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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“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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“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.
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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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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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“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
Corbetta, M., & Shulman, G. L. (2002) · Nature Reviews Neuroscience · doi:10.1038/nrn755 · Cited by 12,881
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
Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J. R., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.127 · Cited by 5,253
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 101–200 of 995
Modular origins of high-amplitude cofluctuations in fine-scale functional connectivity dynamics
Pope, M., Fukushima, M., Betzel, R. F., & Sporns, O. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2109380118 · Cited by 64
Cognitive and Neural State Dynamics of Narrative Comprehension
Song, H., Park, B., Park, H., & Shim, W. M. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.0037-21.2021 · Cited by 91
Spectral signature and behavioral consequence of spontaneous shifts of pupil-linked arousal in human
Podvalny, E., King, L. E., & He, B. J. (2021) · eLife · doi:10.7554/elife.68265 · Cited by 66
It’s about time: Linking dynamical systems with human neuroimaging to understand the brain
John, Y. J., Sawyer, K. S., Srinivasan, K., Müller, E. J., Munn, B., & Shine, J. M. (2022) · Network Neuroscience · doi:10.1162/netn_a_00230 · Cited by 71
Self-citationCohesive network reconfiguration accompanies extended training
Telesford, Q. K., Ashourvan, A., Wymbs, N. F., Grafton, S. T., Vettel, J. M., & Bassett, D. S. (2017) · Human Brain Mapping · doi:10.1002/hbm.23699 · Cited by 62
Ibáñez‐Marcelo, E., Campioni, L., Phinyomark, A., Petri, G., & Santarcangelo, E. L. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.06.044 · Cited by 76
Yan, B., Xu, X., Liu, M., Zheng, K., Liu, J., Li, J., Lei, W., Zhang, B., Lu, H., & Baojuan Li, A. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00191 · Cited by 75
Luppi, A. I., Mediano, P. A. M., Rosas, F. E., Allanson, J. E., Pickard, J. D., Williams, G. B., Craig, M. M., Finoia, P., Peattie, A. R. D., Coppola, P., et al. (2022) · Communications Biology · doi:10.1038/s42003-022-03330-y · Cited by 79
Luppi, A. I., Golkowski, D., Ranft, A., Ilg, R., Jordan, D., Menon, D. K., & Stamatakis, E. A. (2021) · Human Brain Mapping · doi:10.1002/hbm.25405 · Cited by 62
Medaglia, J. D., Satterthwaite, T. D., Kelkar, A., Ciric, R., Moore, T. M., Ruparel, K., Gur, R. C., Gur, R. E., & Bassett, D. S. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.10.048 · Cited by 64
Dynamic changes in brain lateralization correlate with human cognitive performance
Wu, X., Kong, X., Vatansever, D., Liu, Z., Zhang, K., Sahakian, B. J., Robbins, T. W., Feng, J., Thompson, P. M., & Zhang, J. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001560 · Cited by 72
Coherence resonance in neural networks: Theory and experiments
Pisarchik, A. N., & Hramov, A. E. (2022) · Physics Reports · doi:10.1016/j.physrep.2022.11.004 · Cited by 67
Ho, T. C., Sacchet, M. D., Connolly, C. G., Margulies, D. S., Tymofiyeva, O., Paulus, M. P., Simmons, A. N., Gotlib, I. H., & Yang, T. T. (2017) · Neuropsychopharmacology · doi:10.1038/npp.2017.103 · Cited by 73
Information processing speed in multiple sclerosis: Relevance of default mode network dynamics
van Geest, Q., Douw, L., Klooster, S. V. '., Leurs, C. E., Genova, H. M., Wylie, G. R., Steenwijk, M. D., Killestein, J., Geurts, J. J. G., & Hulst, H. E. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.05.015 · Cited by 60
Mueller, J. M., Pritschet, L., Santander, T., Taylor, C. M., Grafton, S. T., Jacobs, E. G., & Carlson, J. M. (2020) · Network Neuroscience · doi:10.1162/netn_a_00169 · Cited by 63
Neuropsychological evidence of multi-domain network hubs in the human thalamus
Hwang, K., Shine, J. M., Bruss, J., Tranel, D., & Boes, A. D. (2021) · eLife · doi:10.7554/elife.69480 · Cited by 71
Self-citationGao, S., Mishne, G., & Scheinost, D. (2021) · Human Brain Mapping · doi:10.1002/hbm.25561 · Cited by 58
Understanding the effects of serotonin in the brain through its role in the gastrointestinal tract
Shine, J. M., O’Callaghan, C., Walpola, I. C., Wainstein, G., Taylor, N. L., Aru, J., Huebner, B., & John, Y. J. (2022) · Brain · doi:10.1093/brain/awac256 · Cited by 68
Self-citationLuppi, A. I., Cain, J. A., Spindler, L. R. B., Górska, U., Toker, D., Hudson, A. E., Brown, E. N., Diringer, M. N., Stevens, R. D., Massimini, M., et al. (2021) · Neurocritical Care · doi:10.1007/s12028-021-01281-6 · Cited by 62
Fluctuations between high- and low-modularity topology in time-resolved functional connectivity
Fukushima, M., Betzel, R. F., He, Y., de Reus, M. A., van den Heuvel, M. P., Zuo, X., & Sporns, O. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.044 · Cited by 54
Saggar, M., Shine, J. M., Liégeois, R., Dosenbach, N. U. F., & Fair, D. A. (2022) · Nature Communications · doi:10.1038/s41467-022-32381-2 · Cited by 65
Self-citationHoy, K. E., McQueen, S., Elliot, D., Herring, S. E., Maller, J. J., & Fitzgerald, P. B. (2019) · Journal of Neurotrauma · doi:10.1089/neu.2018.6097 · Cited by 59
Metastability demystified — the foundational past, the pragmatic present and the promising future
Hancock, F., Rosas, F. E., Luppi, A. I., Zhang, M., Mediano, P. A. M., Cabral, J., Deco, G., Kringelbach, M. L., Breakspear, M. J., Kelso, J. A. S., et al. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-024-00883-1 · Cited by 92
Concurrent EEG- and fMRI-derived functional connectomes exhibit linked dynamics
Wirsich, J., Giraud, A., & Sadaghiani, S. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116998 · Cited by 57
Temporal fluctuations in the brain’s modular architecture during movie-watching
Betzel, R. F., Byrge, L. A., Esfahlani, F. Z., & Kennedy, D. P. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116687 · Cited by 55
Functional brain network reconfiguration during learning in a dynamic environment
Kao, C.-H., Khambhati, A. N., Bassett, D. S., Nassar, M. R., McGuire, J. T., Gold, J. I., & Kable, J. W. (2020) · Nature Communications · doi:10.1038/s41467-020-15442-2 · Cited by 57
Distinct network topology in Alzheimer’s disease and behavioral variant frontotemporal dementia
Ng, A. S. L., Wang, J., Ng, K. K., Chong, J. S. X., Qian, X., Lim, J. K. W., Tan, Y. J., Yong, A. C. W., Chander, R. J., Hameed, S., et al. (2021) · Alzheimer s Research & Therapy · doi:10.1186/s13195-020-00752-w · Cited by 69
fMRI Brain Decoding and Its Applications in Brain–Computer Interface: A Survey
Du, B., Cheng, X., Duan, Y., & Ning, H. (2022) · Brain Sciences · doi:10.3390/brainsci12020228 · Cited by 72
Tompson, S., Falk, E. B., Vettel, J. M., & Bassett, D. S. (2018) · Personality Neuroscience · doi:10.1017/pen.2018.4 · Cited by 51
Keerativittayayut, R., Aoki, R., Sarabi, M. T., Jimura, K., & Nakahara, K. (2018) · eLife · doi:10.7554/elife.32696 · Cited by 54
Resting-state global brain activity affects early β-amyloid accumulation in default mode network
Han, F., Liu, X., Mailman, R. B., Huang, X., Liu, X., & Initiative, T. A. D. N. (2023) · Nature Communications · doi:10.1038/s41467-023-43627-y · Cited by 68
Li, C., Xia, L., Ma, J., Li, S., Liang, S., Ma, X., Wang, T., Li, M., Wen, H., & Jiang, G. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.12.079 · Cited by 57
Robust dynamic brain coactivation states estimated in individuals
Peng, X., Liu, Q., Hubbard, C. S., Wang, D., Zhu, W., Fox, M., & Liu, H. (2023) · Science Advances · doi:10.1126/sciadv.abq8566 · Cited by 52
Guan, X., Zhang, Y., Wei, H., Guo, T., Zeng, Q., Zhou, C., Wang, J., Gao, T., Xuan, M., Gu, Q., et al. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.11.013 · Cited by 56
Signature of consciousness in brain-wide synchronization patterns of monkey and human fMRI signals
Hahn, G., Zamora‐López, G., Uhrig, L., Tagliazucchi, E., Laufs, H., Mantini, D., Kringelbach, M. L., Jarraya, B., & Deco, G. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117470 · Cited by 51
Müller, E. J., Munn, B., & Shine, J. M. (2020) · Nature Communications · doi:10.1038/s41467-020-19716-7 · Cited by 57
Self-citationPupillary Dynamics Link Spontaneous and Task-Evoked Activations Recorded Directly from Human Insula
Kucyi, A., & Parvizi, J. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.0435-20.2020 · Cited by 51
Age-Dependent Contribution of Domain-General Networks to Semantic Cognition
Martin, S., Saur, D., & Hartwigsen, G. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab252 · Cited by 46
Maliske, L., Schurz, M., & Kanske, P. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105080 · Cited by 64
Neuromodulatory control of complex adaptive dynamics in the brain
Shine, J. M. (2023) · Interface Focus · doi:10.1098/rsfs.2022.0079 · Cited by 58
Self-citationStructural brain network of gifted children has a more integrated and versatile topology
Solé‐Casals, J., Serra-Grabulosa, J. M., Romero-García, R., Vilaseca, G., Adán, A., Vilaró, N., Bargalló, N., & Bullmore, E. T. (2019) · Brain Structure and Function · doi:10.1007/s00429-019-01914-9 · Cited by 65
Subgraphs of functional brain networks identify dynamical constraints of cognitive control
Khambhati, A. N., Medaglia, J. D., Karuza, E. A., Thompson‐Schill, S. L., & Bassett, D. S. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006234 · Cited by 41
Network Localization of Executive Function Deficits in Patients with Focal Thalamic Lesions
Hwang, K., Bruss, J., Tranel, D., & Boes, A. D. (2020) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01628 · Cited by 54
The role of the locus coeruleus in shaping adaptive cortical melodies
Wainstein, G., Müller, E. J., Taylor, N. L., Munn, B., & Shine, J. M. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2022.03.006 · Cited by 47
Self-citationMetastable neural dynamics underlies cognitive performance across multiple behavioural paradigms
Alderson, T. H., Bokde, A. L. W., Kelso, J. A. S., Maguire, L., & Coyle, D. (2020) · Human Brain Mapping · doi:10.1002/hbm.25009 · Cited by 56
Jang, H., Mashour, G. A., Hudetz, A. G., & Huang, Z. (2024) · Nature Communications · doi:10.1038/s41467-024-53299-x · Cited by 57
Default mode network scaffolds immature frontoparietal network in cognitive development
Chen, M., He, Y., Lei, H. C., Xu, J., Tian, T., Peng, S., Zhao, G., Lu, J., Zhao, Y., Zhao, H., et al. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac414 · Cited by 55
Cai, W., Warren, S. L., Duberg, K., Pennington, B. F., Hinshaw, S. P., & Menon, V. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01022-3 · Cited by 46
Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain
Madan, C. R. (2021) · Neuroinformatics · doi:10.1007/s12021-021-09519-6 · Cited by 48
Spontaneous brain network activity: Analysis of its temporal complexity
Pedersen, M., Omidvarnia, A., Walz, J. M., Zalesky, A., & Jackson, G. D. (2017) · Network Neuroscience · doi:10.1162/netn_a_00006 · Cited by 41
Lynch, C. J., Breeden, A. L., Gordon, E. M., Cherry, J. B. C., Turkeltaub, P. E., & Vaidya, C. J. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy270 · Cited by 47
Beyond modularity: Fine-scale mechanisms and rules for brain network reconfiguration
Khambhati, A. N., Mattar, M. G., Wymbs, N. F., Grafton, S. T., & Bassett, D. S. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.015 · Cited by 46
A Synergistic Workspace for Human Consciousness Revealed by Integrated Information DecompositionPreprint
Luppi, A. I., Mediano, P. A. M., Rosas, F. E., Allanson, J. E., Pickard, J. D., Carhart-Harris, R. L., Williams, G. B., Craig, M. M., Finoia, P., Owen, A. M., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.25.398081 · Cited by 43
Pini, L., Pizzini, F. B., Galazzo, I. B., Ferrari, C., Galluzzi, S., Cotelli, M., Gobbi, E., Cattaneo, A., Cotelli, M. S., Geroldi, C., et al. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2021.11.005 · Cited by 48
Multitask brain network reconfiguration is inversely associated with human intelligence
Thiele, J. A., Faskowitz, J., Sporns, O., & Hilger, K. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab473 · Cited by 48
Learning how network structure shapes decision-making for bio-inspired computing
Schirner, M., Deco, G., & Ritter, P. (2023) · Nature Communications · doi:10.1038/s41467-023-38626-y · Cited by 55
A synergistic workspace for human consciousness revealed by Integrated Information Decomposition
Luppi, A. I., Mediano, P. A. M., Rosas, F. E., Allanson, J. E., Pickard, J. D., Carhart-Harris, R. L., Williams, G. B., Craig, M. M., Finoia, P., Owen, A. M., et al. (2023) · eLife · doi:10.7554/elife.88173 · Cited by 120
Dynamic switching between brain networks predicts creative ability
Chen, Q., Kenett, Y. N., Cui, Z., Takeuchi, H., Fink, A., Benedek, M., Zeitlen, D. C., Zhuang, K., Lloyd-Cox, J., Kawashima, R., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-07470-9 · Cited by 52
Beyond dimension reduction: stable electric fields emerge from and allow representational drift
Pinotsis, D. A., & Miller, E. K. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119058 · Cited by 37
Taylor, N. L., D’Souza, A., Munn, B., Lv, J., Záborszky, L., Müller, E. J., Wainstein, G., Calamante, F., & Shine, J. M. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119455 · Cited by 42
Self-citationTang, E., Mattar, M. G., Giusti, C., Lydon‐Staley, D. M., Thompson‐Schill, S. L., & Bassett, D. S. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0400-9 · Cited by 46
Lombardo, D., Cassé‐Perrot, C., Ranjeva, J., Troter, A. L., Guye, M., Wirsich, J., Payoux, P., Bartrés‐Faz, D., Bordet, R., Richardson, J. C., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117155 · Cited by 46
Nejati, V., Rasanan, A. H. H., Rad, J. A., Alavi, M. M., Haghi, S., & Nitsche, M. A. (2021) · Neurophysiologie Clinique · doi:10.1016/j.neucli.2021.11.005 · Cited by 45
The diversity and specificity of functional connectivity across spatial and temporal scales
Engel, T. A., Schölvinck, M. L., & Lewis, C. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118692 · Cited by 42
Brain network segregation and integration during an epoch-related working memory fMRI experiment
Fransson, P., Schiffler, B. C., & Thompson, W. H. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.040 · Cited by 42
Differential contributions of static and time-varying functional connectivity to human behavior
Eichenbaum, A., Pappas, I., Lurie, D. J., Cohen, J. R., & D’Esposito, M. (2020) · Network Neuroscience · doi:10.1162/netn_a_00172 · Cited by 37
Facing successfully high mental workload and stressors: An fMRI study
Causse, M., Lepron, E., Mandrick, K., Peysakhovich, V., Berry, I., Callan, D. E., & Rémy, F. (2021) · Human Brain Mapping · doi:10.1002/hbm.25703 · Cited by 59
Grounding the Attentional Boost Effect in Events and the Efficient Brain
Swallow, K. M., Broitman, A. W., Riley, E., & Turker, H. B. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.892416 · Cited by 45
Multiscale organization of neuronal activity unifies scale-dependent theories of brain function
Munn, B., Müller, E. J., Favre‐Bulle, I. A., Scott, E. K., Lizier, J. T., Breakspear, M. J., & Shine, J. M. (2024) · Cell · doi:10.1016/j.cell.2024.10.004 · Cited by 62
Self-citationReducing the influence of intramodular connectivity in participation coefficient
Pedersen, M., Omidvarnia, A., Shine, J. M., Jackson, G. D., & Zalesky, A. (2020) · Network Neuroscience · doi:10.1162/netn_a_00127 · Cited by 40
Self-citationArousal impacts distributed hubs modulating the integration of brain functional connectivity
Lee, K., Horien, C. L., O’Connor, D. A., Garand-Sheridan, B., Tokoglu, F., Scheinost, D., Lake, E. M. R., & Constable, R. T. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119364 · Cited by 38
Multi-view manifold learning of human brain-state trajectories
Busch, E. L., Huang, J., Benz, A., Wallenstein, T., Lajoie, G., Wolf, G., Krishnaswamy, S., & Turk‐Browne, N. B. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00419-0 · Cited by 41
Geng, H., Xu, P., Aleman, A., Qin, S., & Luo, Y. (2024) · Neuroscience Bulletin · doi:10.1007/s12264-023-01168-w · Cited by 48
Kostandyan, M., Bombeke, K., Carsten, T., Krebs, R. M., Notebaert, W., & Boehler, C. N. (2018) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2018.04.032 · Cited by 44
Impaired Evidence Accumulation as a Transdiagnostic Vulnerability Factor in Psychopathology
Sripada, C., & Weigard, A. S. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.627179 · Cited by 44
Schizophrenia Shows Disrupted Links between Brain Volume and Dynamic Functional Connectivity
Abrol, A., Rashid, B., Rachakonda, S., Damaraju, E., & Calhoun, V. D. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00624 · Cited by 37
Antonakakis, M., Dimitriadis, S. I., Zervakis, M. E., Papanicolaou, A. C., & Zouridakis, G. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00090 · Cited by 41
Anderson, E. D., & Barbey, A. K. (2022) · Human Brain Mapping · doi:10.1002/hbm.26164 · Cited by 42
Centralized and distributed cognitive task processing in the human connectome
Amico, E., Arenas, À., & Goñi, J. (2018) · Network Neuroscience · doi:10.1162/netn_a_00072 · Cited by 35
Zuberer, A., Kucyi, A., Yamashita, A., Wu, C. M., Walter, M., Valera, E. M., & Esterman, M. S. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117610 · Cited by 40
Turker, H. B., Riley, E., Luh, W., Colcombe, S. J., & Swallow, K. M. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118047 · Cited by 42
Wainstein, G., Rojas‐Líbano, D., Medel, V., Alnæs, D., Kolskår, K. K., Endestad, T., Laeng, B., Ossandón, T., Crossley, N. A., Matar, E., et al. (2021) · Network Neuroscience · doi:10.1162/netn_a_00205 · Cited by 55
Self-citationLow-dimensional organization of global brain states of reduced consciousness
Perl, Y. S., Pallavicini, C., Piccinini, J., Athena Demertzi, A., Bonhomme, V. L., Martial, C., Panda, R., Alnagger, N., Annen, J., Gosseries, O., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112491 · Cited by 37
Kolskår, K. K., Alnæs, D., Kaufmann, T., Richard, G., Sanders, A., Ulrichsen, K. M., Moberget, T., Andreassen, O. A., Nordvik, J. E., & Westlye, L. T. (2018) · eNeuro · doi:10.1523/eneuro.0092-18.2018 · Cited by 33
Tracking mood fluctuations with functional network patterns
Mirchi, N., Betzel, R. F., Bernhardt, B. C., Dagher, A., & Mišić, B. (2018) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsy107 · Cited by 39
Chen, X., & Yan, C. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118427 · Cited by 36
Latent brain state dynamics and cognitive flexibility in older adults
Lee, B., Cai, W., Young, C. B., Yuan, R., Ryman, S. G., Kim, J., Santini, V. E., Henderson, V. W., Poston, K. L., & Menon, V. (2021) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2021.102180 · Cited by 37
Social cognitive network neuroscience
Krendl, A. C., & Betzel, R. F. (2022) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsac020 · Cited by 40
Zöller, D., Bolton, T. A. W., Karahanoğlu, F. I., Eliez, S., Schaer, M., & Ville, D. V. D. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2863944 · Cited by 30
Nour, M. M., Dahoun, T., McCutcheon, R. A., Adams, R. A., Wall, M. B., & Howes, O. D. (2019) · eLife · doi:10.7554/elife.45045 · Cited by 35
Fukushima, M., & Sporns, O. (2020) · Communications Biology · doi:10.1038/s42003-020-01331-3 · Cited by 34
Allouch, S., Kabbara, A., Duprez, J., Khalil, M. A., Modolo, J., & Hassan, M. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120006 · Cited by 39
Evolutionarily conserved fMRI network dynamics in the mouse, macaque, and human brain
Gutierrez‐Barragan, D., Ramirez, J. S. B., Panzeri, S., Xu, T., & Gozzi, A. (2024) · Nature Communications · doi:10.1038/s41467-024-52721-8 · Cited by 40
Luo, W., Greene, A. S., & Constable, R. T. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118332 · Cited by 34
Maliske, L., & Kanske, P. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.845492 · Cited by 37
Amplification and Suppression of Distinct Brainwide Activity Patterns by Catecholamines
van den Brink, R. L., Nieuwenhuis, S., & Donner, T. H. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.0514-18.2018 · Cited by 27
Optically Modulated Nanofluidic Ionic Transistor for Neuromorphic Functions
Wang, J., Jiang, Y., Xiong, T., Lu, J., He, X., Yu, P., & Mao, L. (2024) · Angewandte Chemie International Edition · doi:10.1002/anie.202418949 · Cited by 35
Schwalm, M., & Jubal, E. R. (2017) · eNeuro · doi:10.1523/eneuro.0293-16.2017 · Cited by 29
Accelerated Aging of Functional Brain Networks Supporting Cognitive Function in Psychotic Disorders
Sheffield, J. M., Rogers, B. P., Blackford, J. U., Heckers, S., & Woodward, N. D. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2018.12.016 · Cited by 34
Unifying framework for cognitive training interventions in brain aging
Turnbull, A. G., Seitz, A. R., Tadin, D., & Lin, F. V. (2022) · Ageing Research Reviews · doi:10.1016/j.arr.2022.101724 · Cited by 34
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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