Variability and task-responsiveness of electrophysiological dynamics: Scale-free stability and oscillatory flexibility

Wainio-Theberge, Soren; Wolff, Annemarie; Gómez‐Pilar, Javier; Zhang, Jianfeng; Northoff, Georg

doi:10.1016/j.neuroimage.2022.119245

Cited by 20 publications

(16 citation statements)

References 91 publications

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“…Specifically, the PLE, ACW and MF remained relatively stable in their closeness and strength while SE and LZC showed stronger changes. That does not only compare well with the core-periphery organization on a dynamic level but also with the recently supposed distinction of stable background dynamic measures and more flexible foreground dynamic measures [82].…”

Section: Relationship Between Different Measures Is Hierarchical and ...supporting

confidence: 70%

“…This indicates that different regions are defined by their temporal structure in the way they respond to external perturbations. LZC and SE on the other hand are more task and layer unspecific, thus, more malleable measures for external perturbation, further their role as foreground measures [82].…”

Section: Task-specific and -Unspecific Effects In Both Topography And...mentioning

confidence: 99%

“…Moreover, applying the graph-theoretic measures, we investigated the relationship among the five dynamic measures. We hypothesized that the dynamic measures like PLE, MF, and ACW are more stable during both rest and task states, operating in the background while information-related measures like SE and LZC are more flexible showing changes during task states thus taking on the role as foreground measures [82].…”

Section: Introductionmentioning

confidence: 99%

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The Intrinsic Hierarchy of Self – Converging Topography and Dynamics

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Gunay

et al. 2022

Preprint

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The brain can be characterized by an intrinsic hierarchy in its topography which, as recently shown for the uni-transmodal distinction of core and periphery, converges with its dynamics. Does such intrinsic hierarchical organization in both topography and dynamic also apply to the brain's inner core itself and its higher-order cognitive functions like self? Applying multiple fMRI data sets, we show how the recently established three-layer topography of self (internal, external, mental) is already present during the resting state and carried over to task states including both task-specific and -unspecific effects. Moreover, the topographic hierarchy converges with corresponding dynamic changes (measured by power-law exponent, autocorrelation window, median frequency, sample entropy, complexity) during both rest and task states. Finally, analogous to the topographic hierarchy, we also demonstrate hierarchy among the different dynamic measures themselves according to background and foreground. Finally, we show task-specific- and un-specific effects in the hierarchies of both dynamics and topography. Together, we demonstrate the existence of an intrinsic topographic hierarchy of self and its convergence with dynamics.

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Section: Relationship Between Different Measures Is Hierarchical and ...supporting

confidence: 70%

Section: Task-specific and -Unspecific Effects In Both Topography And...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Intrinsic Hierarchy of Self – Converging Topography and Dynamics

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Gunay

et al. 2022

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“…The IRASA method (Wen & Liu, 2016 ) was applied to separate oscillatory and fractal components of the power spectrum and previously successfully in EEG/MEG (Wainio‐Theberge et al, 2021 ; Wainio‐Theberge et al, 2022 ). The comparison between the conventionally computed PLE presented above, including both fractal and oscillatory components in the power spectrum, and the IRASA method obtained fractal‐based PLE values (exclusion of oscillatory components) for the SCP and JCP ROIs in rest and task states are displayed in Figure 6 .…”

Section: Resultsmentioning

confidence: 99%

“…To discard the possibility of the oscillatory component of the power spectrum driving our results, we used irregular‐resampling auto‐spectral analysis (IRASA) method to separate fractal component from oscillatory component (Muthukumaraswamy & Liley, 2018; Wainio‐Theberge et al, 2021; Wainio‐Theberge et al, 2022; Wen & Liu, 2016). Briefly, the signal is resampled with a factor

h

ranging from 1.1 to 1.9 with steps of 0.05; and

\frac{1}{h}

.…”

Section: Methodsmentioning

confidence: 99%

Scale‐free dynamics of core‐periphery topography

Klar

Çatal

Langner

et al. 2022

Human Brain Mapping

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The human brain's cerebral cortex exhibits a topographic division into higher‐order transmodal core and lower‐order unimodal periphery regions. While timescales between the core and periphery region diverge, features of their power spectra, especially scale‐free dynamics during resting‐state and their mdulation in task states, remain unclear. To answer this question, we investigated the ~1/f‐like pink noise manifestation of scale‐free dynamics in the core‐periphery topography during rest and task states applying infra‐slow inter‐trial intervals up to 1 min falling inside the BOLD's infra‐slow frequency band. The results demonstrate (1) higher resting‐state power‐law exponent (PLE) in the core compared to the periphery region; (2) significant PLE increases in task across the core and periphery regions; and (3) task‐related PLE increases likely followed the task's atypically low event rates, namely the task's periodicity (inter‐trial interval = 52–60 s; 0.016–0.019 Hz). A computational model and a replication dataset that used similar infra‐slow inter‐trial intervals provide further support for our main findings. Altogether, the results show that scale‐free dynamics differentiate core and periphery regions in the resting‐state and mediate task‐related effects.

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