This research was supported in part by a grant from the National Institute of Child Health and Human Development (P50 HD052120). Views expressed herein are those of the authors and have neither been reviewed nor approved by the granting agencies.

The steps below describe the process of estimating individual differences underlying longitudinal associations between elementary and middle school reading skills into (A) genetic, (C) shared environmental, and (E) non-shared environmental factors using a statistical modeling program, word processor, and software with a graphical user interface (GUI). This study has been approved by the Institutional Review Board at Florida State University.
1. Preparing data for the statistical modeling program
<ol>
	<li>Prepare the data in a format that can be read by the statistical modeling program of choice. Popular statistical modeling programs include Mx, OpenMx in the platform R, and MPlus9. Mx can read data files in .vl or .dat data formats, OpenMx in any data format, and Mplus in a .dat data format. The example demonstrated here is executed in the program MPlus9. 
	NOTE: A sample data file in a .dat format for six randomly chosen participants is available in supplemental files. Variables used in a sample data file reflect variables used in the input coding file.</li>
</ol>
2. Reading data into the statistical modeling program, running the script, and estimating the effects
<ol>
	<li>Open the statistical modeling program.</li>
	<li>Locate the relevant data file to be read into the statistical modeling program by typing &#8220;File is [insert location of your data file on your computer]&#8221;.</li>
	<li>Click on the icon RUN on the ribbon of the statistical modeling program to obtain estimates for genetic, shared environmental, and non-shared environmental influences from the multivariate Cholesky decomposition method. The annotated input script for the multivariate Cholesky decomposition model for four time points as well as its output using MPlus can be found in supplemental coding files.</li>
	<li>Once the statistical modeling program generates estimates for genetic, shared environmental, and non-shared environmental influences, locate the estimates in the output file under stx11 for path a11, stx21 for path a21, &#8230;, sty11 for path c11, sty21 for path c21, &#8230;, stz11 for path e11, stz21 for path e21, etc.</li>
</ol>
3. Creating a table with generated estimates
<ol>
	<li>Open the word processor.</li>
	<li>Copy the generated estimates into a table in a word processor. The table can be created in a format as indicated in Figure 3. For example, in this case, the estimates for the paths a11, a21, a31, and a41 have values of 0.60, 0.24, 0.63, and 0.18, respectively.</li>
</ol>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/60061/60061fig3.jpg" /> 
Figure 3: Multivariate Cholesky decomposition modeling standardized path estimates of genetic and environmental influences. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60061/60061fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
4. Plotting genetic, shared environmental, and non-shared environmental influences
<ol>
	<li>Open the software with a GUI.</li>
	<li>Enter the estimates from the created table into cells F3-F16, G4-G16, H5-H16, and I6-I16. A screenshot from the software with a GUI is depicted in Figure 4.</li>
</ol>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/60061/60061fig4.jpg" /> 
Figure 4: Entering of estimates into the software with a GUI. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60061/60061fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="3">
	<li>Calculate the variance of genetic, shared environmental, and non-shared environmental influences by squaring the estimates in cells F3-F16, G4-G16, H5-H16, and I6-I16. Type the squared values in cells J3-J16, K4-K16, L5-L16, and M6-M16.</li>
	<li>Calculate the percentage variance by multiplying values in cells J3-J16, K4-K16, L5-L16, and M6-M16 by 100. Type the percentage values in cells N3-N16, O4-O16, P5-P16, and Q6-Q16. Steps 4.3 and 4.4 are depicted in Figure 5.</li>
</ol>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/60061/60061fig5.jpg" /> 
Figure 5: Illustration of steps 4.3 and 4.4. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60061/60061fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="5">
	<li>Calculate the extent to which genetic influences carry over (overlap) from elementary to middle school.
	<ol>
		<li>In cell R3, type &#8220;0&#8221;.</li>
		<li>In cell R4, type &#8220;=N4&#8221;. This is the extent to which genetic influences from the first time point carry over to the second time point. In this case, it indicates genetic influences from letter naming fluency in kindergarten carrying over to phoneme segmentation fluency in kindergarten.</li>
		<li>In cell R5, type &#8220;= N5+O5&#8221;. This is the degree to which genetic influences from the first two time points carry over to the third time point. In this case, it indicates genetic influences from letter naming fluency in kindergarten and phoneme segmentation fluency in kindergarten carrying over to word-level reading skills in grade 1.</li>
		<li>In cell R6, type &#8220;= N6+O6+P6&#8221;. This is the extent to which genetic influences from the first three time points carry over to the fourth time point. In this case, it indicates genetic influences from letter naming fluency in kindergarten, phoneme segmentation fluency in kindergarten, and word-level reading skills in grade 1 carrying over to reading comprehension in grade 7.</li>
	</ol>
	</li>
	<li>Calculate the extent to which shared environmental and non-shared environmental influences carry over (overlap) from elementary to middle school much the way as in step 4.5.</li>
	<li>Calculate the extent to which unique genetic, shared environmental, and non-shared environmental factors come online at each particular time point (i.e., grade).
	<ol>
		<li>Copy the percentages from cells N3, O4, P5, and Q6 into cells S3, S4, S5, and S6, respectively, to obtain the extent to which unique genetic factors come online at each grade.</li>
		<li>Copy the percentages from cells N8, O9, P10, and Q11 into cells U3, U4, U5, and U6, respectively, to obtain the extent to which unique shared environmental factors come online at each grade.</li>
		<li>Copy the percentages from cells N13, O14, P15, and Q16 into cells W3, W4, W5, and W6, respectively, to obtain the extent to which unique non-shared environmental factors come online at each grade.</li>
	</ol>
	</li>
	<li>To ensure all calculations are correct, the values in cells R3-W3, R4-W4, R5-W5, and R6-W6 should each add up to 100. Steps 4.5&#8211;4.7 are depicted in Figure 6.</li>
</ol>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/60061/60061fig6.jpg" /> 
Figure 6: Illustration of steps 4.5-4.8. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60061/60061fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="9">
	<li>Plot genetic overlapping as well as genetic unique influences by clicking and dragging the mouse over cells R2&#8211;R6 and S2&#8211;S6 to highlight the data.</li>
	<li>Click on the Insert menu.</li>
	<li>Click on Charts &#62; Stacked Column.</li>
	<li>Repeat steps 4.9&#8211;4.11 for shared environmental and non-shared environmental overlapping as well as unique influences. Choose cells T2&#8211;T6 and U2&#8211;U6 to plot shared environmental influences, and choose cells V2&#8211;V6 and W2&#8211;W6 for non-shared environmental influences.</li>
</ol>

<ol>
	<li>Muter, V., Hulme, C., Snowling, M. J., Stevenson, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Phonemes,+rimes,+vocabulary+and+grammatical+skills+as+foundations+of+early+reading+development:+Evidence+from+a+longitudinal+study.">Phonemes, rimes, vocabulary and grammatical skills as foundations of early reading development: Evidence from a longitudinal study.</a> Developmental Psychology. 40 (5), 665-681 (2004).</li><li>Schatschneider, C., Fletscher, J. M., Francis, D. J., Carlson, C. D., Foorman, B. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Kindergarten+prediction+of+reading+skills:+A+longitudinal+comparative+analysis.">Kindergarten prediction of reading skills: A longitudinal comparative analysis.</a> Journal of Educational Psychology. 96 (2), 265-282 (2004).</li><li>Byrne, 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=Longitudinal+twin+study+of+early+literacy+development:+Preschool+and+kindergarten+phases.">Longitudinal twin study of early literacy development: Preschool and kindergarten phases.</a> Scientific Studies of Reading. 9 (3), 219-235 (2005).</li><li>Christopher, M. E., 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=Genetic+and+environmental+etiologies+of+the+longitudinal+relations+between+prereading+skills+and.">Genetic and environmental etiologies of the longitudinal relations between prereading skills and.</a> Child Development. 86 (2), 342-361 (2015).</li><li>Neale, M. C., Cardon, L. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Methodology for Genetic Studies of Twins and Families. , (1992).</li><li>Byrne, 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=Genetic+and+environmental+influences+on+early+literacy.">Genetic and environmental influences on early literacy.</a> Journal of Research in Reading. 29 (1), 33-49 (2006).</li><li>Byrne, 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=Genetic+and+environmental+influences+on+aspects+of+literacy+and+language+in+early+childhood:+Continuity+and+change+from+preschool+to+grade+2.">Genetic and environmental influences on aspects of literacy and language in early childhood: Continuity and change from preschool to grade 2.</a> Journal of Neurolinguistics. 22 (3), 219-236 (2009).</li><li>Erbeli, F., Hart, S. A., Taylor, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Longitudinal+associations+among+reading+related+skills+and+reading+comprehension:+A+twin+study.">Longitudinal associations among reading related skills and reading comprehension: A twin study.</a> Child Development. 89 (6), e480-e493 (2018).</li><li>Muth&#233;n, L. K., Muth&#233;n, B. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Mplus. The comprehensive modeling program for applied researchers: User&#8217;s guide. , (2012).</li><li>Hart, S. A., 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=Exploring+how+nature+and+nurture+affect+the+development+of+reading:+An+analysis+of+the+Florida+Twin+Project+on+Reading.">Exploring how nature and nurture affect the development of reading: An analysis of the Florida Twin Project on Reading.</a> Developmental Psychology. 49 (10), 1971-1981 (2013).</li><li>Taylor, J., Roehrig, A. D., Hensler, B. S., Connor, C. M., Schatschneider, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Teacher+quality+moderates+the+genetic+effects+on+early+reading.">Teacher quality moderates the genetic effects on early reading.</a> Science. 328 (5977), 512-514 (2010).</li></ol>

Authors have nothing to disclose.

The objective of this study was to demonstrate how the well-established method within behavioral genetics, the multivariate Cholesky decomposition method, can effectively be used for understanding relations across variables in temporal context. Specifically, this method allows estimation of the extent to which unique genetic and environmental influences arise during particular time points (e.g., school grade), as well as demonstrating the overlap of genetic and environmental influences across many time points.
There is one critical step in the protocol, which is estimating the multivariate Cholesky decomposition model. Occasionally, the statistical modeling program script requires adjustments in starting values based on inputted data. Researchers can use different starting values, suggested by the output of the statistical modeling program, to enable smoother iteration processes in generating genetic and environmental estimates.
Modification to the protocol (i.e., the statistical modeling program script) may be necessary if latent variables replaced measured variables. In Mplus, a latent variable is defined using a &#8220;BY&#8221; statement with two or more measured variables. For example, in this annotated input script, the latent variable for each twin is defined in the section &#8220;MODEL:&#8221; as &#8220;FLU0 BY nneworf0* nnewnwf0 (3-4);&#8221; for the first twin in the twin pair, and &#8220;FLU1 BY nneworf1* nnewnwf1 (3-4);&#8221; for the second twin in the twin pair. Another modification refers to the graphical representation of results. Results may be plotted using alternative techniques, such as pie charts. Stacked columns were used here since this is the typical visualization technique in the field of behavior genetics.
There are limitations to the multivariate Cholesky decomposition method. The multivariate Cholesky decomposition is the gold standard analysis in behavior genetics if there is interest in exploring individual differences in longitudinal relations among different variables at different time points. If instead there is interest in testing developmentally driven questions aimed at exploring individual differences in development of one (the same) variable across multiple time points, then a simplex model (depicted in Figure 12) can be used. In the simplex model, A1, C1, and E1 factors present at time 1 partially persist until time 2 (the regression paths from A1 to A2, A2 to A3, &#8230;, C1 to C2, &#8230;, etc.), at which time new factors may enter (the residuals, labeled &#8220;Res.&#8221; in Figure 12).
The multivariate Cholesky decomposition model and simplex model produce exactly the same variance-covariance matrix if there are two time points, but any additional time points separate the two models. With more than two time points, a Cholesky decomposition method produces estimates between all time points. This can result in estimation of relations that are not as developmentally meaningful (e.g., genetic and environmental influences from grade 1 on grade 7). The simplex model, on the other hand, estimates relations that are developmentally relevant (e.g., genetic and environmental influences from grade 1 to grade 2, grade 2 to grade 3, etc.). The latter pattern reflects the natural trajectory of children through school, where they progress from one grade to the next. A more thorough description of the simplex model has been described10.
<img alt="Figure 12" class="xfigimg" src="/files/ftp_upload/60061/60061fig12.jpg" /> 
Figure 12: The simplex model. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60061/60061fig12large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
An additional limitation of the multivariate Cholesky decomposition method is that while it enables quantification of genetic and environmental influences, it does not identify them. Therefore, it cannot be determined specifically which genes influence the traits measured by the variables used in the analysis. Similarly, it can only be surmised which specific environments contributed to the shared environmental or non-shared environmental influences. In the current study, there was some empirical evidence about which potential specific environments may have been at play. For example, classroom reading environment in elementary school has been shown to have longitudinal effects on reading comprehension in middle school (study findings are based on this report)11. However, further progress must rely on additional analyses to identify other environments.
Despite these limitations, the Cholesky decomposition method is a popular approach to longitudinal, multivariate, behavioral genetic research questions. The technique is easy to program and solve. It offers a unique perspective into decomposing genetic and environmental relations among time points, thereby quantifying influences that are time point-specific while distinguishing them from influences that are overlapping across multiple time points.

Becoming a skilled reader with the ability to fluently read and comprehend text is important for children&#8217;s school outcomes. To prevent the development of reading problems, it is vital to understand the extent to which different reading skills predict reading comprehension. Existing research has shown that pre-reading and word-level reading skills in elementary school longitudinally predict reading comprehension in middle school1,2. Individual differences in these predictions mostly point to underlying genetic (and to some extent, environmental) factors from kindergarten up to grade four3,4. However, there is a need to explore whether these same genetic and environmental factors continue to influence these predictions up to middle school grades.
One method to gain a better understanding of individual differences underlying the associations between elementary and middle school reading skills is using behavioral genetic methodology, specifically the Cholesky decomposition method. The Cholesky decomposition method is considered one of the gold standard analyses in behavioral genetics. This method is easy to program and solve and allows for the decomposition of variance and covariance into (A) genetic, (C) shared environmental, and (E) non-shared environmental influences, usually in a sample of twins. An example of a univariate (one variable) Cholesky decomposition is indicated in Figure 1. The A latent factor refers to genetic effects, which are genetic influences inherited from parents. The C latent factor refers to shared environmental effects, which are aspects of the environment that serve to make twins more similar, such as home and school environments. Lastly, the E latent factor refers to non-shared environmental effects, which are environmental influences that are unique to each twin and contribute to differences between twins, such as each&#8217;s own experience. The E factor also captures measurement error.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60061/60061fig1v2.jpg" /> 
Figure 1: Decomposition into (A) genetic, (C) shared environmental, and (E) non-shared environmental influences. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60061/60061fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The A, C, and E factors in Figure 1 estimate the extent to which genes and environments influence one (reading) variable. Still, to investigate individual differences underlying longitudinal associations between more than one reading skill from elementary to middle school, longitudinal analysis is necessary. To answer longitudinally motivated research questions, a multivariate Cholesky decomposition method is used here5. Conceptually, the multivariate Cholesky decomposition method is similar to hierarchical multiple regression, such that independent contribution of genetic and environmental factors is assessed after the contributions of previous factors have been taken into account.
For instance, in a multivariate Cholesky decomposition with longitudinal data at four time points (see Figure 2), the first set of factors [genetic (A1), shared environmental (C1), and non-shared environmental (E1)] contributes to the variance of all variables, represented as paths a11, a21, a31, a41, c11, c21, ..., e11, etc., from A1, C1, E1 factors to each variable. The second set of factors (A2, C2, E2) contributes to the variance of the second and subsequent variables after controlling for the first time point. The second set of factors is represented as paths a22, a32, a42, c22, c32, ..., e22, etc. Then, influences of the third set of factors (A3, C3, E3) are estimated for the third and fourth variables after controlling for the previous two time points. They are represented as paths a33, a43, c33, c43, e33, e43. Finally, influences of the fourth set of factors (A4, C4, E4) are measured for the final time point after controlling for all previous time points. They are represented as paths a44, c44, e44.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/60061/60061fig2.jpg" /> 
Figure 2: Multivariate Cholesky decomposition model for four time points. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60061/60061fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
In this longitudinal application of the multivariate Cholesky decomposition method, genetic and environmental influences at each time point are estimated after the effects of previous time points have been controlled for. As such, this method allows determination of the extent to which unique genetic and environmental influences come online at each particular time point, independent of influences from previous time points (these effects are estimated by paths a11, a22, a33, a44, c11, c22, &#8230;, e11, e22, etc.). In addition, the method also enables examination of the degree to which the same (overlapping) genetic and environmental influences are shared between time points. In other words, it can be determined to which extent genetic and environmental influences carry over from one time point to another (i.e., these effects are estimated by paths a21, a31, a41, a32, a42, a43, c21, c31, ..., e21, etc.). It should be noted that paths a11, c11, and e11 represent all possible genetic and environmental influences up to and including the first time point, which can be either unique or overlapping with previous time points. However, time points prior to the first time point are not estimated; hence, it cannot be accurately determined whether they represent unique or overlapping influences. For simplification purposes, they are included as unique influences in the current report.
The order of the measured variables entered into a Cholesky decomposition is arbitrary. However, the order is usually driven by a theoretical perspective. This is also the case in the current study, in which the order was based on the development of reading skills, such that reading skills in elementary school are predictive of reading comprehension in middle school.
There are several reports in the literature investigating genetic and environmental factors underlying longitudinal associations of reading skills utilizing the Cholesky decomposition method. These prior studies mostly focused on investigating relations between reading skills among elementary schoolers6,7. There is only one published study examining individual differences associated with reading from elementary grades into middle school grades using the multivariate Cholesky decomposition method8. This protocol details the multivariate Cholesky decomposition method from that specific report to explore individual differences in longitudinal relations between kindergarten letter knowledge, kindergarten phonological awareness, first grade word-level reading skills, and seventh grade reading comprehension.
The study findings focus on using the multivariate Cholesky decomposition method to distinguish between two types of genetic and environmental influences. First, it is shown how to estimate genetic and environmental influences that carry over (overlap) from elementary to middle school reading (e.g., estimating paths a43, c43, and e43, which are genetic and environmental influences on word-level reading skills from first grade that affect reading comprehension in seventh grade). Second, it is demonstrated how to estimate unique genetic and environmental influences that come online at each particular grade (e.g., estimating paths a33, c33, and e33, which are unique genetic and environmental influences on word-level reading skills that arise in first grade).

<table>
	<tbody>
		<tr>
			<td>Microsoft Office Excel</td>
			<td>Microsoft</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Microsoft Office Powerpoint</td>
			<td>Microsoft</td>
			<td></td>
			<td></td>
		</tr>
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			<td>Microsoft Office Visio</td>
			<td>Microsoft</td>
			<td></td>
			<td></td>
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			<td>Microsoft Office Word</td>
			<td>Microsoft</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Mplus Statistical Program</td>
			<td>Mplus</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

using cholesky decomposition to explore individual differences in longitudinal relations between reading skills

The Cholesky decomposition method is the gold standard used in the field of behavioral genetics. The method is popular because it is easy to program and solve. Using this method, researchers can explore individual differences in longitudinal relations of different variables across multiple time points. The method allows investigators to decompose variance into (1) unique&#160;genetic, shared and non-shared environmental effects that arise at specific time points as well as (2) overlapping genetic, shared and non-shared environmental effects that carry over from one time point to another. However, the method does not identify the mechanisms or origins underlying these effects. The current report focuses on application of the Cholesky decomposition method in the field of educational psychology. Specifically, it discusses individual differences in longitudinal relations between kindergarten letter knowledge, kindergarten phonological awareness, first grade word-level reading skills, and seventh grade reading comprehension.

Standardized estimates for genetic, shared environmental, and non-shared environmental influences from the multivariate Cholesky decomposition model are depicted in Figure 7. In general, results revealed that individual differences in kindergarten pre-reading and first grade word-level reading skills accounted for a large proportion of the variance of genetic (40%) as well as shared environmental (39%) influences on seventh grade reading comprehension. In addition, results alluded to a degree of unique sources coming into play for each individual reading skill at each grade.
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/60061/60061fig7.jpg" /> 
Figure 7: Full multivariate Cholesky decomposition model with standardized path estimates of genetic and environmental influences. Measured variables are depicted as rectangles, and a latent variable as an oval. LNF = kindergarten letter naming fluency, PSF = kindergarten phoneme segmentation fluency, WLRS = first grade word-level reading skills, RC = seventh grade reading comprehension. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60061/60061fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
As indicated in Figure 8, it appears there was a large share of unique genetic influences (dark green) on letter naming fluency in kindergarten (36%), phoneme segmentation fluency in kindergarten (40%), and reading comprehension in seventh grade (30%). In contrast, word-level reading skills were to a lesser extent associated with unique genetic influences that arise in first grade (20%). Genetic influences on word-level reading skills were mostly overlapping (light green) with genetic influences on letter naming fluency and phoneme segmentation fluency (40%).
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/60061/60061fig8.jpg" /> 
Figure 8: Percentage of unique and overlapping genetic influences on each reading skill. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60061/60061fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Focusing on the shared environmental influences&#160;(see Figure 9), the results implied that overlapping (light blue) shared environment influenced letter naming fluency and phoneme segmentation fluency in kindergarten (9%). Similarly, overlapping shared environmental effects were reflected in word-level reading skills in first grade (15%) and reading comprehension in seventh grade (39%) that were also shared with kindergarten reading skills. Unique shared environmental factors (dark blue) were found for first grade word-level reading skills (15%). These influences were independent of shared environmental influences in kindergarten.
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/60061/60061fig9.jpg" /> 
Figure 9: Percentage of unique and overlapping shared environmental influences on each reading skill. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60061/60061fig9large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
For the non-shared environmental influences (see Figure 10), the results suggested very little overlap between factors (light yellow). Most non-shared environmental influences indicated unique influences (dark yellow) at each individual time point (i.e., grade).
<img alt="Figure 10" class="xfigimg" src="/files/ftp_upload/60061/60061fig10.jpg" /> 
Figure 10: Percentage of unique and overlapping non-shared environmental influences on each reading skill. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60061/60061fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The general representation of genetic and environmental factors underlying reading skills from elementary to middle school is shown in Figure 11. In general, it was shown that reading skills appear to be influenced by both genetic and environmental factors across this developmental period.
<img alt="Figure 11" class="xfigimg" src="/files/ftp_upload/60061/60061fig11.jpg" /> 
Figure 11: Total percentage of genetic, shared environmental, and non-shared environmental influences on each reading skill. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60061/60061fig11large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

This paper demonstrates use of the gold standard method in behavioral genetics, the Cholesky decomposition method, to estimate unique, overlapping genetic and environmental influences on different variables to answer longitudinally motivated research questions.

The Cholesky decomposition method is the gold standard used in the field of behavioral genetics. The method is popular because it is easy to program and ...

using-cholesky-decomposition-to-explore-individual-differences

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Behavior

6.2K Views.  Texas A&M University. This paper demonstrates use of the gold standard method in behavioral genetics, the Cholesky decomposition method, to estimate unique, overlapping genetic and environmental influences on different variables to answer longitudinally motivated research questions.

Video: Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Training Synesthetic Letter-color Associations by Reading in Color

Synesthesia is a rare condition in which a stimulus from one modality automatically and consistently triggers unusual sensations in the same and/or other modalities. A relatively common and well-studied type is grapheme-color synesthesia, defined as the consistent experience of color when viewing, hearing and thinking about letters, words and numbers. We describe our method for investigating to what extent synesthetic associations between letters and colors can be learned by reading in color in nonsynesthetes. Reading in color is a special method for training associations in the sense that the associations are learned implicitly while the reader reads text as he or she normally would and it does not require explicit computer-directed training methods. In this protocol, participants are given specially prepared books to read in which four high-frequency letters are paired with four high-frequency colors. Participants receive unique sets of letter-color pairs based on their pre-existing preferences for colored letters. A modified Stroop task is administered before and after reading in order to test for learned letter-color associations and changes in brain activation. In addition to objective testing, a reading experience questionnaire is administered that is designed to probe for differences in subjective experience. A subset of questions may predict how well an individual learned the associations from reading in color. Importantly, we are not claiming that this method will cause each individual to develop grapheme-color synesthesia, only that it is possible for certain individuals to form letter-color associations by reading in color and these associations are similar in some aspects to those seen in developmental grapheme-color synesthetes. The method is quite flexible and can be used to investigate different aspects and outcomes of training synesthetic associations, including learning-induced changes in brain function and structure.

Reading in color is a new method for training letter-color associations that are typically found only in grapheme-color synesthetes. It involves an implicit form of training that has potential for long-term associative training methods&#160;because the training is a byproduct of reading and any text can be colored.

Synesthesia is a rare condition in which a stimulus from one modality automatically and consistently triggers unusual sensations in the same and/or other ...

Development of a Virtual Reality Assessment of Everyday Living Skills

Cognitive impairments affect the majority of patients with schizophrenia and these impairments predict poor long term psychosocial outcomes.&#160; Treatment studies aimed at cognitive impairment in patients with schizophrenia not only require demonstration of improvements on cognitive tests, but also evidence that any cognitive changes lead to clinically meaningful improvements.&#160; Measures of &#8220;functional capacity&#8221; index the extent to which individuals have the potential to perform skills required for real world functioning. &#160;Current data do not support the recommendation of any single instrument for measurement of functional capacity.&#160; The Virtual Reality Functional Capacity Assessment Tool (VRFCAT) is a novel, interactive gaming based measure of functional capacity that uses a realistic simulated environment to recreate routine activities of daily living. Studies are currently underway to evaluate and establish the VRFCAT&#8217;s sensitivity, reliability, validity, and practicality.&#160;This new measure of functional capacity is practical, relevant, easy to use, and has several features that improve validity and sensitivity of measurement of function in clinical trials of patients with CNS disorders.

A challenge for proving treatment efficacy for cognitive impairments in schizophrenia is finding the optimizing measurement of skills related to everyday functioning.&#160; The Virtual Reality Functional Capacity Assessment Tool (VRFCAT) is an interactive gaming based computerized measure aimed at skills associated with everyday functioning, including baseline impairments and treatment related changes.

Cognitive impairments affect the majority of patients with schizophrenia and these impairments predict poor long term psychosocial outcomes.&#160; ...

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Kinesthetic awareness is important to successfully navigate the environment. When we interact with our daily surroundings, some aspects of movement are deliberately planned, while others spontaneously occur below conscious awareness. The deliberate component of this dichotomy has been studied extensively in several contexts, while the spontaneous component remains largely under-explored. Moreover, how perceptual processes modulate these movement classes is still unclear. In particular, a currently debated issue is whether the visuomotor system is governed by the spatial percept produced by a visual illusion or whether it is not affected by the illusion and is governed instead by the veridical percept. Bistable percepts such as 3D depth inversion illusions (DIIs) provide an excellent context to study such interactions and balance, particularly when used in combination with reach-to-grasp movements. In this study, a methodology is developed that uses a DII to clarify the role of top-down processes on motor action, particularly exploring how reaches toward a target on a DII are affected in both deliberate and spontaneous movement domains.

It is unclear how top-down signals from the ventral visual stream affect movement. We developed a paradigm to test motor behavior towards a target on a 3D depth inversion illusion. Significant differences are reported in both deliberate, goal-directed movements and automatic actions under illusory and veridical viewing conditions.

Kinesthetic awareness is important to successfully navigate the environment. When we interact with our daily surroundings, some aspects of movement are ...

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

A set of behavioral tasks for assessing perceptual and sensorimotor timing abilities in the general population (i.e., non-musicians) is presented here with the goal of uncovering rhythm disorders, such as beat deafness. Beat deafness is characterized by poor performance in perceiving durations in auditory rhythmic patterns or poor synchronization of movement with auditory rhythms (e.g., with musical beats). These tasks include the synchronization of finger tapping to the beat of simple and complex auditory stimuli and the detection of rhythmic irregularities (anisochrony detection task) embedded in the same stimuli. These tests, which are easy to administer, include an assessment of both perceptual and sensorimotor timing abilities under different conditions (e.g., beat rates and types of auditory material) and are based on the same auditory stimuli, ranging from a simple metronome to a complex musical excerpt. The analysis of synchronized tapping data is performed with circular statistics, which provide reliable measures of synchronization accuracy (e.g., the difference between the timing of the taps and the timing of the pacing stimuli) and consistency. Circular statistics on tapping data are particularly well-suited for detecting individual differences in the general population. Synchronized tapping and anisochrony detection are sensitive measures for identifying profiles of rhythm disorders and have been used with success to uncover cases of poor synchronization with spared perceptual timing. This systematic assessment of perceptual and sensorimotor timing can be extended to populations of patients with brain damage, neurodegenerative diseases (e.g., Parkinson&#8217;s disease), and developmental disorders (e.g., Attention Deficit Hyperactivity Disorder).

Behavioral tasks that allow for the assessment of perceptual and sensorimotor timing abilities in the general population (i.e., non-musicians) are presented. Synchronization of finger tapping to the beat of an auditory stimuli and detecting rhythmic irregularities provides a means of uncovering rhythm disorders.

A set of behavioral tasks for assessing perceptual and sensorimotor timing abilities in the general population (i.e., non-musicians) is presented here ...

A Mouse Model of Subchronic and Mild Social Defeat Stress for Understanding Stress-induced Behavioral and Physiological Deficits

Stressful life events often increase the incidence of depression in humans. To study the mechanisms of depression, the development of animal models of depression is essential. Because there are several types of depression, various animal models are needed for a deeper understanding of the disorder. Previously, a mouse model of subchronic and mild social defeat stress (sCSDS) using a modified chronic social defeat stress (CSDS) paradigm was established. In the paradigm, to reduce physical injuries from aggressors, the duration of physical contact between the aggressor and a subordinate was reduced compared to in the original CSDS paradigm. sCSDS mice showed increased body weight gain, food intake, and water intake during the stress period, and their social behaviors were suppressed after the stress period. In terms of the face validity of the stress-induced overeating and overdrinking following the increased body weight gain, the sCSDS mice may show some features related to atypical depression in humans. Thus, a mouse model of sCSDS may be useful for studying the pathogenic mechanisms underlying depression. This protocol will help establish the sCSDS mouse model, especially for studying the mechanisms underlying stress-induced weight gain and polydipsia- and hyperphagia-like symptoms.

Here, methods for developing a mouse model of subchronic and mild social defeat stress are described and used to investigate the pathogenic features of depression including hyperphagia- and polydipsia-like symptoms following increased body weight.

Stressful life events often increase the incidence of depression in humans. To study the mechanisms of depression, the development of animal models of ...

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

In both the East and West, traditional teachings say that the mind and heart are somehow closely correlated, especially during spiritual practice. One difficulty in proving this objectively is that the natures of brain and heart activities are quite different. In this paper, we propose a methodology that uses wavelet entropy to measure the chaotic levels of both electroencephalogram (EEG) and electrocardiogram (ECG) data and show how this may be used to explore the potential coordination between the mind and heart under different experimental conditions. Furthermore, Statistical Parametric Mapping (SPM) was used to identify the brain regions in which the EEG wavelet entropy was the most affected by the experimental conditions. As an illustration, the EEG and ECG were recorded under two different conditions (normal rest and mindful breathing) at the beginning of an 8-week standard Mindfulness-based Stress Reduction (MBSR) training course (pretest) and after the course (posttest). Using the proposed method, the results consistently showed that the wavelet entropy of the brain EEG decreased during the MBSR mindful breathing state as compared to that during the closed-eye resting state. Similarly, a lower wavelet entropy of heartrate was found during MBSR mindful breathing. However, no difference in wavelet entropy during MBSR mindful breathing was found between the pretest and posttest. No correlation was observed between the entropy of brain waves and the entropy of heartrate during normal rest in all participants, whereas a significant correlation was observed during MBSR mindful breathing. Additionally, the most well-correlated brain regions were located in the central areas of the brain. This study provides a methodology for the establishment of evidence that mindfulness practice (i.e., mindful breathing) may increase the coordination between mind and heart activities.

This manuscript describes how to use the wavelet entropy index to analyze high-density electroencephalography (EEG) and electrocardiography (ECG) data. We show that the irregularity of cerebral and cardiac activities became more coordinated during mindfulness-based stress reduction practice.

In both the East and West, traditional teachings say that the mind and heart are somehow closely correlated, especially during spiritual practice. One ...

Central and Divided Visual Field Presentation of Emotional Images to Measure Hemispheric Differences in Motivated Attention

Two dominant theories on lateralized processing of emotional information exist in the literature. One theory posits that unpleasant emotions are processed by right frontal regions, while pleasant emotions are processed by left frontal regions. The other theory posits that the right hemisphere is more specialized for the processing of emotional information overall, particularly in posterior regions.
Assessing the different roles of the cerebral hemispheres in processing emotional information can be difficult without the use of neuroimaging methodologies, which are not accessible or affordable to all scientists. Divided visual field presentation of stimuli can allow for the investigation of lateralized processing of information without the use of neuroimaging technology. 
This study compared central versus divided visual field presentations of emotional images to assess differences in motivated attention between the two hemispheres. The late positive potential (LPP) was recorded using electroencephalography (EEG) and event-related potentials (ERPs) methodologies to assess motivated attention. Future work will pair this paradigm with a more active behavioral task to explore the behavioral impacts on the attentional differences found.

This study compared central versus divided visual field presentations of emotional images to assess differences in motivated attention between the two hemispheres. The late positive potential (LPP) was recorded using electroencephalography (EEG) and event-related potentials (ERPs) methodologies to assess motivated attention.

Two dominant theories on lateralized processing of emotional information exist in the literature. One theory posits that unpleasant emotions are processed ...

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

In a typical eye tracking study using the visual world paradigm, participants' eye movements to objects or pictures in the visual workspace are recorded via an eye tracker as the participant produces or comprehends a spoken language describing the concurrent visual world. This paradigm has high versatility, as it can be used in a wide range of populations, including those who cannot read and/or who cannot overtly give their behavioral responses, such as preliterate children, elderly adults, and patients. More importantly, the paradigm is extremely sensitive to fine grained manipulations of the speech signal, and it can be used to study the online processing of most topics in language comprehension at multiple levels, such as the fine grained acoustic phonetic features, the properties of words, and the linguistic structures. The protocol described in this article illustrates how a typical visual world eye tracking study is conducted, with an example showing how the online processing of some semantically complex statements can be explored with the visual world paradigm.

The visual world paradigm monitors participants' eye movements in the visual workspace as they are listening to or speaking a spoken language. This paradigm can be used to investigate the online processing of a wide range of psycholinguistic questions, including semantically complex statements, such as disjunctive statements.

In a typical eye tracking study using the visual world paradigm, participants' eye movements to objects or pictures in the visual workspace are recorded ...

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading

Regressive eye movements are eye movements that move backwards through the text and comprise approximately 10-25% of eye movements during reading. As such, understanding the causes and mechanisms of regressions plays an important role in understanding eye movement behavior. Inhibition of return (IOR) is an oculomotor effect that results in increased latency to return attention to a previously attended target versus a target that was not previously attended. Thus, IOR may affect regressions. This paper describes how to design materials to distinguish between regressions caused by comprehension-related and oculomotor processes; the latter is subject to IOR. The method allows researchers to identify IOR and control the causes of regressions. While the method requires tightly controlled materials and large numbers of participants and materials, it allows researchers to distinguish and control the types of regressions that occur in their reading studies.

The method was designed to investigate the role of inhibition of return (IOR) in regressive eye movements during reading. The focus is on differentiating between regressions triggered as a result of comprehension difficulty versus those triggered from oculomotor error, including the role of IOR in the two types of regressions.

Regressive eye movements are eye movements that move backwards through the text and comprise approximately 10-25% of eye movements during reading. As ...

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

The Simple View of Reading is a popular model of reading that claims that reading is the product of decoding and language, with each component uniquely predicting reading comprehension. Although researchers have argued whether the sum rather than the product of the components is the better predictor, no researchers have partitioned the variance explained to examine the extent to which the components share variance in predicting reading. To decompose the variance, we subtract the R2 for the language-only model from the full model to obtain the unique R2 for decoding. Second, we subtract the R2 for the decoding-only model from the full model to obtain the unique R2 for language. Third, to obtain the common variance explained by language and decoding, we subtract the sum of the two unique R2 from the R2 for the full model. The method is demonstrated in a regression approach with data from students in grades 1 (n = 372), 6 (n = 309), and 10 (n = 122) using an observed measure of language (receptive vocabulary), decoding (timed word reading), and reading comprehension (standardized test). Results reveal a relatively large amount of variance in reading comprehension explained in grade 1 by the common variance in decoding and language. By grade 10, however, it is the unique effect of language and the common effect of language and decoding that explained the majority of variance in reading comprehension. Results are discussed in the context of an expanded version of the Simple View of Reading that considers unique and shared effects of language and decoding in predicting reading comprehension.

Here we present a protocol for decomposing the variance in reading comprehension into the unique and common effects of language and decoding.

The Simple View of Reading is a popular model of reading that claims that reading is the product of decoding and language, with each component uniquely ...