This work was made possible by the support of the Know Alzheimer Foundation and help from the multimedia production service at the Universidad CEU Cardenal Herrera, especially Enrique Giner. We would like to recognize the work of all the participating pharmacies (SEFAC), and the collaborating doctors from the Society of Primary Care Doctors (SEMERGEN) and Neurology Society (SVN) who helped with the MCI diagnoses, especially Vicente Gassull, Rafael S&#225;nchez, and Jordi P&#233;rez. Finally, we thank all those who agreed to take part in this study.

The methodology applied in this study has been previously published5 in work carried out at the University CEU Cardenal Herrera together with community pharmacies in the region of Valencia (Spain) associated with the Spanish Society of Family and Community Pharmacy (SEFAC). This current study was reviewed and approved by the Research Ethics Committee at the Universidad CEU Cardenal Herrera (approval no. CEI11/001) in March 2011. All individuals involved in the study gave their written informed consent to participation in accordance with the Declaration of Helsinki.
1. Selection of factors associated with mild cognitive impairment
<ol>
	<li>Search for terms related to MCI for use in screening Cochrane Systematic Reviews (e.g., cognitive impairment, dementia, risk factors, etc.).</li>
	<li>Search for terms for which there is some evidence of a relationship with cognitive deterioration or dementia published in the PubMed database; these include demographic factors (sex, age, education level, and economic status), social factors (cognitive and social activities), chronic pathologies (cholesterol, depression, hypertension, diabetes, and obesity), and lifestyle behaviors (alcohol consumption, smoking habit, diet, physical activity, and sleep hours).</li>
	<li>Calculate the odds ratio for qualitative variables or Cohen&#39;s d effect size for quantitative variables15. Select the variables with larger effect sizes for cognitive deterioration or dementia for use in elaborating a questionnaire.</li>
</ol>
2. Design of the questionnaires
<ol>
	<li>Design a questionnaire to collect information about the selected variables, following the guidelines provided by Nardi16. For instance, the variables used in Climent et al.5 were demographic (age, weight, and height [measured with standardized procedures using calibrated scales and stadiometers], sex, education level, and employment type), lifestyle (physical exercise, reading, time spent sleeping overnight and during the day, puzzles, games, TV consumption time, and tobacco and alcohol consumption), and chronic pathologies (hypertension, hyperlipidemia, and diabetes). In addition, record the presence or absence of depression, which is frequently associated with cognitive deterioration.</li>
	<li>Design a pharmacotherapy follow-up sheet to report all the drugs consumed by the participants at the time of the interview, as in Climent et al.5, which used Dader&#39;s method17 to design this sheet.</li>
</ol>
3. Selection of tests for MCI screening
<ol>
	<li>Determine all the tests used to screen for MCI that could be administered by primary healthcare workers (e.g., pharmacists). Reject any tests that must be administered by a specialist. Some of the tests that fulfill these conditions are the Short Portable Mental State Questionnaire (SPMSQ)18, Mini Mental State Examination (MMSE)19, Memory Impairment Screen (MIS)20, Picture Memory Impairment Screen (PMIS)21, Montreal Cognitive Assessment (MoCA)22, Saint Louis University Mental Status (SLUMS)23, and Quick Mild Cognitive Impairment (Qmci)24. An exhaustive review of each MCI test is available in Cullen et al.25.</li>
	<li>Search for a good estimation of the test sensitivities and specificities in the scientific literature.</li>
	<li>Estimate the time required to administer these tests to healthy individuals.</li>
	<li>Consider the basic patient characteristics required for completion of these tests. For example, a minimum education level may be necessary because many MCI tests are not suitable for illiterate participants. A set of MCI screening tests is usually applied to increase sensitivity; however, the minimum number of tests must be quickly administered by pharmacists if the final selective screening is intended for a large population. Climent et al.5 assessed MCI using the MMSE and SPMSQ tests, with the latter being suitable for the large number of individuals who lived through the Spanish civil war who are illiterate.
	<ol>
		<li>Use a variant of the SPMSQ by Pfeiffer18 was validated in Spanish by Mart&#237;nez de la Iglesia26. This test has a maximum score of 10 and the cut-off point for establishing cognitive impairment is 3 or more errors (4 or more for illiterate individuals). This test takes between 8 and 10 minutes to complete.</li>
		<li>Use a NORMACODERM version of the MMSE validated for Spanish speakers by Blesa27 by adapting the original version by Folstein19. This screening test has a maximum score of 30 and is corrected according to the patients&#39; years of schooling and ages. Participants who score less than or equal to 24 are considered as MCI cases. The MMSE is a measure of general cognitive function and includes orientation to time and place, written and spoken language, attention span, calculation, and memory. It was administered to all the participants in this study because it is a very short test which takes only around 5 minutes to complete.</li>
	</ol>
	</li>
</ol>
4. Subject recruitment
<ol>
	<li>Find pharmacists willing to recruit non-institutionalized people to form the study population. The mentioned study by Climent et al.5 included people aged 65 years or more who went regularly to the pharmacy and who agreed to participate in this study. Exclude patients with any difficulty in performing these evaluation tests (e.g., because of blindness, deafness, etc.) or who were already being treated for dementia.</li>
	<li>Provide the participating pharmacists with informed consent forms, which must be completed by every individual taking part in the study. This consent form specifies the title of the research, the objectives of the project, a comprehensible explanation of all the procedures that the participant would take part in, the absence of specific risks, the confidentiality of all the collected data, and the right to withdraw from the study for any reason at any time.</li>
	<li>Train the pharmacists to administer structured personal interviews to the participants, which should last approximately half an hour per person. Collect data for 1 year and send all the forms to the researchers responsible for data protection in the study. Subsequently follow-up with the patients for 3 months.</li>
	<li>Instruct the pharmacists how to identify a probable MCI case using MCI tests. Based on Climent et al.5 we used SPMSQ scores of 4 or more points (for illiterate participants) or 3 or more points for the other participants and scores of 24 points or less were used in the corrected MMSE test.</li>
	<li>Instruct pharmacists how to refer MCI cases to a medical specialist (a neurologist) for their clinical diagnosis-the last step in the flow chart used in this research study (Figure 1).</li>
</ol>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59649/59649fig1.jpg" /> 
Figure 1: Flowchart of the research study and the proposed selective screening. The left side represents the initial study whose data were analyzed with machine-learning techniques to propose the selective screening for early detection of MCI shown in the right panel. This figure was modified from Climent34. <a href="https://www.jove.com/files/ftp_upload/59649/59649fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
5. Pharmacist researcher training
<ol>
	<li>Contact specialists to organize sessions for training the participating pharmacists in basic knowledge related to cognitive impairment and in managing its screening tools, for instance, the SPMSQ and MMSE.</li>
	<li>Ensure that the participating pharmacists are aware of the procedures, data collection protocol, and all the possible issues related to data protection. Inform them that the project was approved by a Research Ethics Committee and of the importance of the consent form according to the Declaration of Helsinki. 
	NOTE: To perform the study described by Climent et al.5, workshops were held at the Official College of Pharmacists and the Cardenal-Herrera CEU University (UCH-CEU), and covered the following: MCI and dementia; diagnostic approaches to MCI and management of the SPMQP and MMSE (taught by the Neurology Service at La Plana Hospital in Castell&#243;n); project presentation and explanation of the methodology by senior community pharmacist researchers; and health education and cognitive training by researchers from the Department of Pharmacy at the UCH-CEU University.</li>
</ol>
6. Study design
<ol>
	<li>Calculate a sample size to assess the feasibility of the project. Because this was an observational study, a larger sample will produce more effective tools. There are two ways to determine sample size: one is based on the estimation of the prevalence and the other is more precise, taking into account effect sizes.
	<ol>
		<li>Calculate an accurate estimation of the prevalence of the condition in the population 
		 
		<img src="/files/ftp_upload/59649/59649eq1.jpg" /> 
		 
		where &#945; is the significance level, p0 is the initial estimation and error is the maximum error expected with a 100(1 - &#945;)% confidence.</li>
		<li>According to the effect sizes found in the literature for each factor, use tools like the pwr package in R to estimate how much power is required to detect differences15,28. 
		NOTE: For instance, in our study5 we designed the first proposal with an error of 3% at 95% confidence and an initial estimation of the prevalence of MCI at 15% in the population aged 65 years or older, resulting in an estimated required sample size of 541 individuals.</li>
	</ol>
	</li>
</ol>
7. Interdisciplinary communication network, pharmacists, primary healthcare physicians, and specialists
<ol>
	<li>Design letters to communicate information about the project to the healthcare centers involved.</li>
	<li>Explain to participating pharmacists how to inform their assigned physicians about the results of the screenings through a letter to the primary healthcare center.</li>
	<li>Send written communications to the medical coordinators of the healthcare centers related to the participating pharmacies and to the Neurology Services of the hospitals to which they are assigned.</li>
	<li>Contact participating neurologists to find out each patient&#39;s definitive diagnosis obtained via specific tests undertaken by specialized healthcare providers. Before this, primary healthcare providers should carry out the following protocol, as summarized by the clinical guidelines (Figure 2).</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/59649/59649fig2.jpg" /> 
Figure 2: Protocol for primary healthcare action. An example of primary healthcare actions that should be considered for early MCI detection before the patient is referred for a medical diagnosis by specialists. <a href="https://www.jove.com/files/ftp_upload/59649/59649fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
8. Statistical analysis and preprocessing
NOTE: Before applying machine-learning techniques a preparatory step is required to transform the original data into a new data set according to the final study objective and the procedures to be applied. For this transformation, several things should be considered, including the characteristics of the algorithms. This is because some of them are sensitive to a lack of variability or sharing of information across columns, although the algorithms used to generate decision trees are particularly robust against these problems. This initial phase aims to categorize qualitative variables and gather values with enough cases for each variable. For efficient screening it is important to choose variables whose acquisition is proven to be easy and accurate. Participants are selected by a short interview in which the algorithms used were constrained to a white-box model, making it easy to check the criteria used to decide if the individual should take the test. We suggest using the rpart29 package in R software for these algorithms, and implementing recursive partitioning.
<ol>
	<li>Collect all the forms from the participating pharmacies and convert them into a table in which every column is a variable and every participating individual is a row.</li>
	<li>Assign an identification number to each participant. Save the identification number and contact information in a different document so that it is not used by the machine-learning algorithm.</li>
	<li>Generate variables to classify whether each drug the patient takes corresponds to second or third ATC30 (Anatomical Therapeutic Chemical) level codes, according to the active principal ingredients on the pharmacotherapy follow-up sheet.</li>
	<li>Perform an initial descriptive analysis.
	<ol>
		<li>For every ordinal variable, choose an adequate contrast for the variable. For categorical variables, select the value considered as the baseline.</li>
		<li>For categorical variables, calculate a univariate logistic regression with a response variable for screening for MCI. Analyze the outcome of the regression with a contingency table, the p-value, sample odds ratio, and the 95% confidence interval of the odds ratio.</li>
		<li>For quantitative variables, calculate the mean, standard deviation, coefficient of logistic regression, and the 95% confidence interval of their coefficients.</li>
	</ol>
	</li>
	<li>Reject variables with missing (unavailable) values, considering these variables difficult to accurately collect.</li>
	<li>Select only variables for which there is at least one statistically significant category (&#945; &#60; 0.01) according to the logistic regression analysis. The outcome of this step produces a reduced data set compared to the initial one.</li>
</ol>
9. Algorithms to create a decision tree
NOTE: Machine-learning algorithms must be properly parameterized to predict which individuals are likely to have a positive MCI test result. One of the main problems while screening for a condition is that the original data is expected to be imbalanced (i.e., few positive cases compared to the negative ones). To get models with balanced data we used a technique called down-sampling, or random sampling, to equalize the frequency with that of the lowest frequency class31. Efficient screening also requires reducing the number of false negatives as much as possible (i.e., increasing the sensitivity of the selection of participants suffering from MCI). One of the techniques used to achieve a greater sensitivity is the introduction of penalties in the calculation of Gini&#39;s impurity index (i.e., the index used by the algorithm to select the best split for the decision tree)32.
<ol>
	<li>Generate a training and test data set with 80% and 20% of the whole data set, respectively using the createDataPartition function in the caret library33.</li>
	<li>Apply the algorithms used to generate decision trees to the training data set. Use the train again function in the caret library33. The following steps are different parameters of the function; for instance, the tree used in this paper was generated with rpart29 (method = &#34;rpart&#34;), but other algorithms are available.
	<ol>
		<li>Select the &#39;down sampling&#39; sampling method and introduce the sampling = &#34;down&#34; parameter into the caret.</li>
		<li>Set the prior probabilities for both classes.</li>
		<li>Provide a loss matrix with the Gini&#39;s impurity index penalties applied in order to focus on the increasing sensitivity.</li>
		<li>For every parameter in the algorithm, choose an appropriate grid of values.</li>
		<li>Use a cross-validation estimation of the receiver operating curve (ROC) values to select the best models within the parameter grid.</li>
	</ol>
	</li>
	<li>Calculate a confusion matrix and the area under the ROC curve (AUC) for the test set prediction to assess the true performance of the model.</li>
</ol>

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A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+review+of+screening+tests+for+cognitive+impairment.">A review of screening tests for cognitive impairment.</a> Journal of Neurology, Neurosurgery &amp; Psychiatry. 78 (8), 790-799 (2007).</li><li>Mart&#237;nez de la Iglesia, J., 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=Adaptaci&#243;n+y+validaci&#243;n+al+castellano+del+cuestionario+de+Pfeiffer+(SPMSQ)+para+detectar+la+existencia+de+deterioro+cognitivo+en+personas+mayores+e+65+a&#241;os.">Adaptaci&#243;n y validaci&#243;n al castellano del cuestionario de Pfeiffer (SPMSQ) para detectar la existencia de deterioro cognitivo en personas mayores e 65 a&#241;os.</a> Medicina Cl&#237;nica. 117 (4), 129-134 (2001).</li><li>Blesa, R., 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=Clinical+validity+of+the+'mini-mental+state'+for+Spanish+speaking+communities.">Clinical validity of the 'mini-mental state' for Spanish speaking communities.</a> Neuropsychologia. 39 (11), 1150-1157 (2001).</li><li>. pwr: Basic Functions for Power Analysis Available from: <a target="_blank" href="https://cran.r-project.org/web/packages/pwr/index.html">https://cran.r-project.org/web/packages/pwr/index.html</a> (2018)</li><li>. rpart: Recursive Partitioning and Regression Trees Available from: <a target="_blank" href="https://cran.r-project.org/web/packages/rpart">https://cran.r-project.org/web/packages/rpart</a> (2018)</li><li>. ATC/DDD Index Available from: <a target="_blank" href="https://www.whocc.no/atc_ddd_index">https://www.whocc.no/atc_ddd_index</a> (2018)</li><li>Kuhn, M., Johnson, 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> Applied Predictive Modeling. 26, (2013).</li><li>Kuhn, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Building+Predictive+Models+in+R+Using+the+caret+Package.">Building Predictive Models in R Using the caret Package.</a> Journal of Statistical Software. 28 (5), 1-26 (2008).</li><li>Climent, M. T., Pardo, J., Mu&#241;oz-Almaraz, F. J., Guerrero, M. D., Moreno, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Decision+Tree+for+Early+Detection+of+Cognitive+Impairment+by+Community+Pharmacists.">Decision Tree for Early Detection of Cognitive Impairment by Community Pharmacists.</a> Frontiers in Pharmacology. 9, 1-12 (2018).</li></ol>

The authors have nothing to disclose.

After searching for terms associated with MCI in Cochrane studies in the PubMed database, a specific questionnaire was created for this study that used the most evident variables with a proven association with MCI. Demographic, lifestyle, and social factors, as well as the patient&#39;s pharmacotherapy and some relevant pathologies were also recorded. Additionally, the SPMSQ and MMSE MCI tests were also selected. Importantly, the SPMSQ was not affected by participants&#39; level of schooling. Pharmacists were trained to ...

Population aging is increasing the prevalence of chronic and degenerative diseases, especially degenerative dementias, which are expected to affect more than 131 million people worldwide by 20501. Among all the degenerative dementias, Alzheimer&#39;s disease (AD) is the most common with an overall prevalence in Europe of 6.88%2. Due to the ever-declining independence of AD patients, this group should start receiving support as soon as AD starts to manifest. Therefore, the early detection of prodromal signs of AD, such as mild cognitive impairment (MCI), is essential.
MCI is defined as an intermediate cognitive decline stage corresponding to normal aging and severe deterioration due to dementia3. According to estimates by Petersen et al.4, the prevalence of MCI is 8.4% among people aged 65-69 years and reaches 25.2% for those aged over 80 years. MCI results in individuals experiencing more difficulties than expected in the execution of low-level cognitive skills, especially those related to memory and language, but does not interfere with the activities of daily living.
Screening is not synonymous with diagnosis; the diagnosis of MCI will always be a clinical task whereas screening methods can only inform us that a patient has a higher probability of suffering from this pathology and that there is a well-founded suspicion of MCI that should be confirmed clinically. Hence, primary healthcare workers (doctors, pharmacists, nurses, etc.) could benefit from the availability of simple screening methods (brief cognitive tests) that can be applied in minutes. Ideally, these would objectively identify patients with a high probability of suffering an MCI so that they can then be clinically tested by general or specialized physicians.
Given that the early detection of MCI is becoming an essential task within the context of public health, this work aimed to identify which characteristics are useful in the targeted identification of MCI in screening tests of elderly populations. These groups would then be more thoroughly tested for MCI in tests administered by primary health care providers. This methodology provides a decision tree with the appropriate algorithms for identifying the population groups to target.
Among these characteristics, age is one of the most consistent factors associated with the development of this pathology. Other relevant characteristics are related to demographics or lifestyle5. Among the latter, some studies have identified the duration of daytime or nighttime sleep as a risk factor that can lead to the diagnosis of MCI5,6,7,8,9. The prolonged consumption of medications such as benzodiazepines, consumed by an estimated 20%-25% of older adults10,11, can also influence sleep hours and the development of MCI12,13. Indeed, prolonged treatments for chronic diseases may be important features useful in the pre-selection of individuals with a high risk of suffering from MCI.
Here, we developed data-based models that use automatic learning algorithms, a decision tree, and a predictive tool to increase the efficiency of the methodology for detecting MCI by discriminating which characteristics play an important role in the early detection of MCI. The resultant decision tree presented here was produced using a specific cohort of Spanish patients using community pharmacies. However, this method would also be useful among other populations with different characteristics.
This work was completed in collaboration with primary healthcare and specialized medical doctors. Community pharmacies were ideal for testing this algorithm because they are close to patients, have long opening hours, and are frequently visited and consulted. Degenerative dementias are complex conditions which are not always well understood by primary health care providers14. Therefore, becoming involved in the process will raise awareness of people suffering from MCI and dementias.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>caret</td>
			<td>Max Kuhn</td>
			<td></td>
			<td>R package</td>
		</tr>
		<tr>
			<td>rpart</td>
			<td>Terry Therneau, Beth Atkinson, Brian Ripley</td>
			<td></td>
			<td>R package</td>
		</tr>
		<tr>
			<td>SPMSQ in Spanish</td>
			<td>Farmaceuticoscomunitarios.org</td>
			<td></td>
			<td><a href="http://farmaceuticoscomunitarios.org/anexos/vol11_n1/ANEXO1.pdf" target="_blank">http://farmaceuticoscomunitarios.org/anexos/vol11_n1/ANEXO1.pdf</a></td>
		</tr>
		<tr>
			<td>SPMSQ in English</td>
			<td>geriatrics.stanford.edu</td>
			<td></td>
			<td><a href="https://geriatrics.stanford.edu/culturemed/overview/assessment/assessment_toolkit/spmsq.html" target="_blank">https://geriatrics.stanford.edu/culturemed/overview/assessment/assessment_toolkit/spmsq.html</a></td>
		</tr>
		<tr>
			<td>MMSE in Spanish</td>
			<td>Farmaceuticoscomunitarios.org</td>
			<td></td>
			<td><a href="http://farmaceuticoscomunitarios.org/anexos/vol11_n1/ANEXO2.pdf" target="_blank">http://farmaceuticoscomunitarios.org/anexos/vol11_n1/ANEXO2.pdf</a></td>
		</tr>
		<tr>
			<td>MMSE in English</td>
			<td>oxfordmedicaleducation.com</td>
			<td></td>
			<td><a href="http://www.oxfordmedicaleducation.com/geriatrics/mini-mental-state-examination-mmse/" target="_blank">http://www.oxfordmedicaleducation.com/geriatrics/mini-mental-state-examination-mmse/</a></td>
		</tr>
	</tbody>
</table>

a machine learning approach to design an efficient selective screening of mild cognitive impairment

Mild cognitive impairment (MCI) is the first sign of dementia among elderly populations and its early detection is crucial in our aging societies. Common MCI tests are time-consuming such that indiscriminate massive screening would not be cost-effective. Here, we describe a protocol that uses machine learning techniques to rapidly select candidates for further screening via a question-based MCI test. This minimizes the number of resources required for screening because only patients who are potentially MCI positive are tested further.
This methodology was applied in an initial MCI research study that formed the starting point for the design of a selective screening decision tree. The initial study collected many demographic and lifestyle variables as well as details about patient medications. The Short Portable Mental Status Questionnaire (SPMSQ) and the Mini-Mental State Examination (MMSE) were used to detect possible cases of MCI. Finally, we used this method to design an efficient process for classifying individuals at risk of MCI. This work also provides insights into lifestyle-related factors associated with MCI that could be leveraged in the prevention and early detection of MCI among elderly populations.

The participating pharmacies gathered data from 728 users and collected demographic variables in addition to the drugs prescribed to the participants. A univariate logistic regression was performed for all the variables34; the error bar graphs shown in Figure 3 and Figure 4 are convenient graphical representations of the confidence interval of the odds ratio (for qualitative variables) and the confidence i...

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

This methodology produces decision trees that target population groups more prone to suffering from mild cognitive impairment and are useful for cost-effective selective screening of the disease.

Mild cognitive impairment (MCI) is the first sign of dementia among elderly populations and its early detection is crucial in our aging societies. Common ...

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7.4K Views.  Universidad CEU Cardenal Herrera. This methodology produces decision trees that target population groups more prone to suffering from mild cognitive impairment and are useful for cost-effective selective screening of the disease.

Video: A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

The Crossmodal Congruency Task as a Means to Obtain an Objective Behavioral Measure in the Rubber Hand Illusion Paradigm

The rubber hand illusion (RHI) is a popular experimental paradigm. Participants view touch on an artificial rubber hand while the participants' own hidden hand is touched. If the viewed and felt touches are given at the same time then this is sufficient to induce the compelling experience that the rubber hand is one's own hand. The RHI can be used to investigate exactly how the brain constructs distinct body representations for one's own body. Such representations are crucial for successful interactions with the external world. To obtain a subjective measure of the RHI, researchers typically ask participants to rate statements such as "I felt as if the rubber hand were my hand". Here we demonstrate how the crossmodal congruency task can be used to obtain an objective behavioral measure within this paradigm.
The variant of the crossmodal congruency task we employ involves the presentation of tactile targets and visual distractors. Targets and distractors are spatially congruent (i.e. same finger) on some trials and incongruent (i.e. different finger) on others. The difference in performance between incongruent and congruent trials - the crossmodal congruency effect (CCE) - indexes multisensory interactions. Importantly, the CCE is modulated both by viewing a hand as well as the synchrony of viewed and felt touch which are both crucial factors for the RHI.
The use of the crossmodal congruency task within the RHI paradigm has several advantages. It is a simple behavioral measure which can be repeated many times and which can be obtained during the illusion while participants view the artificial hand. Furthermore, this measure is not susceptible to observer and experimenter biases. The combination of the RHI paradigm with the crossmodal congruency task allows in particular for the investigation of multisensory processes which are critical for modulations of body representations as in the RHI.

We demonstrate how an objective measure can be employed in the widely employed rubber hand illusion paradigm. This measure is obtained by modifying the well-established crossmodal congruency task. This task allows the investigation of multisensory processes which are critical for modulations of body representations as in the rubber hand illusion.

The rubber hand illusion (RHI) is a popular experimental paradigm. Participants view touch on an artificial rubber hand while the participants' own hidden ...

Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach

The main goal of this study was to assess the usability of a tablet-computer-based application (EmoCogMeter) in investigating the effects of age on cognitive functions across the lifespan in a sample of 378 healthy subjects (age range 18-89 years). Consistent with previous findings we found an age-related cognitive decline across a wide range of neuropsychological domains (memory, attention, executive functions), thereby proving the usability of our tablet-based application. Regardless of prior computer experience, subjects of all age groups were able to perform the tasks without instruction or feedback from an experimenter. Increased motivation and compliance proved to be beneficial for task performance, thereby potentially increasing the validity of the results. Our promising findings underline the great clinical and practical potential of a tablet-based application for detection and monitoring of cognitive dysfunction.

We tested the usability of a tablet-computer-based application (EmoCogMeter) in investigating the effects of age on cognition. Results show an age-related cognitive decline, thereby proving the usability of our application. Findings underline the great clinical and practical potential of a tablet-based application for detection and monitoring of cognitive dysfunction.

The main goal of this study was to assess the usability of a tablet-computer-based application (EmoCogMeter) in investigating the effects of age on ...

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device (ALDM) Test Systems

Fagot &#38; Paleressompoulle1 and Fagot &#38; Bonte2 have published an automated learning device (ALDM) for the study of cognitive abilities of monkeys maintained in semi-free ranging conditions. Data accumulated during the last five years have consistently demonstrated the efficiency of this protocol to investigate individual/physical cognition in monkeys, and have further shown that this procedure reduces stress level during animal testing3. This paper demonstrates that networks of ALDM can also be used to investigate different facets of social cognition and in-group expressed behaviors in monkeys, and describes three illustrative protocols developed for that purpose. The first study demonstrates how ethological assessments of social behavior and computerized assessments of cognitive performance could be integrated to investigate the effects of socially exhibited moods on the cognitive performance of individuals. The second study shows that batteries of ALDM running in parallel can provide unique information on the influence of the presence of others on task performance. Finally, the last study shows that networks of ALDM test units can also be used to study issues related to social transmission and cultural evolution. Combined together, these three studies demonstrate clearly that ALDM testing is a highly promising experimental tool for bridging the gap in the animal literature between research on individual cognition and research on social cognition.

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems

Fagot &#38; Paleressompoulle1 have published an automated learning device (ALDM) aimed at testing individual cognitive abilities in semi-free ranging monkeys. The main goal of our protocol is to use a network of ALDM test units to study social cognition in non-human primates.

Fagot &#38; Paleressompoulle1 and Fagot &#38; Bonte2 have published an automated learning device (ALDM) for the study of cognitive abilities of monkeys ...

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Understanding typical and atypical development remains one of the fundamental questions in developmental human neuroscience. Traditionally, experimental paradigms and analysis tools have been limited to constrained laboratory tasks and contexts due to technical limitations imposed by the available set of measuring and analysis techniques and the age of the subjects. These limitations severely limit the study of developmental neural dynamics and associated neural networks engaged in cognition, perception and action in infants performing &#8220;in action and in context&#8221;. This protocol presents a novel approach to study infants and young children as they freely organize their own behavior, and its consequences in a complex, partly unpredictable and highly dynamic environment. The proposed methodology integrates synchronized high-density active scalp electroencephalography (EEG), inertial measurement units (IMUs), video recording and behavioral analysis to capture brain activity and movement non-invasively in freely-behaving infants. This setup allows for the study of neural network dynamics in the developing brain, in action and context, as these networks are recruited during goal-oriented, exploration and social interaction tasks.

This protocol presents a novel methodology for the neural decoding of intent from freely-behaving infants during unscripted social interaction with an actor. Neural activity is acquired using non-invasive high-density active scalp electroencephalography (EEG). Kinematic data is collected with inertial measurement units and supplemented with synchronized video recording.

Understanding typical and atypical development remains one of the fundamental questions in developmental human neuroscience. Traditionally, experimental ...

Behavioral Disturbances: An Innovative Approach to Monitor the Modulatory Effects of a Nutraceutical Diet

In dogs, diets are often used to modulate behavioral disturbances related to chronic anxiety and stress caused by intense and restless activity. However, the traditional ways to monitor behavioral changes in dogs are complicated and not efficient. In the current clinical evaluation, a new, simple monitoring system was used to assess the effectiveness of a specific diet in positively modulating the intense and restless activity of 24 dogs of different ages and breeds. This protocol describes how to easily and rapidly evaluate improvement in a set of symptoms related to generalized anxiety by using a specific sensor, a mobile phone app, a wireless router, and a computer. The results showed that dogs treated with specific diets showed significant improvement in the times spent active and at rest after 10 days (p &lt; 0.01 and p &lt; 0.05, respectively). These dogs also showed an overall significant improvement in clinical and behavioral symptoms. A specific sensor, along with its related hardware, was demonstrated to successfully monitor behavioral changes relating to movement in dogs.

We report a simple approach to evaluate the effectiveness of a specific diet in positively modulating the daily activity and clinical and behavioral symptoms of dogs with evident behavioral disturbances.

In dogs, diets are often used to modulate behavioral disturbances related to chronic anxiety and stress caused by intense and restless activity. However, ...

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

This protocol describes a modified serial reaction time (SRT) task used to study implicit motor sequence learning. Unlike the classic SRT task that involves finger-pressing movements while sitting, the modified SRT task requires participants to step with both feet while maintaining a standing posture. This stepping task necessitates whole body actions that impose postural challenges. The foot-stepping task complements the classic SRT task in several ways. The foot-stepping SRT task is a better proxy for the daily activities that require ongoing postural control, and thus may help us better understand sequence learning in real-life situations. In addition, response time serves as an indicator of sequence learning in the classic SRT task, but it is unclear whether response time, reaction time (RT) representing mental process, or movement time (MT) reflecting the movement itself, is a key player in motor sequence learning. The foot-stepping SRT task allows researchers to disentangle response time into RT and MT, which may clarify how motor planning and movement execution are involved in sequence learning. Lastly, postural control and cognition are interactively related, but little is known about how postural control interacts with learning motor sequences. With a motion capture system, the movement of the whole body (e.g., the center of mass (COM)) can be recorded. Such measures allow us to reveal the dynamic processes underlying discrete responses measured by RT and MT, and may aid in elucidating the relationship between postural control and the explicit and implicit processes involved in sequence learning. Details of the experimental set-up, procedure, and data processing are described. The representative data are adopted from one of our previous studies. Results are related to response time, RT, and MT, as well as the relationship between the anticipatory postural response and the explicit processes involved in implicit motor sequence learning.

The &quot;Motor&quot; in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

We introduce the foot-stepping serial reaction time (SRT) task. This modified SRT task, complementing the classic SRT task that involves only finger-pressing movement, better approximates daily sequenced activities and allows researchers to study the dynamic processes underlying discrete response measures and disentangle the explicit process operating in implicit sequence learning.

This protocol describes a modified serial reaction time (SRT) task used to study implicit motor sequence learning. Unlike the classic SRT task that ...

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Older adults are slower at recognizing visual objects than younger adults. The same is true for recognizing that a letter string is a real word. People with Alzheimer&#39;s disease (AD) or Mild Cognitive Impairment (MCI) demonstrate even longer responses in written word recognition than elderly controls. Despite the general tendency towards slower recognition in aging and neurocognitive disorders, certain characteristics of words influence word recognition speed regardless of age or neuropathology (e.g., a word&#8217;s frequency of use). We present here a protocol for examining the influence of lexical characteristics on word recognition response times in a simple lexical decision experiment administered to younger and older adults and people with MCI or AD. In this experiment, participants are asked to decide as quickly and accurately as possible whether a given letter string is an actual word or not. We also describe mixed-effects models and principal components analysis that can be used to detect the influence of different types of lexical variables or individual characteristics of participants on word recognition speed.

This article describes how to implement a simple lexical decision experiment to assess written word recognition in neurologically healthy participants and in individuals with dementia and cognitive decline. We also provide a detailed description of reaction time analysis using principal components analysis (PCA) and mixed-effects modeling.

Older adults are slower at recognizing visual objects than younger adults. The same is true for recognizing that a letter string is a real word. People ...

Using Practice Testing, Public Speaking, and Source Monitoring to Examine the Influences of Learning Strategies and Stress on Episodic Memory

Prior research demonstrated that learning information via retrieval practice, which entails studying and taking practice tests, resulted in less memory impairment under stress than learning information via repeated studying. The present experiment combined three experimental procedures to further examine the memory mechanisms underlying the efficacy of retrieval practice in the context of stress. A list-discrimination task was implemented, in which participants learned two distinct wordlists. This was combined with a retrieval-practice manipulation, as half of the participants engaged in practice testing and half engaged in conventional studying during learning. A week later, participants underwent stress induction, using the Trier Social Stress Test. Before and after stress induction, participants completed tests of item and source memory (i.e., list discrimination). The combination of these three procedures yielded informative results: retrieval practice, in the context of stress, improved item memory but not source memory relative to conventional studying. Limitations and future directions for the use of this methodology are discussed.

The present experiment combined three experimental procedures &#8212; a retrieval-practice learning manipulation, a list-discrimination task, and a stress-induction technique &#8212; to examine the influences of different learning strategies and acute stress on multiple measures of episodic memory.

Prior research demonstrated that learning information via retrieval practice, which entails studying and taking practice tests, resulted in less memory ...

Nest Building Behavior as an Early Indicator of Behavioral Deficits in Mice

Nest building is an innate behavior in male and female rodents, even when raised in laboratory settings. As such, many researchers provide rodents synthetic and/or natural materials (such as twine, tissue, cotton, paper, and hay) as a gauge of their overall well-being and as an ancillary assessment to predict the possible decline in cognition. Typically, changes in nesting behaviors, such as failure to create a nest, indicate a change in health or welfare. In addition, nesting behavior is sensitive to many environmental and physiological challenges, as well as many genetic mutations underlying pathological disease states. The following protocol describes a nesting behavior paradigm that explores the usage of four types of nesting material. In addition, the protocol utilizes intraclass correlations to demonstrate that inter-rater reliability is higher when nests are constructed out of shredded paper compared to other common nesting materials such as cotton squares, paper twists, and soft cob bedding. The chosen methodology and statistical considerations (i.e., intraclass correlation) for this assay may be of interest for those conducting experiments assessing the quality of living of mice.&#160;

Here, we present a protocol to quantify nest building behavior in mice, which is known to be impaired in several neurological disorders and diseases. This protocol examines the utility of four materials and offers the opportunity to quantify the rater agreement in scoring, improving the validity and reliability of the assay.

Nest building is an innate behavior in male and female rodents, even when raised in laboratory settings. As such, many researchers provide rodents ...

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency sensors, such as eye-trackers, electrodermal activity wristbands, EEG bands, and motion sensors provide observations at the millisecond level. This level of precision allows researchers to collect large datasets on social interactions. In this paper, I discuss how multiple eye-trackers can capture a fundamental construct in social interactions, joint visual attention (JVA). JVA has been studied by developmental psychologists to understand how children acquire language, learning scientists to understand how small groups of learners work together, and social scientists to understand interactions in small teams. This paper describes a methodology for capturing JVA in colocated settings using mobile eye-trackers. It presents some empirical results and discusses implications of capturing microobservations to understand social interactions.

Using multimodal sensors is a promising way to understand the role of social interactions in educational settings. This paper describes a methodology for capturing joint visual attention from colocated dyads using mobile eye-trackers.

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency ...