The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index; none of the 42 sources its claims cite is checked for support yet.
The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance
Open accessAbstract
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.
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“the human brain traverses between functional states that maximize either segregation into tight-knit communities or integration across otherwise disparate neural regions”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Abstract
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In the paper
“and are associated with dilations in pupil diameter”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Abstract
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In the paper
“Integrated states enable faster and more accurate performance on a cognitive task”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Abstract
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“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)”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Network Cartography Fluctuates with Pupil Diameter
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“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)”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Identifying Regions Related to Global Integration
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“The extent of global network integration in the cartographic profile was positively correlated with drift rate (Figure 3b)”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Investigating the Relationship Between Cartography and Behavior
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In the paper
“as well as a distinct alteration in the cartographic profile when compared to the resting state (Figure 2b)”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Task-based Alterations in the Cartographic Profile
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“whereas segregated states were associated with relatively higher participation within regions in the default mode network”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Fluctuations in Network Cartography
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“Despite this global shift towards integration, the effect was most pronounced within frontoparietal, default mode, striatal and thalamic regions (Figure 2c)”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Task-based Alterations in the Cartographic Profile
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In the paper
“with the majority of time spent in integrated states (70.32 ± 1.4% of rest session; all variability measures reported as standard deviations)”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Fluctuations in Network Cartography
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- Probably not a substantial claim. 14
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“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).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Fluctuations in Network Cartography
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“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)”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Fluctuations in Network Cartography
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In the paper
“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.”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Task-based Alterations in the Cartographic Profile
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“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).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Task-based Alterations in the Cartographic Profile
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“the boundary threshold (results not shown, as no bins survived multiple comparisons correction)”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Investigating the Relationship Between Cartography and Behavior
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“For each comparison, the null hypothesis of no relationship was rejected after false discovery rate correction (p < 0.05).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Investigating the Relationship Between Cartography and Behavior
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In the paper
“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.”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Investigating the Relationship Between Cartography and Behavior
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In the paper
“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.”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Cartographic profiling
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In the paper
“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).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Replication analysis
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In the paper
“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)”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Abstract
What the sources say
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In the paper
“Within the brain, a highly dynamic functional landscape unfolds on a relatively fixed structural scaffold (Deco et al., 2015; Shen et al., 2015)”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Abstract
What the sources say
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In the paper
“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.”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Data pre-processing
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In the paper
“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).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Network Cartography Fluctuates with Pupil Diameter
What the sources say
- Not checked yetcites Kahneman, 1973
- Not checked yetcites McGinley et al., 2015 source
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In the paper
“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).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Discussion
What the sources say
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In the paper
“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).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Fluctuations in Network Cartography
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In the paper
“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.”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Discussion
What the sources say
- Not checked yetcites van den Heuvel et al., 2016 source
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In the paper
“by showing that fluctuations in neural gain are linked to alterations in network topology, that in turn, relate to effective behavioral performance”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Discussion
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In the paper
“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.”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Task-based Alterations in the Cartographic Profile
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In the paper
“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).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Discussion
What the sources say
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In the paper
“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).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Investigating the Relationship Between Cartography and Behavior
What the sources say
- Not checked yetcites Ratcliff, 1978 source
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In the paper
“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)”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Discussion
What the sources say
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In the paper
“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.”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Discussion
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In the paper
“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).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Data pre-processing
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In the paper
“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).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Investigating the Relationship Between Cartography and Behavior
What the sources say
- Not checked yetcites Krienen et al., 2014 source
- Not checked yetcites Sigman and Dehaene, 2008
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In the paper
“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)”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Abstract
What the sources say
- Not checked yetcites Zalesky et al., 2014 source
- Not checked yetcites Betzel et al., 2015
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In the paper
“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).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Data pre-processing
What the sources say
- Not checked yetcites Power et al., 2014 source
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In the paper
“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).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Time-resolved functional connectivity
What the sources say
- Not checked yetcites Shine et al., 2015 source
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In the paper
“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.”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Task-based alterations in the cartographic profile
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In the paper
“however it bears mention that functional divisions may differ across subjects (Laumann et al., 2015)”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Brain parcellation
What the sources say
- Not checked yetcites Laumann et al., 2015 source
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Cartographic profiling
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In the paper
“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.”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Cartographic profiling
What the sources say
- Not checked yetcites Zalesky et al., 2014 source
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Investigating the Relationship Between Cartography and Behavior
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In the paper
“the MTD is more sensitive to changes in covariance than connectivity (Shine et al., 2015)”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Time-resolved functional connectivity
What the sources say
- Not checked yetcites Shine et al., 2015 source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Time- resolved hub structure
What the sources say
- Not checked yetcites Power et al., 2013 source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Most importantly, as we show, our analyses were reliable and replicable using the MTD across multiple datasets.”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Time-resolved functional connectivity
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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”
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In the citing paper
“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
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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
MinorThe text refers to the global-efficiency formula as Equation 5, while the formula's caption labels it Equation 4.
“As global efficiency (Equation 5) cannot be computed from networks with negative weights”
“Equation 4 – global efficiency of a network, where n denotes the total nodes in the network and di,j denotes the shortest path between a node i and neighboring node j.”
Citations
Cites
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Control of goal-directed and stimulus-driven attention in the brain
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Complex network measures of brain connectivity: Uses and interpretations
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Nonparametric permutation tests for functional neuroimaging: A primer with examples
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The minimal preprocessing pipelines for the Human Connectome Project
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The brainweb: phase synchronization and large-scale integration
Varela, F., Lachaux, J.-P., Rodriguez, E., & Martinerie, J. (2001) · Nature Reviews Neuroscience · doi:10.1038/35067550 · Cited by 4,761
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A component based noise correction method (CompCor) for BOLD and perfusion based fMRI
Behzadi, Y., Restom, K., Liau, J., & Liu, T. T. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.04.042 · Cited by 4,502
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Items 201–300 of 995
If Neuroscience Needs Behavior, What Does Psychology Need?
Parada, F. J., & Rossi, A. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.00433 · Cited by 27
Predicting attention across time and contexts with functional brain connectivity
Song, H., & Rosenberg, M. D. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2020.12.007 · Cited by 29
Zarkali, A., Luppi, A. I., Stamatakis, E. A., Reeves, S., McColgan, P., Leyland, L., Lees, A. J., & Weil, R. S. (2022) · Communications Biology · doi:10.1038/s42003-022-03903-x · Cited by 36
Multi-modal and multi-subject modular organization of human brain networks
Puxeddu, M. G., Faskowitz, J., Sporns, O., Astolfi, L., & Betzel, R. F. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119673 · Cited by 31
Beyond the evoked/intrinsic neural process dichotomy
Bolt, T., Anderson, M. L., & Uddin, L. Q. (2017) · Network Neuroscience · doi:10.1162/netn_a_00028 · Cited by 35
Alagapan, S., Riddle, J., Huang, W. A., Hadar, E. J., Shin, H. W., & Frӧhlich, F. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.10.072 · Cited by 33
Cognitive flexibility: neurobehavioral correlates of changing one’s mind
Zühlsdorff, K., Dalley, J. W., Robbins, T. W., & Morein‐Zamir, S. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac431 · Cited by 35
Challenges in the measurement and interpretation of dynamic functional connectivity
Laumann, T. O., Snyder, A. Z., & Gratton, C. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00366 · Cited by 37
Linking brain network reconfiguration and intelligence: Are we there yet?
Girn, M., Mills, C., & Kalina Christoff, A. (2019) · Trends in Neuroscience and Education · doi:10.1016/j.tine.2019.04.001 · Cited by 29
Subthalamic stimulation modulates motor network in Parkinson’s disease: recover, relieve and remodel
Chu, C., Liu, S., He, N., Zeng, Z., Wang, J., Zhang, Z., Zeljic, K., van der Stelt, O., Sun, B., Yan, F., et al. (2023) · Brain · doi:10.1093/brain/awad004 · Cited by 29
A survey of brain functional network extraction methods using fMRI data
Du, Y., Fang, S., He, X., & Calhoun, V. D. (2024) · Trends in Neurosciences · doi:10.1016/j.tins.2024.05.011 · Cited by 33
Network Dynamics of Attention During a Naturalistic Behavioral Paradigm
Weber, R., Alicea, B., Huskey, R., & Mathiak, K. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00182 · Cited by 31
Body-World Coupling, Sensorimotor Mechanisms, and the Ontogeny of Social Cognition
Rojas‐Líbano, D., & Parada, F. J. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.03005 · Cited by 26
Guan, S., Jiang, R., Bian, H., Yuan, J., Xu, P., Meng, C., & Biswal, B. B. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00493 · Cited by 27
Craske, M. G., Herzallah, M. M., Nusslock, R., & Patel, V. (2023) · Nature Mental Health · doi:10.1038/s44220-022-00012-w · Cited by 31
Fukushima, M., & Sporns, O. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006497 · Cited by 25
Coronel‐Oliveros, C., Cofré, R., & Orio, P. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008737 · Cited by 29
Imaging the temporal dynamics of brain states with highly sampled fMRI
Yang, Z., & Lewis, L. D. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.02.005 · Cited by 33
Cross, N. E., Pomares, F. B., Nguyen, A., Perrault, A. A., Jegou, A., Uji, M., Lee, K., Razavipour, F., Ali, O. B. K., Aydın, Ü., et al. (2021) · PLoS Biology · doi:10.1371/journal.pbio.3001232 · Cited by 30
Multimodal analysis demonstrating the shaping of functional gradients in the marmoset brain
Tong, C., Liu, C., Zhang, K., Bo, B., Xia, Y., Yang, H., Feng, Y., & Liang, Z. (2022) · Nature Communications · doi:10.1038/s41467-022-34371-w · Cited by 27
Spatiotemporal Network Markers of Individual Variability in the Human Functional Connectome
Peña-Gómez, C., Avena‐Koenigsberger, A., Sepulcre, J., & Sporns, O. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx170 · Cited by 33
Singleton, S. P., Luppi, A. I., Carhart-Harris, R. L., Cruzat, J., Roseman, L., Nutt, D. J., Deco, G., Kringelbach, M. L., Stamatakis, E. A., & Kuceyeski, A. F. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.14.444193 · Cited by 30
Brainstem as a developmental gateway to social attention
Geva, R., Dital, A., Ramon, D., Yarmolovsky, J., Gidron, M., & Kuint, J. (2017) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12746 · Cited by 30
Directed functional connectivity using dynamic graphical models
Schwab, S., Harbord, R., Zerbi, V., Elliott, L. T., Afyouni, S., Smith, J. Q., Woolrich, M. W., Smith, S. M., & Nichols, T. E. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.03.074 · Cited by 27
Abnormal dynamic resting-state brain network organization in auditory verbal hallucination
Geng, H., Xu, P., Sommer, I. E., Luo, Y., Aleman, A., & Ćurĉić‐Blake, B. (2020) · Brain Structure and Function · doi:10.1007/s00429-020-02119-1 · Cited by 27
Toward an information theoretical description of communication in brain networks
Amico, E., Abbas, K., Duong-Tran, D. A., Tipnis, U., Rajapandian, M., Chumin, E. J., Ventresca, M., Harezlak, J., & Goñi, J. (2021) · Network Neuroscience · doi:10.1162/netn_a_00185 · Cited by 28
Time-varying whole-brain functional network connectivity coupled to task engagement
Xie, H., González-Castillo, J., Handwerker, D. A., Bandettini, P. A., Calhoun, V. D., Chen, G., Damaraju, E., Liu, X., & Mitra, S. D. (2018) · Network Neuroscience · doi:10.1162/netn_a_00051 · Cited by 24
Mennigen, E., Fryer, S. L., Rashid, B., Damaraju, E., Du, Y., Loewy, R. L., Stuart, B. K., Calhoun, V. D., & Mathalon, D. H. (2018) · Brain Connectivity · doi:10.1089/brain.2018.0579 · Cited by 26
On the nature of time-varying functional connectivity in resting fMRIPreprint
Lurie, D. J., Kessler, D. A., Bassett, D. S., et al. (2018) · doi:10.31234/osf.io/xtzre · Cited by 30
Self-citationMulti-policy models of interregional communication in the human connectomePreprint
Betzel, R. F., Faskowitz, J., Mišić, B., Sporns, O., & Seguin, C. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.08.490752 · Cited by 22
Unlocking the Potential of Two-Point Cells for Energy-Efficient and Resilient Training of Deep Nets
Adeel, A., Adetomi, A., Ahmed, K., Hussain, A., Arslan, T., & Phillips, W. A. (2023) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2022.3228537 · Cited by 32
Spina, V., Chisari, C., & Santarcangelo, E. L. (2020) · Neuroscience · doi:10.1016/j.neuroscience.2020.01.042 · Cited by 27
Ferré, P., Jarret, J., Brambati, S. M., Bellec, P., & Joanette, Y. (2020) · Neurobiology of Language · doi:10.1162/nol_a_00007 · Cited by 25
Functional neural network configuration in late childhood varies by age and cognitive state
Le, T. M., Huang, A., O’Rawe, J. F., & Leung, H. (2020) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2020.100862 · Cited by 30
Dynamic Functional Connectivity as a complex random walk: Definitions and the dFCwalk toolbox
Arbabyazd, L., Lombardo, D., Blin, O. J., Didic, M., Battaglia, D. A., & Jirsa, V. (2020) · MethodsX · doi:10.1016/j.mex.2020.101168 · Cited by 25
Dynamic network impairments underlie cognitive fluctuations in Lewy body dementia
Matar, E., Martens, K. A. E., Phillips, J. R., Wainstein, G., Halliday, G. M., Lewis, S. J. G., & Shine, J. M. (2022) · npj Parkinson s Disease · doi:10.1038/s41531-022-00279-x · Cited by 33
Self-citationFunctional connectome stability and optimality are markers of cognitive performance
Corriveau, A., Yoo, K., Kwon, Y. H., Chun, M. M., & Rosenberg, M. D. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac396 · Cited by 24
Neuronal connected burst cascades bridge macroscale adaptive signatures across arousal states
Munn, B., Müller, E. J., Medel, V., Naismith, S. L., Lizier, J. T., Sanders, R. D., & Shine, J. M. (2023) · Nature Communications · doi:10.1038/s41467-023-42465-2 · Cited by 29
Self-citationWass, S. V. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.03.029 · Cited by 29
Large-scale network dynamics of beta-band oscillations underlie auditory perceptual decision-making
Alavash, M., Daube, C., Wöstmann, M., Brandmeyer, A., & Obleser, J. (2017) · Network Neuroscience · doi:10.1162/netn_a_00009 · Cited by 25
Dynamic reconfiguration of functional brain networks during working memory trainingPreprint
Finc, K., Bonna, K., He, X., Lydon‐Staley, D. M., Kühn, S., Duch, W., & Bassett, D. S. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/685487 · Cited by 28
Chen, J.-Z., Tam, A., Kebets, V., Orban, C., Ooi, L. Q. R., Marek, S. A., Dosenbach, N. U. F., Eickhoff, S., Bzdok, D., Holmes, A., et al. (2020) · bioRxiv · doi:10.1101/2020.06.24.168724 · Cited by 24
Liu, Z., Ying, Y., & Wang, Q. (2022) · Science China Technological Sciences · doi:10.1007/s11431-022-2025-0 · Cited by 28
Data-Driven Approaches to Neuroimaging Analysis to Enhance Psychiatric Diagnosis and Therapy
Zhang, X., Braun, U., Tost, H., & Bassett, D. S. (2020) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2019.12.015 · Cited by 31
Wang, R., Su, X., Chang, Z., Lin, P., & Wu, Y. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3119940 · Cited by 26
Network-based approaches to examining stress in the adolescent brain
Ho, T. C., Dennis, E. L., Thompson, P. M., & Gotlib, I. H. (2018) · Neurobiology of Stress · doi:10.1016/j.ynstr.2018.05.002 · Cited by 27
Neurophysiological Profile of Antismoking Campaigns
Modica, E., Rossi, D., Cartocci, G., Perrotta, D., Feo, P. D., Mancini, M., Aricò, P., Inguscio, B. M. S., & Babiloni, F. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/9721561 · Cited by 28
Dajani, D. R., Odriozola, P., Winters, M., Voorhies, W., Marcano, S., Baez, A., Gates, K. M., Dick, A. S., & Uddin, L. Q. (2020) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01536 · Cited by 27
Sobczak, F., Pais‐Roldán, P., Takahashi, K., & Yu, X. (2021) · eLife · doi:10.7554/elife.68980 · Cited by 23
Brain states as wave-like motifs
Foster, M. L., & Scheinost, D. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.03.004 · Cited by 29
Modeling brain, symptom, and behavior in the winds of change
Lydon‐Staley, D. M., Cornblath, E. J., Blevins, A. S., & Bassett, D. S. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-00805-6 · Cited by 25
Abdallah, M., Zahr, N. M., Saranathan, M., Honnorat, N., Farrugia, N., Adolf Pfefferbaum, A., Sullivan, E. V., & Chanraud, S. (2021) · The Cerebellum · doi:10.1007/s12311-021-01241-y · Cited by 27
The modulation of brain network integration and arousal during exploration
Tardiff, N., Medaglia, J. D., Bassett, D. S., & Thompson‐Schill, S. L. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118369 · Cited by 22
Inner Hemispheric and Interhemispheric Connectivity Balance in the Human Brain
Krupnik, R., Yovel, Y., & Assaf, Y. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.1074-21.2021 · Cited by 24
Functional network antagonism and consciousness
Athena Demertzi, A., Kucyi, A., Ponce‐Alvarez, A., Keliris, G. A., Whitfield, S., & Deco, G. (2022) · Network Neuroscience · doi:10.1162/netn_a_00244 · Cited by 29
Dynamical interactions reconfigure the gradient of cortical timescales
Sorrentino, P., Rabuffo, G., Baselice, F., Lopez, E. T., Liparoti, M., Quarantelli, M., Sorrentino, G., Bernard, C., & Jirsa, V. (2022) · Network Neuroscience · doi:10.1162/netn_a_00270 · Cited by 18
Striatal subdivisions that coherently interact with multiple cerebrocortical networks
Ogawa, A., Osada, T., Tanaka, M., Hori, M., Aoki, S., Nikolaidis, A., Milham, M. P., & Konishi, S. (2018) · Human Brain Mapping · doi:10.1002/hbm.24275 · Cited by 24
Schirmer, M. D., Venkataraman, A., Rekik, I., Kim, M., Mostofsky, S. H., Nebel, M. B., Rosch, K. S., Seymour, K. E., Crocetti, D., Irzan, H., et al. (2021) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · Cited by 27
Joint Gaussian graphical model estimation: A survey
Tsai, K. H., Koyejo, S., & Kolar, M. (2022) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1582 · Cited by 26
Self-citationA multi-demand operating system underlying diverse cognitive tasks
Cai, W., Taghia, J., & Menon, V. (2024) · Nature Communications · doi:10.1038/s41467-024-46511-5 · Cited by 32
Arousal Contributions to Resting-State fMRI Connectivity and Dynamics
Gu, Y., Han, F., & Liu, X. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.01190 · Cited by 23
Understanding neural flexibility from a multifaceted definition
Yin, D., & Kaiser, M. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118027 · Cited by 24
Wang, L., Zhou, C., Cheng, W., Rolls, E. T., Huang, P., Ma, N., Liu, Y., Zhang, Y., Guan, X., Guo, T., et al. (2021) · Human Brain Mapping · doi:10.1002/hbm.25745 · Cited by 27
Recollection-related increases in functional connectivity across the healthy adult lifespan
King, D. R., de Chastelaine, M., & Rugg, M. D. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.09.026 · Cited by 19
Time-resolved structure-function coupling in brain networksPreprint
Liu, Z.-Q., Vázquez-Rodríguez, B., Spreng, R. N., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.08.451672 · Cited by 26
Castro, P., Luppi, A. I., Tagliazucchi, E., Perl, Y. S., Naçi, L., Owen, A. M., Sitt, J. D., Alain Destexhe, A., & Cofré, R. (2024) · Communications Biology · doi:10.1038/s42003-024-06858-3 · Cited by 34
Pajkossy, P., Szöllősi, Á., Demeter, G., & Racsmány, M. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.00506 · Cited by 21
Coronel‐Oliveros, C., Castro, S., Cofré, R., & Orio, P. (2021) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2021.687075 · Cited by 18
da Costa, C. R., Soares, J. M., Oliveira‐Silva, P., Sampaio, A., & Coutinho, J. F. P. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.718400 · Cited by 26
Whole-brain modeling explains the context-dependent effects of cholinergic neuromodulation
Coronel‐Oliveros, C., Gießing, C., Medel, V., Cofré, R., & Orio, P. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119782 · Cited by 22
Temporal Mapper: Transition networks in simulated and real neural dynamics
Zhang, M., Chowdhury, S., & Saggar, M. (2022) · Network Neuroscience · doi:10.1162/netn_a_00301 · Cited by 22
Unravelling consciousness and brain function through the lens of time, space, and information
Luppi, A. I., Rosas, F. E., Mediano, P. A. M., Athena Demertzi, A., Menon, D. K., & Stamatakis, E. A. (2024) · Trends in Neurosciences · doi:10.1016/j.tins.2024.05.007 · Cited by 25
Fu, Z., Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Iraji, A., Liu, J., & Calhoun, V. D. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02683-6 · Cited by 22
Symmetry breaking organizes the brain’s resting state manifold
Fousek, J., Rabuffo, G., Gudibanda, K., Sheheitli, H., Petkoski, S., & Jirsa, V. (2024) · Scientific Reports · doi:10.1038/s41598-024-83542-w · Cited by 36
Momi, D., Wang, Z., Parmigiani, S., Mikulan, E., Bastiaens, S. P., Oveisi, M. P., Kadak, K., Gaglioti, G., Waters, A. C., Hill, S., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58187-6 · Cited by 28
A biologically inspired cognitive skills measurement approach
Ahmad, S., Li, K., Eddine, H. A. I., & Khan, M. I. (2018) · Biologically Inspired Cognitive Architectures · doi:10.1016/j.bica.2018.04.006 · Cited by 24
Brain network dynamics during spontaneous strategy shifts and incremental task optimization
Allegra, M., Seyed-Allaei, S., Schuck, N. W., Amati, D., Laio, A., & Reverberi, C. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116854 · Cited by 27
Pupil-based States of Brain Integration across Cognitive States
Mäki-Marttunen, V. (2021) · Neuroscience · doi:10.1016/j.neuroscience.2021.07.016 · Cited by 26
Motivational deficits in schizophrenia relate to abnormalities in cortical learning rate signals
Hernaus, D., Xu, Z., Brown, E. C., Ruiz, R., Frank, M. J., Gold, J. M., & Waltz, J. A. (2018) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-018-0643-z · Cited by 21
Wang, W., Roy, A., Misra, G., Ho, R. L. M., Ribeiro-Dasilva, M., Fillingim, R. B., & Coombes, S. A. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101964 · Cited by 25
Characterization of network switching in disorder of consciousness at multiple time scales
Cai, L., Wei, X., Wang, J., Yi, G., Lu, M., & Dong, Y. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab79f5 · Cited by 26
An anesthetic protocol for preserving functional network structure in the marmoset monkey brain
Ortiz-Rios, M., Sirmpilatze, N., König, J., & Boretius, S. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00230 · Cited by 25
Gong, A., Liu, J., Jiang, C., & Fu, Y. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/4097561 · Cited by 23
Age differences in functional network reconfiguration with working memory training
Iordan, A. D., Moored, K., Katz, B., Cooke, K. A., Buschkuehl, M., Jaeggi, S. M., Polk, T. A., Peltier, S., Jonides, J., & Reuter‐Lorenz, P. A. (2021) · Human Brain Mapping · doi:10.1002/hbm.25337 · Cited by 26
Romano, A., Lopez, E. T., Cipriano, L., Liparoti, M., Minino, R., Polverino, A., Cavaliere, C., Aiello, M., Granata, C., Sorrentino, G., et al. (2023) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2023.08.003 · Cited by 20
Wass, S. V., Greenwood, E., Esposito, G., Smith, C. G., Necef, I., & Phillips, E. (2024) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13960 · Cited by 29
Network Neuroscience: A Framework for Developing Biomarkers in Psychiatry
Lydon‐Staley, D. M., & Bassett, D. S. (2018) · Current topics in behavioral neurosciences · doi:10.1007/7854_2018_41 · Cited by 24
Activation‐based association profiles differentiate network roles across cognitive loads
Zuo, N., Salami, A., Yang, Y., Yang, Z., Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., & Jiang, T. (2019) · Human Brain Mapping · doi:10.1002/hbm.24561 · Cited by 21
State and trait characteristics of anterior insula time-varying functional connectivity
Pasquini, L., Toller, G., Staffaroni, A. M., Brown, J. A., Deng, J., Lee, A. J., Kurcyus, K., Shdo, S. M., Allen, I. E., Sturm, V. E., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116425 · Cited by 20
Integrated Brain Network Architecture Supports Cognitive Task Performance
Schultz, D. H., & Cole, M. W. (2016) · Neuron · doi:10.1016/j.neuron.2016.10.004 · Cited by 22
A Domain-general Cognitive Core defined in Multimodally Parcellated Human CortexPreprint
Assem, M., Glasser, M. F., Essen, D. C. V., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/517599 · Cited by 22
Wainstein, G., Rojas‐Líbano, D., Medel, V., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.04.412551 · Cited by 23
Self-citationTask matters: Individual MEG signatures from naturalistic and neurophysiological brain states
Colenbier, N., Sareen, E., Puntas, T. D. Á., Griffa, A., Pellegrino, G., Mantini, D., Marinazzo, D., Arcara, G., & Amico, E. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120021 · Cited by 18
Same, Same but Different? A Multi-Method Review of the Processes Underlying Executive Control
Toba, M. N., Malkinson, T. S., Howells, H., Mackie, M., & Spagna, A. (2023) · Neuropsychology Review · doi:10.1007/s11065-023-09577-4 · Cited by 26
Integrative Brain States Facilitate the Expression of Parkinson's Tremor
Dirkx, M. F., Shine, J. M., & Helmich, R. C. G. (2023) · Movement Disorders · doi:10.1002/mds.29506 · Cited by 20
Self-citationDynamical rewiring promotes synchronization in memristive FitzHugh-Nagumo neuronal networks
Hu, X., Ding, Q., Wu, Y., Huang, W., Yang, L., & Jia, Y. (2024) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2024.115047 · Cited by 24
Hawco, C. S., Dickie, E. W., Jacobs, G. R., Daskalakis, Z. J., & Voineskos, A. N. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117823 · Cited by 21
The biological role of local and global fMRI BOLD signal variability in human brain organizationPreprint
Baracchini, G., Zhou, Y., Castanheira, J. D. S., Hansen, J. Y., Rieck, J. R., Turner, G. R., Grady, C. L., Mišić, B., Nomi, J. S., Uddin, L. Q., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.22.563476 · Cited by 18
Distinct but cooperating brain networks supporting semantic cognition
Jung, J., & Ralph, M. A. L. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac190 · Cited by 24
Reconfiguration of brain network dynamics in bipolar disorder: a hidden Markov model approach
Zhang, X., Yang, L., Lu, J., Yuan, Y., Li, D., Zhang, H., Yao, R., Xiang, J., & Wang, B. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-03212-3 · Cited by 29
Metadata
- 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)
- 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
- bronze
- doi.org
- 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
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.
A language model drew the claims from the paper on 2026-10-07, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-07 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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