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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48 Own result · 28 Not checked yet · 15 Own interpretation · 13 Own method
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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
plaincorrelationaln = 14- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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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
plain- Asserted: passes — The authors put it forward as true.
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- The quote only partly states this claim. 2
- It may state two separate claims as one. 22
- Probably not a substantial claim. 14
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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
plaincorrelationaln = 92- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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
boostedcorrelationaln = 92- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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
plaindirectn = 92- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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The quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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
hedgedcorrelationaln = 92- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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
plaincorrelationaln = 92- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
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
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
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
“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
plaindirectn = 173- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
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
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
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
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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 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
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
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
hedgeddirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
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
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
“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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
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
hedgedconditionaln = 92- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
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
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
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
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
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
“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
plaindirectn = 92- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
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
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Cartographic profiling
“As global efficiency (Equation 5) cannot be computed from networks with negative weights”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Cartographic profiling
“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
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000) · Cognitive Psychology · doi:10.1006/cogp.1999.0734 · Cited by 15,798
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
Rubinov, M., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.003 · Cited by 11,522
Nonparametric permutation tests for functional neuroimaging: A primer with examples
Nichols, T. E., & Holmes, A. P. (2002) · Human Brain Mapping · doi:10.1002/hbm.1058 · Cited by 6,256
Avants, B., Epstein, C. L., Grossman, M., & Gee, J. C. (2007) · Medical Image Analysis · doi:10.1016/j.media.2007.06.004 · Cited by 5,924
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
Ratcliff, R. (1978) · Psychological Review · doi:10.1037/0033-295x.85.2.59 · Cited by 4,513
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
Aston-Jones, G., & Cohen, J. D. (2005) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.28.061604.135709 · Cited by 4,472
Cited by
Sorted by citations
Gratton, C., Laumann, T. O., Nielsen, A. N., Greene, D. J., Gordon, E. M., Gilmore, A. W., Nelson, S. M., Coalson, R. S., Snyder, A. Z., Schlaggar, B. L., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.03.035 · Cited by 846
Brain network dynamics are hierarchically organized in time
Vidaurre, D., Smith, S. M., & Woolrich, M. W. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1705120114 · Cited by 883
Questions and controversies in the study of time-varying functional connectivity in resting fMRI
Lurie, D. J., Kessler, D., Bassett, D. S., Betzel, R. F., Breakspear, M., Kheilholz, S., Kucyi, A., Liégeois, R., Lindquist, M. A., McIntosh, A. R., et al. (2020) · Network Neuroscience · doi:10.1162/netn_a_00116 · Cited by 648
Self-citationThe role of PFC networks in cognitive control and executive function
Menon, V., & D’Esposito, M. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-021-01152-w · Cited by 654
Human cognition involves the dynamic integration of neural activity and neuromodulatory systems
Shine, J. M., Breakspear, M. J., Bell, P. T., Martens, K. A. E., Shine, R., Koyejo, S., Sporns, O., & Poldrack, R. A. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0312-0 · Cited by 533
Self-citationHuman consciousness is supported by dynamic complex patterns of brain signal coordination
Demertzi, A., Tagliazucchi, E., Dehaene, S., Deco, G., Barttfeld, P., Raimondo, F., Martial, C., Fernández-Espejo, D., Rohaut, B., Voss, H. U., et al. (2019) · Science Advances · doi:10.1126/sciadv.aat7603 · Cited by 513
A cross-disorder connectome landscape of brain dysconnectivity
van den Heuvel, M. P., & Sporns, O. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0177-6 · Cited by 462
Segregated Systems of Human Brain Networks
Wig, G. S. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.09.006 · Cited by 374
A Domain-General Cognitive Core Defined in Multimodally Parcellated Human Cortex
Assem, M., Glasser, M. F., Essen, D. C. V., & Duncan, J. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa023 · Cited by 340
Alves, P. N., Foulon, C., Karolis, V., Bzdok, D., Margulies, D. S., Volle, E., & Thiebaut de Schotten, M. (2019) · Communications Biology · doi:10.1038/s42003-019-0611-3 · Cited by 357
Neuromodulatory Influences on Integration and Segregation in the Brain
Shine, J. M. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.04.002 · Cited by 328
Self-citationNetwork neuroscience theory of human intelligence
Barbey, A. K. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.10.001 · Cited by 355
Task-based dynamic functional connectivity: Recent findings and open questions
González-Castillo, J., & Bandettini, P. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.006 · Cited by 304
Rapid Reconfiguration of the Functional Connectome after Chemogenetic Locus Coeruleus Activation
Zerbi, V., Floriou‐Servou, A., Markicevic, M., Vermeiren, Y., Sturman, O., Privitera, M., von Ziegler, L., Ferrari, K. D., Weber, B., Deyn, P. P. D., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.05.034 · Cited by 343
A Synergistic Core for Human Brain Evolution and Cognition
Luppi, A. I., Mediano, P. A. M., Rosas, F. E., Holland, N., Fryer, T. D., O’Brien, J. T., Rowe, J. B., Menon, D. K., Bor, D., & Stamatakis, E. A. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01070-0 · Cited by 314
The behavioral and cognitive relevance of time-varying, dynamic changes in functional connectivity
Cohen, J. R. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.09.036 · Cited by 306
The impact of the human thalamus on brain-wide information processing
Shine, J. M., Lewis, L. D., Garrett, D. D., & Hwang, K. (2023) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00701-0 · Cited by 335
Self-citationHerbet, G., & Duffau, H. (2020) · Physiological Reviews · doi:10.1152/physrev.00033.2019 · Cited by 321
Wang, R., Liu, M., Cheng, X., Wu, Y., Hildebrandt, A., & Zhou, C. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2022288118 · Cited by 299
Consciousness-specific dynamic interactions of brain integration and functional diversity
Luppi, A. I., Craig, M. M., Pappas, I., Finoia, P., Williams, G. B., Allanson, J., Pickard, J. D., Owen, A. M., Naci, L., Menon, D. K., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12658-9 · Cited by 251
Principles of dynamic network reconfiguration across diverse brain states
Shine, J. M., & Poldrack, R. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.010 · Cited by 247
Self-citationComputational models link cellular mechanisms of neuromodulation to large-scale neural dynamics
Shine, J. M., Müller, E. J., Munn, B., Cabral, J., Moran, R., & Breakspear, M. J. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00824-6 · Cited by 249
Self-citationDynamic reconfiguration of functional brain networks during working memory training
Finc, K., Bonna, K., He, X., Lydon‐Staley, D. M., Kühn, S., Duch, W., & Bassett, D. S. (2020) · Nature Communications · doi:10.1038/s41467-020-15631-z · Cited by 255
Movie viewing elicits rich and reliable brain state dynamics
van der Meer, J. N., Breakspear, M. J., Chang, L. J., Sonkusare, S., & Cocchi, L. (2020) · Nature Communications · doi:10.1038/s41467-020-18717-w · Cited by 229
Dixon, M. L., Andrews‐Hanna, J. R., Spreng, R. N., Irving, Z. C., Mills, C., Girn, M., & Kalina Christoff, A. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.073 · Cited by 239
Towards a new approach to reveal dynamical organization of the brain using topological data analysis
Saggar, M., Sporns, O., Gonzalez-Castillo, J., Bandettini, P. A., Carlsson, G., Glover, G., & Reiss, A. L. (2018) · Nature Communications · doi:10.1038/s41467-018-03664-4 · Cited by 253
Chen, J., Tam, A., Kebets, V., Orbán, C., Ooi, L. Q. R., Asplund, C. L., Marek, S. A., Dosenbach, N. U. F., Eickhoff, S. B., Bzdok, D., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29766-8 · Cited by 240
Mental Time Travel? A Neurocognitive Model of Event Simulation.
Addis, D. R. (2020) · Review of Philosophy and Psychology · doi:10.1007/s13164-020-00470-0 · Cited by 225
Mnemonic Training Reshapes Brain Networks to Support Superior Memory
Dresler, M., Shirer, W. R., Konrad, B. N., Müller, N. C. J., Wagner, I. C., Fernández, G., Czisch, M., & Greicius, M. D. (2017) · Neuron · doi:10.1016/j.neuron.2017.02.003 · Cited by 221
Shine, J. M., Koyejo, S., & Poldrack, R. A. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1604898113 · Cited by 195
Self-citationReplicability of time-varying connectivity patterns in large resting state fMRI samples
Abrol, A., Damaraju, E., Miller, R. L., Stephen, J. M., Claus, E. D., Mayer, A. R., & Calhoun, V. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.09.020 · Cited by 192
Taghia, J., Cai, W., Ryali, S., Kochalka, J., Nicholas, J., Chen, T., & Menon, V. (2018) · Nature Communications · doi:10.1038/s41467-018-04723-6 · Cited by 200
A mechanistic model of connector hubs, modularity and cognition
Bertolero, M. A., Yeo, B. T. T., Bassett, D. S., & D’Esposito, M. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0420-6 · Cited by 213
Stomach-brain synchrony reveals a novel, delayed-connectivity resting-state network in humans
Rebollo, I., Devauchelle, A.-D., Béranger, B., & Tallon-Baudry, C. (2018) · eLife · doi:10.7554/elife.33321 · Cited by 194
“With short-time scales (~10–100 s), spontaneous brain activity is characterized by the emergence and dissolution of network patterns encompassing and extending classical RSN topologies (Ponce-Alvarez et al., 2015; Shine et al., 2016) with rich temporal trajectories (Mitra et al.”
BackgroundThe role of noradrenaline in cognition and cognitive disorders
Holland, N., Robbins, T. W., & Rowe, J. B. (2021) · Brain · doi:10.1093/brain/awab111 · Cited by 216
Hramov, A. E., Александрович, М. В., & Pisarchik, A. N. (2021) · Physics Reports · doi:10.1016/j.physrep.2021.03.002 · Cited by 228
Shine, J. M., Aburn, M. J., Breakspear, M. J., & Poldrack, R. A. (2018) · eLife · doi:10.7554/elife.31130 · Cited by 183
Self-citationA mechanistic model of connector hubs, modularity and cognition
Bertolero, M. A., Yeo, B. T. T., Bassett, D. S., & D’Esposito, M. (2018) · RePEc: Research Papers in Economics · Cited by 181
Hearne, L. J., Cocchi, L., Zalesky, A., & Mattingley, J. B. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0485-17.2017 · Cited by 186
The ascending arousal system shapes neural dynamics to mediate awareness of cognitive states
Munn, B., Müller, E. J., Wainstein, G., & Shine, J. M. (2021) · Nature Communications · doi:10.1038/s41467-021-26268-x · Cited by 188
Self-citationFrom maps to multi-dimensional network mechanisms of mental disorders
Braun, U., Schaefer, A., Betzel, R. F., Tost, H., Meyer-Lindenberg, A., & Bassett, D. S. (2018) · Neuron · doi:10.1016/j.neuron.2017.11.007 · Cited by 177
Functional connectivity predicts changes in attention observed across minutes, days, and months
Rosenberg, M. D., Scheinost, D., Greene, A. S., Avery, E. W., Kwon, Y. H., Finn, E. S., Ramani, R., Qiu, M., Constable, R. T., & Chun, M. M. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912226117 · Cited by 176
LSD alters dynamic integration and segregation in the human brain
Luppi, A. I., Carhart-Harris, R. L., Roseman, L., Pappas, I., Menon, D. K., & Stamatakis, E. A. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117653 · Cited by 165
Braun, U., Harneit, A., Pergola, G., Menara, T., Schäfer, A., Betzel, R. F., Zang, Z., Schweiger, J. I., Zhang, X., Schwarz, K., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23694-9 · Cited by 198
Beaty, R. E., Chen, Q., Christensen, A. P., Qiu, J., Silvia, P. J., & Schacter, D. L. (2017) · Human Brain Mapping · doi:10.1002/hbm.23884 · Cited by 184
Ito, T., Hearne, L. J., & Cole, M. W. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117141 · Cited by 163
Entropy and the Brain: An Overview
Keshmiri, S. (2020) · Entropy · doi:10.3390/e22090917 · Cited by 176
Three distinct sets of connector hubs integrate human brain function
Gordon, E. M., Lynch, C. J., Gratton, C., Laumann, T. O., Gilmore, A. W., Greene, D. J., Ortega, M., Nguyen, A. L., Schlaggar, B. L., Petersen, S. E., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.07.050 · Cited by 149
Why is everyone talking about brain state?
Greene, A. S., Horien, C. L., Barson, D. G., Scheinost, D., & Constable, R. T. (2023) · Trends in Neurosciences · doi:10.1016/j.tins.2023.04.001 · Cited by 161
Tools of the trade: estimating time-varying connectivity patterns from fMRI data
Iraji, A., Faghiri, A., Lewis, N., Fu, Z., Rachakonda, S., & Calhoun, V. D. (2020) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsaa114 · Cited by 155
Brain network dynamics in high-functioning individuals with autism
Watanabe, T., & Rees, G. (2017) · Nature Communications · doi:10.1038/ncomms16048 · Cited by 165
Singleton, S. P., Luppi, A. I., Carhart-Harris, R. L., Cruzat, J., Roseman, L., Nutt, D., Deco, G., Kringelbach, M. L., Stamatakis, E. A., & Kuceyeski, A. F. (2022) · Nature Communications · doi:10.1038/s41467-022-33578-1 · Cited by 149
Cognitive chimera states in human brain networks
Bansal, K., Garcia, J. O., Tompson, S., Verstynen, T., Vettel, J. M., & Muldoon, S. F. (2019) · Science Advances · doi:10.1126/sciadv.aau8535 · Cited by 160
Fukushima, M., Betzel, R. F., He, Y., van den Heuvel, M. P., Zuo, X., & Sporns, O. (2017) · Brain Structure and Function · doi:10.1007/s00429-017-1539-3 · Cited by 137
Just a thought: How mind-wandering is represented in dynamic brain connectivity
Kucyi, A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.001 · Cited by 142
Modeling and interpreting mesoscale network dynamics
Khambhati, A. N., Sizemore, A. E., Betzel, R. F., & Bassett, D. S. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.029 · Cited by 127
Dynamic functional connectivity and brain metastability during altered states of consciousness
Cavanna, F., Vilas, M. G., Palmucci, M., & Tagliazucchi, E. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.09.065 · Cited by 137
From connectome to cognition: The search for mechanism in human functional brain networks
Mill, R. D., Ito, T., & Cole, M. W. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.060 · Cited by 122
Gilat, M., Bell, P. T., Martens, K. A. E., Georgiades, M., Hall, J. M., Walton, C. C., Lewis, S. J. G., & Shine, J. M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.073 · Cited by 136
Self-citationGarcia, J. O., Ashourvan, A., Muldoon, S. F., Vettel, J. M., & Bassett, D. S. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2786710 · Cited by 132
Dopamine Signaling Modulates the Stability and Integration of Intrinsic Brain Networks
Shafiei, G., Zeighami, Y., Clark, C. A., Coull, J. T., Nagano‐Saito, A., Leyton, M., Dagher, A., & Mišić, B. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy264 · Cited by 128
Core and matrix thalamic sub-populations relate to spatio-temporal cortical connectivity gradients
Müller, E. J., Munn, B., Hearne, L. J., Smith, J. B., Fulcher, B., Arnatkevičiūtė, A., Lurie, D. J., Cocchi, L., & Shine, J. M. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117224 · Cited by 119
Self-citationHierarchical functional system development supports executive function
Keller, A. S., Sydnor, V. J., Pines, A. R., Fair, D. A., Bassett, D. S., & Satterthwaite, T. D. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2022.11.005 · Cited by 130
Topography and behavioral relevance of the global signal in the human brain
Li, J., Bolt, T., Bzdok, D., Nomi, J. S., Yeo, B. T. T., Spreng, R. N., & Uddin, L. Q. (2019) · Scientific Reports · doi:10.1038/s41598-019-50750-8 · Cited by 101
Betzel, R. F., Bertolero, M. A., Gordon, E. M., Gratton, C., Dosenbach, N. U. F., & Bassett, D. S. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.07.003 · Cited by 111
Battaglia, D. A., Boudou, T., Hansen, E. C. A., Lombardo, D., Chettouf, S., Daffertshofer, A., McIntosh, A. R., Zimmermann, J., Ritter, P., & Jirsa, V. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117156 · Cited by 98
Transition of the functional brain network related to increasing cognitive demands
Finc, K., Bonna, K., Lewandowska, M., Wolak, T. P., Nikadon, J., Dreszer, J., Duch, W., & Kühn, S. (2017) · Human Brain Mapping · doi:10.1002/hbm.23621 · Cited by 116
Controversies and progress on standardization of large-scale brain network nomenclature
Uddin, L. Q., Betzel, R. F., Cohen, J. R., Damoiseaux, J. S., Brigard, F. D., Eickhoff, S. B., Fornito, A., Gratton, C., Gordon, E. M., Laird, A. R., et al. (2023) · Network Neuroscience · doi:10.1162/netn_a_00323 · Cited by 125
Self-citationDynamic Flexibility in Striatal-Cortical Circuits Supports Reinforcement Learning
Gerraty, R. T., Davidow, J. Y., Foerde, K., Galván, A., Bassett, D. S., & Shohamy, D. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.2084-17.2018 · Cited by 102
Respiratory rhythms of the predictive mind.
Allen, M., Varga, S., & Heck, D. H. (2023) · Psychological Review · doi:10.1037/rev0000391 · Cited by 114
Dynamic expression of brain functional systems disclosed by fine-scale analysis of edge time series
Sporns, O., Faskowitz, J., Teixeira, A. S., Cutts, S. A., & Betzel, R. F. (2021) · Network Neuroscience · doi:10.1162/netn_a_00182 · Cited by 98
Spontaneous cognitive processes and the behavioral validation of time-varying brain connectivity
Kucyi, A., Tambini, A., Sadaghiani, S., Keilholz, S. D., & Cohen, J. R. (2018) · Network Neuroscience · doi:10.1162/netn_a_00037 · Cited by 95
Time-resolved structure-function coupling in brain networks
Liu, Z.-Q., Vázquez-Rodríguez, B., Spreng, R. N., Bernhardt, B. C., Betzel, R. F., & Mišić, B. (2022) · Communications Biology · doi:10.1038/s42003-022-03466-x · Cited by 103
Partial entropy decomposition reveals higher-order information structures in human brain activity
Varley, T. F., Pope, M., Puxeddu, M. G., Joshua, J., & Sporns, O. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2300888120 · Cited by 103
Precision Neuroscience: Dense Sampling of Individual Brains
Poldrack, R. A. (2017) · Neuron · doi:10.1016/j.neuron.2017.08.002 · Cited by 86
Self-citationYoo, K., Rosenberg, M. D., Noble, S., Scheinost, D., Constable, R. T., & Chun, M. M. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.060 · Cited by 92
Temporal stability of functional brain modules associated with human intelligence
Hilger, K., Fukushima, M., Sporns, O., & Fiebach, C. J. (2019) · Human Brain Mapping · doi:10.1002/hbm.24807 · Cited by 100
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Griffa, A., & van den Heuvel, M. P. (2018) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2018.20.2/agriffa · Cited by 94
Catecholaminergic manipulation alters dynamic network topology across cognitive states
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Self-citation
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
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