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Cited by 792Functional Brain Connectivity StudiesNeural dynamics and brain functionNeural and Behavioral Psychology Studies

Dynamic reconfiguration of frontal brain networks during executive cognition in humans

Open access
Braun, Urs, Schäfer, Axel, Walter, Henrik, Erk, Susanne, Romanczuk-Seiferth, Nina, Haddad, Leila, Schweiger, Janina I., Grimm, Oliver +4 more·2015·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.1422487112PMC PMC4577153
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Abstract

Significance: Cognitive flexibility is hypothesized to require dynamic integration between brain areas. However, the time-dependent nature and distributed complexity of this integration remains poorly understood. Using recent advances in network science, we examine the functional integration between brain areas during a quintessential task that requires executive function. By linking brain regions (nodes) by their interactions (time-dependent edges), we uncover nontrivial modular structure: groups of brain regions cluster together into densely interconnected structures whose interactions change during task execution. Individuals with greater network reconfiguration in frontal cortices show enhanced memory performance, and score higher on neuropsychological tests challenging cognitive flexibility, suggesting that dynamic network reconfiguration forms a fundamental neurophysiological mechanism for executive function.

Verified 2026-10-06 · 10 references not checked yet

AI snapshot

Written from the paper’s full text.

TL;DR

Using dynamic network neuroscience on fMRI data from 344 healthy adults performing an n-back working-memory task, the study shows that executive cognition involves transient reconfiguration of frontal brain networks rather than uniform whole-brain flexibility. Greater frontal flexibility and integration were linked to better working-memory accuracy and executive-flexibility measures.

Key findings

  • Whole-brain network flexibility oscillated across task execution and differed by condition [repeated-measures ANOVA: F(2,343) = 20.75, P < 0.001], but whole-brain flexibility did not differ between 2-back and 0-back [t(343) = 1.31, P = 0.19].
  • Reconfiguration was concentrated in frontal systems: frontal vs. nonfrontal consensus partitions were less similar [t(343) = −10.32, P < 0.001], and frontal-related modules showed higher flexibility than nonfrontal modules in 2-back [F(8,343) = 29.95, P < 0.001; post hoc t(343) = 9.42, P < 0.001] and 0-back [F(7,343) = 11.75, P < 0.001; post hoc t(343) = 4.98, P < 0.001].
  • During 2-back, frontal flexibility correlated with task accuracy (Spearman r = 0.12, PFDR-corrected = 0.04, PBonferroni = 0.2), and frontal integration correlated with task accuracy (r = 0.14, PFDR-corrected = 0.024, PBonferroni = 0.096), backward digit span (r = 0.14, PFDR-corrected = 0.024, PBonferroni = 0.072), and TMT-B performance (r = −0.18, PFDR-corrected = 0.016, PBonferroni = 0.016), but not forward digit span (r = 0.004, PFDR-corrected = 0.95, PBonferroni = 1.0).

Methods

  • 344 healthy volunteers (180 women; mean age 33; range 18–51 y) from a multicenter study underwent BOLD fMRI while performing a visual n-back task with alternating 0-back and 2-back blocks.
  • Functional connectivity was estimated using a sliding time window of 15 volumes with no gap and Morlet wavelet coherence in the 0.08–0.15 Hz band, yielding 114 weighted adjacency matrices per subject.
  • Multilayer community detection with 100 repetitions per subject identified time-respecting modules; flexibility measured how often a region changed module allegiance between consecutive windows, and integration measured transient module–module interactions. Behavioral associations used Spearman correlations with FDR and Bonferroni correction.

Limitations

  • The paper states that dynamic community detection is heuristic and node-to-community assignments remain uncertain; fMRI temporal resolution limits dynamic resolution; functional connectivity is not causal; hard partitioning is used; the n-back task cannot distinguish working-memory maintenance from manipulation; and behavioral correlations are small, with some failing Bonferroni correction.
  • The study sample is healthy adults, so the findings may not generalize to clinical populations such as schizophrenia or dementia, which the paper mentions as relevant future contexts.
  • The reported behavioral effects are exploratory in part, and the paper cautions that they should be validated in future studies.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1073/pnas.1422487112

Cites

53 of 63 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

0 of 62 cited sources checked · 0 supported · 0 flagged

  • Findingown data
    § Results

    Several modules were consistently identified in both the 0-back and 2-back conditions: somatomotor, visual, subcortical, and hippocampal modules.

    “Several modules were consistently identified in both the 0-back and 2-back conditions: somatomotor, visual, subcortical, and hippocampal modules.”
    Quote in the paperNot a substantial claim
  • Findingown data
    § Results

    Time-dependent network flexibility oscillates across task execution, reaching clear maxima in time windows dominated by either the 2-back or 0-back (baseline) conditions.

    “We observed that the time-dependent network flexibility oscillates across task execution, reaching clear maxima in time windows dominated by either the 2-back or 0-back (baseline) conditions [Fig. 1F; repeated-measures ANOVA with condition as factor: F(2,343) = 20.75, P < 0.001].”
    Quote in the paperSubstantial claim
  • Findingown data
    § Results

    During the 2-back working-memory condition, frontal flexibility was positively correlated with task accuracy.

    “During the 2-back working-memory condition, frontal flexibility was positively correlated with task accuracy (Fig. 3A; Spearman's rank correlation coefficient r = 0.12, PFDR-corrected = 0.04, PBonferroni = 0.2),”
    Quote in the paperSubstantial claim
  • Findingown data
    § Results

    During the 0-back condition, flexibility was highest in fronto-related systems.

    “This pattern was conserved during the 0-back condition [F(7,343) = 11.75, P < 0.001; post hoc paired t test between fronto-related and nonfrontal modules: t(343) = 4.98, P < 0.001].”
    Quote in the paperSubstantial claim
  • Findingown data
    § Results

    The z score of the Rand coefficient showed significantly higher similarity for nonfrontal regions than frontal regions.

    “A paired t test on the z score for frontal vs. nonfrontal regions showed a significantly higher similarity for nonfrontal regions [t(343) = −10.32, P < 0.001], supporting the visually marked differences between frontal and nonfrontal modules.”
    Quote in the paperSubstantial claim
  • May combine two claims. 27
  • Classified as probably not a substantial claim. 28
  • The quote only partly states this claim. 2

  • May combine two claims.

    Findingown data
    § Results

    The frontoparietal module was composed of more medial structures in the 0-back condition and more lateral structures in the 2-back condition.

    “First, the frontoparietal module was composed of more medial structures in the 0-back condition, and more lateral structures in the 2-back condition;”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Abstract

    In contrast to a control condition, in which dynamic changes in cortical networks were spread evenly across systems, the effortful working-memory condition was characterized by a reconfiguration of frontoparietal and frontotemporal networks.

    “In contrast to a control condition, in which dynamic changes in cortical networks were spread evenly across systems, the effortful working-memory condition was characterized by a reconfiguration of frontoparietal and frontotemporal networks.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Discussion

    Brain network flexibility and its relationship to cognitive demand can be tracked continuously on the order of minutes and undergoes specific and repetitive changes even within a given task.

    “In the current study, we demonstrate that brain network flexibility and its relationship to cognitive demand can be tracked continuously on the order of minutes and undergoes specific and repetitive changes even within a given task.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Methods

    The parietal module contains a large portion of higher-order association areas classified as part of the visual system in Power et al.; 85% of nodes in our parietal module were assigned to Power et al.'s visual module.

    “we observe that the module that we have referred to as the “parietal module” contains a large portion of higher-order association areas that were classified as part of the visual system in Power et al. (36) (i.e., 85% of nodes in our parietal module were assigned to Power et al.’s “visual module”)”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [36] Power JD, et al. Functional network organization of the human brain. Neuron. 2011;72(4):665–678. doi: 10.1016/j.neuron.2011.09.006. [DOI] [PMC free article] [PubMed] [Google Scholar] source

      Not checked in the free run.

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

    Findingown data
    § Methods

    Using the Pearson correlation coefficient, the decomposition of the module allegiance matrices into network modules was replicable.

    “Third, we observed that the decomposition of the module allegiance matrices into network modules was replicable.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § Discussion

    Despite conservation of whole-brain flexibility across working-memory load, there is a redistribution of flexibility across cognitive systems.

    “Despite the conservation of whole-brain flexibility across working-memory load, we did observe a redistribution of flexibility across cognitive systems.”
    Quote in the paperSubstantial claim

  • The quote only partly states this claim.

    Findingown data
    § Methods

    With a shorter window size of 14 volumes, network flexibility oscillates across task performance.

    “We robustly observe that network flexibility oscillates across task performance”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § Discussion

    The amount of reorganization in the modular structure and specifically the amount of interaction between modules is behaviorally relevant to working-memory performance and cognitive flexibility.

    “We provide evidence that the amount of reorganization in the modular structure and specifically the amount of interaction between modules is behaviorally relevant to working-memory performance and cognitive flexibility.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingcitation
    § Discussion

    The identified network modules map well to biologically meaningful systems of intrinsic connectivity and specifically systems commonly engaged during working-memory performance.

    “The identified network modules map well to biologically meaningful systems of intrinsic connectivity (36) and specifically systems commonly engaged during working-memory performance (14, 38, 39)”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [36] Power JD, et al. Functional network organization of the human brain. Neuron. 2011;72(4):665–678. doi: 10.1016/j.neuron.2011.09.006. [DOI] [PMC free article] [PubMed] [Google Scholar] source

      Not checked in the free run.

    • cites [14] Ginestet CE, Simmons A. Statistical parametric network analysis of functional connectivity dynamics during a working memory task. Neuroimage. 2011;55(2):688–704. doi: 10.1016/j.neuroimage.2010.11.030. [DOI] [PubMed] [Google Scholar] source

      Not checked in the free run.

    • cites [38] Hampson M, Driesen NR, Skudlarski P, Gore JC, Constable RT. Brain connectivity related to working memory performance. J Neurosci. 2006;26(51):13338–13343. doi: 10.1523/JNEUROSCI.3408-06.2006. [DOI] [PMC free article] [PubMed] [Google Scholar] source

      Not checked in the free run.

  • May combine two claims.

    Findingown data
    § Discussion

    Functional connectivity during a working-memory task is dynamically organized into adapting modules and this adapting modular structure relates to cognitive demands.

    “In the present study, we have provided evidence that the functional connectivity during a working-memory task is dynamically organized into adapting modules and that this adapting modular structure relates to cognitive demands.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Methods

    Of the 32 regions reported by Owen et al. mapped to our partition, 46% belonged to the frontoparietal module, 27% to the somatomotor module, and 12% to the frontal module.

    “We found 46% of the mapped nodes belonging to the frontoparietal module identified by us, whereas 27% belonged to the somatomotor module and 12% belonged to the frontal module.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [39] Owen AM, McMillan KM, Laird AR, Bullmore E. N-back working memory paradigm: A meta-analysis of normative functional neuroimaging studies. Hum Brain Mapp. 2005;25(1):46–59. doi: 10.1002/hbm.20131. [DOI] [PMC free article] [PubMed] [Google Scholar] source

      Not checked in the free run.

  • The quote only partly states this claim.

    Findingown data
    § Methods

    Using the Pearson correlation coefficient, there was a significant difference in flexibility between 0-back and 2-back.

    “Fourth, we observed a significant difference in flexibility between 0-back and 2-back [repeated-measures ANOVA with task and frontal vs. nonfrontal as categorical factors, main effect of task: F(1,343) = 21.8, P < 0.001], which was a trend in the results based on coherence.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § Discussion

    Network modules differ in their degree of integration with other network modules: stable core-like modules including visual and somatomotor systems show relatively weak integration, whereas frontal and frontal-mediated modules show relatively strong integration.

    “Critically, we observe that network modules differ in their degree of integration with other network modules: stable core-like modules including visual and somatomotor systems show relatively weak integration with other modules, whereas frontal and frontal-mediated modules show relatively strong integration with other modules (Fig. 2D).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Discussion

    The behavioral correlations observed in the study are small and some fail to reach statistical significance under Bonferroni correction.

    “Finally, the behavioral correlations we observed are small and some fail to reach statistical significance under Bonferroni correction, and therefore should be interpreted with caution and validated in future studies.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Findingreasoning
    § Methods

    Each independent run of the Louvain-like modularity optimization algorithm provided slightly different partitions of network nodes into communities across the 114 time slices.

    “Due to the heuristic nature of the algorithm and to the near degeneracy of the optimization landscape of the multilayer modularity quality function, each independent run of the algorithm provided slightly different partitions of network nodes into communities across the 114 time slices (or network layers).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Methods

    The path length is longest between task blocks and shortest within task blocks.

    “The clustering coefficient is highest between task blocks and lowest within task blocks, whereas the path length is longest between task blocks and shortest within task blocks.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § Methods

    The clustering coefficient is highest between task blocks and lowest within task blocks.

    “The clustering coefficient is highest between task blocks and lowest within task blocks, whereas the path length is longest between task blocks and shortest within task blocks.”
    Quote in the paperNot a substantial claim

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

    Premisecitation
    § Abstract

    Theoretical frameworks for working memory call on the interplay of distinct components and their integration in broader cognitive circuits.

    “Theoretical frameworks for working memory call on the interplay of distinct components (12) and their integration in broader cognitive circuits (1).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [12] Baddeley A. Working memory. C R Acad Sci III. 1998;321(2-3):167–173. doi: 10.1016/s0764-4469(97)89817-4. [DOI] [PubMed] [Google Scholar] source

      Not checked in the free run.

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    Premisecitation
    § Abstract

    Working memory requires the integration of large-scale neural circuits.

    “Working memory lies at the interface of perception and action (7) and requires the integration of large-scale neural circuits (8–11).”
    Quote in the paperSubstantial claim0 of 4 sources checked
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      Not checked in the free run.

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      Not checked in the free run.

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    Premisecitation
    § Abstract

    Many so-called domain-general areas engage in multiple functions, differing from domain-specific areas such as primary visual cortex that perform a very specific function.

    “emerging evidence indicates that many so-called “domain-general” areas engage in multiple functions, differing from “domain-specific” areas such as primary visual cortex that perform a very specific function (1, 2).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [1] Shohamy D, Turk-Browne NB. Mechanisms for widespread hippocampal involvement in cognition. J Exp Psychol Gen. 2013;142(4):1159–1170. doi: 10.1037/a0034461. [DOI] [PMC free article] [PubMed] [Google Scholar] source

      Not checked in the free run.

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      Not checked in the free run.

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

    Premisecitation
    § Abstract

    The empirical neuroimaging literature has identified several distinct sets of brain areas underlying working-memory performance.

    “The empirical neuroimaging literature has bolstered these conceptualizations by identifying several distinct sets of brain areas underlying working-memory performance (13–17).”
    Quote in the paperNot a substantial claim0 of 5 sources checked
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      Not checked in the free run.

    • cites [14] Ginestet CE, Simmons A. Statistical parametric network analysis of functional connectivity dynamics during a working memory task. Neuroimage. 2011;55(2):688–704. doi: 10.1016/j.neuroimage.2010.11.030. [DOI] [PubMed] [Google Scholar] source

      Not checked in the free run.

    • cites [15] Collette F, Van der Linden M. Brain imaging of the central executive component of working memory. Neurosci Biobehav Rev. 2002;26(2):105–125. doi: 10.1016/s0149-7634(01)00063-x. [DOI] [PubMed] [Google Scholar] source

      Not checked in the free run.

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      Not checked in the free run.

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      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Premisecitation
    § Abstract

    Working memory lies at the interface of perception and action.

    “Working memory lies at the interface of perception and action (7)”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [7] Linden DE. The working memory networks of the human brain. Neuroscientist. 2007;13(3):257–267. doi: 10.1177/1073858406298480. [DOI] [PubMed] [Google Scholar] source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data
    § Results

    Dynamic network reorganization is taking place predominately in frontal systems and is driven by a constant readaptation and interaction of frontal systems with each other.

    “These results suggest that dynamic network reorganization is taking place predominately in frontal systems and is driven by a constant readaptation and interaction of frontal systems with each other.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Premisecitation
    § Abstract

    The 2-back condition has higher cognitive load than the 0-back condition.

    “Based on the cognitive load of the 2-back condition (20–22)”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [20] Baddeley A. Exploring the central executive. Q J Exp Psychol A. 1996;49(1):5–28. [Google Scholar] source

      Not checked in the free run.

    • cites [21] Miyake A, et al. The unity and diversity of executive functions and their contributions to complex “Frontal Lobe” tasks: A latent variable analysis. Cognit Psychol. 2000;41(1):49–100. doi: 10.1006/cogp.1999.0734. [DOI] [PubMed] [Google Scholar] source

      Not checked in the free run.

    • cites [22] Kane MJ, Conway AR, Miura TK, Colflesh GJ. Working memory, attention control, and the N-back task: A question of construct validity. J Exp Psychol Learn Mem Cogn. 2007;33(3):615–622. doi: 10.1037/0278-7393.33.3.615. [DOI] [PubMed] [Google Scholar] source

      Not checked in the free run.

  • May combine two claims.

    Study design and conductown data
    § Methods

    The previous results are not due to noise and finite data length but show task-specific variations.

    “This analysis confirms our previous results are not due to noise and finite data length, but show task-specific variations.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Interpretationreasoning
    § Methods

    Identical solutions obtained from diverse initial conditions indicate important replicable features of the modularity landscape.

    “Therefore, identical solutions that are obtained from diverse initial conditions actually indicate important replicable features of the landscape, which should be accounted for in subsequent analysis.”
    Quote in the paperSubstantial claim

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

    Premisereasoning
    § Methods

    A common method for dealing with near degeneracy of the modularity quality function is to examine statistics averaged over 100 repetitions of the optimization, with equal weight placed on each solution rather than only on unique solutions.

    “However, as of today, a common method for dealing with this degeneracy is to examine statistics averaged over the 100 repetitions of the optimization, with equal weight being placed on each solution, rather than just on the unique solutions.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [25] Bassett DS, et al. Robust detection of dynamic community structure in networks. Chaos. 2013;23(1):013142. doi: 10.1063/1.4790830. [DOI] [PMC free article] [PubMed] [Google Scholar] source

      Not checked in the free run.

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      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisecitation
    § Discussion

    The human brain is intrinsically organized into modules, creating a high degree of flexibility and adaptability of the system without fundamentally altering underlying structure.

    “As a complex system, the human brain is intrinsically organized into modules, creating a high degree of flexibility and adaptability of the system without fundamentally altering underlying structure (29, 30).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [29] Kirschner M, Gerhart J. Evolvability. Proc Natl Acad Sci USA. 1998;95(15):8420–8427. doi: 10.1073/pnas.95.15.8420. [DOI] [PMC free article] [PubMed] [Google Scholar] source

      Not checked in the free run.

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      Not checked in the free run.

  • May combine two claims.

    Interpretationown data
    § Results

    The dynamic reconfiguration of frontal systems for working-memory function may not be limited to the n-back working-memory paradigm but may be essential for the manipulation of stored information across tasks.

    “This indicates that the dynamic reconfiguration of frontal systems for working-memory function may not be limited to the n-back working-memory paradigm but may be essential for the manipulation of stored information across tasks.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationreasoning
    § Discussion

    The strong integration of frontal systems with other network modules forms a physiological basis for cognitive flexibility, enabling task switching and cognitive control during demanding tasks.

    “It is intuitively plausible that the strong integration of frontal systems with other network modules forms a physiological basis for cognitive flexibility (15, 41), enabling task switching and cognitive control during demanding tasks (21, 42).”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [15] Collette F, Van der Linden M. Brain imaging of the central executive component of working memory. Neurosci Biobehav Rev. 2002;26(2):105–125. doi: 10.1016/s0149-7634(01)00063-x. [DOI] [PubMed] [Google Scholar] source

      Not checked in the free run.

    • cites [41] Stuss DT. Frontal lobes and attention: Processes and networks, fractionation and integration. J Int Neuropsychol Soc. 2006;12(2):261–271. doi: 10.1017/S1355617706060358. [DOI] [PubMed] [Google Scholar] source

      Not checked in the free run.

    • cites [21] Miyake A, et al. The unity and diversity of executive functions and their contributions to complex “Frontal Lobe” tasks: A latent variable analysis. Cognit Psychol. 2000;41(1):49–100. doi: 10.1006/cogp.1999.0734. [DOI] [PubMed] [Google Scholar] source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Premisenone given
    § Methods

    Wavelet-based measures provide significant denoising to the signal, enhancing signal-to-noise ratio in subsequent analyses.

    “Mathematically, the wavelet-based measures provide significant denoising to the signal, enhancing signal-to-noise ratio in subsequent analyses.”
    Quote in the paperNot a substantial claim

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

    Study design and conductreasoning
    § Discussion

    The assignment of nodes to communities on the individual subject level remains a statistical process, accompanied by some degree of uncertainty.

    “due to heuristics in the algorithm and challenges inherent to fMRI data, the assignment of nodes to communities on the individual subject level remains a statistical process, accompanied by some degree of uncertainty.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Premisecitation
    § Methods

    Maximizing the modularity quality function is NP-hard.

    “maximizing the modularity quality function is NP-hard, and we therefore use a Louvain-like locally greedy heuristic algorithm (50) to seek the optimal modularity index Q.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [50] Jutla I, Jeub L, Mucha P. 2011. A generalized Louvain method for community detection implemented in MATLAB (computer program). Available at netwiki.amath.unc.edu. Accessed September 1, 2014.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Study design and conductreasoning
    § Discussion

    The modular changes and behavioral associations observed might be further resolved in time if electrophysiological data were used.

    “we remain limited by the temporal resolution of fMRI data, making it likely that the modular changes and behavioral associations we observed might be further resolved in time if electrophysiological data were used.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Premisecitation
    § Methods

    A 30-s time window is optimal for quantifying dynamic reconfiguration of networks in the frequency range 0.08-0.15 Hz.

    “In a recent paper from Van De Ville and colleague (49), the authors demonstrate the optimality of a 30-s time window for quantifying dynamic reconfiguration of networks in the frequency range 0.08–0.15 Hz.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [49] Leonardi N, Van De Ville D. On spurious and real fluctuations of dynamic functional connectivity during rest. Neuroimage. 2015;104:430–436. doi: 10.1016/j.neuroimage.2014.09.007. [DOI] [PubMed] [Google Scholar] source

      Not checked in the free run.

  • May combine two claims.

    Interpretationnone given
    § Methods

    Flexibility can be driven by transient interactions between modules: the more transient module-module interactions, the more likely regions are to flexibly alter their allegiance to cognitive systems.

    “flexibility can be driven by transient interactions between modules: the more transient module–module interactions, the more likely regions are to flexibly alter their allegiance to cognitive systems—an aspect of reconfiguration that we capture by integration.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation
    § Methods

    The n-back task has been studied extensively in traditional activation and connectivity analyses, identifying a large executive working-memory network dominated by frontal and parietal regions.

    “The n-back task has been studied extensively in the context of traditional activation and connectivity analyses (38, 39), identifying a large (executive) working-memory network dominated by frontal and parietal regions.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [38] Hampson M, Driesen NR, Skudlarski P, Gore JC, Constable RT. Brain connectivity related to working memory performance. J Neurosci. 2006;26(51):13338–13343. doi: 10.1523/JNEUROSCI.3408-06.2006. [DOI] [PMC free article] [PubMed] [Google Scholar] source

      Not checked in the free run.

    • cites [39] Owen AM, McMillan KM, Laird AR, Bullmore E. N-back working memory paradigm: A meta-analysis of normative functional neuroimaging studies. Hum Brain Mapp. 2005;25(1):46–59. doi: 10.1002/hbm.20131. [DOI] [PMC free article] [PubMed] [Google Scholar] source

      Not checked in the free run.

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

    Study design and conductnone given
    § Discussion

    The measure of functional connectivity used does not reflect causal interaction.

    “Third, we use a measure of functional connectivity that does not reflect causal interaction.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Premisecitation
    § Methods

    Connectivity analyses and more complex measures of dynamic reconfiguration of networks in face of different task demands are not competitive with activation studies but provide very complementary insights that directly address the underlying mechanisms of cognitive processes.

    “Indeed, connectivity analyses, and more complex measures of the dynamic reconfiguration of networks in face of different task demands, are not competitive with activation studies but provide very complementary insights that directly address the underlying mechanisms of cognitive processes (61).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [61] Bassett DS, Yang M, Wymbs NF, Grafton ST. Learning-induced autonomy of sensorimotor systems. Nat Neurosci. 2015;18(5):744–751. doi: 10.1038/nn.3993. [DOI] [PMC free article] [PubMed] [Google Scholar] source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data
    § Methods

    Within task blocks, network reconfiguration mainly takes place between modules, resulting in a lower clustering coefficient and an increased path length.

    “These results support the view that, within task blocks, network reconfiguration mainly takes place between modules, thus resulting in a lower clustering coefficient and an increased path length.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning
    § Methods

    The current work can be seen as a direct validation of the role of frontal regions in adaptive behavior, and as clarifying the mechanism of this role as based on dynamic changes in functional connectivity.

    “Thus, the current work can be seen as a direct validation of the role of frontal regions in adaptive behavior, and as clarifying the mechanism of this role as based on dynamic changes in functional connectivity.”
    Quote in the paperNot a substantial claim

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

    Premisecitation
    § Methods

    Dynamic community detection methods have been used in the context of human motor skill learning and lexical processing.

    “Finally, dynamic community detection methods have further been used in the context of human motor skill learning (3) and lexical processing (59).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [3] Bassett DS, et al. Dynamic reconfiguration of human brain networks during learning. Proc Natl Acad Sci USA. 2011;108(18):7641–7646. doi: 10.1073/pnas.1018985108. [DOI] [PMC free article] [PubMed] [Google Scholar] source

      Not checked in the free run.

    • cites [59] Doron KW, Bassett DS, Gazzaniga MS. Dynamic network structure of interhemispheric coordination. Proc Natl Acad Sci USA. 2012;109(46):18661–18668. doi: 10.1073/pnas.1216402109. [DOI] [PMC free article] [PubMed] [Google Scholar] source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisecitation
    § Methods

    In a following methods paper (25), the authors stringently evaluated the methodological choices required to robustly detect dynamic community structure in simulated and empirical data.

    “In a following methods paper (25), we stringently evaluated the methodological choices required to robustly detect dynamic community structure in simulated and empirical data.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [25] Bassett DS, et al. Robust detection of dynamic community structure in networks. Chaos. 2013;23(1):013142. doi: 10.1063/1.4790830. [DOI] [PMC free article] [PubMed] [Google Scholar] source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data
    § Methods

    Although network reconfiguration explains a small amount of variance, it has quantifiable advantages over traditional analyses and offers complementary insights into the neurobiological basis of working memory.

    “Therefore, although network reconfiguration explains a small amount of variance, it has quantifiable advantages over traditional analyses and here offers complementary insights into the neurobiological basis of working memory.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductcitation
    § Methods

    The main effect of task was removed from the data to avoid confounding network measures with large-scale block (co)activations.

    “Third, the main effect of task was removed from the data to not confound network measures with large-scale block (co)activations, as has been done previously (e.g., see ref. 23).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites ref. 23

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductown data
    § Methods

    Only matrix elements whose values were larger than expected in a random network null model were taken into account to account for noise.

    “To account for noise, only matrix elements whose values were larger than expected in a random network null model were taken into account.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Interpretationcitation
    § Methods

    Repeated optimizations of the modularity quality function of the consensus matrix Tcons return a single optimal consensus partition S, which partitions brain regions into representative putative functional modules that the authors interpret as cognitive systems.

    “This procedure can be repeated iteratively, so that repeated optimizations of the modularity quality function of the consensus matrix Tcons return a single optimal consensus partition S, which partitions brain regions into representative putative functional modules that we interpret as cognitive systems (25).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [25] Bassett DS, et al. Robust detection of dynamic community structure in networks. Chaos. 2013;23(1):013142. doi: 10.1063/1.4790830. [DOI] [PMC free article] [PubMed] [Google Scholar] source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductreasoning
    § Methods

    Any element Tij whose value was less than the maximum entry of the random association matrix was set to 0 to remove noise conservatively.

    “To be conservative, we remove such “noise” from the original nodal association matrix T by setting any element Tij whose value is less than the maximum entry of the random association matrix to 0.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Premisecitation
    § Methods

    Connectivity analyses provide direct evidence of neurophysiological mechanisms defining the roles of brain networks in cognition.

    “For example, although classical GLM-based approaches are often interpreted as indicating functional associations between regions, connectivity analyses provide direct evidence of neurophysiological mechanisms defining the roles of brain networks in cognition (2).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [2] Fedorenko E, Thompson-Schill SL. Reworking the language network. Trends Cogn Sci. 2014;18(3):120–126. doi: 10.1016/j.tics.2013.12.006. [DOI] [PMC free article] [PubMed] [Google Scholar] source

      Not checked in the free run.

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

    Premisecitation
    § Methods

    In that methods paper, a framework of null model testing for statistical inference in neuroimaging data was developed.

    “In that paper, we also developed a framework of null model testing for statistical inference in the context of neuroimaging data.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [25] Bassett DS, et al. Robust detection of dynamic community structure in networks. Chaos. 2013;23(1):013142. doi: 10.1063/1.4790830. [DOI] [PMC free article] [PubMed] [Google Scholar] source

      Not checked in the free run.

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

    Premisecitation
    § Methods

    In the original paper (6), the methods were tested on two different kinds of social networks, and results were consistent with prior knowledge regarding these systems.

    “In the original paper (6), the methods were tested on two different kinds of social networks, and results were consistent with prior knowledge regarding these systems.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [6] Mucha PJ, Richardson T, Macon K, Porter MA, Onnela JP. Community structure in time-dependent, multiscale, and multiplex networks. Science. 2010;328(5980):876–878. doi: 10.1126/science.1184819. [DOI] [PubMed] [Google Scholar] source

      Not checked in the free run.

51 of 51

  • The paper relies on this without support.

    The trail-making test B measures executive flexibility by capturing the subject's cognitive ability to switch between task demands.

    “We directly tested the relationship between frontal integration and cognitive flexibility using behavioral scores on the trail-making test B (TMT-B), which measures executive flexibility by capturing the subject's cognitive ability to switch between task demands.”

  • The paper relies on this without support.

    The digit span backward task captures temporary information storage and the active manipulation of stored items.

    “we tested the relationship between frontal integration and performance on a digit span backward task, which captures temporary information storage and the active manipulation of stored items.”

  • The paper relies on this without support.

    The n-back task is a valid manipulation of working memory/executive cognition.

    “during a working-memory challenge (the “n-back” task)”

  • The paper relies on this without support.

    A large score on the TMT-B task indicates poor task switching ability, and a small score indicates good task switching ability.

    “A large score on the TMT-B task (as measured by the time to complete the task) indicates poor task switching ability, and a small score indicates good task switching ability.”

  • The paper relies on this without support.

    The digit span forward task does not require active information manipulation.

    “which does not require active information manipulation.”
5 of 5

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?

Statistics recomputed

Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.

  • Not confirmedt(343) = 2.259reported p < .024 · recomputed p = .025

    “t(343) = 2.259, p < .024”

  • Not confirmedt(343) = 2.974reported p < .003 · recomputed p = .003

    “t(343) = 2.974, p < .003”

  • ConsistentF(2, 343) = 20.75reported p < .001 · recomputed p < .001

    “F(2,343) = 20.75, p < .001”

  • Consistentt(343) = 1.31reported p = .19 · recomputed p = .191

    “t(343) = 1.31, p = .19”

  • Consistentt(343) = -10.32reported p < .001 · recomputed p < .001

    “t(343) = −10.32, p < .001”

  • ConsistentF(8, 343) = 29.95reported p < .001 · recomputed p < .001

    “F(8,343) = 29.95, p < .001”

  • Consistentt(343) = 9.42reported p < .001 · recomputed p < .001

    “t(343) = 9.42, p < .001”

  • ConsistentF(7, 343) = 11.75reported p < .001 · recomputed p < .001

    “F(7,343) = 11.75, p < .001”

  • Consistentt(343) = 4.98reported p < .001 · recomputed p < .001

    “t(343) = 4.98, p < .001”

  • ConsistentF(1, 343) = 6.25reported p < .013 · recomputed p = .013

    “F(1,343) = 6.25, p < .013”

  • ConsistentF(1, 343) = 16.5reported p < .001 · recomputed p < .001

    “F(1,343) = 16.5, p < .001”

  • Consistentt(343) = 2reported p = .045 · recomputed p = .046

    “t(343) = 2.0, p = .045”

  • ConsistentF(1, 343) = 64.6reported p < .001 · recomputed p < .001

    “F(1,343) = 64.6, p < .001”

  • ConsistentF(1, 343) = 21.8reported p < .001 · recomputed p < .001

    “F(1,343) = 21.8, p < .001”

  • ConsistentF(1, 343) = 196.8reported p < .001 · recomputed p < .001

    “F(1,343) = 196.8, p < .001”

  • Consistentt(343) = 6.97reported p < .001 · recomputed p < .001

    “t(343) = 6.97, p < .001”

  • Consistentt(343) = -0.44reported p = .66 · recomputed p = .660

    “t(343) = −0.44, p = .66”

  • Consistentt(343) = -0.417reported p < .677 · recomputed p = .677

    “t(343) = −0.417, p < .677”

  • Consistentt(343) = -0.742reported p < .459 · recomputed p = .459

    “t(343) = −0.742, p < .459”

  • Consistentt(343) = 0.573reported p < .567 · recomputed p = .567

    “t(343) = 0.573, p < .567”

  • Consistentt(343) = -0.204reported p < .839 · recomputed p = .838

    “t(343) = −0.204, p < .839”

21 of 21

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.

  • Incorrect denominator degrees of freedom for the condition ANOVA

    Minor

    For a repeated-measures ANOVA with 344 subjects and a condition numerator degree of freedom of 2, the uncorrected denominator degree of freedom is 2 × (344 − 1) = 686, not 343.

    Reported 343 · recomputed 686

  • Incorrect denominator degrees of freedom for the module ANOVA

    Minor

    For the stated repeated-measures ANOVA across modules in 344 subjects, a numerator degree of freedom of 8 gives an uncorrected denominator degree of freedom of 8 × 343 = 2,744, not 343. The analogous 0-back result, F(7,343), has the same issue.

    Reported 343 · recomputed 2744

  • Overlapping-window percentage is incorrect

    Minor

    Fourteen of 15 volumes is 93.3%, not 91%.

    Reported 91 · recomputed 93.333333

  • Path length within task blocks is described in opposite ways

    Minor

    The text first says path length is shortest within task blocks, then interprets the within-block results as showing an increased path length.

4 of 4

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. R Amthauer, B Brocke, D Liepmann, A Beauducel Intelligenz-Struktur-Test 2000: IST 2000 (Hogrefe and Huber, Göttingen, Germany, 1999).
  2. 10.1038/nn.3993 · DOI · Paper page

Cited by

Sorted by citations

Related

Authors
Braun, U., Schäfer, A., Walter, H., Erk, S., Romanczuk-Seiferth, N., Haddad, L., Schweiger, J. I., Grimm, O., Heinz, A., Tost, H., Meyer-Lindenberg, A., & Bassett, D. S.
Affiliations
  • Heidelberg University (DE)
  • Charité – Universitätsmedizin Berlin (DE)
  • Department of Psychiatry and Psychotherapy, Charité–University Medicine Berlin, Campus Mitte, 10117 Berlin, Germany;
  • Central Institute for Mental Health Mannheim, University of Heidelberg, Medical Faculty Mannheim, 68159 Mannheim, Germany;
  • University of Pennsylvania (US)
ORCID
Venue
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Published
2015-08-31
Volume
112
Issue
37
Pages
11678-11683
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
DOI
10.1073/pnas.1422487112
PMC
PMC4577153
OpenAlex
W1907418661
Cited by
791
References
63
Citations by year
2026: 54 · 2025: 89 · 2024: 73 · 2023: 66 · 2022: 63 · 2021: 100 · 2020: 87 · 2019: 65 · 2018: 74 · 2017: 74 · 2016: 41 · 2015: 3
FWCI
38.51
Type
journal article
Language
en
Open access
Licence
www.pnas.org/site/misc/userlicense.xhtml (vor)
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • John D. and Catherine T. MacArthur Foundation (MacArthur Award)
  • Alfred P. Sloan Foundation (Sloan Fellow)
  • DOD | U.S. Army | AMC | U.S. Army Research Laboratory (W911NF-10-2-0022)
  • HHS | National Institutes of Health (#2-R01-DC-009209-11)
  • DOD | Army Research Office (W911NF-14-1-0679)
  • National Science Foundation (BCS-1441502, BCS-1430087)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Keywords
dynamic network reconfiguration, executive cognition, working memory, n-back task, frontoparietal network, fronto-temporal network, network flexibility, functional brain networks, schizophrenia, dementia
MeSH terms
Adolescent, Adult, Brain, Brain Mapping, Cognition, Female, Frontal Lobe, Humans, Magnetic Resonance Imaging, Male, Memory, Short-Term, Middle Aged, Nerve Net, Young Adult, Executive Function

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-06.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-06.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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