Rejection of the race model indicates that multisensory interactions from redundant sensory sources combine&#160;in a non-linear manner, resulting in a speeding up of RTs (e.g., RT facilitation) to multisensory stimuli.
One main hurdle that multisensory researchers face is how to best quantify integration effects. For instance, in the case of the most basic behavioral multisensory paradigm, where participants are asked to perform a simple reaction time task, information regarding accuracy and speed is collected. Such multisensory data can be used at the face-value or manipulated using various mathematical applications including but not limited to Maximum Likelihood Estimation18,19, CDFs11, and various other statistical approaches. The majority of our previous multisensory studies employed both quantitative and probabilistic approaches where multisensory integrative effects were calculated by 1) subtracting the mean reaction time (RT) to a multisensory event from the mean reaction time (RT) to the shortest unisensory event, and 2) by employing CDFs to determine whether RT facilitation resulted from synergistic interactions facilitated by redundant sensory information8,20,21,22,23. However, the former methodology was likely not sensitive to the individual differences in integrative processes and researchers have since posited that the later methodology (i.e., CDFs) may provide a better proxy for quantifying multisensory integrative effects24.
Gondan and Minakata recently published a tutorial on how to accurately test the Race Model Inequality (RMI) since researchers all too often make countless errors during the acquisition and pre-processing stages of RT data collection and preparation25. First, the authors posit that is unfavorable to apply data trimming procedures where certain a priori minimum and maximum RT limits are set. They recommend that slow and omitted responses be set to infinity, rather than excluded. Second, given that the RMI may be violated at any latency, multiple t-tests are often used to test the RMI at different time points (i.e., quantiles); unfortunately, this practice leads to the increased Type I error and substantially reduced statistical power. To avoid these issues, it is recommended that the RMI be tested over one specific time range. Some researchers have suggested that it makes sense to test the fastest quartile of responses (0-25%)26 or some pre-identified windows (i.e., 10-25%)24,27 as multisensory integration effects are typically observed during that time interval; however, we argue that the percentile range to be tested must be dictated by the actual dataset (see Protocol Section 5). The problem with relying on published data from young adults or computer simulations is that older adults manifest very different RT distributions, likely due to the age-related declines in sensory systems. Race model significance testing should only be tested over violated portions (positive values) of group-averaged difference wave between actual and predicted CDFs from the study cohort.
To this end, a protective effect of multisensory integration in healthy older adults using the conventional test of the race model16 and the principles set forth by Gondan and colleagues25 has been demonstrated. In fact, greater magnitude of visual-somatosensory RMI (a proxy for multisensory integration) was found to be linked to better balance performance, lower probability of incident falls and increased spatial gait performance28,29.
The objective of the current experiment is to provide researchers with a step-by-step tutorial to calculate the magnitude of multisensory integration effects using the RMI, to facilitate the increased production of diverse translational research studies across many different clinical populations. Note that data presented in the current study are from recently published visual-somatosensory experiments conducted on healthy older adults28,29, but this methodology can be applied to various cohorts across many different experimental designs, utilizing a wide-array of multisensory combinations.

<table>
	<tbody>
		<tr>
			<td>stimulus generator</td>
			<td>Zenometrics, LLC; Peekskill, NY, USA</td>
			<td>n/a</td>
			<td>custom-built</td>
		</tr>
		<tr>
			<td>Excel</td>
			<td>Microsoft Corporation</td>
			<td></td>
			<td>spreadsheet program</td>
		</tr>
		<tr>
			<td>Eprime</td>
			<td>Psychology Software Tools (PST)</td>
			<td></td>
			<td>stimulus presentation software</td>
		</tr>
	</tbody>
</table>

using the race model inequality to quantify behavioral multisensory integration effects

Multisensory integration research investigates how the brain processes simultaneous sensory information. Research on animals (mainly cats and primates) and humans reveal that intact multisensory integration is crucial for functioning in the real world, including both cognitive and physical activities. Much of the research conducted over the past several decades documents multisensory integration effects using diverse psychophysical, electrophysiological, and neuroimaging techniques. While its presence has been reported, the methods used to determine the magnitude of multisensory integration effects varies and typically faces much criticism. In what follows, limitations of previous behavioral studies are outlined and a step-by-step tutorial for calculating the magnitude of multisensory integration effects using robust probability models is provided.

The purpose of this study was to provide a step-by-step tutorial of a methodical approach to quantify the magnitude of VS integration effects, to foster the publication of new multisensory studies using similar experimental designs and setups (see Figure 1). Screenshots of each step and calculation needed to derive magnitude of multisensory integration effects, as measured by RMI AUC, are delineated above and illustrated in Figures&#160;2-8.
<stro...

Watch this Scientific Journal Video about Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects at JoVE.com

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

The current study aims to provide a step-by-step tutorial for calculating the magnitude of multisensory integration effects in an effort to facilitate the production of translational research studies across diverse clinical populations.

Multisensory integration research investigates how the brain processes simultaneous sensory information. Research on animals (mainly cats and primates) ...

Establishing a reproducible method for calculating the magnitude of multisensory integration effects is significant, as it will aid in the facilitation of future translational research across diverse clinical populations. The main advantage to our technique is that we're able to quantify a robust phenotype of multisensory integration that is subsequently associated with important cognitive and motor outcomes in aging, like balance, falls, gait, and executive functions. Begin by using stimulus presentation software to program a simple reaction time experiment with three experimental conditions, visual alone, somatosensory alone, and simultaneous visual-somatosensory.Use a stimulus generator with three control boxes. The left and right control boxes contain bilateral blue light-emitting diodes that illuminate for visual stimulation and bilateral motors with 0.8 G vibration amplitude that vibrate for somatosensory stimulation, as well as plastic housing for the stimulators. Next, place a center dummy control box equidistant from the left and right control boxes, and affix a visual target sticker to serve as the fixation point.After the experiment has been set up, escort the participant to the testing room. Have the participant sit upright and comfortably rest their hands upon the left and right control boxes. Strategically place index fingers over the vibratory motors mounted to the back of the control box and thumbs on the front of the control box under the LEDs, to not block the light.Ensure that the somatosensory stimuli are inaudible by providing participants with headphones over which continuous white noise is played at a comfortable level. Have the participant use a foot pedal located under the right foot as the response pad. Finally, have the participant respond to each stimulus as quickly as possible regardless of whether they feel it, see it, or feel it and see it.Begin analysis by excluding participants that are not able to attain an accuracy of 70%correct or greater on any one stimulus condition. Consider trials inaccurate if a participant fails to respond to a stimulus within the set response time period, and set corresponding reaction time, or RT, to infinity rather than excluding the trial from the analysis. RT data are sorted in ascending order by the experimental condition.Place visual, somatosensory, and VS conditions in separate columns of sorted RT data. Ensure each row represents one trial and each cell represents the actual RT.Note, do not employ data-trimming procedures that delete very slow RTs, as this will bias the distribution of RT data. Ensure RTs that are clearly outliers are set to infinity.Then, to bin the RT data, identify the fastest and the slowest RT.Subtract the slowest RT from the fastest in order to calculate the individual's RT range across all test conditions. Bin RT data from the 0%to the 100%in 5%increments by taking the fastest RT and gradually adding 5%to the previously calculated RT range until 100%of the RT data is accounted for, to result in 21 time bins. Next, within a computer spreadsheet, use a frequency function where array one equals the actual RTs for one of the experimental conditions and array two equals the 21 quantized RT bins previously calculated divided by the total number of trials, 45, per condition.Then, create the cumulative distribution frequency, or CDF, by summing the running total of probabilities across the quantized bins for each of three experimental conditions. The CDF of the multisensory condition represents the actual CDF. To calculate the predicted CDF, sum the two unisensory CDFs with an upper limit set to one.Use this formula across each one of the 21 quantized time bins. Start at the zeroth percentile, and continue all the way down to the 100th percentile for bin 21. Next, to conduct the Test of the Race Model Inequality, subtract the predicted CDF from the actual CDF for each of the 21 quantized time bins to obtain the difference values.Plot these 21 values as a line graph, where the x-axis represents each one of the quantized time bins and the y-axis represents the probability difference between the actual and predicted CDFs. Here, positive values at any latency indicate the integration of the unisensory stimuli and reflect a violation of the RMI. To quantify the multisensory effect at a group level, group-average the individual RMI data across all participants.Use a spreadsheet to assign individuals to rows and time bins to columns. Then, in a new spreadsheet, place the previously calculated 21 difference values into individual rows and average values within time bins to create one group-averaged difference waveform. Then, plot the group average 21 values as a line graph, where the x-axis represents each one of the quantized time bins and the y-axis represents the probability difference between the CDFs.Finally, calculate the area under the curve for each individual by using participant one's data as an example. Sum the CDF difference value at time bin one with the CDF difference value at time bin two, and then divide by two. Visually inspect each consecutive pair of time bins containing positive values.Then, sum these results to generate the total AUC of the CDF difference wave during the violated percentile range of 0.00 to 0.10. Results indicate a group-averaged violation occurring over the zero to 10%percentile range for a sample of 333 older adults. The total number of positive values, zero, one, two, or three, for those three quantiles, 0.00 to 0.10, determines which multisensory classification group a person is assigned to, either deficient, poor, good, or superior, respectively.As we have previously described, it is critical to avoid data-trimming procedures, as it biases the RT distributions. Slow reaction times and omitted trials need to be set to infinity. The main objective here was to develop a robust phenotype of multisensory integration.Having said that, we are aware of differential multisensory integration patterns in aging, and our next step will be to uncover the neural networks responsible for such integrative processes while determining how specific structural or functional alterations contribute to the differential integration patterns. We are working on identifying the neural correlates associated with visual-somatosensory integration in aging, and we believe that such developments will provide insights into several diseases, including but not limited to Alzheimer's and Parkinson's.

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6.2K vistas.  Albert Einstein College of Medicine. El establecimiento de un método reproducible para calcular la magnitud de los efectos de integración multisensorial es significativo, ya que ayudará a facilitar la investigación traslacional futura en diversas poblaciones clínicas. La principal ventaja de nuestra técnica es que somos capaces de cuantificar un fenotipo robusto de integración multisensorial que posteriormente se asocia con importantes resultados cognitivos y motores en el envejecimiento, como el equilibrio, las caídas, ...