Periodic/Aperiodic Parameterization of Transient Oscillations (PAPTO): Implications for Healthy Ageing

Model selection for spectral parameterization

Neurophysiological brain activity comprises rhythmic (periodic) and arrhythmic (aperiodic) signal elements, which are increasingly studied in relation to behavioral traits and clinical symptoms. Current methods for spectral parameterization of neural recordings rely on user-dependent parameter selection, which challenges the replicability and robustness of findings. Here, we introduce a principled approach to model selection, relying on Bayesian information criterion, for static and time-resolved spectral parameterization of neurophysiological data. We present extensive tests of the approach with ground-truth and empirical magnetoencephalography recordings. Data-driven model selection enhances both the specificity and sensitivity of spectral and spectrogram decompositions, even in non-stationary contexts. Overall, the proposed spectral decomposition with data-driven model selection minimizes the reliance on user expertise and subjective choices, enabling more robust, reproducible, and interpretable research findings.

[Formula: see text]-[Formula: see text]: A Nonparametric Model for Neural Power Spectra Decomposition

The power spectra estimated from the brain recordings are the mixed representation of aperiodic transient activity and periodic oscillations, i.e., aperiodic component (AC) and periodic component (PC). Quantitative neurophysiology requires precise decomposition preceding parameterizing each component. However, the shape, statistical distribution, scale, and mixing mechanism of AC and PCs are unclear, challenging the effectiveness of current popular parametric models such as FOOOF, IRASA, BOSC, etc. Here, ξ- π was proposed to decompose the neural spectra by embedding the nonparametric spectra estimation with penalized Whittle likelihood and the shape language modeling into the expectation maximization framework. ξ- π was validated on the synthesized spectra with loss statistics and on the sleep EEG and the large sample iEEG with evaluation metrics and neurophysiological evidence. Compared to FOOOF, both the simulation presenting shape irregularities and the batch simulation with multiple isolated peaks indicated that ξ- π improved the fit of AC and PCs with less loss and higher F1-score in recognizing the centering frequencies and the number of peaks; the sleep EEG revealed that ξ- π produced more distinguishable AC exponents and improved the sleep state classification accuracy; the iEEG showed that ξ- π approached the clinical findings in peak discovery. Overall, ξ- π offered good performance in the spectra decomposition, which allows flexible parameterization using descriptive statistics or kernel functions. ξ- π is a seminal tool for brain signal decoding in fields such as cognitive neuroscience, brain-computer interface, neurofeedback, and brain diseases.

Medication-invariant resting aperiodic and periodic neural activity in Parkinson's disease

Parkinson's disease (PD) has been associated with greater total power in canonical frequency bands (i.e., alpha, beta) of the resting electroencephalogram (EEG). However, PD has also been associated with a reduction in the proportion of total power across all frequency bands. This discrepancy may be explained by aperiodic activity (exponent and offset) present across all frequency bands. Here, we examined differences in the eyes-open (EO) and eyes-closed (EC) resting EEG of PD participants (N = 26) on and off medication, and age-matched healthy controls (CTL; N = 26). We extracted power from canonical frequency bands using traditional methods (total alpha and beta power) and extracted separate parameters for periodic (parameterized alpha and beta power) and aperiodic activity (exponent and offset). Cluster-based permutation tests over spatial and frequency dimensions indicated that total alpha and beta power, and aperiodic exponent and offset were greater in PD participants, independent of medication status. After removing the exponent and offset, greater alpha power in PD (vs. CTL) was only present in EO recordings and no reliable differences in beta power were observed. Differences between PD and CTL in the resting EEG are likely driven by aperiodic activity, suggestive of greater relative inhibitory neural activity and greater neuronal spiking. Our findings suggest that resting EEG activity in PD is characterized by medication-invariant differences in aperiodic activity which is independent of the increase in alpha power with EO. This highlights the importance of considering aperiodic activity contributions to the neural correlates of brain disorders.

Novel Cyclic Homogeneous Oscillation Detection Method for High Accuracy and Specific Characterization of Neural Dynamics

Detecting temporal and spectral features of neural oscillations is essential to understanding dynamic brain function. Traditionally, the presence and frequency of neural oscillations are determined by identifying peaks over 1/f noise within the power spectrum. However, this approach solely operates within the frequency domain and thus cannot adequately distinguish between the fundamental frequency of a non-sinusoidal oscillation and its harmonics. Non-sinusoidal signals generate harmonics, significantly increasing the false-positive detection rate - a confounding factor in the analysis of neural oscillations. To overcome these limitations, we define the fundamental criteria that characterize a neural oscillation and introduce the Cyclic Homogeneous Oscillation (CHO) detection method that implements these criteria based on an auto-correlation approach that determines the oscillation's periodicity and fundamental frequency. We evaluated CHO by verifying its performance on simulated sinusoidal and non-sinusoidal oscillatory bursts convolved with 1/f noise. Our results demonstrate that CHO outperforms conventional techniques in accurately detecting oscillations. Specifically, we determined the sensitivity and specificity of CHO as a function of signal-to-noise ratio (SNR). We further assessed CHO by testing it on electrocorticographic (ECoG, 8 subjects) and electroencephalographic (EEG, 7 subjects) signals recorded during the pre-stimulus period of an auditory reaction time task and on electrocorticographic signals (6 SEEG subjects and 6 ECoG subjects) collected during resting state. In the reaction time task, the CHO method detected auditory alpha and pre-motor beta oscillations in ECoG signals and occipital alpha and pre-motor beta oscillations in EEG signals. Moreover, CHO determined the fundamental frequency of hippocampal oscillations in the human hippocampus during the resting state (6 SEEG subjects). In summary, CHO demonstrates high precision and specificity in detecting neural oscillations in time and frequency domains. The method's specificity enables the detailed study of non-sinusoidal characteristics of oscillations, such as the degree of asymmetry and waveform of an oscillation. Furthermore, CHO can be applied to identify how neural oscillations govern interactions throughout the brain and to determine oscillatory biomarkers that index abnormal brain function.

Beta bursts question the ruling power for brain-computer interfaces

Objective: Current efforts to build reliable brain-computer interfaces (BCI) span multiple axes from hardware, to software, to more sophisticated experimental protocols, and personalized approaches. However, despite these abundant efforts, there is still room for significant improvement. We argue that a rather overlooked direction lies in linking BCI protocols with recent advances in fundamental neuroscience.Approach: In light of these advances, and particularly the characterization of the burst-like nature of beta frequency band activity and the diversity of beta bursts, we revisit the role of beta activity in 'left vs. right hand' motor imagery (MI) tasks. Current decoding approaches for such tasks take advantage of the fact that MI generates time-locked changes in induced power in the sensorimotor cortex and rely on band-passed power changes in single or multiple channels. Although little is known about the dynamics of beta burst activity during MI, we hypothesized that beta bursts should be modulated in a way analogous to their activity during performance of real upper limb movements.Main results and Significance: We show that classification features based on patterns of beta burst modulations yield decoding results that are equivalent to or better than typically used beta power across multiple open electroencephalography datasets, thus providing insights into the specificity of these bio-markers.

Test-retest reliability of spectral parameterization by 1/f characterization using SpecParam

SpecParam (formally known as FOOOF) allows for the refined measurements of electroencephalography periodic and aperiodic activity, and potentially provides a non-invasive measurement of excitation: inhibition balance. However, little is known about the psychometric properties of this technique. This is integral for understanding the usefulness of SpecParam as a tool to determine differences in measurements of cognitive function, and electroencephalography activity. We used intraclass correlation coefficients to examine the test-retest reliability of parameterized activity across three sessions (90 minutes apart and 30 days later) in 49 healthy young adults at rest with eyes open, eyes closed, and during three eyes closed cognitive tasks including subtraction (Math), music recall (Music), and episodic memory (Memory). Intraclass correlation coefficients were good for the aperiodic exponent and offset (intraclass correlation coefficients > 0.70) and parameterized periodic activity (intraclass correlation coefficients > 0.66 for alpha and beta power, central frequency, and bandwidth) across conditions. Across all three sessions, SpecParam performed poorly in eyes open (40% of participants had poor fits over non-central sites) and had poor test-retest reliability for parameterized periodic activity. SpecParam mostly provides reliable metrics of individual differences in parameterized neural activity. More work is needed to understand the suitability of eyes open resting data for parameterization using SpecParam.

Bursting with Potential: How Sensorimotor Beta Bursts Develop from Infancy to Adulthood

Beta activity is thought to play a critical role in sensorimotor processes. However, little is known about how activity in this frequency band develops. Here, we investigated the developmental trajectory of sensorimotor beta activity from infancy to adulthood. We recorded EEG from 9-month-old, 12-month-old, and adult humans (male and female) while they observed and executed grasping movements. We analyzed "beta burst" activity using a novel method that combines time-frequency decomposition and principal component analysis. We then examined the changes in burst rate and waveform motifs along the selected principal components. Our results reveal systematic changes in beta activity during action execution across development. We found a decrease in beta burst rate during movement execution in all age groups, with the greatest decrease observed in adults. Additionally, we identified three principal components that defined waveform motifs that systematically changed throughout the trial. We found that bursts with waveform shapes closer to the median waveform were not rate-modulated, whereas those with waveform shapes further from the median were differentially rate-modulated. Interestingly, the decrease in the rate of certain burst motifs occurred earlier during movement and was more lateralized in adults than in infants, suggesting that the rate modulation of specific types of beta bursts becomes increasingly refined with age.SIGNIFICANCE STATEMENT We demonstrate that, like in adults, sensorimotor beta activity in infants during reaching and grasping movements occurs in bursts, not oscillations like thought traditionally. Furthermore, different beta waveform shapes were differentially modulated with age, including more lateralization in adults. Aberrant beta activity characterizes various developmental disorders and motor difficulties linked to early brain injury, so looking at burst waveform shape could provide more sensitivity for early identification and treatment of affected individuals before any behavioral symptoms emerge. More generally, comparison of beta burst activity in typical versus atypical motor development will also be instrumental in teasing apart the mechanistic functional roles of different types of beta bursts.

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.

Diverse beta burst waveform motifs characterize movement-related cortical dynamics

Classical analyses of induced, frequency-specific neural activity typically average band-limited power over trials. More recently, it has become widely appreciated that in individual trials, beta band activity occurs as transient bursts rather than amplitude-modulated oscillations. Most studies of beta bursts treat them as unitary, and having a stereotyped waveform. However, we show there is a wide diversity of burst shapes. Using a biophysical model of burst generation, we demonstrate that waveform variability is predicted by variability in the synaptic drives that generate beta bursts. We then use a novel, adaptive burst detection algorithm to identify bursts from human MEG sensor data recorded during a joystick-based reaching task, and apply principal component analysis to burst waveforms to define a set of dimensions, or motifs, that best explain waveform variance. Finally, we show that bursts with a particular range of waveform motifs, ones not fully accounted for by the biophysical model, differentially contribute to movement-related beta dynamics. Sensorimotor beta bursts are therefore not homogeneous events and likely reflect distinct computational processes.

Cortex-wide topography of 1/f-exponent in Parkinson's disease

Parkinson's disease (PD) is a progressive and debilitating brain disorder. Besides the characteristic movement-related symptoms, the disease also causes decline in sensory and cognitive processing. The extent of symptoms and brain-wide projections of neuromodulators such as dopamine suggest that many brain regions are simultaneously affected in PD. To characterise brain-wide disease-related changes in neuronal function, we analysed resting state magnetoencephalogram (MEG) from two groups: PD patients and healthy controls. Besides standard spectral analysis, we quantified the aperiodic components (κ, λ) of the neural activity by fitting a power law κ/f^λ - f is the frequency, κ and λ are the fitting parameters-to the MEG power spectrum and studied its relationship with age and Unified Parkinson's Disease Rating Scale (UPDRS). Consistent with previous results, the most significant spectral changes were observed in the high theta/low-alpha band (7-10 Hz) in all brain regions. Furthermore, analysis of the aperiodic part of the spectrum showed that in all but frontal regions λ was significantly larger in PD patients than in control subjects. Our results indicate that PD is associated with significant changes in aperiodic activity across the whole neocortex. Surprisingly, even early sensory areas showed a significantly larger λ in patients than in healthy controls. Moreover, λ was not affected by the Levodopa medication. Finally, λ was positively correlated with patient age but not with UPDRS-III. Because λ is closely associated with excitation-inhibition balance, our results propose new hypotheses about neural correlates of PD in cortical networks.

Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference

Biophysically detailed neural models are a powerful technique to study neural dynamics in health and disease with a growing number of established and openly available models. A major challenge in the use of such models is that parameter inference is an inherently difficult and unsolved problem. Identifying unique parameter distributions that can account for observed neural dynamics, and differences across experimental conditions, is essential to their meaningful use. Recently, simulation based inference (SBI) has been proposed as an approach to perform Bayesian inference to estimate parameters in detailed neural models. SBI overcomes the challenge of not having access to a likelihood function, which has severely limited inference methods in such models, by leveraging advances in deep learning to perform density estimation. While the substantial methodological advancements offered by SBI are promising, their use in large scale biophysically detailed models is challenging and methods for doing so have not been established, particularly when inferring parameters that can account for time series waveforms. We provide guidelines and considerations on how SBI can be applied to estimate time series waveforms in biophysically detailed neural models starting with a simplified example and extending to specific applications to common MEG/EEG waveforms using the the large scale neural modeling framework of the Human Neocortical Neurosolver. Specifically, we describe how to estimate and compare results from example oscillatory and event related potential simulations. We also describe how diagnostics can be used to assess the quality and uniqueness of the posterior estimates. The methods described provide a principled foundation to guide future applications of SBI in a wide variety of applications that use detailed models to study neural dynamics.

Using convolutional dictionary learning to detect task-related neuromagnetic transients and ageing trends in a large open-access dataset

Human neuromagnetic activity is characterised by a complex combination of transient bursts with varying spatial and temporal characteristics. The characteristics of these transient bursts change during task performance and normal ageing in ways that can inform about underlying cortical sources. Many methods have been proposed to detect transient bursts, with the most successful ones being those that employ multi-channel, data-driven approaches to minimize bias in the detection procedure. There has been little research, however, into the application of these data-driven methods to large datasets for group-level analyses. In the current work, we apply a data-driven convolutional dictionary learning (CDL) approach to detect neuromagnetic transient bursts in a large group of healthy participants from the Cam-CAN dataset. CDL was used to extract repeating spatiotemporal motifs in 538 participants between the ages of 18-88 during a sensorimotor task. Motifs were then clustered across participants based on similarity, and relevant task-related clusters were analysed for age-related trends in their spatiotemporal characteristics. Seven task-related motifs resembling known transient burst types were identified through this analysis, including beta, mu, and alpha type bursts. All burst types showed positive trends in their activation levels with age that could be explained by increasing burst rate with age. This work validated the data-driven CDL approach for transient burst detection on a large dataset and identified robust information about the complex characteristics of human brain signals and how they change with age.

Robust estimation of 1/f activity improves oscillatory burst detection

Neural oscillations often occur as transient bursts with variable amplitude and frequency dynamics. Quantifying these effects is important for understanding brain-behaviour relationships, especially in continuous datasets. To robustly measure bursts, rhythmical periods of oscillatory activity must be separated from arrhythmical background 1/f activity, which is ubiquitous in electrophysiological recordings. The Better OSCillation (BOSC) framework achieves this by defining a power threshold above the estimated background 1/f activity, combined with a duration threshold. Here we introduce a modification to this approach called fBOSC, which uses a spectral parametrisation tool to accurately model background 1/f activity in neural data. fBOSC (which is openly available as a MATLAB toolbox) is robust to power spectra with oscillatory peaks and can also model non-linear spectra. Through a series of simulations, we show that fBOSC more accurately models the 1/f power spectrum compared with existing methods. fBOSC was especially beneficial where power spectra contained a 'knee' below ~.5-10 Hz, which is typical in neural data. We also found that, unlike other methods, fBOSC was unaffected by oscillatory peaks in the neural power spectrum. Moreover, by robustly modelling background 1/f activity, the sensitivity for detecting oscillatory bursts was standardised across frequencies (e.g., theta- and alpha-bands). Finally, using openly available resting state magnetoencephalography and intracranial electrophysiology datasets, we demonstrate the application of fBOSC for oscillatory burst detection in the theta-band. These simulations and empirical analyses highlight the value of fBOSC in detecting oscillatory bursts, including in datasets that are long and continuous with no distinct experimental trials.

Spontaneous sensorimotor beta power and cortical thickness uniquely predict motor function in healthy aging

BACKGROUND

Spontaneous beta activity in the primary motor cortices has been shown to increase in amplitude with advancing age, and that such increases are tightly coupled to stronger motor-related beta oscillations during movement planning. However, the relationship between these age-related changes in spontaneous beta in the motor cortices, local cortical thickness, and overall motor function remains unclear.

METHODS

We collected resting-state magnetoencephalography (MEG), high-resolution structural MRI, and motor function scores using a neuropsychological battery from 126 healthy adults (56 female; age range = 22-72 years). MEG data were source-imaged and a whole-brain vertex-wise regression model was used to assess age-related differences in spontaneous beta power across the cortex. Cortical thickness was computed from the structural MRI data and local beta power and cortical thickness values were extracted from the sensorimotor cortices. To determine the unique contribution of age, spontaneous beta power, and cortical thickness to the prediction of motor function, a hierarchical regression approach was used.

RESULTS

There was an increase in spontaneous beta power with age across the cortex, with the strongest increase being centered on the sensorimotor cortices. Sensorimotor cortical thickness was not related to spontaneous beta power, above and beyond age. Interestingly, both cortical thickness and spontaneous beta power in sensorimotor regions each uniquely contributed to the prediction of motor function when controlling for age.

DISCUSSION

This multimodal study showed that cortical thickness and spontaneous beta activity in the sensorimotor cortices have dissociable contributions to motor function across the adult lifespan. These findings highlight the complexity of interactions between structure and function and the importance of understanding these interactions in order to advance our understanding of healthy aging and disease.

Understanding brain function in vascular cognitive impairment and dementia with EEG and MEG: A systematic review

Vascular Cognitive Impairment (VCI) is the second most prevalent dementia after Alzheimer's Disease (AD), and cerebrovascular disease (CBVD) is a major comorbid contributor to the progression of most neurodegenerative diseases. Early differentiation of cognitive impairment is critical given both the high prevalence of CBVD, and that its risk factors are modifiable. The ability for electroencephalogram (EEG) and magnetoencephalogram (MEG) to detect changes in brain functioning for other dementias suggests that they may also be promising biomarkers for early VCI. The present systematic review aims to summarize the literature regarding electrophysiological patterns of mild and major VCI. Despite considerable heterogeneity in clinical definition and electrophysiological methodology, common patterns exist when comparing patients with VCI to healthy controls (HC) and patients with AD, though there is a low specificity when comparing between VCI subgroups. Similar to other dementias, slowed frequency patterns and disrupted inter- and intra-hemispheric connectivity are repeatedly reported for VCI patients, as well as longer latencies and smaller amplitudes in evoked responses. Further study is needed to fully establish MEG and EEG as clinically useful biomarkers, including a clear definition of VCI and standardized methodology, allowing for comparison across groups and consolidation of multicenter efforts.

Time-resolved parameterization of aperiodic and periodic brain activity

Macroscopic neural dynamics comprise both aperiodic and periodic signal components. Recent advances in parameterizing neural power spectra offer practical tools for evaluating these features separately. Although neural signals vary dynamically and express non-stationarity in relation to ongoing behaviour and perception, current methods yield static spectral decompositions. Here, we introduce Spectral Parameterization Resolved in Time (SPRiNT) as a novel method for decomposing complex neural dynamics into periodic and aperiodic spectral elements in a time-resolved manner. First, we demonstrate, with naturalistic synthetic data, SPRiNT's capacity to reliably recover time-varying spectral features. We emphasize SPRiNT's specific strengths compared to other time-frequency parameterization approaches based on wavelets. Second, we use SPRiNT to illustrate how aperiodic spectral features fluctuate across time in empirical resting-state EEG data (n=178) and relate the observed changes in aperiodic parameters over time to participants' demographics and behaviour. Lastly, we use SPRiNT to demonstrate how aperiodic dynamics relate to movement behaviour in intracranial recordings in rodents. We foresee SPRiNT responding to growing neuroscientific interests in the parameterization of time-varying neural power spectra and advancing the quantitation of complex neural dynamics at the natural time scales of behaviour.