Single-trial characterization of neural rhythms: potential and challenges

Kosciessa, Julian Q.; Grandy, Thomas H.; Garrett, Douglas D.; Werkle‐Bergner, Markus

doi:10.1101/356089

Cited by 29 publications

(89 citation statements)

References 59 publications

(31 reference statements)

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“…To counter the effect of intrinsically high correlations between frequency patterns due to the 1/frequency power spectrum (Schönauer et al, 2017), we removed the mean background noise spectrum from the log-transformed TFRs following previously established procedures (i.e., as suggested by the “Better oscillation detection” (BOSC) method; Caplan et al, 2001; Kosciessa et al, 2018; Whitten et al, 2011). Because of structured noise, correlations between different activity patterns are very high and almost never at or below zero, meaning that the true null-distribution is higher than zero.…”

Section: Methodsmentioning

confidence: 99%

Neural pattern similarity differentially affects memory performance of younger and older adults: Age differences in neural similarity and memory

Sommer

Fandakova

Grandy

et al. 2019

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18Age-related memory decline is associated with changes in neural functioning but little is known 19 about how aging affects the quality of information representation in the brain. Whereas a 20 long-standing hypothesis of the aging literature links cognitive impairments to less distinct 21 neural representations in old age, memory studies have shown that high similarity between 22 activity patterns benefits memory performance for the respective stimuli. Here, we addressed 23 this apparent conflict by investigating between-item representational similarity in 50 younger 24 (19-27 years old) and 63 older (63-75 years old) human adults (male and female) who studied 25 scene-word associations using a mnemonic imagery strategy while electroencephalography was 26 recorded. We compared the similarity of spatiotemporal frequency patterns elicited during 27 encoding of items with different subsequent memory fate. Compared to younger adults, older 28 adults' memory representations were more similar to each other but items that elicited the 29 most similar activity patterns early in the encoding trial were those that were best remembered 30 by older adults. In contrast, young adults' memory performance benefited from decreased 31 similarity between earlier and later periods in the encoding trials, which might reflect their 32 better success in forming unique memorable mental images of the joint picture-word pair. 33Our results advance the understanding of the representational properties that give rise to 34 memory quality as well as how these properties change in the course of aging. 35 3 Significance statement 36Declining memory abilities are one of the most evident limitations for humans when growing 37 older. Despite recent advances of our understanding of how the brain represents and sto-38 res information in distributed activation patterns, little is known about how the quality of 39 information representation changes during aging and thus affects memory performance. We 40 investigated how the similarity between neural representations relates to subsequent memory 41 quality in younger and older adults. We present novel evidence that the interaction of pattern 42 similarity and memory performance differs between age groups: Older adults benefited from 43 increased similarity during early encoding whereas young adults benefited from decreased si-44 milarity between early and later encoding. These results provide insights into the nature of 45 memory and age-related memory deficits. 46 4 47 A long-standing hypothesis in the cognitive neuroscience of aging holds that neural represen-48 tations become less specific with advancing age, with detrimental effects on cognitive perfor-49 mance. Reduced neural distinctiveness in older compared to young adults (Li et al., 2001) 50 has been observed as increased similarity and/or reduced discriminability between neural 51 activity patterns during different memory tasks (Carp et al., 2010; St-Laurent et al., 2011), 52 between different stimulus categories (Carp et al., 2011; Park e...

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Section: Methodsmentioning

confidence: 99%

Neural pattern similarity differentially affects memory performance of younger and older adults: Age differences in neural similarity and memory

Sommer

Fandakova

Grandy

et al. 2019

Preprint

Self Cite

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“…peaks (Haller et al, 2018;Kosciessa et al, 2019) with the latter themselves being temporal 761 averages of potentially non-stationary spectral events (Kosciessa et al, 2019). Notably, 762…”

Section: Discussion 732 733mentioning

confidence: 99%

“…A 5-cycle wavelet was used to provide 425 the time-frequency transformations for 49 logarithmically-spaced center frequencies between 426 1 and 64 Hz. Rhythmic episodes were detected as described in Kosciessa et al (2019). 427…”

Section: Hypotheses and Current Study 227 228mentioning

confidence: 99%

“…shape (Sherman et al, 2016; Shin, Law, Tsutsui, Moore, & Jones, 2017), while occupying a 711 lower total duration during rest than alpha rhythms (Kosciessa et al, 2019). This may explain 712 a divergence of entropy estimates from spectral power as it should be the rate of stereotypic 713 spectral events that reduces pattern irregularity rather than the overall power within a frequency 714 band.…”

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confidence: 99%

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Standard multiscale entropy reflects neural dynamics at mismatched temporal scales: What’s signal irregularity got to do with it?

Kosciessa

Kloosterman

Garrett³

2019

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AbstractMultiscale Entropy (MSE) is used to characterize the temporal irregularity of neural time series patterns. Due to its’ presumed sensitivity to non-linear signal characteristics, MSE is typically considered a complementary measure of brain dynamics to signal variance and spectral power. However, the divergence between these measures is often unclear in application. Furthermore, it is commonly assumed (yet sparingly verified) that entropy estimated at specific time scales reflects signal irregularity at those precise time scales of brain function. We argue that such assumptions are not tenable. Using simulated and empirical electroencephalogram (EEG) data from 47 younger and 52 older adults, we indicate strong and previously underappreciated associations between MSE and spectral power, and highlight how these links preclude traditional interpretations of MSE time scales. Specifically, we show that the typical definition of temporal patterns via “similarity bounds” biases coarse MSE scales – that are thought to reflect slow dynamics – by high-frequency dynamics. Moreover, we demonstrate that entropy at fine time scales – presumed to indicate fast dynamics – is highly sensitive to broadband spectral power, a measure dominated by low-frequency contributions. Jointly, these issues produce counterintuitive reflections of frequency-specific content on MSE time scales. We emphasize the resulting inferential problems in a conceptual replication of cross-sectional age differences at rest, in which scale-specific entropy age effects could be explained by spectral power differences at mismatched temporal scales. Furthermore, we demonstrate how such problems may be alleviated, resulting in the indication of scale-specific age differences in rhythmic irregularity. By controlling for narrowband contributions, we indicate that spontaneous alpha rhythms during eyes open rest transiently reduce broadband signal irregularity. Finally, we recommend best practices that may better permit a valid estimation and interpretation of neural signal irregularity at time scales of interest.Author SummaryBrain signals exhibit a wealth of dynamic patterns that that are thought to reflect ongoing neural computations. Multiscale sample entropy (MSE) intends to describe the temporal irregularity of such patterns at multiple time scales of brain function. However, the notion of time scales may often be unintuitive. In particular, traditional implementations of MSE are sensitive to slow fluctuations at fine time scales, and fast dynamics at coarse time scales. This conceptual divergence is often overlooked and may lead to difficulties in establishing the unique contribution of MSE to effects of interest over more established spectral power. Using simulations and empirical data, we highlight these issues and provide evidence for their relevance for valid practical inferences. We further highlight that standard MSE and traditional spectral power are highly collinear in our example. Finally, our analyses indicate that spectral filtering can be used to estimate temporal signal irregularity at matching and intuitive time scales. To guide future studies, we make multiple recommendations based on our observations. We believe that following these suggestions may advance our understanding of the unique contributions of neural signal irregularity to neural and cognitive function across the lifespan.

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“…Therefore, only cycles that are determined to be part of a theta oscillatory burst were analyzed. However, the task of identifying the segments of the signal with oscillatory components is challenging and currently unsolved (Kosciessa et al, 2018) . It is unclear if there are discrete times in which an oscillator is on and off, so perhaps there is no objective solution.…”

Section: Theta Cycle Analysismentioning

confidence: 99%

Hippocampal theta bursting and waveform shape reflect CA1 spiking patterns

Cole

Voytek

2018

Preprint

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Brain rhythms are nearly always analyzed in the spectral domain in terms of their power, phase, and frequency. While this conventional approach has uncovered spike-field coupling, as well as correlations to normal behaviors and pathological states, emerging work has highlighted the physiological and behavioral importance of multiple novel oscillation features. Oscillatory bursts, for example, uniquely index a variety of cognitive states, and the nonsinusoidal shape of oscillations relate to physiological changes, including Parkinson's disease. Open questions remain regarding how bursts and nonsinusoidal features relate to circuit-level processes, and how they interrelate. By analyzing unit and local field recordings in the rodent hippocampus, we uncover a number of significant relationships between oscillatory bursts, nonsinusoidal waveforms, and local inhibitory and excitatory spiking patterns. Bursts of theta oscillations are surprisingly related to a decrease in pyramidal neuron synchrony, and have no detectable effect on firing sequences, despite significant increases in neuronal firing rates during periods of theta bursting. Theta burst duration is predicted by the asymmetries of its first cycle, and cycle asymmetries relate to firing rate, synchrony, and sequences of pyramidal neurons and interneurons. These results provide compelling physiological evidence that time-domain features, of both nonsinusoidal hippocampal theta waveform and the theta bursting state, reflects local circuit properties. These results point to the possibility of inferring circuit states from local field potential features in the hippocampus and perhaps other brain regions with other rhythms.

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Single-trial characterization of neural rhythms: potential and challenges

Cited by 29 publications

References 59 publications

Neural pattern similarity differentially affects memory performance of younger and older adults: Age differences in neural similarity and memory

Neural pattern similarity differentially affects memory performance of younger and older adults: Age differences in neural similarity and memory

Standard multiscale entropy reflects neural dynamics at mismatched temporal scales: What’s signal irregularity got to do with it?

Hippocampal theta bursting and waveform shape reflect CA1 spiking patterns

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