The Dynamics of Functional Brain Networks Integrated

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Please cite this article in press as Shine et al The Dynamics of Functional Brain Networks Integrated Network States during Cognitive Task Perfor. mance Neuron 2016 http dx doi org 10 1016 j neuron 2016 09 018. The Dynamics of Functional Brain,Networks Integrated Network States. during Cognitive Task Performance, James M Shine 1 2 5 Patrick G Bissett 1 Peter T Bell 3 Oluwasanmi Koyejo 1 Joshua H Balsters 4. Krzysztof J Gorgolewski 1 Craig A Moodie 1 and Russell A Poldrack1. 1Department of Psychology Stanford University Stanford CA 94305 USA. 2Neuroscience Research Australia University of New South Wales Sydney NSW 2052 Australia. 3University of Queensland Centre for Clinical Research University of Queensland Brisbane QLD 4072 Australia. 4Department of Health Sciences and Technology Neural Control of Movement Laboratory ETH Zurich 8092 Zurich Switzerland. 5Lead Contact,Correspondence macshine stanford edu. http dx doi org 10 1016 j neuron 2016 09 018, SUMMARY provide a sensitive method for non invasively identifying time. sensitive shifts in inter areal synchrony which has been. Higher brain function relies upon the ability to flexibly proposed as a key mechanism for effective communication be. integrate information across specialized commu tween distant neural regions Fries 2015 Varela et al 2001. nities of brain regions however it is unclear how To this end recent experiments using fMRI data have demon. this mechanism manifests over time In this study strated that global brain signals transition between states of. we used time resolved network analysis of fMRI high and low connectivity strength over time Zalesky et al. 2014 and that these fluctuations are related to coordinated pat. data to demonstrate that the human brain tra, terns of network topology Betzel et al 2016 however the psy.
verses between functional states that maximize, chological relevance of these fluctuations in network topology. either segregation into tight knit communities or remains poorly understood. integration across otherwise disparate neural re In the present work we show that dynamic fluctuations. gions Integrated states enable faster and more ac in network structure relate to ongoing cognitive function and. curate performance on a cognitive task and are further demonstrate a relation between these fluctuations. associated with dilations in pupil diameter sug and integration within a network of frontoparietal striatal and. gesting that ascending neuromodulatory systems thalamic regions that track with the ascending neuromodulatory. may govern the transition between these alterna system of the brain as characterized using pupillometry Joshi. tive modes of brain function Together our results et al 2016 Together the results of our experiments provide. confirm a direct link between cognitive performance mechanistic evidence to support the role of global network. integration in effective cognitive performance, and the dynamic reorganization of the network struc. ture of the brain,INTRODUCTION Fluctuations in Network Cartography. To elucidate fluctuations in the network structure of the brain. Within the brain a highly dynamic functional landscape unfolds over time we computed a windowed estimate of functional con. on a relatively fixed structural scaffold Deco et al 2015 Shen nectivity Shine et al 2015 from a cohort of 92 unrelated sub. et al 2015 in which the emergence of momentary neural coali jects obtained from the Human Connectome Project HCP see. tions forms the basis for complex cognitive functions Bassett Experimental Procedures Smith et al 2013 After identifying. et al 2015 Cole et al 2014 learning Bassett et al 2011 the community structure of the brain s functional connectivity. and consciousness Barttfeld et al 2015 Godwin et al 2015 network Rubinov and Sporns 2010 we estimated the impor. This view of brain function highlights the role of individual brain tance of each region for maintaining this evolving network struc. regions within the context of a broader neural network Bullmore ture by calculating its connectivity both within WT and between. and Sporns 2012 Others have noted the importance of time BT each community see Experimental Procedures Guimera. sensitive descriptions of brain activity in understanding the func and Nunes Amaral 2005 Sporns and Betzel 2016 While previ. tional relevance of alterations in this network structure under ous studies have clustered these metrics at the regional level us. different behavioral conditions Varela et al 2001 ing pre defined cartographic boundaries Guimera and Nunes. Time resolved analyses of functional neuroimaging data pro Amaral 2005 Mattar et al 2015 we hypothesized that the brain. vide a unique opportunity to examine these time varying re should fluctuate as a whole between cartographic extremes. configurations in global network structure These experiments that were characterized by either segregation i e the extent. Neuron 92 1 11 October 19 2016 2016 Published by Elsevier Inc 1. NEURON 13348, Please cite this article in press as Shine et al The Dynamics of Functional Brain Networks Integrated Network States during Cognitive Task Perfor. mance Neuron 2016 http dx doi org 10 1016 j neuron 2016 09 018. Figure 1 Dynamic Fluctuations in Cartography, A Upper a representative time series of the mean BT for a single individual from the Discovery cohort HCP 100307 Lower each temporal window was.
partitioned into one of two topological states using k means clustering red segregated blue integrated. B The mean cartographic profile of both the segregated and integrated states HCP Discovery cohort n 92. C Regions with greater WT in the integrated than segregated state. D Regions with greater BT in the integrated than segregated state. to which communication occurs primarily within tight knit com com macshine coupling for a demonstration of the fluctuations. munities of regions or integration i e the degree of communi of the cartographic profile over time. cation between distinct regions Deco et al 2015 which might The two states also showed differential patterns of regional. otherwise be obscured by reduction into classes defined by inter modular connectivity Figures 1C and 2D with the inte. these arbitrary cartographic boundaries grated states characterized by a global increase in inter. To test this hypothesis in the resting state we created a novel modular communication across the brain false discovery rate. analysis technique to assess the temporal classification into two FDR a 0 05 for all 375 individual parcels This was also. states without requiring the grouping of each region into a pre reflected in graph theoretic measures of network wide integra. defined cartographic class Guimera and Nunes Amaral 2005 tion temporal windows associated with segregated states had. which we refer to here as the cartographic profile Subject significantly elevated modularity QS 0 55 0 1 versus QI. level k means clustering of these full profiles across time 0 42 0 2 Cohen s d 0 9 p 10 11 Sporns and Betzel. k 2 with stable clustering at higher values of k see Experi 2016 whereas those associated with the integrated states. mental Procedures Figure S1 available online identified modes had greater global efficiency ES 0 18 0 03 versus EI. of information processing that were characterized by either inte 0 24 0 05 d 1 5 p 10 8 Bullmore and Sporns 2012. gration or segregation Figure 1A The resting brain explored The shift toward integration was most prominent in sensory. a dynamical repertoire within this topological regime greater and attentional networks Figure 1D FDR a 0 05 whereas. than expected by a stationary null model fluctuating aperiodi segregated states were associated with relatively higher partic. cally between the integrated and segregated temporal states ipation within regions in the default mode network suggesting. with the majority of time spent in integrated states 70 32 that the cartographic profile may reflect changes in the engage. 1 4 of rest session all variability measures reported as ment of attention and cognition over time Corbetta and Shul. SDs Although the majority of the group level fluctuations man 2002 Importantly the fluctuations in global network. occurred in inter modular connectivity i e BT values transi topology occurred independently of the mean framewise. tioned between high and low states en masse we also observed displacement in each repetition time TR mean r 0 01. window to window fluctuations in intra modular connectivity 0 01 nuisance signals from cerebrospinal fluid CSF and. WT within individual parcels see Video 1 at https github deep cerebral white matter WM mean r 0 02 0 01. 2 Neuron 92 1 11 October 19 2016,NEURON 13348, Please cite this article in press as Shine et al The Dynamics of Functional Brain Networks Integrated Network States during Cognitive Task Perfor. mance Neuron 2016 http dx doi org 10 1016 j neuron 2016 09 018. Figure 2 Alteration of Cartographic Profile during Task Performance. A Time series plot demonstrating the close temporal relationship between mean BT across 100 subjects thick black line individual subject data plotted in gray. and task block regressors blue line Pearson s correlation between regressor and group mean BT r 0 521. B Regions of the 2D joint histogram that were significantly different between N back task blocks and the resting state paired samples t test Colored points. indicate regions that survived false discovery correction FDR a 0 05 red yellow increased frequency during N back task blocks blue light blue increased. frequency during resting state FDR a 0 05, C Surface projections of parcels associated with higher WT left or BT right during the N back task when compared the resting state frontoparietal and. subcortical hub regions showed elevated BT during task whereas WT was elevated in primary systems and decreased in default mode regions. D A plot quantifying the shift away from the cartographic profile in the resting state along the between module BT connectivity axis across the six tasks in the. HCP dataset error bars reflect SD across the Discovery cohort. and number of modules estimated within each temporal win Together these results suggest that the brain transitions into a. dow mean r 0 03 0 10 state of higher global integration in order to meet extrinsic task. demands Indeed all of the 375 regions showed a significant. Task Based Alterations in the Cartographic Profile shift toward greater inter modular connectivity BT during the. We next examined whether the balance between network inte N back task when compared to the resting state FDR a 0 05. gration and segregation tracked with ongoing cognitive function for all 375 regions Despite this global shift toward integration. using data from a cognitively demanding N back task Barch the effect was most pronounced within frontoparietal default. et al 2013 We observed a strong correlation between fluctua mode striatal and thalamic regions Figure 2C many of which. tions in cartography across all parcels and the blocks of the have been previously identified as belonging to a rich club of. experimental task group mean Pearson s r 0 521 R2 0 27 densely interconnected high degree hub nodes that are crit. p 10 10 Figure 2A Video 2 https github com macshine ical for the resilience and stability of the global brain network. coupling as well as a distinct alteration in the cartographic pro van den Heuvel and Sporns 2013 Importantly the involvement. file when compared to the resting state Figure 2B These of these highly interconnected hub regions during the task would. changes were coincident with increased task driven connectiv likely facilitate effective communication between specialist re. ity between frontoparietal dorsal attention cingulo opercular gions that would otherwise remain isolated thus affording a. and visual networks 2 back versus 0 back blocks FDR q larger repertoire of potential responses to deal with the chal. 0 05 Figure S4 suggesting that global integration may have lenges of the task. facilitated communication between otherwise segregated sys To determine whether network topology was sensitive to spe. tems during the more challenging 2 back condition Importantly cific task demands we calculated the cartographic profile in the. the extent of integration remained correlated with the task remaining six tasks from the HCP in the same cohort of 92. regressor even after controlling for the global signal mean r subjects Barch et al 2013 While the performance of each. 0 452 0 21 p 10 10 and the mean time resolved connectivity task also led to an increase in global integration relative to rest. across all parcels mean r 0 393 0 14 p 10 9 suggesting the effect was less pronounced than the lateral shift observed. that the fluctuations in topology were not simply driven by con in the N back task particularly when compared to the relatively. straints imposed by the task structure simple motor task 88 8 of parcels showed higher BT in the. Neuron 92 1 11 October 19 2016 3,NEURON 13348, Please cite this article in press as Shine et al The Dynamics of Functional Brain Networks Integrated Network States during Cognitive Task Perfor. mance Neuron 2016 http dx doi org 10 1016 j neuron 2016 09 018. Figure 3 Relationship between Task Per,formance and the Cartographic Profile. A A graphical depiction of the drift diffusion,model which uses the mean and SD of a subject s.
reaction time and performance accuracy to esti,mate the drift rate or rate of evidence accumu. Neuron Article The Dynamics of Functional Brain Networks Integrated Network States during Cognitive Task Performance James M Shine 1 2 5 Patrick G Bissett 1 Peter T Bell 3 Oluwasanmi Koyejo 1 Joshua H Balsters 4

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