scholarly journals Separating neural oscillations from aperiodic 1/f activity: challenges and recommendations

2021 ◽  
Author(s):  
Moritz Gerster ◽  
Gunnar Waterstraat ◽  
Vladimir Litvak ◽  
Klaus Lehnertz ◽  
Alfons Schnitzler ◽  
...  
Keyword(s):  
A Priori ◽  
Field Potential ◽  
Power Spectra ◽  
Neural Oscillations ◽  
Periodic Part ◽  
Spectral Features ◽  
Spectral Peaks ◽  
Aperiodic Component ◽  
Simulation Parameters ◽  
Periodic Components

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

scholarly journals Parameterizing neural power spectra

2018 ◽  
Author(s):  
Matar Haller ◽  
Thomas Donoghue ◽  
Erik Peterson ◽  
Paroma Varma ◽  
Priyadarshini Sebastian ◽  
...  
Keyword(s):  
Large Scale ◽  
A Priori ◽  
Power Spectra ◽  
Neuronal Population ◽  
Task Demands ◽  
Center Frequency ◽  
Cognitive State ◽  
Periodic Signal ◽  
Periodic Oscillations ◽  
Aperiodic Signal

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

scholarly journals Multinational qEEG developmental surfaces

2019 ◽  
Author(s):  
Shiang Hu ◽  
Ally Ngulugulu ◽  
Jorge Bosch-Bayard ◽  
Maria L. Bringas-Vega ◽  
Pedro A. Valdes-Sosa
Keyword(s):  
Power Spectra ◽  
Two Dimensions ◽  
Spectral Features ◽  
Linear Mixed Effects ◽  
Independent Variables ◽  
Eeg Power Spectra ◽  
The Usa ◽  
Specific Factors ◽  
Quantitative Electroencephalogram ◽  
The Individual

AbstractThe quantitative electroencephalogram (qEEG) is a diagnostic method based on the spectral features of the resting state EEG. The departure of spectral features from normality is gauged by the z transform with respect to the age-adjusted mean and deviation of normative databases – known as the developmental equations/surfaces. However, the extent to which the data collected from different countries with various equipment require separate developmental equations remains unanswered. Here, we analyzed the EEG of 535 subjects from 3 countries, Switzerland, the USA and Cuba. The EEG power spectra of all samples were log transformed and their relations to the covariables (‘age’, ‘frequency’, ‘country’ and ‘individual’) were analyzed using the linear mixed effects model. We found that the origin ‘country’ of the subjects did not play a significant effect on the log spectra, even without interactions with other independent variables, whereas, ‘age’ and ‘frequency’ were highly significant. To estimate the developmental surfaces in greater detail, we carried out kernel regression (lowess) in two dimensions of log-age and frequency. We found two main phenomena: 1) slow rhythms (δ, θ) predominated in the lower ages and then decreased with a tendency to disappear at higher ages; 2) α rhythm was absent at lower ages, but gradually appeared more relevant in occipital and parietal regions, and increased with aging with an increasing centering frequency of α rhythm. We consider both phenomena as an expression of healthy neurodevelopmental and maturation related to age. It is the first study of multinational qEEG developmental surfaces accounting for ‘country’. The results demonstrate the possibility of creating international qEEG norms since the ‘individual’ and ‘age’ variability are much larger than the specific factors like ‘country’, or the technology employed ‘device’.

scholarly journals ANGULAR POWER SPECTRUM IN MODULAR INVARIANT INFLATION MODEL

2007 ◽  
Vol 22 (12) ◽  
pp. 2223-2237 ◽  
Author(s):  
MITSUO J. HAYASHI ◽  
SHIRO HIRAI ◽  
TOMOYUKI TAKAMI ◽  
YUSUKE OKAMEI ◽  
KENJI TAKAGI ◽  
...  
Keyword(s):  
Scalar Potential ◽  
Field Potential ◽  
Power Spectra ◽  
Scalar Fields ◽  
Modular Invariant ◽  
Angular Power Spectrum ◽  
Inflation Model ◽  
Density Perturbations ◽  
Cosmological Data ◽  
Single Field

We propose a scalar potential of inflation, motivated by modular invariant supergravity, and compute the angular power spectra of the adiabatic density perturbations that result from this model. The potential consists of three scalar fields, S, Y and T, together with two free parameters. By fitting the parameters to cosmological data at the fixed point T = 1, we find that the potential behaves like the single-field potential of S, which slowly rolls down along the minimized trajectory in Y. We further show that the inflation predictions corresponding to this potential provide a good fit to the recent three-year WMAP data, e.g. the spectral index ns = 0.951. The TT and TE angular power spectra obtained from our model almost completely coincide with the corresponding results obtained from the ΛCDM model. We conclude that our model is considered to be an adequate theory of inflation that explains the present data, although the theoretical basis of this model should be further explicated.

scholarly journals SU(2) Yang-Mills thermodynamics: A priori estimate and radiative corrections

2018 ◽  
Vol 182 ◽  
pp. 02053 ◽  
Author(s):  
Ralf Hofmann
Keyword(s):  
Ground State ◽  
A Priori ◽  
Power Spectra ◽  
A Priori Estimate ◽  
Coarse Graining ◽  
Late Time ◽  
Radiative Corrections ◽  
Priori Estimate ◽  
Yang Mills ◽  
The One

We review and explain essential characteristics of the a priori estimate of the thermal ground state and its excitations in the deconfining phase of SU(2) Quantum Yang-Mills thermodynamics. This includes the spatially central and peripheral structure of Harrington-Shepard (anti)calorons, a sketch on how a spatial coarse-graining over (anti)caloron centers yields an inert scalar field, which is responsible for an adjoint Higgs mechanism, the identification of (anti)caloron action with ħ, a discussion of how, owing to (anti)caloron structure, the thermal ground state can be excited (wave-like and particlelike massless modes, massive thermal quasiparticle fluctuations), the principle role of and accounting for radiative corrections, the exclusion of energy-sign combinations due to constraints on momenta transfers in four-vertices in a completely fixed, physical gauge, dihedral diagrams and their resummation up to infinite loop order in the massive sector, and the resummation of the one-loop polarisation tensor of the massless modes. We also outline applications of deconfining SU(2) Yang-Mills thermodynamics to the Cosmic Microwave Background (CMB) which affect the cosmological model at high redshifts, the redshift for re-ionization of the Universe, the CMB angular power spectra at low l, and the late-time emergence of intergalactic magnetic fields.

scholarly journals Aplication of A New Observational Strategy to the Study of Gravitational Solar Modes

1998 ◽  
Vol 185 ◽  
pp. 51-52
Author(s):  
I. Martin Mateos ◽  
P.L. Pallé
Keyword(s):  
Time Series ◽  
Solar Disk ◽  
Power Spectra ◽  
Phase Relations ◽  
Basic Data ◽  
Spectral Features ◽  
Velocity Measurements ◽  
Geometrical Properties ◽  
Temporal Properties ◽  
Observational Strategy

The aim of the present work is the detection of solar g-modes, making use of their spatial and temporal properties, by means of a new observational strategy. The basic data, gathered at the Observatorio del Teide in 1993, consists on daily solar velocity measurements taken continuous and sequentially at six different and symmetric positions on the solar disk. By correlating the time series obtained from different positions, and considering the geometrical properties of different modes (l, m) on the Sun‘s surface, some of them can selectively be eliminated or enhanced. In particular, the main spectral features present in the resulting power spectra must have precise phase relations if they correspond to global solar g-modes.

scholarly journals Unpacking Transient Event Dynamics in Electrophysiological Power Spectra

2019 ◽  
Vol 32 (6) ◽  
pp. 1020-1034 ◽  
Author(s):  
Andrew J. Quinn ◽  
Freek van Ede ◽  
Matthew J. Brookes ◽  
Simone G. Heideman ◽  
Magdalena Nowak ◽  
...  
Keyword(s):  
Markov Models ◽  
Spectral Power ◽  
A Priori ◽  
Power Spectra ◽  
Simulated Data ◽  
Burst Detection ◽  
Spectral Content ◽  
Transient Event ◽  
Electrophysiological Recordings ◽  
Event Dynamics

AbstractElectrophysiological recordings of neuronal activity show spontaneous and task-dependent changes in their frequency-domain power spectra. These changes are conventionally interpreted as modulations in the amplitude of underlying oscillations. However, this overlooks the possibility of underlying transient spectral ‘bursts’ or events whose dynamics can map to changes in trial-average spectral power in numerous ways. Under this emerging perspective, a key challenge is to perform burst detection, i.e. to characterise single-trial transient spectral events, in a principled manner. Here, we describe how transient spectral events can be operationalised and estimated using Hidden Markov Models (HMMs). The HMM overcomes a number of the limitations of the standard amplitude-thresholding approach to burst detection; in that it is able to concurrently detect different types of bursts, each with distinct spectral content, without the need to predefine frequency bands of interest, and does so with less dependence on a priori threshold specification. We describe how the HMM can be used for burst detection and illustrate its benefits on simulated data. Finally, we apply this method to empirical data to detect multiple burst types in a task-MEG dataset, and illustrate how we can compute burst metrics, such as the task-evoked timecourse of burst duration.

scholarly journals Co-Increasing Neuronal Noise and Beta Power in the Developing Brain

2019 ◽  
Author(s):  
Wei He ◽  
Thomas Donoghue ◽  
Paul F Sowman ◽  
Robert A Seymour ◽  
Jon Brock ◽  
...  
Keyword(s):  
Childhood Development ◽  
Beta Band ◽  
Neuronal Oscillations ◽  
Age Related ◽  
Beta Power ◽  
Oscillatory Power ◽  
Aperiodic Component ◽  
Periodic Components ◽  
Aperiodic Signal ◽  
Age Related Changes

ABSTRACTAccumulating evidence across species indicates that brain oscillations are superimposed upon an aperiodic 1/f - like power spectrum. Maturational changes in neuronal oscillations have not been assessed in tandem with this underlying aperiodic spectrum. The current study uncovers co-maturation of the aperiodic component alongside the periodic components (oscillations) in spontaneous magnetoencephalography (MEG) data. Beamformer-reconstructed MEG time-series allowed a direct comparison of power in the source domain between 24 children (8.0 ± 2.5 years, 17 males) and 24 adults (40.6 ± 17.4 years, 16 males). Our results suggest that the redistribution of oscillatory power from lower to higher frequencies that is observed in childhood does not hold once the age-related changes in the aperiodic signal are controlled for. When estimating both the periodic and aperiodic components, we found that power increases with age in the beta band only, and that the 1/f signal is flattened in adults compared to children. These results suggest a pattern of co-maturing beta oscillatory power with the aperiodic 1/f signal in typical childhood development.

scholarly journals Random recurrent networks near criticality capture the broadband power distribution of human ECoG dynamics

2016 ◽  
Author(s):  
Rishidev Chaudhuri ◽  
Biyu He ◽  
Xiao-Jing Wang
Keyword(s):  
Power Spectrum ◽  
Power Distribution ◽  
Random Network ◽  
Field Potential ◽  
Power Spectra ◽  
Low Frequency ◽  
Recurrent Network ◽  
Network Activity ◽  
Brain Diseases ◽  
Low Frequency Power

The power spectrum of brain electric field potential recordings is dominated by an arrhythmic broadband signal but a mechanistic account of its underlying neural network dynamics is lacking. Here we show how the broadband power spectrum of field potential recordings can be explained by a simple random network of nodes near criticality. Such a recurrent network produces activity with a combination of a fast and a slow autocorrelation time constant, with the fast mode corresponding to local dynamics and the slow mode resulting from recurrent excitatory connections across the network. These modes are combined to produce a power spectrum similar to that observed in human intracranial EEG (i.e., electrocorticography, ECoG) recordings. Moreover, such a network naturally converts input correlations across nodes into temporal autocorrelation of the network activity. Consequently, increased independence between nodes results in a reduction in low-frequency power, which offers a possible explanation for observed changes in ECoG power spectra during task performance. Lastly, changes in network coupling produce changes in network activity power spectra reminiscent of those seen in human ECoG recordings across different arousal states. This model thus links macroscopic features of the empirical ECoG power spectrum to a parsimonious underlying network structure and proposes potential mechanisms for changes in ECoG power spectra observed across behavioral and arousal states. This provides a computational framework within which to generate and test hypotheses about the cellular and network mechanisms underlying whole brain electrical dynamics, their variations across behavioral states as well as abnormalities associated with brain diseases.

scholarly journals Spectrum Degradation of Hippocampal LFP During Euthanasia

2021 ◽  
Vol 15 ◽  
Author(s):  
Yuchen Zhou ◽  
Alex Sheremet ◽  
Jack P. Kennedy ◽  
Nicholas M. DiCola ◽  
Carolina B. Maciel ◽  
...  
Keyword(s):  
Entorhinal Cortex ◽  
Fault Tolerant ◽  
Field Potential ◽  
Power Spectra ◽  
Direct Consequence ◽  
Synaptic Activity ◽  
Low Frequencies ◽  
Second Stage ◽  
Gamma Rhythm ◽  
Neural Entrainment

The hippocampal local field potential (LFP) exhibits a strong correlation with behavior. During rest, the theta rhythm is not prominent, but during active behavior, there are strong rhythms in the theta, theta harmonics, and gamma ranges. With increasing running velocity, theta, theta harmonics and gamma increase in power and in cross-frequency coupling, suggesting that neural entrainment is a direct consequence of the total excitatory input. While it is common to study the parametric range between the LFP and its complementing power spectra between deep rest and epochs of high running velocity, it is also possible to explore how the spectra degrades as the energy is completely quenched from the system. Specifically, it is unknown whether the 1/f slope is preserved as synaptic activity becomes diminished, as low frequencies are generated by large pools of neurons while higher frequencies comprise the activity of more local neuronal populations. To test this hypothesis, we examined rat LFPs recorded from the hippocampus and entorhinal cortex during barbiturate overdose euthanasia. Within the hippocampus, the initial stage entailed a quasi-stationary LFP state with a power-law feature in the power spectral density. In the second stage, there was a successive erosion of power from high- to low-frequencies in the second stage that continued until the only dominant remaining power was <20 Hz. This stage was followed by a rapid collapse of power spectrum toward the absolute electrothermal noise background. As the collapse of activity occurred later in hippocampus compared with medial entorhinal cortex, it suggests that the ability of a neural network to maintain the 1/f slope with decreasing energy is a function of general connectivity. Broadly, these data support the energy cascade theory where there is a cascade of energy from large cortical populations into smaller loops, such as those that supports the higher frequency gamma rhythm. As energy is pulled from the system, neural entrainment at gamma frequency (and higher) decline first. The larger loops, comprising a larger population, are fault-tolerant to a point capable of maintaining their activity before a final collapse.

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