DynamicConnectivity.bib

@article{AnderssonJ.SmithS.andJenkinson2008,
author = {{Andersson, J., Smith, S., and Jenkinson}, M.},
journal = {Human Brain Mapping},
title = {{Fnirt-fmrib's non-linear image registration tool.}},
year = {2008}
}
@article{Bassett2011,
abstract = {Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes--flexibility and selection--must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we investigate the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.},
annote = {NULL},
author = {Bassett, Danielle S and Wymbs, Nicholas F and Porter, Mason A and Mucha, Peter J and Carlson, Jean M and Grafton, Scott T},
doi = {10.1073/pnas.1018985108},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Bassett et al. - 2011 - Dynamic reconfiguration of human brain networks during learning.pdf:pdf},
issn = {1091-6490},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
month = {may},
number = {18},
pages = {7641--6},
pmid = {21502525},
publisher = {National Academy of Sciences},
title = {{Dynamic reconfiguration of human brain networks during learning.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21502525 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3088578},
volume = {108},
year = {2011}
}
@article{Bassett2013,
abstract = {As a person learns a new skill, distinct synapses, brain regions, and circuits are engaged and change over time. In this paper, we develop methods to examine patterns of correlated activity across a large set of brain regions. Our goal is to identify properties that enable robust learning of a motor skill. We measure brain activity during motor sequencing and characterize network properties based on coherent activity between brain regions. Using recently developed algorithms to detect time-evolving communities, we find that the complex reconfiguration patterns of the brain's putative functional modules that control learning can be described parsimoniously by the combined presence of a relatively stiff temporal core that is composed primarily of sensorimotor and visual regions whose connectivity changes little in time and a flexible temporal periphery that is composed primarily of multimodal association regions whose connectivity changes frequently. The separation between temporal core and periphery changes over the course of training and, importantly, is a good predictor of individual differences in learning success. The core of dynamically stiff regions exhibits dense connectivity, which is consistent with notions of core-periphery organization established previously in social networks. Our results demonstrate that core-periphery organization provides an insightful way to understand how putative functional modules are linked. This, in turn, enables the prediction of fundamental human capacities, including the production of complex goal-directed behavior.},
author = {Bassett, Danielle S. and Wymbs, Nicholas F. and Rombach, M. Puck and Porter, Mason A. and Mucha, Peter J. and Grafton, Scott T.},
doi = {10.1371/journal.pcbi.1003171},
editor = {Sporns, Olaf},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Bassett et al. - 2013 - Task-Based Core-Periphery Organization of Human Brain Dynamics.pdf:pdf},
issn = {1553-7358},
journal = {PLoS Computational Biology},
month = {sep},
number = {9},
pages = {e1003171},
publisher = {Public Library of Science},
title = {{Task-Based Core-Periphery Organization of Human Brain Dynamics}},
url = {http://dx.plos.org/10.1371/journal.pcbi.1003171},
volume = {9},
year = {2013}
}
@article{Bates,
abstract = {Maximum likelihood or restricted maximum likelihood (REML) estimates of the pa-rameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed-and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We de-scribe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer.},
author = {Bates, Douglas and M{\"{a}}chler, Martin and Bolker, Benjamin M and Walker, Steven C},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Bates et al. - Unknown - Fitting linear mixed-effects models using lme4.pdf:pdf},
journal = {Journal of Statistical Software 67.},
keywords = {Bates2015,Cholesky decomposition,linear mixed models,penalized least squares,sparse matrix methods},
mendeley-tags = {Bates2015},
title = {{Fitting linear mixed-effects models using lme4}},
url = {https://arxiv.org/pdf/1406.5823.pdf},
year = {2015}
}
@article{Baum2017a,
abstract = {The human brain is organized into large-scale functional modules that have been shown to evolve in childhood and adolescence. However, it remains unknown whether the underlying white matter architecture is similarly refined during development, potentially allowing for improvements in executive function. In a sample of 882 participants (ages 8-22) who underwent diffusion imaging as part of the Philadelphia Neurodevelopmental Cohort, we demonstrate that structural network modules become more segregated with age, with weaker connections between modules and stronger connections within modules. Evolving modular topology facilitates global network efficiency and is driven by age-related strengthening of hub edges present both within and between modules. Critically, both modular segregation and network efficiency are associated with enhanced executive performance and mediate the improvement of executive functioning with age. Together, results delineate a process of structural network maturation that supports executive function in youth.},
author = {Baum, Graham L and Ciric, Rastko and Roalf, David R and Betzel, Richard F and Moore, Tyler M and Shinohara, Russell T and Kahn, Ari E and Vandekar, Simon N and Rupert, Petra E and Quarmley, Megan and Cook, Philip A and Elliott, Mark A and Ruparel, Kosha and Gur, Raquel E and Gur, Ruben C and Bassett, Danielle S and Satterthwaite, Theodore D},
doi = {10.1016/j.cub.2017.04.051},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Baum et al. - 2017 - Modular Segregation of Structural Brain Networks Supports the Development of Executive Function in Youth.pdf:pdf},
issn = {1879-0445},
journal = {Current biology : CB},
keywords = {DTI,MRI,adolescence,brain,connectome,development,executive,module,network,tractography},
month = {jun},
number = {11},
pages = {1561--1572.e8},
pmid = {28552358},
publisher = {Elsevier},
title = {{Modular Segregation of Structural Brain Networks Supports the Development of Executive Function in Youth.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/28552358 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC5491213},
volume = {27},
year = {2017}
}
@article{Baum2017,
author = {Baum, Graham L. and Ciric, Rastko and Roalf, David R. and Betzel, Richard F. and Moore, Tyler M. and Shinohara, Russell T. and Kahn, Ari E. and Vandekar, Simon N. and Rupert, Petra E. and Quarmley, Megan and Cook, Philip A. and Elliott, Mark A. and Ruparel, Kosha and Gur, Raquel E. and Gur, Ruben C. and Bassett, Danielle S. and Satterthwaite, Theodore D.},
doi = {10.1016/j.cub.2017.04.051},
issn = {09609822},
journal = {Current Biology},
month = {jun},
number = {11},
pages = {1561--1572.e8},
title = {{Modular Segregation of Structural Brain Networks Supports the Development of Executive Function in Youth}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0960982217304967},
volume = {27},
year = {2017}
}
@article{Casey2008,
abstract = {Adolescence is a developmental period characterized by suboptimal decisions and actions that are associated with an increased incidence of unintentional injuries, violence, substance abuse, unintended pregnancy, and sexually transmitted diseases. Traditional neurobiological and cognitive explanations for adolescent behavior have failed to account for the nonlinear changes in behavior observed during adolescence, relative to both childhood and adulthood. This review provides a biologically plausible model of the neural mechanisms underlying these nonlinear changes in behavior. We provide evidence from recent human brain imaging and animal studies that there is a heightened responsiveness to incentives and socioemotional contexts during this time, when impulse control is still relatively immature. These findings suggest differential development of bottom-up limbic systems, implicated in incentive and emotional processing, to top-down control systems during adolescence as compared to childhood and adulthood. This developmental pattern may be exacerbated in those adolescents prone to emotional reactivity, increasing the likelihood of poor outcomes.},
author = {Casey, B J and Jones, Rebecca M and Hare, Todd A},
doi = {10.1196/annals.1440.010},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Casey, Jones, Hare - 2008 - The adolescent brain.pdf:pdf},
issn = {0077-8923},
journal = {Annals of the New York Academy of Sciences},
month = {mar},
pages = {111--26},
pmid = {18400927},
publisher = {NIH Public Access},
title = {{The adolescent brain.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18400927 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC2475802},
volume = {1124},
year = {2008}
}
@article{Chai2017a,
abstract = {Cognitive function evolves significantly over development, enabling flexible control of human behavior. Yet, how these functions are instantiated in spatially distributed and dynamically interacting networks, or graphs, that change in structure from childhood to adolescence is far from understood. Here we applied a novel machine-learning method to track continuously overlapping and time-varying subgraphs in the brain at rest within a sample of 200 healthy youth (ages 8–11 and 19–22) drawn from the Philadelphia Neurodevelopmental Cohort. We uncovered a set of subgraphs that capture surprisingly integrated and dynamically changing interactions among known cognitive systems. We observed that subgraphs that were highly expressed were especially transient, flexibly switching between high and low expression over time. This transience was particularly salient in a subgraph predominantly linking frontoparietal regions of the executive system, which increases in both expression and flexibility from childhood to yo...},
author = {Chai, Lucy R. and Khambhati, Ankit N. and Ciric, Rastko and Moore, Tyler M. and Gur, Ruben C. and Gur, Raquel E. and Satterthwaite, Theodore D. and Bassett, Danielle S.},
doi = {10.1162/NETN_a_00001},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Chai et al. - 2017 - Evolution of brain network dynamics in neurodevelopment.pdf:pdf},
issn = {2472-1751},
journal = {Network Neuroscience},
keywords = {Energy,Entropy,Executive function,Flexibility,Matrix factorization,Neurodevelopment,Subgraph},
month = {feb},
number = {1},
pages = {14--30},
publisher = { MIT Press One Rogers Street, Cambridge, MA 02142-1209USAjournals-info@mit.edu },
title = {{Evolution of brain network dynamics in neurodevelopment}},
url = {http://www.mitpressjournals.org/doi/10.1162/NETN{\_}a{\_}00001},
volume = {1},
year = {2017}
}
@article{Chai2017,
abstract = {Cognitive function evolves significantly over development, enabling flexible control of human behavior. Yet, how these functions are instantiated in spatially distributed and dynamically interacting networks, or graphs, that change in structure from childhood to adolescence is far from understood. Here we applied a novel machine-learning method to track continuously overlapping and time-varying subgraphs in the brain at rest within a sample of 200 healthy youth (ages 8–11 and 19–22) drawn from the Philadelphia Neurodevelopmental Cohort. We uncovered a set of subgraphs that capture surprisingly integrated and dynamically changing interactions among known cognitive systems. We observed that subgraphs that were highly expressed were especially transient, flexibly switching between high and low expression over time. This transience was particularly salient in a subgraph predominantly linking frontoparietal regions of the executive system, which increases in both expression and flexibility from childhood to yo...},
author = {Chai, Lucy R. and Khambhati, Ankit N. and Ciric, Rastko and Moore, Tyler M. and Gur, Ruben C. and Gur, Raquel E. and Satterthwaite, Theodore D. and Bassett, Danielle S.},
doi = {10.1162/NETN_a_00001},
issn = {2472-1751},
journal = {Network Neuroscience},
keywords = {Energy,Entropy,Executive function,Flexibility,Matrix factorization,Neurodevelopment,Subgraph},
month = {feb},
number = {1},
pages = {14--30},
publisher = { MIT Press One Rogers Street, Cambridge, MA 02142-1209USAjournals-info@mit.edu },
title = {{Evolution of brain network dynamics in neurodevelopment}},
url = {http://www.mitpressjournals.org/doi/10.1162/NETN{\_}a{\_}00001},
volume = {1},
year = {2017}
}
@article{Cohen2010,
abstract = {Previous work has shown that human adolescents may be hypersensitive to rewards, but it is not known which aspect of reward processing is responsible for this. We separated decision value and prediction error signals and found that neural prediction error signals in the striatum peaked in adolescence, whereas neural decision value signals varied depending on how value was modeled. This suggests that heightened dopaminergic prediction error responsivity contributes to adolescent reward seeking.},
author = {Cohen, Jessica R and Asarnow, Robert F and Sabb, Fred W and Bilder, Robert M and Bookheimer, Susan Y and Knowlton, Barbara J and Poldrack, Russell A},
doi = {10.1038/nn.2558},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Cohen et al. - 2010 - A unique adolescent response to reward prediction errors.pdf:pdf},
issn = {1546-1726},
journal = {Nature neuroscience},
month = {jun},
number = {6},
pages = {669--71},
pmid = {20473290},
publisher = {NIH Public Access},
title = {{A unique adolescent response to reward prediction errors.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/20473290 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC2876211},
volume = {13},
year = {2010}
}
@article{DamienA.FairNicoU.F.DosenbachJessicaA.ChurchAlexanderL.CohenShefaliBrahmbhattFrancisM.MiezinDeannaM.BarchMarcusE.RaichleStevenE.Petersen2007,
abstract = {Human attentional control is unrivaled. We recently proposed that adults depend on distinct frontoparietal and cinguloopercular networks for adaptive online task control versus more stable set control, respectively. During development, both experience-dependent evoked activity and spontaneous waves of synchronized cortical activity are thought to support the formation and maintenance of neural networks. Such mechanisms may encourage tighter “integration” of some regions into networks over time while “segregating” other sets of regions into separate networks. Here we use resting state functional connectivity MRI, which measures correlations in spontaneous blood oxygenation level-dependent signal fluctuations between brain regions to compare previously identified control networks between children and adults. We find that development of the proposed adult control networks involves both segregation (i.e., decreased short-range connections) and integration (i.e., increased long-range connections) of the brain regions that comprise them. Delay/disruption in the developmental processes of segregation and integration may play a role in disorders of control, such as autism, attention deficit hyperactivity disorder, and Tourette's syndrome.},
author = {{Damien, A. Fair, Nico, U. F. Dosenbach, Jessica, A. Church, Alexander, L. Cohen, Shefali, Brahmbhatt, Francis, M. Miezin, Deanna, M Barch, Marcus, E. Raichle, Steven, E. Petersen, Bradley}, L. Schlaggar},
journal = {PNAS},
keywords = {attentionconnectivityfunctional MRIspontaneous act},
number = {33},
pages = {13507--13512},
title = {{Development of distinct control networks through segregation and integration}},
url = {http://www.pnas.org/content/104/33/13507.full},
volume = {104},
year = {2007}
}
@article{Davidow2016,
abstract = {Adolescents are notorious for engaging in reward-seeking behaviors, a tendency attributed to heightened activity in the brain's reward systems during adolescence. It has been suggested that reward sensitivity in adolescence might be adaptive, but evidence of an adaptive role has been scarce. Using a probabilistic reinforcement learning task combined with reinforcement learning models and fMRI, we found that adolescents showed better reinforcement learning and a stronger link between reinforcement learning and episodic memory for rewarding outcomes. This behavioral benefit was related to heightened prediction error-related BOLD activity in the hippocampus and to stronger functional connectivity between the hippocampus and the striatum at the time of reinforcement. These findings reveal an important role for the hippocampus in reinforcement learning in adolescence and suggest that reward sensitivity in adolescence is related to adaptive differences in how adolescents learn from experience.},
author = {Davidow, Juliet Y. and Foerde, Karin and Galvan, Adriana and Shohamy, Daphna},
doi = {10.1016/j.neuron.2016.08.031},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Davidow et al. - 2016 - An Upside to Reward Sensitivity The Hippocampus Supports Enhanced Reinforcement Learning in Adolescence(2).pdf:pdf},
isbn = {1097-4199 (Electronic) 0896-6273 (Linking)},
issn = {10974199},
journal = {Neuron},
pmid = {27710793},
title = {{An Upside to Reward Sensitivity: The Hippocampus Supports Enhanced Reinforcement Learning in Adolescence}},
year = {2016}
}
@article{Daw2011,
abstract = {Researchers have recently begun to integrate computational models into the analysis of neural and behavioural data, particularly in experiments on reward learning and decision making. This chapter aims to review and rationalize these methods. It exposes these tools as instances of broadly applicable statistical techniques, considers the questions they are suited to answer, provides a practical tutorial and tips for their effective use, and, finally, suggests some directions for extension or improvement. The techniques are illustrated with fits of simple models to simulated datasets. Throughout, the chapter flags interpretational and technical pitfalls of which authors, reviewers, and readers should be aware.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Daw, Nathaniel D.},
doi = {10.1093/acprof:oso/9780199600434.003.0001},
eprint = {arXiv:1011.1669v3},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Daw - 2009 - Trial-by-trial data analysis using computational models.pdf:pdf},
isbn = {9780191725623},
issn = {0199600430},
journal = {Decision Making, Affect, and Learning: Attention and Performance XXIII},
keywords = {Computational models,Data analysis,Decision making,Neural data,Reward learning,Statistical methods},
pages = {3--38},
pmid = {16683413},
title = {{Trial-by-trial data analysis using computational models}},
url = {https://pdfs.semanticscholar.org/43c3/d7653710bbb477df108fc2ed2729429d053c.pdf},
volume = {23},
year = {2011}
}
@article{Dosenbach2007,
abstract = {Control regions in the brain are thought to provide signals that configure the brain's moment-to-moment information processing. Previously, we identified regions that carried signals related to task-control initiation, maintenance, and adjustment. Here we characterize the interactions of these regions by applying graph theory to resting state functional connectivity MRI data. In contrast to previous, more unitary models of control, this approach suggests the presence of two distinct task-control networks. A frontoparietal network included the dorsolateral prefrontal cortex and intraparietal sulcus. This network emphasized start-cue and error-related activity and may initiate and adapt control on a trial-by-trial basis. The second network included dorsal anterior cingulate/medial superior frontal cortex, anterior insula/frontal operculum, and anterior prefrontal cortex. Among other signals, these regions showed activity sustained across the entire task epoch, suggesting that this network may control goal-directed behavior through the stable maintenance of task sets. These two independent networks appear to operate on different time scales and affect downstream processing via dissociable mechanisms.},
author = {Dosenbach, Nico U F and Fair, Damien A and Miezin, Francis M and Cohen, Alexander L and Wenger, Kristin K and Dosenbach, Ronny A T and Fox, Michael D and Snyder, Abraham Z and Vincent, Justin L and Raichle, Marcus E and Schlaggar, Bradley L and Petersen, Steven E},
doi = {10.1073/pnas.0704320104},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Dosenbach et al. - 2007 - Distinct brain networks for adaptive and stable task control in humans.pdf:pdf},
issn = {1091-6490},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
month = {jun},
number = {26},
pages = {11073--8},
pmid = {17576922},
publisher = {National Academy of Sciences},
title = {{Distinct brain networks for adaptive and stable task control in humans.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17576922 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC1904171},
volume = {104},
year = {2007}
}
@article{Fair2009,
abstract = {The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward ‘segregation' (a general decrease in correlation strength) between regions close in anatomical space and ‘integration' (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more “distributed” architecture in young adults. We argue that this “local to distributed” developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing “small-world”-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways.},
author = {Fair, Damien A. and Cohen, Alexander L. and Power, Jonathan D. and Dosenbach, Nico U. F. and Church, Jessica A. and Miezin, Francis M. and Schlaggar, Bradley L. and Petersen, Steven E.},
doi = {10.1371/journal.pcbi.1000381},
editor = {Sporns, Olaf},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Fair et al. - 2009 - Functional Brain Networks Develop from a “Local to Distributed” Organization(2).pdf:pdf},
issn = {1553-7358},
journal = {PLoS Computational Biology},
month = {may},
number = {5},
pages = {e1000381},
publisher = {Public Library of Science},
title = {{Functional Brain Networks Develop from a “Local to Distributed” Organization}},
url = {http://dx.plos.org/10.1371/journal.pcbi.1000381},
volume = {5},
year = {2009}
}
@article{Gerraty2018,
abstract = {15 16 17 Summary 18 Complex learned behaviors involve the integrated action of distributed brain 19 circuits. While the contributions of individual regions to learning have been 20 extensively investigated, understanding how distributed brain networks 21 orchestrate their activity over the course of learning remains elusive. To 22 address this gap, we used fMRI combined with tools from dynamic network 23 neuroscience to obtain time-­‐resolved descriptions of network coordination 24 during reinforcement learning. We found that reinforcement learning 25 involves dynamic changes in network coupling between the striatum and 26 distributed brain networks. Moreover, we found that the degree of flexibility 27 in whole-­‐brain circuit dynamics correlates with participants' learning rate, as 28 derived from reinforcement learning models. Finally, we found that episodic 29 memory, measured in the same participants at the same time, was related to 30 dynamic connectivity in distinct brain networks. These results support the 31 idea that dynamic changes in network communication provide a mechanism 32 for information integration during reinforcement learning.},
author = {Gerraty, Raphael T and Davidow, Juliet Y and Foerde, Karin and Galvan, Adriana and Bassett, Danielle S and Shohamy, Daphna},
doi = {10.1101/094383},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Gerraty et al. - Unknown - Dynamic flexibility in striatal-­‐cortical circuits supports reinforcement 1 learning 2 3.pdf:pdf},
title = {{Dynamic flexibility in striatal-­‐cortical circuits supports reinforcement learning}},
url = {http://dx.doi.org/10.1101/094383},
year = {2018}
}
@article{Gu2015,
abstract = {Adult human cognition is supported by systems of brain regions, or modules, that are functionally coherent at rest and collectively activated by distinct task requirements. However, an understanding of how the formation of these modules supports evolving cognitive capabilities has not been delineated. Here, we quantify the formation of network modules in a sample of 780 youth (aged 8-22 y) who were studied as part of the Philadelphia Neurodevelopmental Cohort. We demonstrate that the brain's functional network organization changes in youth through a process of modular evolution that is governed by the specific cognitive roles of each system, as defined by the balance of within- vs. between-module connectivity. Moreover, individual variability in these roles is correlated with cognitive performance. Collectively, these results suggest that dynamic maturation of network modules in youth may be a critical driver for the development of cognition.},
author = {Gu, Shi and Satterthwaite, Theodore D and Medaglia, John D and Yang, Muzhi and Gur, Raquel E and Gur, Ruben C and Bassett, Danielle S},
doi = {10.1073/pnas.1502829112},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Gu et al. - 2015 - Emergence of system roles in normative neurodevelopment.pdf:pdf},
issn = {1091-6490},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
keywords = {brain network,graph theory,modularity,network science,neurodevelopment},
month = {nov},
number = {44},
pages = {13681--6},
pmid = {26483477},
publisher = {National Academy of Sciences},
title = {{Emergence of system roles in normative neurodevelopment.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/26483477 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4640772},
volume = {112},
year = {2015}
}
@article{Ishii2002,
abstract = {In reinforcement learning, the duality between exploitation and exploration has long been an important issue. This paper presents a new method that controls the balance between exploitation and exploration. Our learning scheme is based on model-based re-inforcement learning, in which the Bayes inference with forgetting effect estimates the state-transition probability of the environment. The balance parameter, which corre-sponds to the randomness in action selection, is controlled based on variation of action results and perception of environmental change. When applied to maze tasks, our method successfully obtains good controls by adapting to environmental changes. Recently, Usher et al. [60] has suggested that noradrenergic neurons in the locus coeruleus may control the exploitation-exploration balance in a real brain and that the balance may correspond to the level of animal's selective attention. According to this scenario, we also discuss a possible implementation in the brain.},
author = {Ishii, Shin and Yoshida, Wako and Yoshimoto, Junichiro},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Ishii, Yoshida, Yoshimoto - 2002 - Exploitation-exploration control Control of exploitation-exploration meta-parameter in reinforcement.pdf:pdf},
number = {4},
pages = {665--687},
title = {{Exploitation-exploration control Control of exploitation-exploration meta-parameter in reinforcement learning}},
url = {https://pdfs.semanticscholar.org/1c5e/37e86f2a45d85a5ed96483fe08369e5b7d9e.pdf},
volume = {15},
year = {2002}
}
@article{JutlaI.S.JeubL.G.andMucha2011,
author = {{Jutla, I.S., Jeub, L.G., and Mucha}, P.J.},
keywords = {Jutla2011},
mendeley-tags = {Jutla2011},
title = {{A generalized Louvain method for community detection implemented in MATLAB}},
url = {http://netwiki.amath.unc.edu/GenLouvain},
year = {2011}
}
@article{Masel2010,
abstract = {Why isn't random variation always deleterious? Are there factors that sometimes make adaptation easier? Biological systems are extraordinarily robust to perturbation by mutations, recombination and the environment. It has been proposed that this robustness might make them more evolvable. Robustness to mutation allows genetic variation to accumulate in a cryptic state. Switching mechanisms known as evolutionary capacitors mean that the amount of heritable phenotypic variation available can be correlated to the degree of stress and hence to the novelty of the environment and remaining potential for adaptation. There have been two somewhat separate literatures relating robustness to evolvability. One has focused on molecular phenotypes and new mutations, the other on morphology and cryptic genetic variation. Here, we review both literatures, and show that the true distinction is whether recombination rates are high or low. In both cases, the evidence supports the claim that robustness promotes evolvability.},
author = {Masel, Joanna and Trotter, Meredith V},
doi = {10.1016/j.tig.2010.06.002},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Masel, Trotter - 2010 - Robustness and evolvability.pdf:pdf},
issn = {0168-9525},
journal = {Trends in genetics : TIG},
month = {sep},
number = {9},
pages = {406--14},
pmid = {20598394},
publisher = {NIH Public Access},
title = {{Robustness and evolvability.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/20598394 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3198833},
volume = {26},
year = {2010}
}
@article{Mattar2015,
abstract = {One of the most remarkable features of the human brain is its ability to adapt rapidly and efficiently to external task demands. Novel and non-routine tasks, for example, are implemented faster than structural connections can be formed. The neural underpinnings of these dynamics are far from understood. Here we develop and apply novel methods in network science to quantify how patterns of functional connectivity between brain regions reconfigure as human subjects perform 64 different tasks. By applying dynamic community detection algorithms, we identify groups of brain regions that form putative functional communities, and we uncover changes in these groups across the 64-task battery. We summarize these reconfiguration patterns by quantifying the probability that two brain regions engage in the same network community (or putative functional module) across tasks. These tools enable us to demonstrate that classically defined cognitive systems—including visual, sensorimotor, auditory, default mode, fronto-parietal, cingulo-opercular and salience systems—engage dynamically in cohesive network communities across tasks. We define the network role that a cognitive system plays in these dynamics along the following two dimensions: (i) stability vs. flexibility and (ii) connected vs. isolated. The role of each system is therefore summarized by how stably that system is recruited over the 64 tasks, and how consistently that system interacts with other systems. Using this cartography, classically defined cognitive systems can be categorized as ephemeral integrators, stable loners, and anything in between. Our results provide a new conceptual framework for understanding the dynamic integration and recruitment of cognitive systems in enabling behavioral adaptability across both task and rest conditions. This work has important implications for understanding cognitive network reconfiguration during different task sets and its relationship to cognitive effort, individual variation in cognitive performance, and fatigue.},
author = {Mattar, Marcelo G. and Cole, Michael W. and Thompson-Schill, Sharon L. and Bassett, Danielle S.},
doi = {10.1371/journal.pcbi.1004533},
editor = {Honey, Christopher J},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Mattar et al. - 2015 - A Functional Cartography of Cognitive Systems.pdf:pdf},
issn = {1553-7358},
journal = {PLOS Computational Biology},
month = {dec},
number = {12},
pages = {e1004533},
publisher = {Public Library of Science},
title = {{A Functional Cartography of Cognitive Systems}},
url = {http://dx.plos.org/10.1371/journal.pcbi.1004533},
volume = {11},
year = {2015}
}
@article{Medaglia2018,
abstract = {Adolescence is marked by rapid development of executive function. Mounting evidence suggests that executive function in adults may be driven by dynamic control of neurophysiological processes. Yet, how these dynamics evolve over adolescence and contribute to cognitive development is unknown. In a sample of 780 youth aged 8–22 yr (42.7{\%} male) from the Philadelphia Neurodevelopment Cohort, we use a dynamic graph approach to extract activation states in BOLD fMRI data from 264 brain regions. We construct a graph in which each observation in time is a node and the similarity in brain states at two different times is an edge. Using this graphical approach, we identify two primary brain states reminiscent of intrinsic and task-evoked systems. We show that time spent in these two states is higher in older adolescents, as is the flexibility with which the brain switches between them. Increasing time spent in primary states and flexibility among states relates to increases in a complex executive accuracy factor score over adolescence. Flexibility is more positively associated with accuracy toward early adulthood. These findings suggest that brain state dynamics are associated with complex executive function across a critical period of adolescence.},
author = {Medaglia, John D. and Satterthwaite, Theodore D. and Kelkar, Apoorva and Ciric, Rastko and Moore, Tyler M. and Ruparel, Kosha and Gur, Ruben C. and Gur, Raquel E. and Bassett, Danielle S.},
doi = {10.1016/J.NEUROIMAGE.2017.10.048},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Medaglia et al. - 2018 - Brain state expression and transitions are related to complex executive cognition in normative neurodevelopment.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
month = {feb},
pages = {293--306},
publisher = {Academic Press},
title = {{Brain state expression and transitions are related to complex executive cognition in normative neurodevelopment}},
url = {https://www.sciencedirect.com/science/article/pii/S1053811917308753},
volume = {166},
year = {2018}
}
@article{Mucha2010,
abstract = {Network science is an interdisciplinary endeavor, with methods and appli-cations drawn from across the natural, social, and information sciences. A prominent problem in network science is the algorithmic detection of tightly-connected groups of nodes known as communities. We developed a general-ized framework of network quality functions that allowed us to study the com-munity structure of arbitrary multislice networks, which are combinations of individual networks coupled through links that connect each node in one net-work slice to itself in other slices. This framework allows one to study},
archivePrefix = {arXiv},
arxivId = {arXiv:0911.1824v3},
author = {Mucha, Peter J and Richardson, Thomas and Macon, Kevin and Porter, Mason A and Onnela, Jukka-Pekka},
eprint = {arXiv:0911.1824v3},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Mucha et al. - 2010 - Community Structure in Time-Dependent, Multiscale, and Multiplex Networks(2).pdf:pdf},
title = {{Community Structure in Time-Dependent, Multiscale, and Multiplex Networks}},
url = {https://arxiv.org/pdf/0911.1824.pdf},
year = {2010}
}
@article{Pagnoni2002,
abstract = {Activity in human ventral striatum locked to errors of reward prediction},
author = {Pagnoni, Giuseppe and Zink, Caroline F. and Montague, P. Read and Berns, Gregory S.},
doi = {10.1038/nn802},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Pagnoni et al. - 2002 - Activity in human ventral striatum locked to errors of reward prediction.pdf:pdf},
issn = {10976256},
journal = {Nature Neuroscience},
month = {feb},
number = {2},
pages = {97--98},
publisher = {Nature Publishing Group},
title = {{Activity in human ventral striatum locked to errors of reward prediction}},
url = {http://www.nature.com/doifinder/10.1038/nn802},
volume = {5},
year = {2002}
}
@article{Power2012,
abstract = {Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements.},
author = {Power, Jonathan D. and Barnes, Kelly A. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
doi = {10.1016/J.NEUROIMAGE.2011.10.018},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Power et al. - 2012 - Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
month = {feb},
number = {3},
pages = {2142--2154},
publisher = {Academic Press},
title = {{Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion}},
url = {https://www.sciencedirect.com/science/article/pii/S1053811911011815},
volume = {59},
year = {2012}
}
@article{Satterthwaite2013,
abstract = {Several independent studies have demonstrated that small amounts of in-scanner motion systematically bias estimates of resting-state functional connectivity. This confound is of particular importance for studies of neurodevelopment in youth because motion is strongly related to subject age during this period. Critically, the effects of motion on connectivity mimic major findings in neurodevelopmental research, specifically an age-related strengthening of distant connections and weakening of short-range connections. Here, in a sample of 780 subjects ages 8-22, we re-evaluate patterns of change in functional connectivity during adolescent development after rigorously controlling for the confounding influences of motion at both the subject and group levels. We find that motion artifact inflates both overall estimates of age-related change as well as specific distance-related changes in connectivity. When motion is more fully accounted for, the prevalence of age-related change as well as the strength of distance-related effects is substantially reduced. However, age-related changes remain highly significant. In contrast, motion artifact tends to obscure age-related changes in connectivity associated with segregation of functional brain modules; improved preprocessing techniques allow greater sensitivity to detect increased within-module connectivity occurring with development. Finally, we show that subject's age can still be accurately estimated from the multivariate pattern of functional connectivity even while controlling for motion. Taken together, these results indicate that while motion artifact has a marked and heterogeneous impact on estimates of connectivity change during adolescence, functional connectivity remains a valuable phenotype for the study of neurodevelopment.},
author = {Satterthwaite, Theodore D and Wolf, Daniel H and Ruparel, Kosha and Erus, Guray and Elliott, Mark A and Eickhoff, Simon B and Gennatas, Efstathios D and Jackson, Chad and Prabhakaran, Karthik and Smith, Alex and Hakonarson, Hakon and Verma, Ragini and Davatzikos, Christos and Gur, Raquel E and Gur, Ruben C},
doi = {10.1016/j.neuroimage.2013.06.045},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Satterthwaite et al. - 2013 - Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Adolescence,Connectivity,Connectome,Development,Motion artifact,Network,Resting-state,fMRI},
month = {dec},
pages = {45--57},
pmid = {23792981},
publisher = {NIH Public Access},
title = {{Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity during youth.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23792981 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3874413},
volume = {83},
year = {2013}
}
@article{Satterthwaite2013a,
abstract = {Several recent reports in large, independent samples have demonstrated the influence of motion artifact on resting-state functional connectivity MRI (rsfc-MRI). Standard rsfc-MRI preprocessing typically includes regression of confounding signals and band-pass filtering. However, substantial heterogeneity exists in how these techniques are implemented across studies, and no prior study has examined the effect of differing approaches for the control of motion-induced artifacts. To better understand how in-scanner head motion affects rsfc-MRI data, we describe the spatial, temporal, and spectral characteristics of motion artifacts in a sample of 348 adolescents. Analyses utilize a novel approach for describing head motion on a voxelwise basis. Next, we systematically evaluate the efficacy of a range of confound regression and filtering techniques for the control of motion-induced artifacts. Results reveal that the effectiveness of preprocessing procedures on the control of motion is heterogeneous, and that improved preprocessing provides a substantial benefit beyond typical procedures. These results demonstrate that the effect of motion on rsfc-MRI can be substantially attenuated through improved preprocessing procedures, but not completely removed.},
author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
doi = {10.1016/J.NEUROIMAGE.2012.08.052},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Satterthwaite et al. - 2013 - An improved framework for confound regression and filtering for control of motion artifact in the prepr(2).pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Satterthwaite2012a},
mendeley-tags = {Satterthwaite2012a},
month = {jan},
pages = {240--256},
publisher = {Academic Press},
title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
url = {https://www.sciencedirect.com/science/article/pii/S1053811912008609},
volume = {64},
year = {2012}
}
@article{Satterthwaite2012,
abstract = {It has recently been reported (Van Dijk et al., 2011) that in-scanner head motion can have a substantial impact on MRI measurements of resting-state functional connectivity. This finding may be of particular relevance for studies of neurodevelopment in youth, confounding analyses to the extent that motion and subject age are related. Furthermore, while Van Dijk et al. demonstrated the effect of motion on seed-based connectivity analyses, it is not known how motion impacts other common measures of connectivity. Here we expand on the findings of Van Dijk et al. by examining the effect of motion on multiple types of resting-state connectivity analyses in a large sample of children and adolescents (n=456). Following replication of the effect of motion on seed-based analyses, we examine the influence of motion on graphical measures of network modularity, dual-regression of independent component analysis, as well as the amplitude and fractional amplitude of low frequency fluctuation. In the entire sample, subject age was highly related to motion. Using a subsample where age and motion were unrelated, we demonstrate that motion has marked effects on connectivity in every analysis examined. While subject age was associated with increased within-network connectivity even when motion was accounted for, controlling for motion substantially attenuated the strength of this relationship. The results demonstrate the pervasive influence of motion on multiple types functional connectivity analysis, and underline the importance of accounting for motion in studies of neurodevelopment.},
author = {Satterthwaite, Theodore D. and Wolf, Daniel H. and Loughead, James and Ruparel, Kosha and Elliott, Mark A. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E.},
doi = {10.1016/J.NEUROIMAGE.2011.12.063},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Satterthwaite et al. - 2012 - Impact of in-scanner head motion on multiple measures of functional connectivity Relevance for studies of.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
keywords = {Satterthwaite2012b},
mendeley-tags = {Satterthwaite2012b},
month = {mar},
number = {1},
pages = {623--632},
publisher = {Academic Press},
title = {{Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth}},
url = {https://www.sciencedirect.com/science/article/pii/S1053811911014650},
volume = {60},
year = {2012}
}
@article{Smith2004,
abstract = {The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important research area in its own right. In this paper, we present a review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). This research has focussed on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data. The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL).},
author = {Smith, Stephen M. and Jenkinson, Mark and Woolrich, Mark W. and Beckmann, Christian F. and Behrens, Timothy E.J. and Johansen-Berg, Heidi and Bannister, Peter R. and {De Luca}, Marilena and Drobnjak, Ivana and Flitney, David E. and Niazy, Rami K. and Saunders, James and Vickers, John and Zhang, Yongyue and {De Stefano}, Nicola and Brady, J. Michael and Matthews, Paul M.},
doi = {10.1016/J.NEUROIMAGE.2004.07.051},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Smith et al. - 2004 - Advances in functional and structural MR image analysis and implementation as FSL.pdf:pdf},
issn = {1053-8119},
journal = {NeuroImage},
month = {jan},
pages = {S208--S219},
publisher = {Academic Press},
title = {{Advances in functional and structural MR image analysis and implementation as FSL}},
url = {https://www.sciencedirect.com/science/article/pii/S1053811904003933},
volume = {23},
year = {2004}
}
@article{Sutton,
author = {Sutton, Richard S and Barto, Andrew G},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Sutton, Barto - Unknown - Reinforcement Learning An Introduction.pdf:pdf},
title = {{Reinforcement Learning: An Introduction}},
url = {https://s3.amazonaws.com/academia.edu.documents/38529120/9780262257053{\_}index.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A{\&}Expires=1526057445{\&}Signature=hQ23cDlVaJ8d9BD28r9h7{\%}2FNo23U{\%}3D{\&}response-content-disposition=inline{\%}3B filename{\%}3DReinforcement{\_}Learning{\_}ind}
}
@article{Sutton1981,
abstract = {Many adaptive neural network theories are based on neuronlike adaptive elements that can behave as single unit analogs of associative conditioning. In this article we develop a similar adaptive element, but one which is more closely in accord with the facts of animal learning theory than elements commonly studied in adaptive network research. We suggest that an essential feature of classical conditioning that has been largely overlooked by adaptive network theorists is its predictive nature. The adaptive element we present learns to increase its response rate in anticipation of increased stimulation, producing a conditioned response before the occurrence of the unconditioned stimulus. The element also is in strong agreement with the behavioral data regarding the effects of stimulus context, since it is a temporally refined extension of the Rescorla-Wagner model. We show by computer simulation that the element becomes sensitive to the most reliable, nonredundant, and earliest predictors of reinforcement. We also point out that the model solves many of the stability and saturation problems en-countered in network simulations. Finally, we discuss our model in light of recent advances in the physiology and biochemistry of synaptic mechanisms. One way to bridge the gap between be-havioral and neural views of learning is to postulate neural analogs of behavioral mod-ification paradigms. Hebb's suggestion that when a cell A repeatedly and persistently takes part in firing another cell B, then A's efficiency in firing B is increased, is the most familiar of these postulates (Hebb, 1949). This rule for synaptic plasticity is a neural analog of associative conditioning and con-tinues to exert a powerful influence on the-oretical and experimental research in learn-ing and memory. Neural network models designed to explore the behavioral possibil-ities of modifiable structures typically},
author = {Sutton, Richard S and Barto, Andrew G},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Sutton, Barto - 1981 - Toward a Modern Theory of Adaptive Networks Expectation and Prediction.pdf:pdf},
journal = {Psychological Review},
number = {2},
pages = {135--170},
title = {{Toward a Modern Theory of Adaptive Networks: Expectation and Prediction}},
url = {http://psycnet.apa.org/fulltext/1981-20731-001.pdf},
volume = {88},
year = {1981}
}
@article{Tang2017,
abstract = {As the human brain develops, it increasingly supports coordinated control of neural activity. The mechanism by which white matter evolves to support this coordination is not well understood. Here we use a network representation of diffusion imaging data from 882 youth ages 8–22 to show that white matter connectivity becomes increasingly optimized for a diverse range of predicted dynamics in development. Notably, stable controllers in subcortical areas are negatively related to cognitive performance. Investigating structural mechanisms supporting these changes, we simulate network evolution with a set of growth rules. We find that all brain networks are structured in a manner highly optimized for network control, with distinct control mechanisms predicted in child vs. older youth. We demonstrate that our results cannot be explained by changes in network modularity. This work reveals a possible mechanism of human brain development that preferentially optimizes dynamic network control over static network architecture.},
author = {Tang, Evelyn and Giusti, Chad and Baum, Graham L. and Gu, Shi and Pollock, Eli and Kahn, Ari E. and Roalf, David R. and Moore, Tyler M. and Ruparel, Kosha and Gur, Ruben C. and Gur, Raquel E. and Satterthwaite, Theodore D. and Bassett, Danielle S.},
doi = {10.1038/s41467-017-01254-4},
file = {:Users/chelseaharmon/Library/Application Support/Mendeley Desktop/Downloaded/Tang et al. - 2017 - Developmental increases in white matter network controllability support a growing diversity of brain dynamics(2).pdf:pdf},
issn = {2041-1723},
journal = {Nature Communications},
keywords = {Applied mathematics,Biological physics,Cognitive neuroscience,Network models,Neuronal development},
month = {dec},
number = {1},
pages = {1252},
publisher = {Nature Publishing Group},
title = {{Developmental increases in white matter network controllability support a growing diversity of brain dynamics}},
url = {http://www.nature.com/articles/s41467-017-01254-4},
volume = {8},
year = {2017}
}