Variability and stability of large-scale cortical oscillation patterns

Cox, Roy; Schapiro, Anna C.; Stickgold, Robert

doi:10.1101/093005

Cited by 8 publications

(57 citation statements)

References 70 publications

(96 reference statements)

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“…Importantly, individual topographical variability was stable both across sleep stages and nights (Fig. 5), analogous to topographical EEG stability during wakefulness 31 , indicating these patterns constitute robust traits. Together, these findings underscore that group averages reflect a mixture of quite idiosyncratic spatial spindle patterns.…”

Section: Discussionmentioning

confidence: 77%

Individual differences in frequency and topography of slow and fast sleep spindles

Cox

Schapiro

Manoach

et al. 2017

Preprint

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Sleep spindles are transient oscillatory waveforms occurring during non--rapid eye movement (NREM) sleep and are implicated in plasticity and memory processes. In humans, spindles can be classified as either slow or fast, but large individual differences in spindle frequency as well as methodological difficulties have hindered progress towards understanding their function. Using two nights of high--density electroencephalography recordings from healthy individuals, we first characterize the individual variability of NREM spectra and demonstrate the difficulty of determining subject--specific spindle frequencies. We then introduce a novel spatial filtering approach that can reliably separate subject--specific spindle activity into slow and fast components that are stable across nights and across N2 and N3 sleep. We then proceed to provide detailed analyses of the topographical expression of individualized slow and fast spindle activity. Group--level analyses conform to known spatial properties of spindles, but also uncover novel differences between sleep stages and spindle classes. Moreover, subject--specific examinations reveal that individual topographies show considerable variability that is stable across nights. Finally, we demonstrate that topographical maps depend nontrivially on the spindle metric employed. In sum, our findings indicate that group--level approaches mask substantial individual variability of spindle dynamics, in both the spectral and spatial domains. We suggest that leveraging, rather than ignoring, such differences may prove useful to further our understanding of the physiology and functional role of sleep spindles.

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Section: Discussionmentioning

confidence: 77%

Individual differences in frequency and topography of slow and fast sleep spindles

Cox

Schapiro

Manoach

et al. 2017

Preprint

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“…For a detailed overview of experimental procedures, see (Cox et al 2016). Briefly, 21 healthy volunteers completed the first visit of this study and 14 returned for a second visit, 3-8 months later.…”

Section: Resultsmentioning

confidence: 99%

“…A detailed overview of methods, including the task and network similarity analyses, can be found in our companion report (Cox et al 2016). Here, we report analyses specific to the present study.…”

Section: Methodsmentioning

confidence: 99%

“…In a companion paper (Cox et al 2016), we offer an in depth characterization of the network structure of distributed oscillatory activity across and within individuals, as measured by electroencephalography (EEG). Briefly, we found that different individuals have distinct, stable, and identifiable network profiles, even though oscillatory organization demonstrated robust group-level correspondences for different frequency bands and oscillatory features.…”

Section: Introductionmentioning

confidence: 99%

“…In the current report, we use the same dataset to examine whether the individual oscillatory profiles described in (Cox et al 2016) serve a functional role. Based on the aforementioned evidence that episodic memory networks are distributed widely across the brain (Burke et al 2013; Watrous et al 2013; King et al 2015), and that oscillatory activity during encoding (Park et al 2014; Jiang et al 2015) and rest (Klimesch et al 1990; Grandy et al 2013; Brokaw et al 2016) may be sensitive to aspects of memory performance, we here investigate whether large-scale oscillatory networks serve as a marker for memory processing.…”

Section: Introductionmentioning

confidence: 99%

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Distributed alpha networks underlie individual differences in memory performance

Cox

Schapiro

Stickgold

2016

Preprint

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Episodic memory, our ability to remember specific events, varies considerably across individuals. However, little is known about the neural basis of this variability. To address this issue, we investigated the role of distributed networks of oscillatory activity, as measured through electroencephalography (EEG). We observed that individual differences in alpha network structure reliably predict individual memory capacity. Specifically, individuals whose network profiles during encoding were most different from their resting state networks exhibited greatest subsequent memory performance, suggesting that optimal information processing requires substantial shifts in large-scale oscillatory organization. Furthermore, these results were not observed in circumscribed topographical regions or individual connections, indicating that distributed network approaches were more sensitive to functional processes than more conventional methods. These findings uncover a physiological correlate of individual differences in episodic memory and demonstrate the utility of multivariate EEG techniques to uncover brain-behavior correlates.

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Aperiodic neural activity is a better predictor of schizophrenia than neural oscillations

Rosen

Belger

et al. 2017

Preprint

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Schizophrenia has been associated with separate irregularities in several neural oscillatory frequency bands, including theta, alpha, and gamma. Our multivariate classification suggests that instead of irregularities in many frequency bands, schizophrenia-related EEG differences may better be explained by an overall shift in neural noise, reflected by a change in the 1/f slope of the power spectrum. Significance statementUnderstanding the neurobiological origins of schizophrenia, and identifying reliable biomarkers, are both of critical importance in improving treatment of that disease. While we lack predictive biomarkers, numerous studies have observed disruptions to neural oscillations in schizophrenia patients. This literature has, in part, lead to schizophrenia being characterized as disease of disrupted neural coordination. We report however that changes to background noise (i.e., 1/f noise) are a substantially better predictor of schizophrenia than oscillatory power. The observed alterations in neural noise are consistent with inhibitory neuron dysfunctions associated with schizophrenia, allowing for a direct link between noninvasive EEG and neurobiological deficits.

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Variability and stability of large-scale cortical oscillation patterns

Cited by 8 publications

References 70 publications

Individual differences in frequency and topography of slow and fast sleep spindles

Individual differences in frequency and topography of slow and fast sleep spindles

Distributed alpha networks underlie individual differences in memory performance

Aperiodic neural activity is a better predictor of schizophrenia than neural oscillations

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