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Cited by 998Functional Brain Connectivity StudiesNeural dynamics and brain functionAdvanced Neuroimaging Techniques and Applications

The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance

Open access
Shine, James M., Bissett, Patrick G., Bell, Peter T., Koyejo, Oluwasanmi, Balsters, Joshua H., Gorgolewski, Krzysztof J., Moodie, Craig A., Poldrack, Russell A.·2016·Neuron
DOI 10.1016/j.neuron.2016.09.018PMC PMC5073034
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Abstract

Higher brain function relies upon the ability to flexibly integrate information across specialized communities of brain regions, however it is unclear how this mechanism manifests over time. In this study, we used time-resolved network analysis of functional magnetic resonance imaging data to demonstrate that the human brain traverses between functional states that maximize either segregation into tight-knit communities or integration across otherwise disparate neural regions. Integrated states enable faster and more accurate performance on a cognitive task, and are associated with dilations in pupil diameter, suggesting that ascending neuromodulatory systems may govern the transition between these alternative modes of brain function. Together, our results confirm a direct link between cognitive performance and the dynamic reorganization of the network structure of the brain.

Integrity signals

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Crossref · 10.1016/j.neuron.2016.09.018

Cites

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Claims the paper makes

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  • Findingown data

    The human brain moves between functional states that maximize either segregation into tight-knit communities or integration across otherwise disparate neural regions.

    “the human brain traverses between functional states that maximize either segregation into tight-knit communities or integration across otherwise disparate neural regions”
    Quote in the paperSubstantial claim
  • Findingown data

    Integrated states are associated with dilations in pupil diameter.

    “and are associated with dilations in pupil diameter”
    Quote in the paperSubstantial claim
  • Findingown data

    Integrated states are associated with faster and more accurate performance on a cognitive task.

    “Integrated states enable faster and more accurate performance on a cognitive task”
    Quote in the paperSubstantial claim
  • Findingown data

    In a separate resting-state dataset of 14 individuals, pupil diameter was positively correlated with mean BT (group mean r = 0.241 +/− 0.06; R2 = 0.06; p = 10−5).

    “we observed a positive correlation between pupil diameter and mean BT (group mean r = 0.241 +/− 0.06; R2 = 0.06; p = 10−5; Figure 4)”
    Quote in the paperSubstantial claim
  • Findingown data

    A parcel-wise conjunction analysis identified a right-lateralized network of frontal, parietal, thalamic and striatal regions with consistently elevated BT across drift rate, non-decision time and pupil diameter comparisons.

    “This analysis revealed a right-lateralized network of frontal, parietal, thalamic and striatal regions that were associated with consistently elevated BT across the three comparisons (blue; Figure 4c)”
    Quote in the paperSubstantial claim
  • The quote only partly states this claim. 2
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    Findingown data

    In resting-state data from 92 HCP subjects, k-means clustering of full cartographic profiles identified two modes characterized by integration or segregation.

    “Subject-level k-means clustering of these full profiles across time (k = 2, with stable clustering at higher values of k; see Materials and Methods and Figure S1) identified modes of information processing that were characterized by either integration or segregation (Figure 1a).”
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    Findingown data

    Group-level fluctuations occurred mainly in inter-modular connectivity, with BT values transitioning between high and low states en masse.

    “Although the majority of the group-level fluctuations occurred in inter-modular connectivity (i.e. BT values transitioned between high and low states en masse)”
    Quote in the paperNot a substantial claim

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    Findingown data

    The extent of reconfiguration varied by task: the simple Motor task was associated with greatest segregation, while the complex N-back task was associated with greatest integration.

    “These results demonstrate that the extent of reconfiguration varies as a function of task: the relatively simple motor task, which involved repetitive movements of specific effectors, was associated with greatest segregation, whereas the more complex N-back task, which required complex working memory updating and cognitive control, was associated with greatest integration.”
    Quote in the paperSubstantial claim

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    Findingown data

    Each of the remaining six HCP tasks increased global integration relative to rest, but less than the N-back task, especially compared with the Motor task (88.8% of parcels showed higher BT in N-back; FDR α < 0.05).

    “While the performance of each task also led to an increase in global integration relative to rest, the effect was less pronounced than the lateral shift observed in the N-back task, particularly when compared to the relatively simple Motor task (88.8% of parcels showed higher BT in the N-back task; FDR α < 0.05).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    No bins survived multiple comparisons correction for the relationship between the cartographic profile and the diffusion boundary threshold.

    “the boundary threshold (results not shown, as no bins survived multiple comparisons correction)”
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    Findingown data

    Correlations between cartographic profile bins and drift rate and non-decision time were significant after false discovery rate correction (p < 0.05).

    “For each comparison, the null hypothesis of no relationship was rejected after false discovery rate correction (p < 0.05).”
    Quote in the paperSubstantial claim

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    Findingown data

    The cartographic profile showed a similar relationship with median reaction time and accuracy, with integration associated with better performance.

    “We also compared the cartographic profile with median reaction time and accuracy for both the cohorts and observed a similar relationship between integration and improved performance.”
    Quote in the paperSubstantial claim

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    Findingown data

    In the discovery cohort, 16.1 ± 1.1% of temporal windows exceeded the 95th percentile of the VAR null model, indicating that the resting state had significant dynamic fluctuations in topology.

    “In the discovery cohort, 16.1 ± 1.1% of temporal windows were associated with deviations ≥ 95th percentile of the VAR null model (i.e. greater than the predicted 5%), suggesting that the resting state was associated with significant dynamic fluctuations in topology.”
    Quote in the paperSubstantial claim

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    Findingown data

    These results were confirmed in a unique cohort of 92 unrelated HCP participants: graph measures rWT = 0.971, rBT = 0.967; mean cartographic profiles rCart = 0.973.

    “We also confirmed the presence of these results in a unique cohort of 92 unrelated participants from the HCP: graph measures – rWT = 0.971, rBT = 0.967; and mean cartographic profiles – rCart = 0.973 (Figure S2).”
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    Premisecitation

    Momentary neural coalitions underlie complex cognitive functions, learning, and consciousness.

    “the emergence of momentary neural coalitions forms the basis for complex cognitive functions (Bassett et al., 2015; Cole et al., 2014), learning (Bassett et al., 2011) and consciousness (Barttfeld et al., 2015; Godwin et al., 2015)”
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    Premisecitation

    The brain's functional landscape is highly dynamic and unfolds on a relatively fixed structural scaffold.

    “Within the brain, a highly dynamic functional landscape unfolds on a relatively fixed structural scaffold (Deco et al., 2015; Shen et al., 2015)”
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    Study design and conductown data

    After co-registration, 11 of 173 NKI scans (6.3%) were discarded due to insufficient coverage of orbitofrontal cortex, temporopolar cortex and/or cerebellum.

    “After co-registration, data were manually inspected and of the 173 original participants, 11 [6.3%] scans were discarded due insufficient coverage of orbitofrontal cortex, temporopolar cortex and/or cerebellum.”
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    Premisecitation

    Non-luminance-related pupil diameter fluctuations track neural firing in ascending neuromodulatory systems such as the locus coeruleus, and pupil diameter is a surrogate for arousal and task engagement.

    “Recent invasive electrophysiological recordings in non-human primates have shown that non-luminance-related fluctuations pupil diameter tracks with neural firing in ascending neuromodulatory systems, such as the locus coeruleus, confirming the well-established proposal (Kahneman, 1973) that pupil diameter is a surrogate measure for arousal and task engagement (McGinley et al., 2015).”
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    Premisecitation

    The brain traverses a metastable state-space over time, balancing segregated processing with global coordination and integration.

    “There is now growing evidence to support the notion that the brain traverses a metastable state-space in time (Deco et al., 2015), balancing the opposing tendencies for specialized, segregated processing with the need for global coordination and integration (Tognoli and Kelso, 2014).”
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    Study design and conductown data

    Fluctuations in global network topology were independent of mean framewise displacement, CSF and deep white matter nuisance signals, and the number of modules per window.

    “Importantly, the fluctuations in global network topology occurred independently of the mean framewise displacement in each TR (mean r = 0.01 ± 0.01), nuisance signals from cerebrospinal fluid and deep cerebral white matter (mean r = −0.02 ± 0.01) and of the number of modules estimated within each temporal window (mean r = 0.03 ± 0.10).”
    Quote in the paperSubstantial claim

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    Interpretationcitation

    Global integration is an important candidate mechanism for the evolution of complex brain networks and for explaining how the brain creates complex, adaptive behavior.

    “As such, global integration is an important candidate mechanism responsible for the evolution of complex brain networks (van den Heuvel et al., 2016), and hence, for explaining the mechanism through which the brain creates complex, adaptive behavior.”
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    Interpretationreasoning

    Fluctuations in neural gain are linked to alterations in network topology that relate to effective behavioral performance.

    “by showing that fluctuations in neural gain are linked to alterations in network topology, that in turn, relate to effective behavioral performance”
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    Interpretationreasoning

    Involvement of highly interconnected hub regions during the task would likely facilitate effective communication between specialist regions that would otherwise remain isolated, affording a larger repertoire of responses.

    “Importantly, the involvement of these highly interconnected hub regions during the task would likely facilitate effective communication between specialist regions that would otherwise remain isolated, thus affording a larger repertoire of potential responses to deal with the challenges of the task.”
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  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    The N-back task is often used as a measure of cognitive control, a complex construct with dissociable sub-components that likely rely on overlapping but distinct neural architectures.

    “The N-back task is often used as a measure of cognitive control, which itself is a complex construct composed of dissociable sub-components, such as updating, set-shifting and response inhibition (Miyake et al., 2000), that likely rely on overlapping, yet distinct, neural architectures (Duncan, 2010; Poldrack et al., 2011).”
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    Premisecitation

    The diffusion model decomposes behavioral performance into latent variables for speed and accuracy of information processing, non-decision time, and response caution.

    “The diffusion model provides a decomposition of behavioral performance into cognitively-relevant latent variables representing the speed and accuracy of information processing (drift rate – ‘v’), the speed of perceptual and motor processes not directly related to the decision process (non-decision time – ‘t’) and a flexible measure of response caution (boundary separation – ‘a’; Ratcliff, 1978).”
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    Premisecitation

    Previous studies have shown that fluctuations in network topology relate to distinct patterns of behavior during cognitive tasks.

    “others have recently shown that fluctuations in network topology relate to distinct patterns of behavior during cognitive tasks (Alavash et al., 2016; Vatansever et al., 2015)”
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    Interpretationown data

    Integration within the functional connectome correlated with increases in pupil diameter, highlighting a potential neurobiological mechanism for modulating network-level dynamics.

    “We then showed that integration within the functional connectome correlated with increases in pupil diameter (Figure 4), highlighting a potential neurobiological mechanism responsible for modulating network-level dynamics in the human brain.”
    Quote in the paperSubstantial claim

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    Study design and conductown data

    Participants with more than 20% of resting time points exceeding FD > 0.5 mm or DVARS > 5% were excluded (HCP group 1: 8/100; HCP group 2: 8/100; NKI: 10/162).

    “Frames associated with FD > 0.5mm or DVARS > 5% were identified, and participants with greater than 20% of the resting time points exceeding these values were excluded from further analysis (HCP group 1: 8/100; HCP group 2: 8/100; NKI group: 10/162).”
    Quote in the paperSubstantial claim

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    Interpretationcitation

    A globally efficient, integrated network architecture supports fast, effective computation throughout the cognitive processing stream, potentially through facilitation of parallel processing.

    “Together, these results suggest that a globally efficient, integrated network architecture supports fast, effective computation throughout the cognitive processing stream (Krienen et al., 2014), potentially through the facilitation of parallel processing mechanisms (Sigman and Dehaene, 2008).”
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    Premisecitation

    Previous fMRI studies show global brain signals transition between high and low connectivity strength over time and these fluctuations relate to coordinated network topology patterns.

    “recent experiments using functional MRI data have demonstrated that global brain signals transition between states of high and low connectivity strength over time (Zalesky et al., 2014) and that these fluctuations are related to coordinated patterns of network topology (Betzel et al., 2015)”
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    Study design and conductown data

    The main analysis did not scrub data, but results from scrubbed data showed strong correspondence with the main outcome measures.

    “Due to concerns associated with the alteration of the temporal structure of the images, the data used in the main analysis were not ‘scrubbed’ (Power et al., 2014), however we did compare the results of our experiment with scrubbed data (missing values corrected using interpolation) and found strong correspondence between the outcome measures of the two studies (see Validation).”
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    Premisecitation

    Simulated data indicate that the MTD detects dynamic connectivity changes best with a seven-time-point window, optimizing both sensitivity and specificity.

    “Previous work has shown that, when using the MTD, a window length of seven time points provides optimal sensitivity and specificity for detecting dynamic changes in functional connectivity structure in simulated time series data (Shine et al., 2015).”
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    Study design and conductown data

    Task-evoked activity was controlled for by regressing HRF-convolved task block data from each time series before connectivity analysis.

    “To control for spurious patterns of connectivity associated with task-evoked activity, we first regressed the HRF-convolved task block data from each time series.”
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    Study design and conductcitation

    Functional divisions may differ across subjects, which is a limitation of the parcellation.

    “however it bears mention that functional divisions may differ across subjects (Laumann et al., 2015)”
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    Study design and conductown data

    The integrated and segregated states were matched on graph density, indicating that BT fluctuations were not merely due to changes in network sparsity over time.

    “Importantly, the two states were matched on graph density, suggesting that the fluctuations in BT did not occur simply due to alterations in network sparsity over time.”
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    Premisecitation

    A VAR model order of 11 appropriately mimics the expected temporal signature of the BOLD response in 0.72-s TR data.

    “In keeping with Zalesky et al. (Zalesky et al., 2014), VAR model order was set at 11, appropriately mimicking the expected temporal signature of the BOLD response in 0.72s TR data.”
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    Study design and conductown data

    The data contained no evidence of contaminants; the few fast responses (110 RTs <400 ms across both samples) were not guesses, as their 93% accuracy matched the overall 93% accuracy.

    “We searched for evidence of contaminants in our data and found no evidence of them (i.e. the few fast responses [110 RTs <400ms across both samples] were not guesses [93% accuracy was the same as the 93% accuracy for all trials).”
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    Premisecitation

    The MTD responds more strongly to changes in covariance than to changes in connectivity.

    “the MTD is more sensitive to changes in covariance than connectivity (Shine et al., 2015)”
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    Premisecitation

    The participation coefficient BT has previously been used successfully to characterize hubs in brain networks.

    “The participation coefficient, BT, quantifies the extent to which a region connects across all modules (i.e. between-module strength) and has previously been used to successfully characterize hubs within brain networks (e.g. see Power et al., 2013).”
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    Study design and conductown data

    The analyses reported here were reliable and could be replicated with the MTD across several datasets.

    “Most importantly, as we show, our analyses were reliable and replicable using the MTD across multiple datasets.”
    Quote in the paperSubstantial claim
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  • The paper relies on this without support.

    Flexible integration of information across specialized brain communities is necessary for higher brain function.

    “Higher brain function relies upon the ability to flexibly integrate information across specialized communities of brain regions”

  • The paper relies on this without support.

    The separate resting-state pupil dataset is comparable to the main datasets for linking pupil diameter to network topology.

    “we measured pupil diameter from individuals in a separate resting state dataset (14 individuals; TR = 2s; 3.5mm3 voxels; 204 volumes; Murphy et al., 2014) and compared alterations in pupil diameter with the cartographic profile (w = 10 TRs)”

  • The paper relies on this without support.

    Pupil diameter fluctuations reflect activity in ascending neuromodulatory systems.

    “To test the hypothesis that fluctuations in cartography related to activity in ascending neuromodulatory systems, we acquired a separate dataset of 14 individuals (mean age: 29 years; 8/14 male) in which pupil diameter was measured over time during the quiet resting state”
3 of 3

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • This conceptualization incorporates the understanding of whole brain networks as dynamic entities whose activity and connectivity shift with situational demands.

    “this conceptualization takes into account our increasing understanding of these whole brain networks as dynamic entities, with patterns of activity and connectivity (both within-network and with other networks) shifting as required by ever-changing situational demands (Cabral, Kringelbach, & Deco, 2014;Shine et al., 2016).”

    From Mental Time Travel? A Neurocognitive Model of Event Simulation. (2020) · cites this paper as Shine et al., 2016

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Global-efficiency equation has conflicting numbers

    Minor

    The text refers to the global-efficiency formula as Equation 5, while the formula's caption labels it Equation 4.

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Authors
Shine, J. M., Bissett, P. G., Bell, P. T., Koyejo, O., Balsters, J. H., Gorgolewski, K. J., Moodie, C. A., & Poldrack, R. A.
Affiliations
  • UNSW Sydney (AU)
  • Stanford University (US)
  • The University of Queensland (AU)
  • ETH Zurich (CH)
Corresponding author
Shine, James M.
ORCID
Venue
Neuron
Publisher
Elsevier BV
Published
2016-10
Volume
92
Issue
2
Pages
544-554
ISSN
0896-6273 (ISSN-L)
DOI
10.1016/j.neuron.2016.09.018
PMC
PMC5073034
OpenAlex
W2526529257
Semantic Scholar
dab91b6faf1b613b99bc8cc00464fcef244a776e
Cited by
886
References
65
Citations by year
2026: 107 · 2025: 106 · 2024: 99 · 2023: 94 · 2022: 100 · 2021: 114 · 2020: 82 · 2019: 70 · 2018: 66 · 2017: 45 · 2016: 3
FWCI
39.03
Type
journal article
Language
en
Open access
Licence
Locations
  • Neuron (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
National Institute on Drug Abuse (F32DA041773)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Keywords
functional brain networks, network dynamics, community structure, cognitive task performance, pupil dilation, neuromodulation, network reorganization
MeSH terms
Adult, Brain, Brain Mapping, Cognition, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Nerve Net, Neural Pathways, Pupil, Task Performance and Analysis, Functional Neuroimaging

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