On noise in the nervous system.

Pinneo, Lawrence R.

doi:10.1037/h0023240

Cited by 47 publications

(36 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In these models, anatomical connectivity is best reflected by rsFC at coarse timescales and is only weakly correlated with rsFC at finer timescales (Honey et al, 2007). The observation that hubs are functionally variable (Honey et al, 2007;Mišić et al, 2011), especially with each other (Allen et al, 2014), seems contradictory to the evidence that hubs are densely structurally interconnected. Presumably, these connections should mostly limit the functional interactions of hubs to other hubs.…”

Section: Introductionmentioning

confidence: 65%

“…At higher dosages, isoflurane has cooccurring effects on cerebral blood flow (CBF), blood volume, and metabolic rate that can result in neurovascular decoupling-confounding observations of neural changes (Masamoto and Kanno, 2012). However, the level used in the present study has been shown to preserve CBF autoregulation (Eger, 1984;Li et al, 2013) and result in slow continuous EEG activity and not burst suppression, a pattern characteristic of isoflurane at higher dosages (Vincent et al, 2007). Dose-dependent analysis has shown stable spatial and temporal FC patterns at doses Ͻ1.50%, suggesting a preservation of intrinsic functional organization .…”

Section: Methodsmentioning

confidence: 92%

“…Isoflurane is a commonly used anesthetic in resting-state investigations of nonhuman primates, revealing robust and homologous FC patterns (Vincent et al, 2007;Shmuel and Leopold, 2008;Teichert et al, 2010;Hutchison et al, 2011;Mars et al, 2011; for review, see Hutchison and Everling, 2012) while eliminating training requirements, physiological stress, and motion. At higher dosages, isoflurane has cooccurring effects on cerebral blood flow (CBF), blood volume, and metabolic rate that can result in neurovascular decoupling-confounding observations of neural changes (Masamoto and Kanno, 2012).…”

Section: Methodsmentioning

confidence: 99%

“…The window was advanced in increments of one time point along the entire time series and the correlation recalculated. Window sizes were selected based on previous studies of rsFC dynamics (Hutchison et al, 2013b;Allen et al, 2014). Each FC matrix was corrected for multiple comparisons using a false discovery rate (FDR) procedure (Benjamini et al, 2006) with the FDR set to p Ͻ 0.05.…”

Section: Methodsmentioning

confidence: 99%

“…Functional connectivity (FC) varies significantly across timescales, even in the absence of an explicit task (Hutchison et al, 2013a). Resting-state FC (rsFC) dynamics are thought to reflect the brain's exploration of different network configurations.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Network Structure Shapes Spontaneous Functional Connectivity Dynamics

et al. 2015

View full text Add to dashboard Cite

The structural organization of the brain constrains the range of interactions between different regions and shapes ongoing information processing. Therefore, it is expected that large-scale dynamic functional connectivity (FC) patterns, a surrogate measure of coordination between brain regions, will be closely tied to the fiber pathways that form the underlying structural network. Here, we empirically examined the influence of network structure on FC dynamics by comparing resting-state FC (rsFC) obtained using BOLD-fMRI in macaques (Macaca fascicularis) to structural connectivity derived from macaque axonal tract tracing studies. Consistent with predictions from simulation studies, the correspondence between rsFC and structural connectivity increased as the sample duration increased. Regions with reciprocal structural connections showed the most stable rsFC across time. The data suggest that the transient nature of FC is in part dependent on direct underlying structural connections, but also that dynamic coordination can occur via polysynaptic pathways. Temporal stability was found to be dependent on structural topology, with functional connections within the rich-club core exhibiting the greatest stability over time. We discuss these findings in light of highly variable functional hubs. The results further elucidate how large-scale dynamic functional coordination exists within a fixed structural architecture.

show abstract

Section: Introductionmentioning

confidence: 65%

Section: Methodsmentioning

confidence: 92%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Network Structure Shapes Spontaneous Functional Connectivity Dynamics

et al. 2015

View full text Add to dashboard Cite

show abstract

Spatiotemporal complexity patterns of resting‐state bioelectrical activity explain fluid intelligence: Sex matters

Dreszer

Grochowski

Lewandowska

et al. 2020

Human Brain Mapping

View full text Add to dashboard Cite

Neural complexity is thought to be associated with efficient information processing but the exact nature of this relation remains unclear. Here, the relationship of fluid intelligence (gf) with the resting‐state EEG (rsEEG) complexity over different timescales and different electrodes was investigated. A 6‐min rsEEG blocks of eyes open were analyzed. The results of 119 subjects (57 men, mean age = 22.85 ± 2.84 years) were examined using multivariate multiscale sample entropy (mMSE) that quantifies changes in information richness of rsEEG in multiple data channels at fine and coarse timescales. gf factor was extracted from six intelligence tests. Partial least square regression analysis revealed that mainly predictors of the rsEEG complexity at coarse timescales in the frontoparietal network (FPN) and the temporo‐parietal complexities at fine timescales were relevant to higher gf. Sex differently affected the relationship between fluid intelligence and EEG complexity at rest. In men, gf was mainly positively related to the complexity at coarse timescales in the FPN. Furthermore, at fine and coarse timescales positive relations in the parietal region were revealed. In women, positive relations with gf were mostly observed for the overall and the coarse complexity in the FPN, whereas negative associations with gf were found for the complexity at fine timescales in the parietal and centro‐temporal region. These outcomes indicate that two separate time pathways (corresponding to fine and coarse timescales) used to characterize rsEEG complexity (expressed by mMSE features) are beneficial for effective information processing.

show abstract

Decreased long‐range temporal correlations in the resting‐state functional magnetic resonance imaging blood‐oxygen‐level‐dependent signal reflect motor sequence learning up to 2 weeks following training

Jäger,

Bailey,

Huntenburg

et al. 2023

Human Brain Mapping

View full text Add to dashboard Cite

Decreased long‐range temporal correlations (LRTC) in brain signals can be used to measure cognitive effort during task execution. Here, we examined how learning a motor sequence affects long‐range temporal memory within resting‐state functional magnetic resonance imaging signal. Using the Hurst exponent (HE), we estimated voxel‐wise LRTC and assessed changes over 5 consecutive days of training, followed by a retention scan 12 days later. The experimental group learned a complex visuomotor sequence while a complementary control group performed tightly matched movements. An interaction analysis revealed that HE decreases were specific to the complex sequence and occurred in well‐known motor sequence learning associated regions including left supplementary motor area, left premotor cortex, left M1, left pars opercularis, bilateral thalamus, and right striatum. Five regions exhibited moderate to strong negative correlations with overall behavioral performance improvements. Following learning, HE values returned to pretraining levels in some regions, whereas in others, they remained decreased even 2 weeks after training. Our study presents new evidence of HE's possible relevance for functional plasticity during the resting‐state and suggests that a cortical subset of sequence‐specific regions may continue to represent a functional signature of learning reflected in decreased long‐range temporal dependence after a period of inactivity.

show abstract

On noise in the nervous system.

Cited by 47 publications

References 19 publications

Network Structure Shapes Spontaneous Functional Connectivity Dynamics

Network Structure Shapes Spontaneous Functional Connectivity Dynamics

Spatiotemporal complexity patterns of resting‐state bioelectrical activity explain fluid intelligence: Sex matters

Decreased long‐range temporal correlations in the resting‐state functional magnetic resonance imaging blood‐oxygen‐level‐dependent signal reflect motor sequence learning up to 2 weeks following training

Contact Info

Product

Resources

About