Name: a method for investigating age-related differences in the functional connectivity of cognitive control networks associated with dimensional change card sort performance
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Description: Watch this Scientific Journal Video about A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort P

This research was made possible with the support of grants from the National Science and Engineering Research Council (NSERC) to J. Bruce Morton.

1. Obtain Approval for Working with Human Subjects
2. fMRI Data Acquisition
<ol>
	<li>Acquire fMRI data following procedures suitable for young children (see Raschle, et al.12). Make every effort to limit possible age-related differences in task performance and motion, as these differences introduce unwanted confounds that limit one&#39;s capacity to draw inferences about developmentally-relevant differences in brain activation and functional connectivity. 
	Note: In t...

<ol>
	<li>Diamond, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Normal+Development+of+Prefrontal+Cortex+from+Birth+to+Young+Adulthood:+Cognitive+Functions+Anatomy,+and+Biochemistry.">Normal Development of Prefrontal Cortex from Birth to Young Adulthood: Cognitive Functions Anatomy, and Biochemistry.</a> Principles of Frontal Lobe Function. , 1-38 (2002).</li><li>Zelazo, P. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Dimensional+Change+Card+Sort+(DCCS):+a+method+of+assessing+executive+function+in+children.">The Dimensional Change Card Sort (DCCS): a method of assessing executive function in children.</a> Nat Protoc. 1, 297-301 (2006).</li><li>Monsell, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Task+switching.">Task switching.</a> Trends Cogn Sci (Regul Ed. 7, 134-140 (2003).</li><li>Crone, E. A., Bunge, S. A., van der Molen, M. W., Ridderinkhof, K. 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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=Development+of+distinct+control+networks+through+segregation+and+integration.">Development of distinct control networks through segregation and integration.</a> Proc Natl Acad Sci USA. 104, 13507-13512 (2007).</li><li>Uddin, L. Q., Supekar, K., Menon, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Typical+and+atypical+development+of+functional+human+brain+networks:+insights+from+resting-state+FMRI.">Typical and atypical development of functional human brain networks: insights from resting-state FMRI.</a> Frontiers in systems neuroscience. 4, (2010).</li><li>Calhoun, V. D., Adali, T., Pearlson, G. D., Pekar, J. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+method+for+making+group+inferences+from+functional+MRI+data+using+independent+component+analysis.">A method for making group inferences from functional MRI data using independent component analysis.</a> Human brain mapping. 14, 140-151 (2001).</li><li>Fransson, P., 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=Resting-state+networks+in+the+infant+brain.">Resting-state networks in the infant brain.</a> Proceedings of the National Academy of Sciences of the United States of America. 104, 15531-15536 (2007).</li><li>Supekar, K., Menon, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Developmental+maturation+of+dynamic+causal+control+signals+in+higher-order+cognition:+a+neurocognitive+network+model.">Developmental maturation of dynamic causal control signals in higher-order cognition: a neurocognitive network model.</a> PLoS computational biology. 8, (2012).</li><li>Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., Petersen, S. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spurious+but+systematic+correlations+in+functional+connectivity+MRI+networks+arise+from+subject+motion.">Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.</a> Neuroimage. 59, 2142-2154 (2012).</li><li>Raschle, N. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Making+MR+Imaging+Child's+Play+-+Pediatric+Neuroimaging+Protocol,+Guidelines+and+Procedure.">Making MR Imaging Child's Play - Pediatric Neuroimaging Protocol, Guidelines and Procedure.</a> (29), (2009).</li><li>Morton, J. B., Bosma, R., Ansari, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Age-related+changes+in+brain+activation+associated+with+dimensional+shifts+of+attention:+an+fMRI+study.">Age-related changes in brain activation associated with dimensional shifts of attention: an fMRI study.</a> Neuroimage. 46, 249-256 (2009).</li><li>Ezekiel, F., Bosma, R., Morton, J. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dimensional+Change+Card+Sort+performance+associated+with+age-related+differences+in+functional+connectivity+of+lateral+prefrontal+cortex.">Dimensional Change Card Sort performance associated with age-related differences in functional connectivity of lateral prefrontal cortex.</a> Developmental Cognitive Neuroscience. , (2013).</li><li>Calhoun, V. D., Kiehl, K. A., Pearlson, G. 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D., Liu, J., Adali, T. <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+group+ICA+for+fMRI+data+and+ICA+for+joint+inference+of+imaging,+genetic,+and+ERP+data.">A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data.</a> Neuroimage. 45, (2009).</li><li>Allen, E. 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=Tracking+whole-brain+connectivity+dynamics+in+the+resting+state.">Tracking whole-brain connectivity dynamics in the resting state.</a> Cerebral Cortex. , (2012).</li></ol>

Higher-order mental operations, such as the ability to switch sorting rules, develop rapidly throughout childhood and adolescence. Because these mental operations involve interactions between multiple distributed brain regions, there is growing interest in exploring the relationship between the development of higher-order cognition and age-related changes in the organization of broad-scale cortical networks. We present a method based on group independent component analysis applied to task-based fMRI data as a means of ex...

The ability to regulate behavior develops gradually in childhood and adolescence (for review, see Diamond1). In the Dimensional Change Card Sort task, for example, participants switch from sorting cards one way, such as shape, to sorting them a different way, such as color2 (see Figure 2). Switching exacts a small performance cost, or switch cost, such that responses are typically slower and more error-prone on switch trials in which the sorting rule changes as compared to repeat trials in which the sorting rule remains the same3. The magnitude of these costs typically gets smaller as children grow older4, illustrating the fact that the capacity for behavioral regulation undergoes continued development early in life.
Because complex mental operations, such as switching, involve rapid interactions between multiple brain regions5, there is growing interest in relating the development of higher-order cognition to changes in the functional organization of broad-scale cortical networks6.
One approach to investigating developmental change in broad-scale networks is through the use of seed-based functional connectivity analysis6,7. The first step in this technique is to consult with available research literature and define a priori regions of interest, or ROIs, that seem to be relevant to the behavior in question. These ROIs, or nodes, define the basic skeleton of the network. Next, low-frequency fluctuations in activity (or T2*-weighted signal intensity) in these ROIs are measured for 5&#160;to 10&#160;min&#160;while participants are at rest in an MRI scanner. Functional connectivity between any two nodes of the network is then quantified as the correlation of their respective time courses. Nodes that are strongly connected functionally should have similar, and thus highly correlated, signal time courses. On the other hand, nodes that are weakly connected functionally should have dissimilar and thus weakly correlated, signal time courses. To complete a model of the network, edges (or links) are drawn between nodes whose time courses correlate above a chosen threshold. Tests for age-related differences in functional connectivity within a network can be conducted on any single node-to-node connection, or on the topology of the entire set of nodes and edges. These differences in functional connectivity can then be related to measures of cognitive performance collected offline.
In this paper, a different approach is described that is based on group independent component analysis of task-based fMRI data8. Independent component analysis (or ICA) is a statistical procedure for blindly revealing hidden sources underlying a set of observations such that the revealed sources are maximally independent. Applied to the analysis of fMRI data, the procedure assumes that each volume is a mixture of a finite number of spatially-independent sources. Using one of a variety of different algorithms, such as the infomax algorithm, ICA then estimates an unmixing matrix, which when applied to the original data yields a set of maximally independent sources, or components. Each component can be thought of as a network, insofar as it comprises of a set of voxels that share a common time course. Group ICA is a particular type of ICA in which a common set of group components is first estimated from an entire data set, and then participant-specific sets of the group components are computed in a back-reconstruction step. Once an entire data set is decomposed into a set of components, the next step is to discard artifactual components that represent noise sources, and identify theoretically meaningful components that correspond with networks of interest. This can be achieved either by modeling component time courses in the context of a GLM to identify networks that activate in a predicted manner, spatially correlating components with a template of a network of interest, or both. The resulting set of components can then be submitted to a group comparison to test for possible age-related differences in functional connectivity within theoretically interesting networks7,9,10.
Studying age-related changes in functional connectivity through the application of group ICA to task-based fMRI data has several advantages over the application of seed-based techniques to resting-state fMRI data. First, unlike seed-based techniques that focus on a small set of a priori defined ROIs, the current group ICA approach utilizes all voxels comprising a volumetric time series. This diminishes opportunities for bias that necessarily arise when a small group of seeds are selected a priori as regions of interest. Second, applying functional connectivity analysis (ICA-based or otherwise) to task- rather than resting-state fMRI data has the advantage of allowing network organization and network function to be more directly associated. If, for example, examining the cognitive or behavioral implications of functional connectivity (such as variation in DCCS performance) is a priority, it is important to show that the network of interest is associated with task performance. With resting-state protocols, this is very difficult because the researcher has no record of any cognitive, behavioral, or affective states experienced by the participant during data acquisition. It is therefore impossible to provide direct evidence that any network of interest is relevant for task performance. By contrast, when functional connectivity analysis, such as ICA, is applied to task-data, it is possible to confirm that the network of interest is at least associated with the performance of a task. Finally, ICA is less subject to the adverse influence of noise. Noise sources, such as those associated with subject motion and the cardiac rhythm, have unique spatio-temporal profiles. Therefore, in the context of a group ICA, these sources are isolated and assigned to separate components, leaving remaining components relatively free of these unwelcome sources of variance. Because seed-based analyses use raw time courses in the estimation of functional connectivity, and time courses are, by definition, mixtures of neurophysiological signal and artifactual noise, group differences in functional connectivity estimates can reflect true group differences in underlying neurophysiology, group differences in the structure of noise, or both11.

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			<td height="30" style="height:30px;width:185px;">Name of equipment</td>
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a method for investigating age-related differences in the functional connectivity of cognitive control networks associated with dimensional change card sort performance

The ability to adjust behavior to sudden changes in the environment develops gradually in childhood and adolescence. For example, in the Dimensional Change Card Sort task, participants switch from sorting cards one way, such as shape, to sorting them a different way, such as color. Adjusting behavior in this way exacts a small performance cost, or switch cost, such that responses are typically slower and more error-prone on switch trials in which the sorting rule changes as compared to repeat trials in which the sorting rule remains the same. The ability to flexibly adjust behavior is often said to develop gradually, in part because behavioral costs such as switch costs typically decrease with increasing age. Why aspects of higher-order cognition, such as behavioral flexibility, develop so gradually remains an open question. One hypothesis is that these changes occur in association with functional changes in broad-scale cognitive control networks. On this view, complex mental operations, such as switching, involve rapid interactions between several distributed brain regions, including those that update and maintain task rules, re-orient attention, and select behaviors. With development, functional connections between these regions strengthen, leading to faster and more efficient switching operations. The current video describes a method of testing this hypothesis through the collection and multivariate analysis of fMRI data from participants of different ages.

Group ICA, even on a relatively small fMRI data set, will return a set of components comparable to those observed in other studies. Figure 4&#160;is a superimposition of 5 such components and their associated time courses unmixed from a sample of 12 children and 13 adults, with approximately 800 volumes per participant. As shown in Figure 4, default mode, fronto-parietal, cingulo-insular, and visual networks can readily be seen from the results of this decomposition. As well, notice how ...

Watch this Scientific Journal Video about A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort P

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

This video presents a method of examining age-related changes in functional connectivity of cognitive control networks engaged by targeted tasks/processes. The technique is based on multi-variate analysis of fMRI data.

The ability to adjust behavior to sudden changes in the environment develops gradually in childhood and adolescence. For example, in the Dimensional ...

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