Aperiodic Activity Research Articles

Good luck or bad luck? The influence of social comparison on risk‐taking decision and the underlying neural mechanism

AbstractThe present study aimed to investigate the impact of social comparison on risk‐taking behaviors and the neural underpinnings within a competitive context. Participants who thought they were playing against a stranger in a gambling task were actually playing against a programmed computer. Eighty‐eight college students were assigned to one of three comparison conditions (downward, upward, and parallel) by varying the probability of gain. Behavioral results showed that disadvantage led to increased risk‐taking. Event‐related potential data analyses showed, in the parallel comparison condition, a significantly larger Feedback‐Related Negativity (FRN) was induced by the self's safe decision than the risky decision and by loss rather than gain. However, in the upward and downward comparison conditions, larger FRN emerged solely in response to the loss of risky rather than safe decisions. On the P3 component, participants in the upward comparison condition showed no significant difference in response to their gain or loss, while the other two conditions did. The highest P3 amplitude, delta/theta power, and aperiodic activity were found in the closely matched condition. Finally, in the downward comparison condition, a stronger delta/theta power was correlated with a less risky decision. Overall, the findings indicate that parity heightens emotional arousal and engages more cognitive resources.

EEG aperiodic dynamics from early through late childhood: Associations with ADHD, cognition, and development

BackgroundAperiodic resting electroencephalography (EEG) activity is dynamic, reflecting shifting excitatory:inhibitory (E:I) balance with changing environmental conditions. We examined developmental and cognitive correlates of aperiodic and dynamic aperiodic indices in a cross-sequential cohort of early, middle, and late childhood youth with and without attention deficit hyperactivity disorder (ADHD). MethodsTwo-hundred eighty-five children ages 2 – 14 years provided resting EEG during high- and low-visual input conditions. Licensed clinical psychologists determined ADHD diagnosis or likelihood (in young children). Linear regressions were estimated to examine associations between aperiodic features and age, ADHD diagnosis, IQ, and experimental condition. ResultsFrom early to middle childhood, the aperiodic exponent increased linearly, indicating lower E:I, followed by a decreasing trajectory in late childhood. The aperiodic exponent was greater with high versus low visual input in young children, but this effect reversed with age. The ADHD group had a decreased aperiodic exponent, overall. Dynamic aperiodic activity, i.e. shifts in E:I balance, was associated with IQ. ConclusionsThe aperiodic exponent and aperiodic dynamics are proxies for age-related cortical maturation and E:I balance, and have distinct associations with ADHD symptoms and cognitive ability. SignificanceWe provide novel evidence that dynamic aperiodic activity is a candidate marker of cortical efficiency in childhood.

The development of aperiodic neural activity in the human brain.

The neurophysiological mechanisms supporting brain maturation are fundamental to attention and memory capacity across the lifespan. Human brain regions develop at different rates, with many regions developing into the third and fourth decades of life. Here, in this preregistered study ( https://osf.io/gsru7 ), we analyzed intracranial EEG (iEEG) recordings from widespread brain regions in a large developmental cohort. Using task-based (i.e., attention to-be-remembered visual stimuli) and task-free (resting-state) data from 101 children and adults (5.93 - 54.00 years, 63 males; n electrodes = 5691), we mapped aperiodic (1/ƒ-like) activity, a proxy of excitation:inhibition (E:I) balance with steeper slopes indexing inhibition and flatter slopes indexing excitation. We reveal that aperiodic slopes flatten with age into young adulthood in both association and sensorimotor cortices, challenging models of early sensorimotor development based on brain structure. In prefrontal cortex (PFC), attentional state modulated age effects, revealing steeper task-based than task-free slopes in adults and the opposite in children, consistent with the development of cognitive control. Age-related differences in task-based slopes also explained age-related gains in memory performance, linking the development of PFC cognitive control to the development of memory. Last, with additional structural imaging measures, we reveal that age-related differences in gray matter volume are differentially associated with aperiodic slopes in association and sensorimotor cortices. Our findings establish developmental trajectories of aperiodic activity in localized brain regions and illuminate the development of PFC inhibitory control during adolescence in the development of attention and memory.

Impact of sleep deprivation on aperiodic activity: a resting-state EEG study.

Sleep deprivation (SD) has been shown to have a negative impact on alertness, as evidenced by behavioral and electroencephalographic studies. Nevertheless, in prior studies utilizing conventional fixed-bandwidth spectral analysis the aperiodic and periodic components were often confused, and some important periodic parameters (i.e., center frequency, bandwidth) were ignored. Here, based on a large open-access dataset of SD, we employed a standardized process for multiple-electrode analysis and group inference. We found that, compared to the healthy sleep control state (SC), the aperiodic offset shifted overall after SD, primarily in the occipital region. This shift was associated with a reduction in subjective alertness. Regarding periodic components, we did not find any power change in the alpha rhythm, but there was an increase in bandwidth of alpha within different regions distributed in the occipital and temporal lobes. These findings highlight the potential significance and value of aperiodic parameters in behavioral and electrophysiological research.NEW & NOTEWORTHY Aperiodic and periodic components were separated in a large open-access EEG dataset of sleep deprivation. Aperiodic offsets increase after deprivation, particularly in the occipital region, reflecting a decline in self-reported vigilance. Parameterized alpha bandwidth, which was ignored in previous studies, is found to be relevant to sleep deprivation. Increase in bandwidth of alpha was focused in the occipital and temporal lobes.

Aperiodic neural activity reflects metacontrol in task-switching

“Metacontrol” refers to the ability to find the right balance between more persistent and more flexible cognitive control styles, depending on task demands. Recent research on tasks involving response conflict regulation indicates a consistent link between aperiodic EEG activity and task conditions that demand a more or less persistent control style. In this study, we explored whether this connection between metacontrol and aperiodic activity also applies to cognitive flexibility. We examined EEG and behavioral data from two separate samples engaged in a task-switching paradigm, allowing for an internal replication of our findings. Both studies revealed that aperiodic activity significantly decreased during task switching compared to task repetition. Our results support the predictions of metacontrol theory but contradict those of traditional control theories which would have predicted the opposite pattern of results. We propose that aperiodic activity observed in EEG signals serves as a valid indicator of dynamic neuroplasticity in metacontrol, suggesting that truly adaptive metacontrol does not necessarily bias processing towards persistence in response to every control challenge, but chooses between persistence and flexibility biases depending on the nature of the challenge.

Aperiodic EEG Predicts Variability of Visual Temporal Processing.

The human brain exhibits both oscillatory and aperiodic, or 1/f, activity. Although a large body of research has focused on the relationship between brain rhythms and sensory processes, aperiodic activity has often been overlooked as functionally irrelevant. Prompted by recent findings linking aperiodic activity to the balance between neural excitation and inhibition, we investigated its effects on the temporal resolution of perception. We recorded electroencephalography (EEG) from participants (both sexes) during the resting state and a task in which they detected the presence of two flashes separated by variable interstimulus intervals. Two-flash discrimination accuracy typically follows a sigmoid function whose steepness reflects perceptual variability or inconsistent integration/segregation of the stimuli. We found that individual differences in the steepness of the psychometric function correlated with EEG aperiodic exponents over posterior scalp sites. In other words, participants with flatter EEG spectra (i.e., greater neural excitation) exhibited increased sensory noise, resulting in shallower psychometric curves. Our finding suggests that aperiodic EEG is linked to sensory integration processes usually attributed to the rhythmic inhibition of neural oscillations. Overall, this correspondence between aperiodic neural excitation and behavioral measures of sensory noise provides a more comprehensive explanation of the relationship between brain activity and sensory integration and represents an important extension to theories of how the brain samples sensory input over time.

The Mindful Brain at Rest: Neural Oscillations and Aperiodic Activity in Experienced Meditators

ObjectivesPrevious research has demonstrated that mindfulness meditation is associated with a variety of benefits, including improved mental health. Researchers have suggested these benefits may be underpinned by differences in neural oscillations. However, previous studies measuring neural oscillations have not controlled for non-oscillatory neural activity, the power spectrum of which follows a 1/f distribution (whereby the power of neural activity at each frequency is inversely proportional to that frequency (such that as frequency increases, power decreases)) and contributes to power measurements within oscillation frequencies of interest. We applied recently developed methods to determine if past findings related to neural oscillations in meditation are present even after controlling for non-oscillatory 1/f activity.MethodForty-eight experienced meditators and 44 demographically matched non-meditators provided resting electroencephalography (EEG) recordings. Whole-scalp EEG comparisons (topographical ANOVAs) were used to test for differences between meditators and non-meditators in the distribution or global power of activity for theta, alpha, beta, and gamma oscillations, and for the 1/f components slope and intercept using the extended Better OSCillation detection toolbox.ResultsResults indicated that meditators showed differences in theta, alpha, and gamma oscillatory power compared to non-meditators (all p < 0.05). Post hoc testing suggested that the oscillatory differences were primarily driven by differences in the distribution of neural activity between meditators and non-meditators, rather than differences in the overall power across all scalp electrodes.ConclusionsOur results suggest that experience with meditation is associated with higher oscillatory power and altered distributions of theta, alpha, and gamma oscillations, even after controlling for non-oscillatory 1/f activity. Band-specific differences in oscillatory activity may be a mechanism through which meditation leads to neurophysiological benefits.PreregistrationThis study was not preregistered.

Structural inequality and temporal brain dynamics across diverse samples.

Structural income inequality - the uneven income distribution across regions or countries - could affect brain structure and function, beyond individual differences. However, the impact of structural income inequality on the brain dynamics and the roles of demographics and cognition in these associations remains unexplored. Here, we assessed the impact of structural income inequality, as measured by the Gini coefficient on multiple EEG metrics, while considering the subject-level effects of demographic (age, sex, education) and cognitive factors. Resting-state EEG signals were collected from a diverse sample (countries=10; healthy individuals=1394 from Argentina, Brazil, Colombia, Chile, Cuba, Greece, Ireland, Italy, Turkey and United Kingdom). Complexity (fractal dimension, permutation entropy, Wiener entropy, spectral structure variability), power spectral and aperiodic components (1/f slope, knee, offset), as well as graph-theoretic measures were analysed. Despite variability in samples, data collection methods, and EEG acquisition parameters, structural inequality systematically predicted electrophysiological brain dynamics, proving to be a more crucial determinant of brain dynamics than individual-level factors. Complexity and aperiodic activity metrics captured better the effects of structural inequality on brain function. Following inequality, age and cognition emerged as the most influential predictors. The overall results provided convergent multimodal metrics of biologic embedding of structural income inequality characterised by less complex signals, increased random asynchronous neural activity, and reduced alpha and beta power, particularly over temporoposterior regions. These findings might challenge conventional neuroscience approaches that tend to overemphasise the influence of individual-level factors, while neglecting structural factors. Results pave the way for neuroscience-informed public policies aimed at tackling structural inequalities in diverse populations.

Evaluating and Comparing Measures of Aperiodic Neural Activity.

Neuro-electrophysiological recordings contain prominent aperiodic activity - meaning irregular activity, with no characteristic frequency - which has variously been referred to as 1/f (or 1/f-like activity), fractal, or 'scale-free' activity. Previous work has established that aperiodic features of neural activity is dynamic and variable, relating (between subjects) to healthy aging and to clinical diagnoses, and also (within subjects) tracking conscious states and behavioral performance. There are, however, a wide variety of conceptual frameworks and associated methods for the analyses and interpretation of aperiodic activity - for example, time domain measures such as the autocorrelation, fractal measures, and/or various complexity and entropy measures, as well as measures of the aperiodic exponent in the frequency domain. There is a lack of clear understanding of how these different measures relate to each other and to what extent they reflect the same or different properties of the data, which makes it difficult to synthesize results across approaches and complicates our overall understanding of the properties, biological significance, and demographic, clinical, and behavioral correlates of aperiodic neural activity. To address this problem, in this project we systematically survey the different approaches for measuring aperiodic neural activity, starting with an automated literature analysis to curate a collection of the most common methods. We then evaluate and compare these methods, using statistically representative time series simulations. In doing so, we establish consistent relationships between the measures, showing that much of what they capture reflects shared variance - though with some notable idiosyncrasies. Broadly, frequency domain methods are more specific to aperiodic features of the data, whereas time domain measures are more impacted by oscillatory activity. We extend this analysis by applying the measures to a series of empirical EEG and iEEG datasets, replicating the simulation results. We conclude by summarizing the relationships between the multiple methods, emphasizing opportunities for reexamining previous findings and for future work.

Resting-state EEG correlates of sustained attention in healthy ageing: Cross-sectional findings from the LEISURE study

While structural and biochemical brain changes are well-documented in ageing, functional neuronal network differences, as indicated by electrophysiological markers, are less clear. Moreover, age-related changes in sustained attention and their associated electrophysiological correlates are still poorly understood. To address this, we analysed cross-sectional baseline electroencephalography (EEG) and cognitive data from the Lifestyle Intervention Study for Dementia Risk Reduction (LEISURE). Participants were 96 healthy older adults, aged 50–84. We examined resting-state EEG periodic (individual alpha frequency [IAF], aperiodic-adjusted individual alpha power [aIAP]) and aperiodic (exponent and offset) activity, and their associations with age and sustained attention. Results showed associations between older age and slower IAF, but not aIAP or global aperiodic exponent and offset. Additionally, hierarchical linear regression revealed that after controlling for demographic variables, faster IAF was associated with better Sustained Attention to Response Task performance, and mediation analysis confirmed IAF as a mediator between age and sustained attention performance. These findings indicate that IAF may be an important marker of ageing, and a slower IAF may signal diminished cognitive processing capacity for sustained attention in older adults.

Differential effects of haloperidol on neural oscillations during wakefulness and sleep

The electrical activity of the brain, characterized by its frequency components, reflects a complex interplay between periodic (oscillatory) and aperiodic components. These components are associated with various neurophysiological processes, such as the excitation-inhibition balance (aperiodic activity) or interregional communication (oscillatory activity). However, we do not fully understand whether these components are truly independent or if different neuromodulators affect them in different ways. The dopaminergic system has a critical role for cognition and motivation, being a potential modulator of these power spectrum components. To improve our understanding of these questions, we investigated the differential effects of this system on these components using electrocorticogram recordings in cats, which show clear oscillations and aperiodic 1/f activity. Specifically, we focused on the effects of haloperidol (a D2 receptor antagonist) on oscillatory and aperiodic dynamics during wakefulness and sleep. By parameterizing the power spectrum into these two components, our findings reveal a robust modulation of oscillatory activity by the D2 receptor across the brain. Surprisingly, aperiodic activity was not significantly affected and exhibited inconsistent changes across the brain. This suggests a nuanced interplay between neuromodulation and the distinct components of brain oscillations, providing insights into the selective regulation of oscillatory dynamics in awake states.

Aperiodic Component Analysis in Quantification of Steady-State Visually Evoked Potentials.

In this study, we aimed to investigate whether and how the aperiodic component in electroencephalograms affects different quantitative processes of steady-state visually evoked potentials and the performance of corresponding brain-computer interfaces. We applied the Fitting Oscillations & One-Over-F method to parameterize power spectra as a combination of periodic oscillations and an aperiodic component. Electroencephalographic responses and system performance were measured and compared using four prevailing methods: power spectral density analysis, canonical correlation analysis, filter bank canonical correlation analysis and the state-of-the-art method, task discriminant component analysis. We found that controlling for the aperiodic component prominently downgraded the performance of brain-computer interfaces measured by canonical correlation analysis (94.9% to 82.8%), filter bank canonical correlation analysis (94.1% to 87.6%), and task discriminant component analysis (96.5% to 70.3%). However, it had almost no effect on that measured by power spectral density analysis (80.4% to 78.7%). This was accompanied by a differential aperiodic impact between power spectral density analysis and the other three methods on the differentiation of the target and non-target stimuli. The aperiodic component distinctly impacts the quantification of steady-state visually evoked potentials and the performance of corresponding brain-computer interfaces. Our work underscores the significance of taking into account the dynamic nature of aperiodic activities in research related to the quantification of steady-state visually evoked potentials. The source code for our approach is available at https://github.com/didi226/scut_ssvep_aperiod.

Changes in electrophysiological aperiodic activity during cognitive control in Parkinson’s disease

Abstract Cognitive symptoms in Parkinson’s disease are common and can significantly affect patients’ quality of life. Therefore, there is an urgent clinical need to identify a signature derived from behavioural and/or neuroimaging indicators that could predict which patients are at increased risk for early and rapid cognitive decline. Recently, converging evidence identified that aperiodic activity of the EEG reflects meaningful physiological information associated with age, development, cognitive and perceptual states or pathologies. In this study, we aimed to investigate aperiodic activity in Parkinson’s disease during cognitive control and characterize its possible association with behaviour. Here, we recorded high-density EEG in 30 healthy controls and 30 Parkinson’s disease patients during a Simon task. We analysed task-related behavioural data in the context of the activation–suppression model and extracted aperiodic parameters (offset, exponent) at both scalp and source levels. Our results showed lower behavioural performances in cognitive control as well as higher offsets in patients in the parieto-occipital areas, suggesting increased excitability in Parkinson’s disease. A small congruence effect on aperiodic parameters in pre- and post-central brain areas was also found, possibly associated with task execution. Significant differences in aperiodic parameters between the resting-state, pre- and post-stimulus phases were seen across the whole brain, which confirmed that the observed changes in aperiodic activity are linked to task execution. No correlation was found between aperiodic activity and behaviour or clinical features. Our findings provide evidence that EEG aperiodic activity in Parkinson’s disease is characterized by greater offsets, and that aperiodic parameters differ depending on arousal state. However, our results do not support the hypothesis that the behaviour-related differences observed in Parkinson’s disease are related to aperiodic changes. Overall, this study highlights the importance of considering aperiodic activity contributions in brain disorders and further investigating the relationship between aperiodic activity and behaviour.

Aperiodic Activity Indexes Neural Hyperexcitability in Generalized Epilepsy.

Generalized epilepsy (GE) encompasses a heterogeneous group of hyperexcitability disorders that clinically manifest as seizures. At the whole-brain level, distinct seizure patterns as well as interictal epileptic discharges (IEDs) reflect key signatures of hyperexcitability in magneto- and electroencephalographic (M/EEG) recordings. Moreover, it had been suggested that aperiodic activity, specifically the slope of the 1/ƒx decay function of the power spectrum, might index neural excitability. However, it remained unclear if hyperexcitability as encountered at the cellular level directly translates to putative large-scale excitability signatures, amenable to M/EEG. In order to test whether the power spectrum is altered in hyperexcitable states, we recorded resting-state MEG from male and female GE patients (n = 51; 29 females; 28.82 ± 12.18 years; mean ± SD) and age-matched healthy controls (n = 49; 22 females; 32.10 ± 12.09 years). We parametrized the power spectra using FOOOF ("fitting oscillations and one over f") to separate oscillatory from aperiodic activity to directly test whether aperiodic activity is systematically altered in GE patients. We further identified IEDs to quantify the temporal dynamics of aperiodic activity around overt epileptic activity. The results demonstrate that aperiodic activity indexes hyperexcitability in GE at the whole-brain level, especially during epochs when no IEDs were present (p = 0.0130; d = 0.52). Upon IEDs, large-scale circuits transiently shifted to a less excitable network state (p = 0.001; d = 0.68). In sum, these results uncover that MEG background activity might index hyperexcitability based on the current brain state and does not rely on the presence of epileptic waveforms.

EEG During Dynamic Facial Emotion Processing Reveals Neural Activity Patterns Associated with Autistic Traits in Children.

Altered brain connectivity and atypical neural oscillations have been observed in autism, yet their relationship with autistic traits in non-clinical populations remains underexplored. Here, we employ electroencephalography (EEG) to examine functional connectivity, oscillatory power, and broadband aperiodic activity during a dynamic facial emotion processing (FEP) task in 101 typically developing children aged 4-12 years. We investigate associations between these electrophysiological measures of brain dynamics and autistic traits as assessed by the Social Responsiveness Scale, 2nd Edition (SRS-2). Our results revealed that increased FEP-related connectivity across theta (4-7 Hz) and beta (13-30 Hz) frequencies correlated positively with higher SRS-2 scores, predominantly in right-lateralized (theta) and bilateral (beta) cortical networks. Additionally, a steeper 1/f-like aperiodic slope (spectral exponent) across fronto-central electrodes was associated with higher SRS-2 scores. Greater aperiodic-adjusted theta and alpha oscillatory power further correlated with both higher SRS-2 scores and steeper aperiodic slopes. These findings underscore important links between FEP-related brain dynamics and autistic traits in typically developing children. Future work could extend these findings to assess these EEG-derived markers as potential mechanisms underlying behavioural difficulties in autism.

Multiple Intrinsic Timescales Govern Distinct Brain States in Human Sleep.

Human sleep exhibits multiple, recurrent temporal regularities, ranging from circadian rhythms to sleep stage cycles and neuronal oscillations during nonrapid eye movement sleep. Moreover, recent evidence revealed a functional role of aperiodic activity, which reliably discriminates different sleep stages. Aperiodic activity is commonly defined as the spectral slope χ of the 1/frequency (1/fχ) decay function of the electrophysiological power spectrum. However, several lines of inquiry now indicate that the aperiodic component of the power spectrum might be better characterized by a superposition of several decay processes with associated timescales. Here, we determined multiple timescales, which jointly shape aperiodic activity using human intracranial electroencephalography. Across three independent studies (47 participants, 23 female), our results reveal that aperiodic activity reliably dissociated sleep stage-dependent dynamics in a regionally specific manner. A principled approach to parametrize aperiodic activity delineated several, spatially and state-specific timescales. Lastly, we employed pharmacological modulation by means of propofol anesthesia to disentangle state-invariant timescales that may reflect physical properties of the underlying neural population from state-specific timescales that likely constitute functional interactions. Collectively, these results establish the presence of multiple intrinsic timescales that define the electrophysiological power spectrum during distinct brain states.

The development of aperiodic and periodic resting-state power between early childhood and adulthood: New insights from optically pumped magnetometers

Neurophysiological signals, comprised of both periodic (e.g., oscillatory) and aperiodic (e.g., non-oscillatory) activity, undergo complex developmental changes between childhood and adulthood. With much of the existing literature primarily focused on the periodic features of brain function, our understanding of aperiodic signals is still in its infancy. Here, we are the first to examine age-related changes in periodic (peak frequency and power) and aperiodic (slope and offset) activity using optically pumped magnetometers (OPMs), a new, wearable magnetoencephalography (MEG) technology that is particularly well-suited for studying development. We examined age-related changes in these spectral features in a sample (N=65) of toddlers (1–3 years), children (4–5 years), young adults (20–26 years), and adults (27–38 years). Consistent with the extant literature, we found significant age-related decreases in the aperiodic slope and offset, and changes in peak frequency and power that were frequency-specific; we are the first to show that the effect sizes of these changes also varied across brain regions. This work not only adds to the growing body of work highlighting the advantages of using OPMs, especially for studying development, but also contributes novel information regarding the variation of neurophysiological changes with age across the brain.

Test-Retest Reliability of EEG Aperiodic Components in Resting and Mental Task States.

Aperiodic activity is derived from the electroencephalography (EEG) power spectrum and reflects changes in the slope and shifts of the broadband spectrum. Studies have shown inconsistent test-retest reliability of the aperiodic components. This study systematically measured how the test-retest reliability of the aperiodic components was affected by data duration (1, 2, 3, 4, and 5min), states (resting with eyes closed, resting with eyes open, performing mental arithmetic, recalling the events of the day, and mentally singing songs), and methods (the Fitting Oscillations and One-Over-F (FOOOF) and Linear Mixed-Effects Regression (LMER)) at both short (90-min) and long (one-month) intervals. The results showed that aperiodic components had fair, good, or excellent test-retest reliability (ranging from 0.53 to 0.91) at both short and long intervals. It is recommended that better reliability of the aperiodic components be obtained using data durations longer than 3min, the resting state with eyes closed, the mental arithmetic task state, and the LMER method.

Theta-phase locking of single neurons during human spatial memory.

The precise timing of single-neuron activity in relation to local field potentials may support various cognitive functions. Extensive research in rodents, along with some evidence in humans, suggests that single-neuron activity at specific phases of theta oscillations plays a crucial role in memory processes. Our fundamental understanding of such theta-phase locking in humans and its dependency on basic electrophysiological properties of the local field potential is still limited, however. Here, using single-neuron recordings in epilepsy patients performing a spatial memory task, we thus aimed at improving our understanding of factors modulating theta-phase locking in the human brain. Combining a generalized-phase approach for frequency-adaptive theta-phase estimation with time-resolved spectral parameterization, our results show that theta-phase locking is a strong and prevalent phenomenon across human medial temporal lobe regions, both during spatial memory encoding and retrieval. Neuronal theta-phase locking increased during periods of elevated theta power, when clear theta oscillations were present, and when aperiodic activity exhibited steeper slopes. Theta-phase locking was similarly strong during successful and unsuccessful memory, and most neurons activated at similar theta phases between encoding and retrieval. Some neurons changed their preferred theta phases between encoding and retrieval, in line with the idea that different memory processes are separated within the theta cycle. Together, these results help disentangle how different properties of local field potentials and memory states influence theta-phase locking of human single neurons. This contributes to a better understanding of how interactions between single neurons and local field potentials may support human spatial memory.

Aperiodic and periodic components of oscillatory brain activity in relation to cognition and symptoms in pediatric ADHD

Abstract Children with attention-deficit/hyperactivity disorder show deficits in processing speed, as well as aberrant neural oscillations, including both periodic (oscillatory) and aperiodic (1/f-like) activity, reflecting the pattern of power across frequencies. Both components were suggested as underlying neural mechanisms of cognitive dysfunctions in attention-deficit/hyperactivity disorder. Here, we examined differences in processing speed and resting-state-Electroencephalogram neural oscillations and their associations between 6- and 12-year-old children with (n = 33) and without (n = 33) attention-deficit/hyperactivity disorder. Spectral analyses of the resting-state EEG signal using fast Fourier transform revealed increased power in fronto-central theta and beta oscillations for the attention-deficit/hyperactivity disorder group, but no differences in the theta/beta ratio. Using the parameterization method, we found a higher aperiodic exponent, which has been suggested to reflect lower neuronal excitation-inhibition, in the attention-deficit/hyperactivity disorder group. While fast Fourier transform–based theta power correlated with clinical symptoms for the attention-deficit/hyperactivity disorder group only, the aperiodic exponent was negatively correlated with processing speed across the entire sample. Finally, the aperiodic exponent was correlated with fast Fourier transform–based beta power. These results highlight the different and complementary contribution of periodic and aperiodic components of the neural spectrum as metrics for evaluation of processing speed in attention-deficit/hyperactivity disorder. Future studies should further clarify the roles of periodic and aperiodic components in additional cognitive functions and in relation to clinical status.