Parameterizing neural power spectra into periodic and aperiodic components

Nature Neuroscience, Год журнала: 2020, Номер 23(12), С. 1655 - 1665

Опубликована: Ноя. 23, 2020

Язык: Английский

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Complex Oscillatory Waves Emerging from Cortical Organoids Model Early Human Brain Network Development

Cell stem cell, Год журнала: 2019, Номер 25(4), С. 558 - 569.e7

Опубликована: Авг. 29, 2019

Язык: Английский

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Closed-loop stimulation of temporal cortex rescues functional networks and improves memory

Nature Communications, Год журнала: 2018, Номер 9(1)

Опубликована: Янв. 19, 2018

Abstract Memory failures are frustrating and often the result of ineffective encoding. One approach to improving memory outcomes is through direct modulation brain activity with electrical stimulation. Previous efforts, however, have reported inconsistent effects when using open-loop stimulation target hippocampus medial temporal lobes. Here we use a closed-loop system monitor decode neural from recordings in humans. We apply targeted lateral cortex report that this rescues periods poor This also improves later recall, revealing reliable for enhancement. Taken together, our results suggest such systems may provide therapeutic treating dysfunction.

Язык: Английский

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An electrophysiological marker of arousal level in humans

eLife, Год журнала: 2020, Номер 9

Опубликована: Июль 28, 2020

Deep non-rapid eye movement sleep (NREM) and general anesthesia with propofol are prominent states of reduced arousal linked to the occurrence synchronized oscillations in electroencephalogram (EEG). Although rapid (REM) is also associated diminished levels, it characterized by a desynchronized, ‘wake-like’ EEG. This observation implies that not necessarily only defined synchronous oscillatory activity. Using intracranial surface EEG recordings four independent data sets, we demonstrate 1/f spectral slope electrophysiological power spectrum, which reflects non-oscillatory, scale-free component neural activity, delineates wakefulness from anesthesia, NREM REM sleep. Critically, discriminates solely based on neurophysiological brain state. Taken together, our findings describe common marker tracks arousal, including different stages as well humans.

Язык: Английский

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The spectral exponent of the resting EEG indexes the presence of consciousness during unresponsiveness induced by propofol, xenon, and ketamine

NeuroImage, Год журнала: 2019, Номер 189, С. 631 - 644

Опубликована: Янв. 11, 2019

Despite the absence of responsiveness during anesthesia, conscious experience may persist. However, reliable, easily acquirable and interpretable neurophysiological markers presence consciousness in unresponsive states are still missing. A promising marker is based on decay-rate power spectral density (PSD) resting EEG. We acquired electroencephalogram (EEG) three groups healthy participants (n = 5 each), before anesthesia induced by either xenon, propofol or ketamine. Dosage each anesthetic agent was tailored to yield unresponsiveness (Ramsay score 6). Delayed subjective reports assessed whether present (‘Conscious report’) absent/inaccessible recall (‘No Report’). estimated decay PSD EEG—after removing oscillatory peaks—via exponent β, for a broad band (1–40 Hz) narrower sub-bands (1–20 Hz, 20–40 Hz). Within-subject changes β were assessed. Furthermore, ‘Conscious report’ discriminated against ‘no states. Finally, we evaluated correlation with recently proposed index consciousness, Perturbational Complexity Index (PCI), derived from previous TMS-EEG study. The EEG which (wakefulness, ketamine) where reduced abolished (xenon, propofol). Loss substantially decreased (negative) broad-band subject undergoing xenon anesthesia—indexing an overall steeper decay. Conversely, ketamine displayed similar that wakefulness—consistent preservation consciousness—yet it showed flattening high-frequencies (20–40 Hz)—consistent its specific mechanism action. highly correlated PCI, corroborating interpretation as consciousness. reliably indexed unconsciousness beyond sheer unresponsiveness.

Язык: Английский

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Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture

eLife, Год журнала: 2020, Номер 9

Опубликована: Ноя. 23, 2020

Complex cognitive functions such as working memory and decision-making require information maintenance over seconds to years, from transient sensory stimuli long-term contextual cues. While theoretical accounts predict the emergence of a corresponding hierarchy neuronal timescales, direct electrophysiological evidence across human cortex is lacking. Here, we infer timescales invasive intracranial recordings. Timescales increase along principal sensorimotor-to-association axis entire cortex, scale with single-unit within macaques. Cortex-wide transcriptomic analysis shows alignment between expression excitation- inhibition-related genes, well genes specific voltage-gated transmembrane ion transporters. Finally, are functionally dynamic: prefrontal expand during individual performance, while cortex-wide compress aging. Thus, follow cytoarchitectonic gradients relevant for cognition in both short long terms, bridging microcircuit physiology macroscale dynamics behavior.

Язык: Английский

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Behavior needs neural variability

Neuron, Год журнала: 2021, Номер 109(5), С. 751 - 766

Опубликована: Фев. 17, 2021

Язык: Английский

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Decomposing alpha and 1/f brain activities reveals their differential associations with cognitive processing speed

NeuroImage, Год журнала: 2019, Номер 205, С. 116304 - 116304

Опубликована: Окт. 22, 2019

Research in cognitive neuroscience has extensively demonstrated that the temporal dynamics of brain activity are associated with functioning. The mainly include oscillatory and 1/f noise-like, non-oscillatory activities coexist many forms confound each other's variability. As such, observed functional associations narrowband oscillations might have been confounded broadband component. Here, we investigated relationship between resting-state EEG efficiency functioning N = 180 individuals. We show plays an essential role accounting for between-person variability speed – a can be mistaken as originating from using conventional power spectrum analysis. At first glance, alpha appeared to predictive speed. However, when dissociating pure activity, only predicted speed, whereas vanished. With this highly powered study, disambiguate relevance law pattern resting state neural substantiate necessity isolating component studying spontaneous activities.

Язык: Английский

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Methodological considerations for studying neural oscillations

European Journal of Neuroscience, Год журнала: 2021, Номер 55(11-12), С. 3502 - 3527

Опубликована: Июль 16, 2021

Neural oscillations are ubiquitous across recording methodologies and species, broadly associated with cognitive tasks, amenable to computational modelling that investigates neural circuit generating mechanisms population dynamics. Because of this, offer an exciting potential opportunity for linking theory, physiology cognition. However, despite their prevalence, there many concerns-new old-about how our analysis assumptions violated by known properties field data. For investigations be properly interpreted, ultimately developed into mechanistic theories, it is necessary carefully consider the underlying methods we employ. Here, discuss seven methodological considerations analysing oscillations. The (1) verify presence oscillations, as they may absent; (2) validate oscillation band definitions, address variable peak frequencies; (3) account concurrent non-oscillatory aperiodic activity, which might otherwise confound measures; measure (4) temporal variability (5) waveform shape often bursty and/or nonsinusoidal, potentially leading spurious results; (6) separate spatially overlapping rhythms, interfere each other; (7) required signal-to-noise ratio obtaining reliable estimates. topic, provide relevant examples, demonstrate errors interpretation, suggestions these issues. We primarily focus on univariate measures, such power phase estimates, though issues can propagate multivariate measures. These recommendations a helpful guide measuring interpreting

Язык: Английский

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Cycle-by-cycle analysis of neural oscillations

Journal of Neurophysiology, Год журнала: 2019, Номер 122(2), С. 849 - 861

Опубликована: Июль 3, 2019

Neural oscillations are widely studied using methods based on the Fourier transform, which models data as sums of sinusoids. This has successfully uncovered numerous links between and cognition or disease. However, neural nonsinusoidal, these nonsinusoidal features increasingly linked to a variety behavioral cognitive states, pathophysiology, underlying neuronal circuit properties. We present new analysis framework, one that is complementary existing Hilbert transform-based approaches, quantifies oscillatory in time domain cycle-by-cycle basis. have released this suite “bycycle,” fully documented, open-source Python package with detailed tutorials troubleshooting cases. approach performs tests assess whether an oscillation at any given moment and, if so, each cycle by its amplitude, period, waveform symmetry, latter missed use conventional approaches. In series simulated event-related studies, we show how transform approaches can conflate changes burst duration increased amplitude change frequency, even though those were unchanged simulation. Our avoids errors. Furthermore, validate simulation against experimental recordings patients Parkinson’s disease, who known beta (12–30 Hz) oscillations. NEW & NOTEWORTHY introduce package, bycycle, for analyzing traditional but specific pitfalls. First, bycycle confirms present, avoid aperiodic, nonoscillatory Next, it aspects oscillations, physiology, diseases. tested real data.

Язык: Английский

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