Scaling and Criticality in Large-Scale Neuronal Activity

Linkenkaer‐Hansen, Klaus

doi:10.1007/3-540-44832-2_18

Cited by 4 publications

(4 citation statements)

References 37 publications

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“…The generality of the mechanism of self‐organized criticality makes it a strong candidate for explaining long‐range correlations in the brain (Linkenkaer‐Hansen, 2003), which has activity‐dependent mechanisms for changing the neuronal connectivity that can last from milliseconds to a lifetime (Marder, 1998; Penn & Shatz, 1999). We would like to emphasize that long‐range temporal correlations at the network level can be much longer than the time scales of the individual components in the system (Longtin et al ., 2003).…”

Section: Discussionmentioning

confidence: 99%

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Stimulus‐induced change in long‐range temporal correlations and scaling behaviour of sensorimotor oscillations

Linkenkaer‐Hansen

Nikulin

Palva

et al. 2004

Eur J of Neuroscience

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141

129

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The human brain spontaneously generates large-scale network oscillations at around 10 and 20 Hz. The amplitude envelope of these oscillations fluctuates intermittently and was recently reported to exhibit power-law decay of the autocorrelation for hundreds of seconds. This indicates that the underlying networks are in a dynamic state resembling the self-organized critical state known to exist in many complex systems. Based on the mechanism of how correlations emerge in these systems, we hypothesized that the physiological basis of long-range power-law correlations is the buildup of a memory of past activity by a continuous modification of the network's functional connectivity by the ongoing oscillations. In this framework, exogenous perturbations of ongoing oscillations would degrade or abolish this dynamic network memory. We investigated the sensitivity of the temporal correlations in sensorimotor 10- and 20-Hz oscillations to median nerve stimulation that is known to have immediate effects on ongoing oscillations. Our results show that the amplitude fluctuations of these oscillations were effectively modulated by the somatosensory stimuli but still exhibited long-range temporal correlations and power-law scaling behaviour. The magnitude of the temporal correlations was, however, attenuated and the power-law exponents were decreased. This implies that the stimuli indeed degraded the network's memory of its past.

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Section: Discussionmentioning

confidence: 99%

“…Note that all the analyses of temporal correlation reported in the present paper were performed on the amplitude envelope of oscillatory activity (thick lines in C). Modified from Linkenkaer‐Hansen (2003).…”

Section: Methodsmentioning

confidence: 99%

Stimulus‐induced change in long‐range temporal correlations and scaling behaviour of sensorimotor oscillations

Linkenkaer‐Hansen

Nikulin

Palva

et al. 2004

Eur J of Neuroscience

Self Cite

141

129

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“…Linkenkaer-Hansen et al ( 2001 ) report long-range temporal correlations and scaling with 10–20 Hz brain oscillations. Pursuing this observation in more detail, Linkenkaer-Hansen ( 2003 ) and Linkenkaer-Hansen et al ( 2004a ) suggest that the long-term spatial-temporal structure of the complex ongoing EEG activity may reflect a memory of the system's dynamics extending beyond just a few seconds, possibly by a continuous modification of functional brain networks in the sense of SOC. In these tests, somatosensory stimuli attenuate temporal correlations and power-law scaling behavior, suggesting that stimuli degrade the network memory of its past.…”

Section: Power-law Scaling In Neuronal Structures and Processesmentioning

confidence: 98%

Fractals in the nervous system: conceptual implications for theoretical neuroscience

Werner¹

2010

Front. Physio.

175

180

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This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power-law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review.

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“…Another prominent theory for how optimal signal variability is able to represent brain processing is that of criticality within neuronal networks (Beggs & Plenz, 2003 ; He, 2011 ; Linkenkaer‐Hansen, 2003 ; Shew & Plenz, 2013 ; Simola et al, 2017 ). Brain criticality indicates that neural networks operate near a tipping point between order and disorder, allowing for flexibility and adaptation.…”

Section: Discussionmentioning

confidence: 99%

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

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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.

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Scaling and Criticality in Large-Scale Neuronal Activity

Cited by 4 publications

References 37 publications

Stimulus‐induced change in long‐range temporal correlations and scaling behaviour of sensorimotor oscillations

Stimulus‐induced change in long‐range temporal correlations and scaling behaviour of sensorimotor oscillations

Fractals in the nervous system: conceptual implications for theoretical neuroscience

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

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