Modality-specific tracking of attention and sensory statistics in the human electrophysiological spectral exponent

eLife, Journal Year: 2021, Volume and Issue: 10

Published: Oct. 21, 2021

A hallmark of electrophysiological brain activity is its 1/f-like spectrum – power decreases with increasing frequency. The steepness this ‘roll-off’ approximated by the spectral exponent, which in invasively recorded neural populations reflects balance excitatory to inhibitory (E:I balance). Here, we first establish that exponent non-invasive electroencephalography (EEG) recordings highly sensitive general (i.e., anaesthesia-driven) changes E:I balance. Building on EEG as a viable marker E:I, then demonstrate sensitivity focus selective attention an experiment during participants detected targets simultaneous audio-visual noise. In addition these endogenous balance, exponents over auditory and visual sensory cortices also tracked stimulus exponents, respectively. Individuals’ degree stimulus–brain coupling predicted behavioural performance. Our results highlight rich information contained activity, providing window into diverse processes previously thought be inaccessible human recordings.

Language: Английский

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Changes in EEG multiscale entropy and power‐law frequency scaling during the human sleep cycle

Human Brain Mapping, Journal Year: 2018, Volume and Issue: 40(2), P. 538 - 551

Published: Sept. 26, 2018

We explored changes in multiscale brain signal complexity and power‐law scaling exponents of electroencephalogram (EEG) frequency spectra across several distinct global states consciousness induced the natural physiological context human sleep cycle. specifically aimed to link EEG a statistically unified representation neural power spectrum. Further, by utilizing surrogate‐based tests nonlinearity we also examined whether any stage‐dependent entropy were separable from linear stochastic effects contained Our results indicate that throughout cycle are strongly time‐scale dependent. Slow wave was characterized reduced at short time scales increased long scales. Temporal (at scales) slope appear, large extent, capture common phenomenon neuronal noise, putatively reflecting cortical balance between excitation inhibition. Nonlinear dynamical properties signals accounted for smaller portion changes, especially stage 2 sleep.

Language: Английский

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EEG power spectral slope differs by ADHD status and stimulant medication exposure in early childhood

Journal of Neurophysiology, Journal Year: 2019, Volume and Issue: 122(6), P. 2427 - 2437

Published: Oct. 17, 2019

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental characterized by hyperactivity/impulsivity and inattentiveness. Efforts toward the development of biologically based diagnostic test have identified differences in EEG power spectrum; most consistently reported an increased ratio theta to beta during resting state those with disorder, compared controls. Current approaches calculate theta/beta using fixed frequency bands, but observed may be confounded other relevant features spectrum, including shifts peak oscillation altered slope or offset aperiodic 1/

Language: Английский

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Parameterizing neural power spectra

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2018, Volume and Issue: unknown

Published: April 11, 2018

Abstract Electrophysiological signals across species and recording scales exhibit both periodic aperiodic features. Periodic oscillations have been widely studied linked to numerous physiological, cognitive, behavioral, disease states, while the “background” 1/f component of neural power spectra has received far less attention. Most analyses are conducted on a priori , canonically-defined frequency bands without consideration underlying structure, or verification that signal even exists in addition signal. This is problematic, as recent evidence shows dynamic, changing with age, task demands, cognitive state. It also relative excitation/inhibition neuronal population. means standard analytic approaches easily conflate changes one another because parameters—along oscillation center frequency, power, bandwidth—are all dynamic physiologically meaningful, but likely different, ways. In order overcome limitations traditional narrowband reduce potentially deleterious effects conflating these features, we introduce novel algorithm for automatic parameterization spectral densities (PSDs) combination putative oscillations. Notably, this requires no specification band limits accounts potentially-overlapping minimizing degree which they confounded another. amenable large-scale data exploration analysis, providing researchers tool quickly accurately parameterize spectra.

Language: Английский

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Longitudinal changes in aperiodic and periodic activity in electrophysiological recordings in the first seven months of life

Developmental Cognitive Neuroscience, Journal Year: 2020, Volume and Issue: 47, P. 100895 - 100895

Published: Dec. 10, 2020

Neuronal oscillations emerge in early human development. These periodic are thought to rapidly change infancy and stabilize during maturity. Given their numerous connections physiological cognitive processes, understanding the trajectory of oscillatory development is important for healthy brain This complicated by recent evidence that assessment neuronal confounded aperiodic activity, an inherent feature electrophysiological recordings. Recent cross-sectional shows this signal progressively shifts from childhood through adulthood, adulthood into later life. None these studies, however, have been performed infants, nor they examined longitudinally. Here, we analyzed longitudinal non-invasive EEG data 22 typically developing ranging between 38 203 days old. We show progressive flattening power spectrum begins very development, continuing first months results highlight importance separating signals, because can bias measurement oscillations. infrequent, bursting nature recommend using quantitative time domain approaches isolate bursts uncover changes waveform properties bursts.

Language: Английский

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1/f neural noise and electrophysiological indices of contextual prediction in aging

Brain Research, Journal Year: 2018, Volume and Issue: 1691, P. 34 - 43

Published: April 18, 2018

Language: Английский

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The development of theta and alpha neural oscillations from ages 3 to 24 years

Developmental Cognitive Neuroscience, Journal Year: 2021, Volume and Issue: 50, P. 100969 - 100969

Published: May 31, 2021

Intrinsic, unconstrained neural activity exhibits rich spatial, temporal, and spectral organization that undergoes continuous refinement from childhood through adolescence. The goal of this study was to investigate the development theta (4−8 Hertz) alpha (8−12 oscillations early adulthood (years 3–24), as these play a fundamental role in cognitive function. We analyzed eyes-open, resting-state EEG data 96 participants estimate genuine separately aperiodic (1/f) signal. examined age-related differences signal (slope offset), well peak frequency power dominant posterior oscillation. For signal, we found both slope offset decreased with age. oscillation, frequency, but not power, increased Critically, (ages 3–7) characterized by dominance electrodes, whereas oscillation range between ages 7 24. Furthermore, displayed topographical transition electrodes anterior adulthood. Our results provide quantitative description oscillations.

Language: Английский

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Measurement of excitation-inhibition ratio in autism spectrum disorder using critical brain dynamics

Scientific Reports, Journal Year: 2020, Volume and Issue: 10(1)

Published: June 8, 2020

Abstract Balance between excitation (E) and inhibition (I) is a key principle for neuronal network organization information processing. Consistent with this notion, excitation-inhibition imbalances are considered pathophysiological mechanism in many brain disorders including autism spectrum disorder (ASD). However, methods to measure E/I ratios human networks lacking. Here, we present method quantify functional ratio ( fE / I ) from oscillations, validate it healthy subjects children ASD. We define structural an silico network, investigate how relates power long-range temporal correlations (LRTC) of the network’s activity, use these relationships design algorithm. Application algorithm EEGs adults showed that balanced at population level decreased through GABAergic enforcement. In ASD, observed larger variability stronger LRTC compared typically developing (TDC). Interestingly, visual grading EEG abnormalities thought reflect revealed elevated ASD normal TDC or abnormal EEG. speculate our approach will help understand physiological heterogeneity also other disorders.

Language: Английский

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Separating Neural Oscillations from Aperiodic 1/f Activity: Challenges and Recommendations

Neuroinformatics, Journal Year: 2022, Volume and Issue: 20(4), P. 991 - 1012

Published: April 7, 2022

Electrophysiological power spectra typically consist of two components: An aperiodic part usually following an 1/f law [Formula: see text] and periodic components appearing as spectral peaks. While the investigation parts, commonly referred to neural oscillations, has received considerable attention, study only recently gained more interest. The is quantified by center frequencies, powers, bandwidths, while parameterized y-intercept exponent text]. For either part, however, it essential separate components. In this article, we scrutinize frequently used methods, FOOOF (Fitting Oscillations & One-Over-F) IRASA (Irregular Resampling Auto-Spectral Analysis), that are from component. We evaluate these methods using diverse obtained with electroencephalography (EEG), magnetoencephalography (MEG), local field potential (LFP) recordings relating three independent research datasets. Each method each dataset poses distinct challenges for extraction both parts. specific features hindering separation highlighted simulations emphasizing features. Through comparison simulation parameters defined a priori, parameterization error quantified. Based on real simulated spectra, advantages discuss common challenges, note which impede separation, assess computational costs, propose recommendations how use them.

Language: Английский

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Periodic and aperiodic neural activity displays age-dependent changes across early-to-middle childhood

Developmental Cognitive Neuroscience, Journal Year: 2022, Volume and Issue: 54, P. 101076 - 101076

Published: Jan. 22, 2022

The neurodevelopmental period spanning early-to-middle childhood represents a time of significant growth and reorganisation throughout the cortex. Such changes are critical for emergence maturation range social cognitive processes. Here, we utilised both eyes open closed resting-state electroencephalography (EEG) to examine maturational in oscillatory (i.e., periodic) non-oscillatory (aperiodic, '1/f-like') activity large cohort participants ranging from 4-to-12 years age (N = 139, average age=9.41 years, SD=1.95). EEG signal was parameterised into aperiodic periodic components, linear regression models were used evaluate if chronological could predict exponent offset, as well peak frequency power within alpha beta ranges. Exponent offset found decrease with age, while aperiodic-adjusted increased age; however, there no association between band. Age also unrelated spectral either or bands, despite ranges being correlated signal. Overall, these results highlight capacity features elucidate age-related functional developing brain.

Language: Английский

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