When brain rhythms aren't 'rhythmic': implication for their mechanisms and meaning.

doi:10.1016/J.CONB.2016.06.010

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When brain rhythms aren't 'rhythmic': implication for their mechanisms and meaning.

Journal Article•DOI•

When brain rhythms aren't 'rhythmic': implication for their mechanisms and meaning.

Stephanie R. Jones¹•Institutions (1)

Brown University¹

01 Oct 2016-Current Opinion in Neurobiology (Elsevier Current Trends)-Vol. 40, pp 72-80

TL;DR: Evidence showing time-domain signals with vastly different waveforms can exhibit identical spectral-domain frequency and power and non-oscillatory waveform feature can create spurious high spectral power is reviewed.

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About: This article is published in Current Opinion in Neurobiology.The article was published on 2016-10-01 and is currently open access. It has received 220 citations till now.

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Journal Article•DOI•

Brain Oscillations and the Importance of Waveform Shape

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Scott R. Cole¹, Bradley Voytek•Institutions (1)

University of California, San Diego¹

01 Feb 2017-Trends in Cognitive Sciences

TL;DR: It is shown here that there are numerous instances in which neural oscillations are nonsinusoidal, and approaches to characterize nonsinusoid features and account for them in traditional spectral analysis are highlighted.

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380 citations

Cites background from "When brain rhythms aren't 'rhythmic..."

...Numerous reports have used both real and simulated data to show that nonsinusoidal oscillations with stereotyped sharp transients increase PAC estimates [11,36,38,41,42,44,45,87]....
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...While such approaches require multiple oscillatory cycles to yield useful metrics, studying the temporal dynamics of single oscillatory cycles can also reveal crucial physiological information, as previously suggested [25,38]....
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...Importantly, this idea has been hinted at or directly mentioned in several earlier reports [12,23,36,38,43,49,51,84,85]; however, such reports of waveform shape have been brief and sparse in the literature of neural oscillations....
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...It may even be possible to move past the sinusoidal assumptions of the Fourier transform and toward more biologically informed decomposition methods, perhaps consisting of a ‘dictionary’ of neurophysiological basis functions (as similarly suggested in [38])....
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Journal Article•DOI•

Frontal Cortex and the Hierarchical Control of Behavior.

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David Badre¹, Derek Evan Nee²•Institutions (2)

Brown University¹, Florida State University²

01 Feb 2018-Trends in Cognitive Sciences

TL;DR: The review of the evidence supports neither a unitary model of lateral frontal function nor a unidimensional abstraction gradient, rather, separate frontal networks interact via local and global hierarchical structure to support diverse task demands.

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364 citations

Cites background from "When brain rhythms aren't 'rhythmic..."

...The precise physiological correlate of these oscillatory signals is still a matter of open research [78,79]....
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Journal Article•DOI•

Where Does EEG Come From and What Does It Mean

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Michael X Cohen¹•Institutions (1)

Radboud University Nijmegen¹

01 Apr 2017-Trends in Neurosciences

TL;DR: It is argued that the authors know shockingly little about the answer to where do EEG signals come from and what do they mean, and how modern neuroscience technologies that allow us to measure and manipulate neural circuits with high spatiotemporal accuracy might finally bring us some answers.

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355 citations

Cites background from "When brain rhythms aren't 'rhythmic..."

...There are noteworthy cases of nonsinusoidal neural oscillations, including up–down states during anesthesia and rat hippocampal theta during exploration [71,76]....
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Journal Article•DOI•

Beyond the Status Quo: A Role for Beta Oscillations in Endogenous Content (Re)Activation.

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Bernhard Spitzer¹, Saskia Haegens², Saskia Haegens³•Institutions (3)

University of Oxford¹, Columbia University², Radboud University Nijmegen³

01 Jul 2017

TL;DR: It is suggested that beta-mediated ensemble formation within and between cortical areas may awake, rather than merely preserve, an endogenous cognitive set in the service of current task demands.

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Abstract: Among the rhythms of the brain, oscillations in the beta frequency range (∼13–30 Hz) have been considered the most enigmatic. Traditionally associated with sensorimotor functions, beta oscillations have recently become more broadly implicated in top-down processing, long-range communication, and preservation of the current brain state. Here, we extend and refine these views based on accumulating new findings of content-specific beta-synchronization during endogenous information processing in working memory (WM) and decision making. We characterize such content-specific beta activity as short-lived, flexible network dynamics supporting the endogenous (re)activation of cortical representations. Specifically, we suggest that beta-mediated ensemble formation within and between cortical areas may awake, rather than merely preserve, an endogenous cognitive set in the service of current task demands. This proposal accommodates key aspects of content-specific beta modulations in monkeys and humans, integrates with timely computational models, and outlines a functional role for beta that fits its transient temporal characteristics.

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354 citations

Cites background from "When brain rhythms aren't 'rhythmic..."

..., 2016), (3) their burst-like temporal dynamics (Jones, 2016), (4) their presumed role in the flexible formation and Review 8 of 15...
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...First, under this framework, content-specific beta episodes are expected to be relatively short lived (see also Jones, 2016; Sherman et al., 2016), since they would reflect neither latent nor active representations per se, but only a (presumably brief) transition period between the two (Fig....
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...…top-down processing (Engel and Fries, 2010; Wang, 2010) and (2) long-range communication (Kopell et al., 2000; Varela et al., 2001; Sherman et al., 2016), (3) their burst-like temporal dynamics (Jones, 2016), (4) their presumed role in the flexible formation and July/August 2017, 4(4) e0170-17....
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...This proposal accommodates accumulating findings in animals and humans and outlines a functional role for beta that may fit its “burst-like” temporal characteristics (Jones, 2016)....
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Journal Article•DOI•

The rate of transient beta frequency events predicts behavior across tasks and species

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Hyeyoung Shin¹, Robert Law¹, Robert Law², Shawn Tsutsui¹, Christopher I Moore¹, Stephanie R. Jones¹, Stephanie R. Jones² - Show less +3 more•Institutions (2)

Brown University¹, Providence VA Medical Center²

06 Nov 2017-eLife

TL;DR: It is shown that functionally relevant differences in averaged beta power in primary somatosensory neocortex reflect a difference in the number of high-power beta events per trial, i.e. event rate.

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Abstract: Beta oscillations (15-29Hz) are among the most prominent signatures of brain activity. Beta power is predictive of healthy and abnormal behaviors, including perception, attention and motor action. In non-averaged signals, beta can emerge as transient high-power 'events'. As such, functionally relevant differences in averaged power across time and trials can reflect changes in event number, power, duration, and/or frequency span. We show that functionally relevant differences in averaged beta power in primary somatosensory neocortex reflect a difference in the number of high-power beta events per trial, i.e. event rate. Further, beta events occurring close to the stimulus were more likely to impair perception. These results are consistent across detection and attention tasks in human magnetoencephalography, and in local field potentials from mice performing a detection task. These results imply that an increased propensity of beta events predicts the failure to effectively transmit information through specific neocortical representations.

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219 citations

Cites background from "When brain rhythms aren't 'rhythmic..."

...…are sustained for several cycles (e.g. occipital alpha rhythms during eye closure, slow wave sleep rhythms), rhythmic activity can often be transient in non-averaged data (Feingold et al., 2015; Jones, 2016; Jones et al., 2009; Lundqvist et al., 2016; Sherman et al., 2016; Tinkhauser et al., 2017)....
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...Because power spectral values are non-negative, the accumulation of events across trials creates a continuous band of activity in the average, often misinterpreted as a sustained rhythm (Figure 2i) (Jones, 2016)....
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...The temporal signatures of rhythmic activity may prove crucial to understanding their importance in brain function (Cole and Voytek, 2017; Jensen et al., 2016; Jones, 2016)....
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References

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Open Access

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Journal Article•DOI•

EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.

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Arnaud Delorme¹, Scott Makeig¹•Institutions (1)

University of California, San Diego¹

15 Mar 2004-Journal of Neuroscience Methods

TL;DR: EELAB as mentioned in this paper is a toolbox and graphic user interface for processing collections of single-trial and/or averaged EEG data of any number of channels, including EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), Independent Component Analysis (ICA) and time/frequency decomposition including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling.

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17,362 citations

"When brain rhythms aren't 'rhythmic..." refers background in this paper

...The latter could also be reflected as inter-trial phase coherence [42,43], which some studies have associated with the resetting of ongoing oscillations [44–46]....
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Journal Article•DOI•

Matching pursuits with time-frequency dictionaries

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Stéphane Mallat¹, Zhifeng Zhang¹•Institutions (1)

New York University¹

01 Aug 1993-IEEE Transactions on Signal Processing

TL;DR: The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions, chosen in order to best match the signal structures.

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Abstract: The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal structures. Matching pursuits are general procedures to compute adaptive signal representations. With a dictionary of Gabor functions a matching pursuit defines an adaptive time-frequency transform. They derive a signal energy distribution in the time-frequency plane, which does not include interference terms, unlike Wigner and Cohen class distributions. A matching pursuit isolates the signal structures that are coherent with respect to a given dictionary. An application to pattern extraction from noisy signals is described. They compare a matching pursuit decomposition with a signal expansion over an optimized wavepacket orthonormal basis, selected with the algorithm of Coifman and Wickerhauser see (IEEE Trans. Informat. Theory, vol. 38, Mar. 1992). >

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9,380 citations

Book•

Rhythms of the brain

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György Buzsáki¹•Institutions (1)

Rutgers University¹

01 Jan 2006

TL;DR: The brain's default state: self-organized oscillations in rest and sleep, and perturbation of the default patterns by experience.

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Abstract: Prelude. Cycle 1. Introduction. Cycle 2. Structure defines function. Cycle 3. Diversity of cortical functions is provided by inhibition. Cycle 4. Windows on the brain. Cycle 5. A system of rhythms: from simple to complex dynamics. Cycle 6. Synchronization by oscillation. Cycle 7. The brain's default state: self-organized oscillations in rest and sleep. Cycle 8. Perturbation of the default patterns by experience. Cycle 9. The gamma buzz: gluing by oscillations in the waking brain. Cycle 10. Perceptions and actions are brain state-dependent. Cycle 11. Oscillations in the "other cortex:" navigation in real and memory space. Cycle 12. Coupling of systems by oscillations. Cycle 13. The tough problem. References.

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4,266 citations

Journal Article•DOI•

Measuring phase synchrony in brain signals

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Jean-Philippe Lachaux¹, Eugenio Rodríguez¹, Jacques Martinerie¹, Francisco J. Varela¹•Institutions (1)

Centre national de la recherche scientifique¹

01 Jan 1999-Human Brain Mapping

TL;DR: It is argued that whereas long‐scale effects do reflect cognitive processing, short‐scale synchronies are likely to be due to volume conduction, and ways to separate such conduction effects from true signal synchrony are discussed.

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Abstract: This article presents, for the first time, a practical method for the direct quantification of frequency-specific synchronization (i.e., transient phase-locking) between two neuroelectric signals. The motivation for its development is to be able to examine the role of neural synchronies as a putative mechanism for long-range neural integration during cognitive tasks. The method, called phase-locking statistics (PLS), measures the significance of the phase covariance between two signals with a reasonable time-resolution (,100 ms). Unlike the more traditional method of spectral coherence, PLS separates the phase and amplitude components and can be directly interpreted in the framework of neural integration. To validate synchrony values against background fluctuations, PLS uses surrogate data and thus makes no a priori assumptions on the nature of the experimental data. We also apply PLS to investigate intracortical recordings from an epileptic patient performing a visual discrimination task. We find large-scale synchronies in the gamma band (45 Hz), e.g., between hippocampus and frontal gyrus, and local synchronies, within a limbic region, a few cm apart. We argue that whereas long-scale effects do reflect cognitive processing, short-scale synchronies are likely to be due to volume conduction. We discuss ways to separate such conduction effects from true signal synchrony. Hum Brain Mapping 8:194-208, 1999. r 1999 Wiley-Liss, Inc.

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3,397 citations

"When brain rhythms aren't 'rhythmic..." refers background in this paper

...The latter could also be reflected as inter-trial phase coherence [42,43], which some studies have associated with the resetting of ongoing oscillations [44–46]....
[...]

Journal Article•DOI•

Dynamic predictions: Oscillations and synchrony in top–down processing

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Andreas K. Engel, Pascal Fries¹, Pascal Fries², Wolf Singer³•Institutions (3)

F.C. Donders Centre for Cognitive Neuroimaging¹, National Institutes of Health², Max Planck Society³

01 Oct 2001-Nature Reviews Neuroscience

TL;DR: It is argued that coherence among subthreshold membrane potential fluctuations could be exploited to express selective functional relationships during states of expectancy or attention, and these dynamic patterns could allow the grouping and selection of distributed neuronal responses for further processing.

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Abstract: Classical theories of sensory processing view the brain as a passive, stimulus-driven device. By contrast, more recent approaches emphasize the constructive nature of perception, viewing it as an active and highly selective process. Indeed, there is ample evidence that the processing of stimuli is controlled by top-down influences that strongly shape the intrinsic dynamics of thalamocortical networks and constantly create predictions about forthcoming sensory events. We discuss recent experiments indicating that such predictions might be embodied in the temporal structure of both stimulus-evoked and ongoing activity, and that synchronous oscillations are particularly important in this process. Coherence among subthreshold membrane potential fluctuations could be exploited to express selective functional relationships during states of expectancy or attention, and these dynamic patterns could allow the grouping and selection of distributed neuronal responses for further processing.

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3,330 citations