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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“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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“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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“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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“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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“The other five tasks recruited levels of integration between these two extremes.”
§ 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 resting brain explored a dynamical repertoire within this topological regime (greater than expected by a stationary null model)”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Fluctuations in Network Cartography
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“whereas those associated with the integrated states had greater global efficiency (ES = 0.18 ± 0.03 vs. EI = 0.24 ± 0.05; d = 1.5; p = 10−8; Bullmore and Sporns, 2012)”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Fluctuations in Network Cartography
What the sources say
- Not checked yetcites Bullmore and Sporns, 2012 source
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“that was maximal within frontoparietal, striatal and thalamic regions”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Network Cartography Fluctuates with Pupil Diameter
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“We observed a strong correlation between fluctuations in cartography across all parcels and the blocks of the experimental task (group mean Pearson’s r = 0.521; R2 = 0.27; p = 10−10; Figure 2a & Video 2)”
§ 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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“fluctuating aperiodically between the integrated and segregated temporal states”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Fluctuations in Network Cartography
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“with the integrated states characterized by a global increase in inter-modular communication across the brain (FDR α < 0.05 for all 375 individual parcels)”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Fluctuations in Network Cartography
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“we also observed window-to-window fluctuations in intra-modular connectivity (WT) within individual parcels”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Fluctuations in Network Cartography
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“inversely correlated with non-decision time (Figure 3c)”
§ 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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“temporal windows associated with segregated states had significantly elevated modularity (QS = 0.55 ± 0.1 vs. QI = 0.42 ± 0.2; Cohen’s d = 0.9; p = 10−11; Sporns and Betzel, 2015)”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Fluctuations in Network Cartography
What the sources say
- Not checked yetcites Sporns and Betzel, 2015
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“and had no relationship to the boundary threshold”
§ 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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“and a set of regions in visual cortex and insula that were associated with elevated WT (red; 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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In the paper
“The shift towards integration was most prominent in sensory and attentional networks (Figure 1d; FDR α < 0.05)”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Fluctuations in Network Cartography
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“Indeed, all of the 375 regions showed a significant shift towards greater inter-modular connectivity (BT) during the N-back task when compared to the resting state (FDR α < 0.05 for all 375 regions).”
§ 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 cartographic profile observed in the resting state was modulated by the performance of a range of cognitive tasks in proportion to task demands (Figure 2).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Discussion
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“These changes were coincident with increased task-driven connectivity between frontoparietal, dorsal attention, cingulo-opercular and visual networks (2-back versus 0-back blocks; FDR q < 0.05; Figure S4)”
§ 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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“For both drift rate and non-decision time (and in both the Discovery and Replication cohort), the relationship between cognitive function and integration was most pronounced across frontoparietal, striatal, thalamic and pallidal regions (FDR α < 0.05; Figure 3b and 3c).”
§ 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 expected, all 375 parcels demonstrated higher BT in the more Integrated states, whereas none of the 375 parcels showed significantly different WT in either state (FDR α < 0.05).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Cartographic profiling
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In the paper
“this test demonstrated that the first two principle components for each subject were associated with the integrated (20.2 ± 1.4% variance) or segregated state (4.9 ± 2.3% of variance).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Cartographic profiling
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In the paper
“Finally, the linear relationships between behavioral performance and the cartographic profile were consistent across the discovery and replication datasets.”
§ 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 mean of these regressors was then correlated with the cartographic profile across all temporal windows for each of the 14 subjects (mean correlation: r = 0.241 ± 0.06).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Network Cartography Fluctuates with Pupil Diameter
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In the paper
“As expected, all 375 parcels demonstrated higher BT in the more Integrated states, whereas none of the 375 parcels showed significantly different WT in either state (FDR α < 0.05).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Cartographic profiling
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In the paper
“A spatial correlation between the two datasets was strongly positive for both the relationship with drift rate (r = 0.613; R2 = 0.37; p = 10−11; Figure S3)”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Replication analysis
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“and non-decision time (r = 0.681; R2 = 0.46; p = 10−15; Figure S3)”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Replication analysis
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“In addition, the same fluctuations observed in the HCP dataset were also present in the NKI dataset (see Video 3 at http://github.com/macshine/coupling).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Replication analysis
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In the paper
“but the null hypothesis could not be rejected for the diffusion boundary (p > 0.500).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Replication analysis
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“Overall, we observed a strong positive correlation between the outcome measures identified in the two sessions (for all statistical tests, p < 0.001): graph measures – rWT, = 0.982, rBT = 0.957; and mean cartographic profiles rCart = 0.982 (Figure S2).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Replication analysis
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“We also performed a targeted analysis to determine whether activity and connectivity within the default network were related to fluctuations in BT (activity: group mean r = −0.044 ± 0.09; p = 10−5; and connectivity: group mean r = 0.127 ± 0.09; p = 10−12).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Cartographic profiling
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In the paper
“We also performed a targeted analysis to determine whether activity and connectivity within the default network were related to fluctuations in BT (activity: group mean r = −0.044 ± 0.09; p = 10−5; and connectivity: group mean r = 0.127 ± 0.09; p = 10−12).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Cartographic profiling
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In the paper
“We also observed similarly positive relationships between the group-level outcome measures estimated from the HCP and NKI data (for all statistical tests, p < 0.001): graph measures – rWT = 0.941, rBT = 0.857; and mean cartographic profiles – rCart = 0.927 (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
“We observed a rightward deviation in the mean cartographic profile during the 2-back vs 0-back block”
§ 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
“Together, our results confirm a direct link between cognitive performance and the dynamic reorganization of the network structure of the brain.”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Abstract
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In the paper
“suggesting that ascending neuromodulatory systems may govern the transition between these alternative modes of brain function”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Abstract
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In the paper
“Together, our results demonstrate that global brain integration is closely related to cognitive function during an N-back task.”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Discussion
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In the paper
“The locus coeruleus (Aston-Jones and Cohen, 2005) is known to modulate pupil diameter (Joshi et al., 2016), and thus by inference, may play a role in the modulation of fluctuations in global network topology through phasic alterations in neural gain (Eldar et al., 2013).”
§ 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
“Together, these results suggest that the brain transitions into a state of higher global integration in order to meet extrinsic task demands.”
§ 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
“Each of these patterns was replicated in a separate cohort of 92 subjects.”
§ 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
“Finally, we were able to demonstrate that a network of right-lateralized frontoparietal, striatal and thalamic regions were responsible for mediating the effects of integration on cognitive function (Figure 4c).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Discussion
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In the paper
“Together, these results suggest that integration may be particularly important for more difficult tasks, perhaps involving cognitive control”
§ 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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“these results suggest that the observed global fluctuations in network structure over time may have been driven by ongoing dynamic alterations in ascending neuromodulatory input to the cortex and subcortex, which through the modulation of neural gain, may have mediated increases in connectivity between otherwise segregated regions of the brain.”
§ 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 Eldar et al., 2013 source
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In the paper
“Here, we extend these studies by demonstrating fluctuations in network topology that relate to computationally-meaningful measures of effective behavioral performance.”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Discussion
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In the paper
“These results suggest that the time-resolved measures identified in this study were reliable across sessions, individuals and independent datasets collected using different scanners and imaging protocols.”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Reproducibility
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“many of which have been previously identified as belonging to a ‘rich club’ of densely-interconnected, high degree ‘hub’ nodes that are critical for the resilience and stability of the global brain network (van den Heuvel and Sporns, 2013)”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Task-based Alterations in the Cartographic Profile
What the sources say
- Not checked yetcites van den Heuvel and Sporns, 2013 source
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In the paper
“Importantly, the extent of integration remained correlated with the task regressor even after controlling for the global signal (mean r = 0.452 ± 0.21; p = 10−10) and the mean time-resolved connectivity across all parcels (mean r = 0.393 ± 0.14; p = 10−9)”
§ 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
“our results suggest that system-wide alterations in network topology facilitate more effective behavioral performance”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Discussion
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“suggesting that the cartographic profile may reflect changes in the engagement of attention and cognition over time (Corbetta and Shulman, 2002)”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Fluctuations in Network Cartography
What the sources say
- Not checked yetcites Corbetta and Shulman, 2002 source
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In the paper
“By catalyzing communication between specialist regions of the brain that would otherwise remain segregated, global integration increases an individuals ability to accomplish complex cognitive tasks”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Discussion
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In the paper
“Together, these results highlight a distributed network of brain regions that mediate the computational integration required for effective cognitive processing.”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Identifying Regions Related to Global Integration
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In the paper
“suggesting that global integration may have facilitated communication between otherwise segregated systems during more challenging 2-back condition”
§ 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
“These experiments provide a sensitive method for non-invasively identifying time-sensitive shifts in inter-areal synchrony, which has been proposed as a key mechanism for effective communication between distant neural regions (Fries, 2015; Varela et al., 2001).”
§ 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
“In the task data, each of the relationships identified between the cartographic profile and behavior were replicated in the second set of 92 individuals from the HCP (both r > 0.610; p < 0.001; Figure S2).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Results > Reproducibility
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesWhat 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 replicated the analysis after separately regressing the global signal and the mean MTD value across all parcels (global signal: mean r = 0.452 ± 0.21, p = 10−10; mean MTD: mean r = 0.393 ± 0.14 p = 10−9).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Task-based alterations in the cartographic profile
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“We used the EZ-diffusion model instead of alternative diffusion fitting routines (e.g. fast-dm or DMAT) because previous work has shown that the EZ-diffusion model is particularly effective for recovering individual differences in parameter values, which were of particular interest in this experiment (van Ravenzwaaij and Oberauer, 2009).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Investigating the Relationship Between Cartography and Behavior
What the sources say
- Not checked yetcites van Ravenzwaaij and Oberauer, 2009
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“others have shown that covariance is a more reliable marker of coupling strength in BOLD data (Cole et al., 2016).”
§ 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 Cole et al., 2016 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesWhat 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
“The partition identified at each value of k was strongly similar to the pattern identified at k = 2 (mean mutual information = 0.400 ± 0.02; Figure S1).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Cartographic profiling
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“Therefore, we proceeded with the EZ-diffusion model, which performs as well or better than more complicated fitting routines when contaminants are not present (Ratcliff et al., 2015; van Ravenzwaaij and Oberauer, 2009).”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Investigating the Relationship Between Cartography and Behavior
What the sources say
- Not checked yetcites Ratcliff et al., 2015 source
- Not checked yetcites van Ravenzwaaij and Oberauer, 2009
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“As global efficiency (Equation 5) cannot be computed from networks with negative weights (Barch et al., 2013), we first thresholded the connectivity matrix within each window to include only positive edge weights before calculating global efficiency on the remaining connected component.”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Cartographic profiling
What the sources say
- Not checked yetcites Barch et al., 2013 source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“we used a recently described statistical technique (Multiplication of Temporal Derivatives [MTD]; Shine et al., 2015) that allows greater temporal resolution of time-resolved connectivity in BOLD time series data when compared to the conventional sliding-window Pearson’s correlation coefficient (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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesWhat 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 significant fluctuations along the BT axis remained after correcting for ongoing changes in the number of modules per temporal window.”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Cartographic profiling
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe 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
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
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.
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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Strength:hedged· 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. 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
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. 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
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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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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
Strength:boosted· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Importantly, the two states were matched on graph density, suggesting that the fluctuations in BT did not occur simply due to alterations in network sparsity over time.”
§ The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance > Experimental Procedures > Cartographic profiling
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
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
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
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In the citing paper
“this conceptualization takes into account our increasing understanding of these whole brain networks as dynamic entities, with patterns of activity and connectivity (both within-network and with other networks) shifting as required by ever-changing situational demands (Cabral, Kringelbach, & Deco, 2014;Shine et al., 2016).”
From Mental Time Travel? A Neurocognitive Model of Event Simulation. (2020) · cites this paper as Shine et al., 2016
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Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Global-efficiency equation has conflicting numbers
MinorThe text refers to the global-efficiency formula as Equation 5, while the formula's caption labels it Equation 4.
“As global efficiency (Equation 5) cannot be computed from networks with negative weights”
“Equation 4 – global efficiency of a network, where n denotes the total nodes in the network and di,j denotes the shortest path between a node i and neighboring node j.”
Citations
Cites
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Control of goal-directed and stimulus-driven attention in the brain
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
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A component based noise correction method (CompCor) for BOLD and perfusion based fMRI
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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-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
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.
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