Linked Sources of Neural Noise Contribute to Age-related Cognitive Decline

Mnemonic But Not Contextual Feedback Signals Defy Dedifferentiation in the Aging Early Visual Cortex

Perception is an intricate interplay between feedforward visual input and internally generated feedback signals that comprise concurrent contextual and time-distant mnemonic (episodic and semantic) information. Yet, an unresolved question is how the composition of feedback signals changes across the lifespan and to what extent feedback signals undergo age-related dedifferentiation, that is, a decline in neural specificity. Previous research on this topic has focused on feedforward perceptual representation and episodic memory reinstatement, suggesting reduced fidelity of neural representations at the item and category levels. In this fMRI study, we combined an occlusion paradigm that filters feedforward input to the visual cortex and multivariate analysis techniques to investigate the information content in cortical feedback, focusing on age-related differences in its composition. We further asked to what extent differentiation in feedback signals (in the occluded region) is correlated to differentiation in feedforward signals. Comparing younger (18-30 years) and older female and male adults (65-75 years), we found that contextual but not mnemonic feedback was prone to age-related dedifferentiation. Semantic feedback signals were even better differentiated in older adults, highlighting the growing importance of generalized knowledge across ages. We also found that differentiation in feedforward signals was correlated with differentiation in episodic but not semantic feedback signals. Our results provide evidence for age-related adjustments in the composition of feedback signals and underscore the importance of examining dedifferentiation in aging for both feedforward and feedback processing.

Resting EEG Periodic and Aperiodic Components Predict Cognitive Decline Over 10 Years

Measures of intrinsic brain function at rest show promise as predictors of cognitive decline in humans, including EEG metrics such as individual α peak frequency (IAPF) and the aperiodic exponent, reflecting the strongest frequency of α oscillations and the relative balance of excitatory/inhibitory neural activity, respectively. Both IAPF and the aperiodic exponent decrease with age and have been associated with worse executive function and working memory. However, few studies have jointly examined their associations with cognitive function, and none have examined their association with longitudinal cognitive decline rather than cross-sectional impairment. In a preregistered secondary analysis of data from the longitudinal Midlife in the United States (MIDUS) study, we tested whether IAPF and aperiodic exponent measured at rest predict cognitive function (N = 235; age at EEG recording M = 55.10, SD = 10.71) over 10 years. The IAPF and the aperiodic exponent interacted to predict decline in overall cognitive ability, even after controlling for age, sex, education, and lag between data collection time points. Post hoc tests showed that "mismatched" IAPF and aperiodic exponents (e.g., higher exponent with lower IAPF) predicted greater cognitive decline compared to "matching" IAPF and aperiodic exponents (e.g., higher exponent with higher IAPF; lower IAPF with lower aperiodic exponent). These effects were largely driven by measures of executive function. Our findings provide the first evidence that IAPF and the aperiodic exponent are joint predictors of cognitive decline from midlife into old age and thus may offer a useful clinical tool for predicting cognitive risk in aging.

Subthalamic nucleus dynamics track microlesion effect in Parkinson's disease

Parkinson's Disease (PD) is characterized by the temporary alleviation of motor symptoms following electrode implantation (or nucleus destruction), known as the microlesion effect (MLE). Electrophysiological studies have explored different PD stages, but understanding electrophysiological characteristics during the MLE period remains unclear. The objective was to examine the characteristics of local field potential (LFP) signals in the subthalamic nucleus (STN) during the hyperacute period following implantation (within 2 days) and 1 month post-implantation. 15 patients diagnosed with PD were enrolled in this observational study, with seven simultaneous recordings of bilateral STN-LFP signals using wireless sensing technology from an implantable pulse generator. Recordings were made in both on and off medication states over 1 month after implantation. We used a method to parameterize the neuronal power spectrum to separate periodic oscillatory and aperiodic components effectively. Our results showed that beta power exhibited a significant increase in the off medication state 1 month after implantation, compared to the postoperative hyperacute period. Notably, this elevation was effectively attenuated by levodopa administration. Furthermore, both the exponents and offsets displayed a decrease at 1 month postoperatively when compared to the hyperacute postoperative period. Remarkably, levodopa medication exerted a modulatory effect on these aperiodic parameters, restoring them back to levels observed during the hyperacute period. Our findings suggest that both periodic and aperiodic components partially capture distinct electrophysiological characteristics during the MLE. It is crucial to adequately evaluate such discrepancies when exploring the mechanisms of MLE and optimizing adaptive stimulus protocols.

Resting EEG Periodic and Aperiodic Components Predict Cognitive Decline Over 10 Years

Measures of intrinsic brain function at rest show promise as predictors of cognitive decline in humans, including EEG metrics such as individual alpha peak frequency (IAPF) and the aperiodic exponent, reflecting the strongest frequency of alpha oscillations and the relative balance of excitatory:inhibitory neural activity, respectively. Both IAPF and the aperiodic exponent decrease with age and have been associated with worse executive function and working memory. However, few studies have jointly examined their associations with cognitive function, and none have examined their association with longitudinal cognitive decline rather than cross-sectional impairment. In a preregistered secondary analysis of data from the longitudinal Midlife in the United States (MIDUS) study, we tested whether IAPF and aperiodic exponent measured at rest predict cognitive function (N = 235; age at EEG recording M = 55.10, SD = 10.71) over 10 years. The IAPF and the aperiodic exponent interacted to predict decline in overall cognitive ability, even after controlling for age, sex, education, and lag between data collection timepoints. Post-hoc tests showed that "mismatched" IAPF and aperiodic exponents (e.g., higher exponent with lower IAPF) predicted greater cognitive decline compared to "matching" IAPF and aperiodic exponents (e.g., higher exponent with higher IAPF; lower IAPF with lower aperiodic exponent). These effects were largely driven by measures of executive function. Our findings provide the first evidence that IAPF and the aperiodic exponent are joint predictors of cognitive decline from midlife into old age and thus may offer a useful clinical tool for predicting cognitive risk in aging.

Development of Postanesthesia Care Unit Delirium Is Associated with Differences in Aperiodic and Periodic Alpha Parameters of the Electroencephalogram during Emergence from General Anesthesia: Results from a Prospective Observational Cohort Study

BACKGROUND

Intraoperative alpha-band power in frontal electrodes may provide helpful information about the balance of hypnosis and analgesia and has been associated with reduced occurrence of delirium in the postanesthesia care unit. Recent studies suggest that narrow-band power computations from neural power spectra can benefit from separating periodic and aperiodic components of the electroencephalogram. This study investigates whether such techniques are more useful in separating patients with and without delirium in the postanesthesia care unit at the group level as opposed to conventional power spectra.

METHODS

Intraoperative electroencephalography recordings of 32 patients who developed perioperative neurocognitive disorders and 137 patients who did not were considered in this post hoc secondary analysis. The power spectra were calculated using conventional methods and the "fitting oscillations and one over f" algorithm was applied to separate aperiodic and periodic components to see whether the electroencephalography signature is different between groups.

RESULTS

At the group level, patients who did not develop perioperative neurocognitive disorders presented with significantly higher alpha-band power and a broadband increase in power, allowing a "fair" separation based on conventional power spectra. Within the first third of emergence, the difference in median absolute alpha-band power amounted to 8.53 decibels (area under the receiver operator characteristics curve, 0.74 [0.65; 0.82]), reaching its highest value. In relative terms, the best separation was achieved in the second third of emergence, with a difference in medians of 7.71% (area under the receiver operator characteristics curve, 0.70 [0.61; 0.79]). The area under the receiver operator characteristics curve values were generally lower toward the end of emergence with increasing arousal.

CONCLUSIONS

Increased alpha-band power during emergence in patients who did not develop perioperative neurocognitive disorders can be traced back to an increase in oscillatory alpha activity and an overall increase in aperiodic broadband power. Although the differences between patients with and without perioperative neurocognitive disorders can be detected relying on traditional methods, the separation of the signal allows a more detailed analysis. This may enable clinicians to detect patients at risk for developing perioperative neurocognitive disorders in the postanesthesia care unit early in the emergence phase.

EDITOR’S PERSPECTIVE

Associations between age-related differences in occipital alpha power and the broadband parameters of the EEG power spectrum: A cross-sectional cohort study

In adulthood, neurological structure and function are often affected by aging, with negative implications for daily life as well as laboratory-based tasks. Some of these changes include decreased efficiency modulating cortical activity and lower signal-to-noise ratios in neural processing (as inferred from surface electroencephalography). To better understand mechanisms influencing age-related changes in cortical activity, we explored the effects of aging on narrow-band alpha power (7.5-12.5 Hz) and broadband/aperiodic components that span a wider range (1.5-30.5 Hz) over the occipital region during eyes-open and eyes-closed wakeful rest in 19 healthy young adults (18-35 years) and 21 community-dwelling older adults (59+ years). Older adults exhibited a smaller change in alpha power across conditions compared to younger adults. Older adults also showed flatter aperiodic slopes in both conditions. These changes in narrow-band alpha are consistent with previous work and suggest that older adults may have a reduced ability to modulate state-specific activity. Differences in the aperiodic slope suggest age-related changes in the signal-noise-ratio in cortical oscillations. However, the relationship between narrow-band alpha modulation and the aperiodic slope was unclear, warranting further investigation into how these variables relate to each other in the aging process. In summary, aging is associated with a broadband flattening of the EEG power spectrum and reduced state-specific modulation of narrow-band alpha power, but these changes appear to be (at least partially) independent of each other. The present findings suggest that separate mechanisms may underlie age-related differences in aperiodic power and narrow-band oscillations.

Hierarchical network structure as the source of hierarchical dynamics (power-law frequency spectra) in living and non-living systems: How state-trait continua (body plans, personalities) emerge from first principles in biophysics

Living systems are hierarchical control systems that display a small world network structure. In such structures, many smaller clusters are nested within fewer larger ones, producing a fractal-like structure with a 'power-law' cluster size distribution (a mereology). Just like their structure, the dynamics of living systems shows fractal-like qualities: the timeseries of inner message passing and overt behavior contain high frequencies or 'states' (treble) that are nested within lower frequencies or 'traits' (bass), producing a power-law frequency spectrum that is known as a 'state-trait continuum' in the behavioral sciences. Here, we argue that the power-law dynamics of living systems results from their power-law network structure: organisms 'vertically encode' the deep spatiotemporal structure of their (anticipated) environments, to the effect that many small clusters near the base of the hierarchy produce high frequency signal changes and fewer larger clusters at its top produce ultra-low frequencies. Such ultra-low frequencies exert a tonic regulatory pressure that produces morphological as well as behavioral traits (i.e., body plans and personalities). Nested-modular structure causes higher frequencies to be embedded within lower frequencies, producing a power-law state-trait continuum. At the heart of such dynamics lies the need for efficient energy dissipation through networks of coupled oscillators, which also governs the dynamics of non-living systems (e.q., earthquakes, stock market fluctuations). Since hierarchical structure produces hierarchical dynamics, the development and collapse of hierarchical structure (e.g., during maturation and disease) should leave specific traces in system dynamics (shifts in lower frequencies, i.e. morphological and behavioral traits) that may serve as early warning signs to system failure. The applications of this idea range from (bio)physics and phylogenesis to ontogenesis and clinical medicine.

Resting EEG power spectra across middle to late life: associations with age, cognition, APOE-ɛ4 carriage, and cardiometabolic burden

We investigated how resting electroencephalography (EEG) measures are associated with risk factors for late-life cognitive impairment and dementia, including age, apolipoprotein E ɛ4 (APOE-ɛ4) carriage, and cardiometabolic burden. Resting EEG was recorded from 86 adults (50-80 years of age). Participants additionally completed the Addenbrooke's Cognitive Examination (ACE) III and had blood drawn to assess APOE-ɛ4 carriage status and cardiometabolic burden. EEG power spectra were decomposed into sources of periodic and aperiodic activity to derive measures of aperiodic component slope and alpha (7-14 Hz) and beta (15-30 Hz) peak power and peak frequency. Alpha and beta peak power measures were corrected for aperiodic activity. The aperiodic component slope was correlated with ACE-III scores but not age. Alpha peak frequency decreased with age. Individuals with higher cardiometabolic burden had lower alpha peak frequencies and lower beta peak power. APOE-ɛ4 carriers had lower beta peak frequencies. Our findings suggest that the slope of the aperiodic component of resting EEG power spectra is more closely associated with measures of cognitive performance rather than chronological age in older adults.

Age differentially modulates the cortical tracking of the lower and higher level linguistic structures during speech comprehension

Speech comprehension requires listeners to rapidly parse continuous speech into hierarchically-organized linguistic structures (i.e. syllable, word, phrase, and sentence) and entrain the neural activities to the rhythm of different linguistic levels. Aging is accompanied by changes in speech processing, but it remains unclear how aging affects different levels of linguistic representation. Here, we recorded magnetoencephalography signals in older and younger groups when subjects actively and passively listened to the continuous speech in which hierarchical linguistic structures of word, phrase, and sentence were tagged at 4, 2, and 1 Hz, respectively. A newly-developed parameterization algorithm was applied to separate the periodically linguistic tracking from the aperiodic component. We found enhanced lower-level (word-level) tracking, reduced higher-level (phrasal- and sentential-level) tracking, and reduced aperiodic offset in older compared with younger adults. Furthermore, we observed the attentional modulation on the sentential-level tracking being larger for younger than for older ones. Notably, the neuro-behavior analyses showed that subjects' behavioral accuracy was positively correlated with the higher-level linguistic tracking, reversely correlated with the lower-level linguistic tracking. Overall, these results suggest that the enhanced lower-level linguistic tracking, reduced higher-level linguistic tracking and less flexibility of attentional modulation may underpin aging-related decline in speech comprehension.

Combining aperiodic 1/f slopes and brain simulation: An EEG/MEG proxy marker of excitation/inhibition imbalance in Alzheimer's disease

INTRODUCTION

Accumulation and interaction of amyloid-beta (Aβ) and tau proteins during progression of Alzheimer's disease (AD) are shown to tilt neuronal circuits away from balanced excitation/inhibition (E/I). Current available techniques for noninvasive interrogation of E/I in the intact human brain, for example, magnetic resonance spectroscopy (MRS), are highly restrictive (i.e., limited spatial extent), have low temporal and spatial resolution and suffer from the limited ability to distinguish accurately between different neurotransmitters complicating its interpretation. As such, these methods alone offer an incomplete explanation of E/I. Recently, the aperiodic component of neural power spectrum, often referred to in the literature as the '1/f slope', has been described as a promising and scalable biomarker that can track disruptions in E/I potentially underlying a spectrum of clinical conditions, such as autism, schizophrenia, or epilepsy, as well as developmental E/I changes as seen in aging.

METHODS

Using 1/f slopes from resting-state spectral data and computational modeling, we developed a new method for inferring E/I alterations in AD.

RESULTS

We tested our method on recent freely and publicly available electroencephalography (EEG) and magnetoencephalography (MEG) datasets of patients with AD or prodromal disease and demonstrated the method's potential for uncovering regional patterns of abnormal excitatory and inhibitory parameters.

DISCUSSION

Our results provide a general framework for investigating circuit-level disorders in AD and developing therapeutic interventions that aim to restore the balance between excitation and inhibition.

Slope of the power spectral density flattens at low frequencies (<150 Hz) with healthy aging but also steepens at higher frequency (>200 Hz) in human electroencephalogram

The power spectral density (PSD) of the brain signals is characterized by two distinct features: oscillations, which are represented as distinct "bumps," and broadband aperiodic activity, that reduces in power with increasing frequency and is characterized by the slope of the power falloff. Recent studies have shown a change in the slope of the aperiodic activity with healthy aging and mental disorders. However, these studies analyzed slopes over a limited frequency range (<100 Hz). To test whether the PSD slope is affected over a wider frequency range with aging and mental disorder, we analyzed the slope till 800 Hz in electroencephalogram data recorded from elderly subjects (>49 years) who were healthy (n = 217) or had mild cognitive impairment (MCI; n = 11) or Alzheimer's Disease (AD; n = 5). Although the slope reduced up to ~ 150 Hz with healthy aging (as shown previously), surprisingly, at higher frequencies (>200 Hz), it increased with age. These results were observed in all electrodes, for both eyes open and eyes closed conditions, and for different reference schemes. However, slopes were not significantly different in MCI/AD subjects compared with healthy controls. Overall, our results constrain the biophysical mechanisms that are reflected in the PSD slopes in healthy and pathological aging.

The development of oscillatory and aperiodic resting state activity is linked to a sensitive period in humans

Congenital blindness leads to profound changes in electroencephalographic (EEG) resting state activity. A well-known consequence of congenital blindness in humans is the reduction of alpha activity which seems to go together with increased gamma activity during rest. These results have been interpreted as indicating a higher excitatory/inhibitory (E/I) ratio in visual cortex compared to normally sighted controls. Yet it is unknown whether the spectral profile of EEG during rest would recover if sight were restored. To test this question, the present study evaluated periodic and aperiodic components of the EEG resting state power spectrum. Previous research has linked the aperiodic components, which exhibit a power-law distribution and are operationalized as a linear fit of the spectrum in log-log space, to cortical E/I ratio. Moreover, by correcting for the aperiodic components from the power spectrum, a more valid estimate of the periodic activity is possible. Here we analyzed resting state EEG activity from two studies involving (1) 27 permanently congenitally blind adults (CB) and 27 age-matched normally sighted controls (MCB); (2) 38 individuals with reversed blindness due to bilateral, dense, congenital cataracts (CC) and 77 age-matched sighted controls (MCC). Based on a data driven approach, aperiodic components of the spectra were extracted for the low frequency (Lf-Slope 1.5 to 19.5 Hz) and high frequency (Hf-Slope 20 to 45 Hz) range. The Lf-Slope of the aperiodic component was significantly steeper (more negative slope), and the Hf-Slope of the aperiodic component was significantly flatter (less negative slope) in CB and CC participants compared to the typically sighted controls. Alpha power was significantly reduced, and gamma power was higher in the CB and the CC groups. These results suggest a sensitive period for the typical development of the spectral profile during rest and thus likely an irreversible change in the E/I ratio in visual cortex due to congenital blindness. We speculate that these changes are a consequence of impaired inhibitory circuits and imbalanced feedforward and feedback processing in early visual areas of individuals with a history of congenital blindness.

Poorer White Matter Microstructure Predicts Slower and More Variable Reaction Time Performance: Evidence for a Neural Noise Hypothesis in a Large Lifespan Cohort

Most prior research has focused on characterizing averages in cognition, brain characteristics, or behavior, and attempting to predict differences in these averages among individuals. However, this overwhelming focus on mean levels may leave us with an incomplete picture of what drives individual differences in behavioral phenotypes by ignoring the variability of behavior around an individual's mean. In particular, enhanced white matter (WM) structural microstructure has been hypothesized to support consistent behavioral performance by decreasing Gaussian noise in signal transfer. Conversely, lower indices of WM microstructure are associated with greater within-subject variance in the ability to deploy performance-related resources, especially in clinical populations. We tested a mechanistic account of the "neural noise" hypothesis in a large adult lifespan cohort (Cambridge Centre for Ageing and Neuroscience) with over 2500 adults (ages 18-102; 1508 female; 1173 male; 2681 behavioral sessions; 708 MRI scans) using WM fractional anisotropy to predict mean levels and variability in reaction time performance on a simple behavioral task using a dynamic structural equation model. By modeling robust and reliable individual differences in within-person variability, we found support for a neural noise hypothesis (Kail, 1997), with lower fractional anisotropy predicted individual differences in separable components of behavioral performance estimated using dynamic structural equation model, including slower mean responses and increased variability. These effects remained when including age, suggesting consistent effects of WM microstructure across the adult lifespan unique from concurrent effects of aging. Crucially, we show that variability can be reliably separated from mean performance using advanced modeling tools, enabling tests of distinct hypotheses for each component of performance.SIGNIFICANCE STATEMENT Human cognitive performance is defined not just by the long-run average, but trial-to-trial variability around that average. However, investigations of cognitive abilities and changes during aging have largely ignored this variability component of behavior. We provide evidence that white matter (WM) microstructure predicts individual differences in mean performance and variability in a sample spanning the adult lifespan (18-102). Unlike prior studies of cognitive performance and variability, we modeled variability directly and distinct from mean performance using a dynamic structural equation model, which allows us to decouple variability from mean performance and other complex features of performance (e.g., autoregression). The effects of WM were robust above the effect of age, highlighting the role of WM in promoting fast and consistent performance.

Noise improves the association between effects of local stimulation and structural degree of brain networks

Stimulation to local areas remarkably affects brain activity patterns, which can be exploited to investigate neural bases of cognitive function and modify pathological brain statuses. There has been growing interest in exploring the fundamental action mechanisms of local stimulation. Nevertheless, how noise amplitude, an essential element in neural dynamics, influences stimulation-induced brain states remains unknown. Here, we systematically examine the effects of local stimulation by using a large-scale biophysical model under different combinations of noise amplitudes and stimulation sites. We demonstrate that noise amplitude nonlinearly and heterogeneously tunes the stimulation effects from both regional and network perspectives. Furthermore, by incorporating the role of the anatomical network, we show that the peak frequencies of unstimulated areas at different stimulation sites averaged across noise amplitudes are highly positively related to structural connectivity. Crucially, the association between the overall changes in functional connectivity as well as the alterations in the constraints imposed by structural connectivity with the structural degree of stimulation sites is nonmonotonically influenced by the noise amplitude, with the association increasing in specific noise amplitude ranges. Moreover, the impacts of local stimulation of cognitive systems depend on the complex interplay between the noise amplitude and average structural degree. Overall, this work provides theoretical insights into how noise amplitude and network structure jointly modulate brain dynamics during stimulation and introduces possibilities for better predicting and controlling stimulation outcomes.

Neural and cognitive correlates of performance in dynamic multi-modal settings

The endeavour to understand human cognition has largely relied upon investigation of task-related brain activity. However, resting-state brain activity can also offer insights into individual information processing and performance capabilities. Previous research has identified electroencephalographic resting-state characteristics (most prominently: the individual alpha frequency; IAF) that predict cognitive function. However, it has largely overlooked a second component of electrophysiological signals: aperiodic 1/ƒ activity. The current study examined how both oscillatory and aperiodic resting-state EEG measures, alongside traditional cognitive tests, can predict performance in a dynamic and complex, semi-naturalistic cognitive task. Participants' resting-state EEG was recorded prior to engaging in a Target Motion Analysis (TMA) task in a simulated submarine control room environment (CRUSE), which required participants to integrate dynamically changing information over time. We demonstrated that the relationship between IAF and cognitive performance extends from simple cognitive tasks (e.g., digit span) to complex, dynamic measures of information processing. Further, our results showed that individual 1/ƒ parameters (slope and intercept) differentially predicted performance across practice and testing sessions, whereby flatter slopes and higher intercepts were associated with improved performance during learning. In addition to the EEG predictors, we demonstrate a link between cognitive skills most closely related to the TMA task (i.e., spatial imagery) and subsequent performance. Overall, the current study highlights (1) how resting-state metrics - both oscillatory and aperiodic - have the potential to index higher-order cognitive capacity, while (2) emphasising the importance of examining these electrophysiological components within more dynamic settings and over time.

Periodic and aperiodic contributions to theta-beta ratios across adulthood

The ratio of fronto-central theta (4-7 Hz) to beta oscillations (13-30 Hz), known as the theta-beta ratio, is negatively correlated with attentional control, reinforcement learning, executive function, and age. Although theta-beta ratios have been found to decrease with age in adolescents and young adults, theta has been found to increase with age in older adults. Moreover, age-related decrease in individual alpha peak frequency and flattening of the 1/f aperiodic component may artifactually inflate the association between theta-beta ratio and age. These factors lead to an incomplete understanding of how theta-beta ratio varies across the lifespan and the extent to which variation is due to a conflation of aperiodic and periodic activity. We conducted a partially preregistered analysis examining the cross-sectional associations between age and resting canonical fronto-central theta-beta ratio, individual alpha peak frequency, and aperiodic component (n = 268; age 36-84, M = 55.8, SD = 11.0). Age was negatively associated with theta-beta ratios, individual peak alpha frequencies, and the aperiodic exponent. The correlation between theta-beta ratios and age remained after controlling for individual peak alpha frequencies, but was nonsignificant when controlling for the aperiodic exponent. Aperiodic exponent fully mediated the relationship between theta-beta ratio and age, although beta remained significantly associated with age after controlling for theta, individual peak alpha, and aperiodic exponent. Results replicate previous observations and show age-related decreases in theta-beta ratios are not due to age-related decrease in individual peak alpha frequencies but primarily explained by flattening of the aperiodic component with age.

Rapid adaptation of predictive models during language comprehension: Aperiodic EEG slope, individual alpha frequency and idea density modulate individual differences in real-time model updating

Predictive coding provides a compelling, unified theory of neural information processing, including for language. However, there is insufficient understanding of how predictive models adapt to changing contextual and environmental demands and the extent to which such adaptive processes differ between individuals. Here, we used electroencephalography (EEG) to track prediction error responses during a naturalistic language processing paradigm. In Experiment 1, 45 native speakers of English listened to a series of short passages. Via a speaker manipulation, we introduced changing intra-experimental adjective order probabilities for two-adjective noun phrases embedded within the passages and investigated whether prediction error responses adapt to reflect these intra-experimental predictive contingencies. To this end, we calculated a novel measure of speaker-based, intra-experimental surprisal (“speaker-based surprisal”) as defined on a trial-by-trial basis and by clustering together adjectives with a similar meaning. N400 amplitude at the position of the critical second adjective was used as an outcome measure of prediction error. Results showed that N400 responses attuned to speaker-based surprisal over the course of the experiment, thus indicating that listeners rapidly adapt their predictive models to reflect local environmental contingencies (here: the probability of one type of adjective following another when uttered by a particular speaker). Strikingly, this occurs in spite of the wealth of prior linguistic experience that participants bring to the laboratory. Model adaptation effects were strongest for participants with a steep aperiodic (1/f) slope in resting EEG and low individual alpha frequency (IAF), with idea density (ID) showing a more complex pattern. These results were replicated in a separate sample of 40 participants in Experiment 2, which employed a highly similar design to Experiment 1. Overall, our results suggest that individuals with a steep aperiodic slope adapt their predictive models most strongly to context-specific probabilistic information. Steep aperiodic slope is thought to reflect low neural noise, which in turn may be associated with higher neural gain control and better cognitive control. Individuals with a steep aperiodic slope may thus be able to more effectively and dynamically reconfigure their prediction-related neural networks to meet current task demands. We conclude that predictive mechanisms in language are highly malleable and dynamic, reflecting both the affordances of the present environment as well as intrinsic information processing capabilities of the individual.

Separating the aperiodic and periodic components of neural activity in Parkinson's disease

Most studies on electrophysiology have not separated aperiodic activity from the spectra but have rather evaluated a combined periodic oscillatory component and the aperiodic component. As the understanding of aperiodic activity gradually deepens, its potential physiological significance has acquired increased appreciation. Herein, we investigated the two components in scalp electroencephalogram in 16 healthy controls and 15 patients with Parkinson's disease (PD); the results revealed that aperiodic parameters were approximately symmetrically distributed in topography in patients with PD and were significantly modulated by dopaminergic medication in channels C4, C3, CP5 and FC5. In sum, our findings might provide indicators for evaluating treatment response in PD and highlight the importance of re-evaluating the neuronal power spectra parameterization.

Biophysical mechanism underlying compensatory preservation of neural synchrony over the adult lifespan

We propose that the preservation of functional integration, estimated from measures of neural synchrony, is a key objective of neurocompensatory mechanisms associated with healthy human ageing. To support this proposal, we demonstrate how phase-locking at the peak alpha frequency in Magnetoencephalography recordings remains invariant over the lifespan in a large cohort of human participants, aged 18-88 years. Using empirically derived connection topologies from diffusion tensor imaging data, we create an in-silico model of whole-brain alpha dynamics. We show that enhancing inter-areal coupling can cancel the effect of increased axonal transmission delays associated with age-related degeneration of white matter tracts, albeit at slower network frequencies. By deriving analytical solutions for simplified connection topologies, we further establish the theoretical principles underlying compensatory network re-organization. Our findings suggest that frequency slowing with age- frequently observed in the alpha band in diverse populations- may be viewed as an epiphenomenon of the underlying compensatory mechanism.

Analysis of MEG data from healthy participants and whole-brain network modeling suggests that the brain compensates for age-related disruptions in connectivity by slowing down the frequency of neural synchronization.

Slow fluctuations in ongoing brain activity decrease in amplitude with ageing yet their impact on task-related evoked responses is dissociable from behavior

In humans, ageing is characterized by decreased brain signal variability and increased behavioral variability. To understand how reduced brain variability segregates with increased behavioral variability, we investigated the association between reaction time variability, evoked brain responses and ongoing brain signal dynamics, in young (N=36) and older adults (N=39). We studied the electroencephalogram (EEG) and pupil size fluctuations to characterize the cortical and arousal responses elicited by a cued go/no-go task. Evoked responses were strongly modulated by slow (<2 Hz) fluctuations of the ongoing signals, which presented reduced power in the older participants. Although variability of the evoked responses was lower in the older participants, once we adjusted for the effect of the ongoing signal fluctuations, evoked responses were equally variable in both groups. Moreover, the modulation of the evoked responses caused by the ongoing signal fluctuations had no impact on reaction time, thereby explaining why although ongoing brain signal variability is decreased in older individuals, behavioral variability is not. Finally, we showed that adjusting for the effect of the ongoing signal was critical to unmask the link between neural responses and behavior as well as the link between task-related evoked EEG and pupil responses.

Globally elevated excitation-inhibition ratio in children with autism spectrum disorder and below-average intelligence

Background

Altered neuronal excitation–inhibition (E–I) balance is strongly implicated in ASD. However, it is not known whether the direction and degree of changes in the E–I ratio in individuals with ASD correlates with intellectual disability often associated with this developmental disorder. The spectral slope of the aperiodic 1/f activity reflects the E–I balance at the scale of large neuronal populations and may uncover its putative alternations in individuals with ASD with and without intellectual disability.

Methods

Herein, we used magnetoencephalography (MEG) to test whether the 1/f slope would differentiate ASD children with average and below–average (< 85) IQ. MEG was recorded at rest with eyes open/closed in 49 boys with ASD aged 6–15 years with IQ ranging from 54 to 128, and in 49 age-matched typically developing (TD) boys. The cortical source activity was estimated using the beamformer approach and individual brain models. We then extracted the 1/f slope by fitting a linear function to the log–log-scale power spectra in the high-frequency range.

Results

The global 1/f slope averaged over all cortical sources demonstrated high rank-order stability between the two conditions. Consistent with previous research, it was steeper in the eyes-closed than in the eyes-open condition and flattened with age. Regardless of condition, children with ASD and below-average IQ had flatter slopes than either TD or ASD children with average or above-average IQ. These group differences could not be explained by differences in signal-to-noise ratio or periodic (alpha and beta) activity.

Limitations

Further research is needed to find out whether the observed changes in E–I ratios are characteristic of children with below-average IQ of other diagnostic groups.

Conclusions

The atypically flattened spectral slope of aperiodic activity in children with ASD and below-average IQ suggests a shift of the global E–I balance toward hyper-excitation. The spectral slope can provide an accessible noninvasive biomarker of the E–I ratio for making objective judgments about treatment effectiveness in people with ASD and comorbid intellectual disability.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13229-022-00498-2.

Spectral parameterization for studying neurodevelopment: How and why

A growing body of literature suggests that the explicit parameterization of neural power spectra is important for the appropriate physiological interpretation of periodic and aperiodic electroencephalogram (EEG) activity. In this paper, we discuss why parameterization is an imperative step for developmental cognitive neuroscientists interested in cognition and behavior across the lifespan, as well as how parameterization can be readily accomplished with an automated spectral parameterization ("specparam") algorithm (Donoghue et al., 2020a). We provide annotated code for power spectral parameterization, via specparam, in Jupyter Notebook and R Studio. We then apply this algorithm to EEG data in childhood (N = 60; Mage = 9.97, SD = 0.95) to illustrate its utility for developmental cognitive neuroscientists. Ultimately, the explicit parameterization of EEG power spectra may help us refine our understanding of how dynamic neural communication contributes to normative and aberrant cognition across the lifespan. Data and annotated analysis code for this manuscript are available on GitHub as a supplement to the open-access specparam toolbox.

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

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

Power signatures of habenular neuronal signals in patients with bipolar or unipolar depressive disorders correlate with their disease severity

The habenula is an epithalamic structure implicated in negative reward mechanisms and plays a downstream modulatory role in regulation of dopaminergic and serotonergic functions. Human and animal studies show its hyperactivity in depression which is curtailed by the antidepressant response of ketamine. Deep brain stimulation of habenula (DBS) for major depression have also shown promising results. However, direct neuronal activity of habenula in human studies have rarely been reported. Here, in a cross-sectional design, we acquired both spontaneous resting state and emotional task-induced neuronal recordings from habenula from treatment resistant depressed patients undergoing DBS surgery. We first characterise the aperiodic component (1/f slope) of the power spectrum, interpreted to signify excitation-inhibition balance, in resting and task state. This aperiodicity for left habenula correlated between rest and task and which was significantly positively correlated with depression severity. Time-frequency responses to the emotional picture viewing task show condition differences in beta and gamma frequencies for left habenula and alpha for right habenula. Notably, alpha activity for right habenula was negatively correlated with depression severity. Overall, from direct habenular recordings, we thus show findings convergent with depression models of aberrant excitatory glutamatergic output of the habenula driving inhibition of monoaminergic systems.

Resting EEG spectral slopes are associated with age-related differences in information processing speed

BACKGROUND

Previous research has shown the slope of the EEG power spectrum differentiates between older and younger adults in various experimental cognitive tasks. We extend that work, assessing the relation between the EEG power spectrum and performance on the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS).

METHODS

Twenty-one younger and twenty-three older adults completed the RBANS with EEG data collected at rest. Using spectral parameterization, we tested the mediating effect of the spectral slope on differences in subsequent cognitive task performance.

RESULTS

Older adults performed reliably worse on the RBANS overall, and on the Attention and Delayed Memory domains specifically. However, evidence of mediation was only found for the Coding subtest.

CONCLUSIONS

The slope of the resting EEG power spectrum mediated age-related differences in cognition, but only in a task requiring speeded processing. Mediation was not statistically significant for delayed memory, even though age-related differences were present.

Ongoing 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? Ongoing fluctuations of neuronal excitability are currently hypothesized to underlie the trial-by-trial variability in sensory processing. To test this, we capitalized on intracranial 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.

Understanding the Error in Psychopathology: Notable Intraindividual Differences in Neural Variability of Performance Monitoring

BACKGROUND

Abnormal performance monitoring is a possible transdiagnostic marker of psychopathology. Research on neural indices of performance monitoring, including the error-related negativity (ERN), typically examines group and interindividual (between-person) differences in mean/average scores. Intraindividual (within-person) variability in activity captures the capacity to dynamically adjust from moment to moment, and excessive variability appears maladaptive. Intraindividual variability in ERN represents a unique and largely unexamined dimension that might impact functioning. We tested whether psychopathology group differences (major depressive disorder [MDD], generalized anxiety disorder [GAD], obsessive-compulsive disorder [OCD]) or corresponding psychiatric symptoms account for intraindividual variability in single-trial ERN scores.

METHODS

High-density electroencephalogram (Electrical Geodesics, Inc.) was recorded during a semantic flanker task in 51 participants with MDD, 44 participants with GAD, 31 participants with OCD, and 56 psychiatrically-healthy participants. Mean ERN amplitude was scored 0-125ms following participant response across four fronto-central sites. Multilevel location-scale models were used to simultaneously examine interindividual and intraindividual differences in ERN.

RESULTS

Analyses indicated considerable intraindividual variability in ERN that was common across groups. However, we did not find strong evidence to support relationships between ERN and psychopathology groups or transdiagnostic symptoms.

CONCLUSIONS

These findings point to important methodological implications for studies of performance monitoring in healthy and clinical populations-the common assumption of fixed intraindividual variability (i.e., residual variance) may be inappropriate for ERN studies. Implementation of multilevel location-scale models in future research can leverage between-person differences in intraindividual variability in performance monitoring to gain a rich understanding of trial-to-trial performance monitoring dynamics.

Sleep-related hypermotor epilepsy and non-rapid eye movement parasomnias: Differences in the periodic and aperiodic component of the electroencephalographic power spectra

Over the last two decades, our understanding of clinical and pathophysiological aspects of sleep-related epileptic and non-epileptic paroxysmal behaviours has improved considerably, although it is far from complete. Indeed, even if many core characteristics of sleep-related hypermotor epilepsy and non-rapid eye movement parasomnias have been clarified, some crucial points remain controversial, and the overlap of the behavioural patterns between these disorders represents a diagnostic challenge. In this work, we focused on segments of multichannel sleep electroencephalogram free from clinical episodes, from two groups of subjects affected by sleep-related hypermotor epilepsy (N = 15) and non-rapid eye movement parasomnias (N = 16), respectively. We examined sleep stages N2 and N3 of the first part of the night (cycles 1 and 2), and assessed the existence of differences in the periodic and aperiodic components of the electroencephalogram power spectra between the two groups, using the Fitting Oscillations & One Over f (FOOOF) toolbox. A significant difference in the gamma frequency band was found, with an increased relative power in sleep-related hypermotor epilepsy subjects, during both N2 (p < .001) and N3 (p < .001), and a significant higher slope of the aperiodic component in non-rapid eye movement parasomnias, compared with sleep-related hypermotor epilepsy, during N3 (p = .012). We suggest that the relative power of the gamma band and the slope extracted from the aperiodic component of the electroencephalogram signal may be helpful to characterize differences between subjects affected by non-rapid eye movement parasomnias and those affected by sleep-related hypermotor epilepsy.

Resting-state aperiodic neural dynamics predict individual differences in visuomotor performance and learning

An emerging body of work has demonstrated that resting-state non-oscillatory, or aperiodic, 1/f neural activity is a functional and behaviorally relevant marker of cognitive function capacity. In the motor domain, previous work has only applied 1/f analyses to investigations of motor coordination and performance measures. The value of aperiodic resting-state neural dynamics as a marker of individual visuomotor performance capacity remains unknown. Accordingly, the aim of this work was to investigate if individual 1/f intercept and slope parameters of aperiodic resting-state neural activity predict reaction time and perceptual sensitivity in an immersive virtual reality marksmanship task. The marksmanship task required speeded selection of target stimuli and avoidance of selecting non-target stimuli. Motor and perceptual demands were incrementally increased across task blocks and participants performed the task across three training sessions spanning one week. When motor demands were high, steeper individual 1/f slope predicted shorter reaction time. This relationship did not change with practice. Increased 1/f intercept and a steeper 1/f slope were associated with higher perceptual sensitivity, measured as d'. However, this association was only observed under the highest levels of perceptual demand and only in the initial exposure to these conditions. Individuals with a lower 1/f intercept and a shallower 1/f slope demonstrated the greatest gains in perceptual sensitivity from task practice. These findings demonstrate that individual differences in motor and perceptual performance can be accounted for with resting-state aperiodic neural dynamics. The 1/f aperiodic parameters are most informative in predicting visuomotor performance under complex and demanding task conditions. In addition to predicting capacity for high visuomotor performance with a novel task, 1/f aperiodic parameters might also be useful in predicting which individuals might derive the most improvements from practice.

Behavioral and cognitive correlates of the aperiodic (1/f-like) exponent of the EEG power spectrum in adolescents with and without ADHD

Efficient information processing facilitates cognition and may be disrupted in a number of neurodevelopmental conditions. And yet, the role of inefficient information processing and its neural underpinnings remains poorly understood. In the current study, we examined the cognitive and behavioral correlates of the aperiodic exponent of the electroencephalogram (EEG) power spectrum, a putative marker of disrupted, inefficient neural communication, in a sample of adolescents with and without ADHD (n = 184 nADHD = 87; Mage = 13.95 years, SD = 1.36). Exponents were calculated via FOOOF (Donoghue et al., 2020a) from EEG data recorded during an 8-minute baseline episode. Reaction time speed and variability, as well as drift diffusion parameters (including the drift rate parameter, a cognitive parameter directly related to inefficient information processing) were calculated. Adolescents with ADHD had smaller aperiodic exponents (a "flattened" EEG power spectrum) relative to their typically-developing peers. After controlling for ADHD, aperiodic exponents were related to reaction time variability and the drift rate parameter, but not in the expected direction. Our findings lend support for the aperiodic exponent as a neural correlate of disrupted information processing, and provide insight into the role of cortical excitation/inhibition imbalance in the pathophysiology of ADHD.

Oscillations as a window into neuronal mechanisms underlying dorsal anterior cingulate cortex function

The function of dorsal Anterior Cingulate Cortex (dACC) remains poorly understood. While many methods, spanning bottom-up and top-down approaches, have been deployed, the view they offer is often conflicting. Integrating bottom-up and top-down approaches requires an intermediary with sufficient explanatory power, theoretical development, and empirical support. Oscillations in the local field potential (LFP) provide such a link. LFP oscillations arise from empirically well-characterized neuronal circuit motifs. Synchronizing the firing of individual units has appealing properties to bind disparate brain regions and propagate information, including gating, routing, and coding. Moreover, the LFP, rather than single unit activity, more closely relates to macro-scale recordings, such as the electroencephalogram and functional magnetic resonance imaging. Thus, LFP oscillations are a critical link that allow for the inference of neuronal micro-circuitry underlying macroscopic brain recordings.

Parameterizing neural power spectra into periodic and aperiodic components

Electrophysiological signals exhibit both periodic and aperiodic properties. Periodic oscillations have been linked to numerous physiological, cognitive, behavioral and disease states. Emerging evidence demonstrates that the aperiodic component has putative physiological interpretations and that it dynamically changes with age, task demands and cognitive states. Electrophysiological neural activity is typically analyzed using canonically defined frequency bands, without consideration of the aperiodic (1/f-like) component. We show that standard analytic approaches can conflate periodic parameters (center frequency, power, bandwidth) with aperiodic ones (offset, exponent), compromising physiological interpretations. To overcome these limitations, we introduce an algorithm to parameterize neural power spectra as a combination of an aperiodic component and putative periodic oscillatory peaks. This algorithm requires no a priori specification of frequency bands. We validate this algorithm on simulated data, and demonstrate how it can be used in applications ranging from analyzing age-related changes in working memory to large-scale data exploration and analysis.