Decomposing age effects in EEG alpha power

Increasing life expectancy is prompting the need to understand how the brain changes during healthy aging. Research utilizing Electroencephalography (EEG) has found that the power of alpha oscillations decrease from adulthood on. However, non-oscillatory (aperiodic) components in the data may confound results and thus require re-investigation of these findings. The present report aims at analyzing a pilot and two additional independent samples (total N = 533) of resting-state EEG from healthy young and elderly individuals. A newly developed algorithm will be utilized that allows the decomposition of the measured signal into aperiodic and aperiodic-adjusted signal components. By using multivariate sequential Bayesian updating of the age effect in each signal component, evidence across the datasets will be accumulated. It is hypothesized that previously reported age-related alpha power differences will disappear when absolute power is adjusted for the aperiodic signal component. Consequently, age-related differences in the intercept and slope of the aperiodic signal component are expected. Importantly, using a battery of neuropsychological tests, we will assess how the previously reported relationship between cognitive functions and alpha oscillations changes when taking the aperiodic signal into account; this will be done on data of the young and aged individuals separately. The aperiodic signal components and adjusted alpha parameters could potentially offer a promising biomarker for cognitive decline, thus finally the test–retest reliability of the aperiodic and aperiodic-adjusted signal components will be assessed.

Connectomics of Human Electrophysiology

We present both a scientific overview and conceptual positions concerning the challenges and assets of electrophysiological measurements in the search for the nature and functions of the human connectome. We discuss how the field has been inspired by findings and approaches from functional magnetic resonance imaging (fMRI) and informed by a small number of significant multimodal empirical studies, which show that the canonical networks that are commonplace in fMRI are in fact rooted in electrophysiological processes. This review is also an opportunity to produce a brief, up-to-date critical survey of current data modalities and signal processing methods available for deriving both static and dynamic connectomes using electrophysiological data. We review hurdles that challenge the significance and impact of current electrophysiology connectome research. We then encourage the field to take a leap of faith and embrace the wealth of electrophysiological signals, despite their apparent, disconcerting complexity. Our position is that electrophysiology connectomics is poised to inform testable mechanistic models of information integration in hierarchical brain networks, constructed from observable oscillatory and aperiodic signal components and their polyrhythmic interactions.

Spontaneous neural oscillations influence behavior and sensory representations by suppressing neuronal excitability

The ability to process and respond to external input is critical for adaptive behavior. Why, then, do neural and behavioral responses vary across repeated presentations of the same sensory input? Spontaneous fluctuations of neuronal excitability are currently hypothesized to underlie the trial-by-trial variability in sensory processing. To test this, we capitalized on invasive electrophysiology in neurosurgical patients performing an auditory discrimination task with visual cues: specifically, we examined the interaction between prestimulus alpha oscillations, excitability, task performance, and decoded neural stimulus representations. We found that strong prestimulus oscillations in the alpha+ band (i.e., alpha and neighboring frequencies), rather than the aperiodic signal, correlated with a low excitability state, indexed by reduced broadband high-frequency activity. This state was related to slower reaction times and reduced neural stimulus encoding strength. We propose that the alpha+ rhythm modulates excitability, thereby resulting in variability in behavior and sensory representations despite identical input.

Decomposing the role of alpha oscillations during brain maturation

During childhood and adolescence, the human brain undergoes various micro- and macroscopic changes. Understanding the neurophysiological changes within this reorganizational process is crucial, as many major psychiatric disorders emerge during this critical phase of life. In electroencephalography (EEG), a widely studied signal component are alpha oscillations (~8-13 Hz), which have been linked to developmental changes throughout the lifespan. Previous neurophysiological studies have demonstrated an increase of the alpha peak frequency and a decrease of alpha power to be related to brain maturation. The latter results have been questioned by recent developments in EEG signal processing techniques, as it could be demonstrated that aperiodic (non-oscillatory) components in the EEG signal conflate findings on periodic (oscillatory) changes, and thus need to be decomposed accordingly. We therefore analyzed a large, openly available pediatric dataset of 1485 children and adolescents in the age range of 5 to 21 years, in order to clarify the role of alpha oscillations and aperiodic signal components in this period of life. We first replicated previous findings of an increase of alpha peak frequency with age. Our results further suggest that alpha oscillatory power decreases with increasing age, however, when controlling for the aperiodic signal component, this effect inverted such as the aperiodic adjusted alpha power parameters significantly increase with advanced brain maturation, while the aperiodic signal component flattens and its offset decreases. Thus, interpretations of these oscillatory changes should be done with caution and incorporate changes in the aperiodic signal. These findings highlight the importance of taking aperiodic signal components into account when investigating age related changes of EEG spectral power parameters.

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

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

On the Aperiodic Signal Amplification by Rescaled Vibrational Resonance in Fractional-Order Duffing Oscillators

In the former works of Yang et al., they put forward two vibrational resonance (VR) methods in fractional Duffing oscillators to amplify the weak harmonic character signal with arbitrary high-frequency. Moreover, the aperiodic character signal is also a common signal form in practical engineering. For the “fast-changing” aperiodic character signal in time domain, the classical VR method is difficult to amplify it effectively. To solve this problem, an aperiodic signal amplification method based on rescaled VR in fractional-order Duffing oscillators is proposed. Take the bipolar binary random signals with arbitrary minimal pulse width as an example, the rescaled VR under the overdamped and underdamped conditions can be realized by matching the signal parameters and system parameters effectively, and the amplification of aperiodic signal can be achieved.

Formation of Minimax Ensembles of Aperiodic Gold Codes

Introduction. Signals constructed on the basis of ensembles of code sequences are widely used in digital communication systems. During development of such systems, the most attention is paid to analysis, synthesis and implementation of periodic signal ensembles. Theoretic methods for synthesis of periodic signal ensembles are developed and are in use. Considerably fewer results are received regarding construction of aperiodic signal ensembles with given properties. Theoretical methods for synthesis of such ensembles are practically nonexistent.Aim. To construct aperiodic Gold code ensembles with the best ratios of code length to ensemble volume among the most known binary codes.Materials and methods. Methods of directed search and discrete choice of the best ensemble based on unconditional preference criteria are used.Results. Full and truncated aperiodic Gold code ensembles with given length and ensemble volume were constructed. Parameters and shape of auto- and mutual correlation functions were shown for a number of constructed ensembles. Comparison of the paper results with known results for periodic Gold code ensembles has been conducted regarding growth of minimax correlation function values depending on code length and ensemble volume.Conclusion. The developed algorithms, unlike the known ones, make it possible to form both complete ensembles and ensembles taking into account the limitation of their volume. In addition, the algorithms can be extended to the tasks of forming ensembles from other families, for example, assembled from code sequences belonging to different families.

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

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.

Multiple mechanisms link prestimulus neural oscillations to sensory responses

Spontaneous fluctuations of neural activity may explain why sensory responses vary across repeated presentations of the same physical stimulus. To test this hypothesis, we recorded electroencephalography in humans during stimulation with identical visual stimuli and analyzed how prestimulus neural oscillations modulate different stages of sensory processing reflected by distinct components of the event-related potential (ERP). We found that strong prestimulus alpha- and beta-band power resulted in a suppression of early ERP components (C1 and N150) and in an amplification of late components (after 0.4 s), even after controlling for fluctuations in 1/f aperiodic signal and sleepiness. Whereas functional inhibition of sensory processing underlies the reduction of early ERP responses, we found that the modulation of non-zero-mean oscillations (baseline shift) accounted for the amplification of late responses. Distinguishing between these two mechanisms is crucial for understanding how internal brain states modulate the processing of incoming sensory information.