EEG fingerprinting: Subject-specific signature based on the aperiodic component of power spectrum

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.

Fingerprints of brain disease: connectome identifiability in Alzheimer's disease

Functional connectivity patterns in the human brain, like the friction ridges of a fingerprint, can uniquely identify individuals. Does this "brain fingerprint" remain distinct even during Alzheimer's disease (AD)? Using fMRI data from healthy and pathologically ageing subjects, we find that individual functional connectivity profiles remain unique and highly heterogeneous during mild cognitive impairment and AD. However, the patterns that make individuals identifiable change with disease progression, revealing a reconfiguration of the brain fingerprint. Notably, connectivity shifts towards functional system connections in AD and lower-order cognitive functions in early disease stages. These findings emphasize the importance of focusing on individual variability rather than group differences in AD studies. Individual functional connectomes could be instrumental in creating personalized models of AD progression, predicting disease course, and optimizing treatments, paving the way for personalized medicine in AD management.

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

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.

Prior Context and Individual Alpha Frequency Influence Predictive Processing during Language Comprehension

The extent to which the brain predicts upcoming information during language processing remains controversial. To shed light on this debate, the present study reanalyzed Nieuwland and colleagues' (2018) [Nieuwland, M. S., Politzer-Ahles, S., Heyselaar, E., Segaert, K., Darley, E., Kazanina, N., et al. Large-scale replication study reveals a limit on probabilistic prediction in language comprehension. eLife, 7, e33468, 2018] replication of DeLong and colleagues (2015) [DeLong, K. A., Urbach, T. P., & Kutas, M. Probabilistic word pre-activation during language comprehension inferred from electrical brain activity. Nature Neuroscience, 8, 1117-1121, 2005]. Participants (n = 356) viewed sentences containing articles and nouns of varying predictability, while their EEG was recorded. We measured ERPs preceding the critical words (namely, the semantic prediction potential), in conjunction with postword N400 patterns and individual neural metrics. ERP activity was compared with two measures of word predictability: cloze probability and lexical surprisal. In contrast to prior literature, semantic prediction potential amplitudes did not increase as cloze probability increased, suggesting that the component may not reflect prediction during natural language processing. Initial N400 results at the article provided evidence against phonological prediction in language, in line with Nieuwland and colleagues' findings. Strikingly, however, when the surprisal of the prior words in the sentence was included in the analysis, increases in article surprisal were associated with increased N400 amplitudes, consistent with prediction accounts. This relationship between surprisal and N400 amplitude was not observed when the surprisal of the two prior words was low, suggesting that expectation violations at the article may be overlooked under highly predictable conditions. Individual alpha frequency also modulated the relationship between article surprisal and the N400, emphasizing the importance of individual neural factors for prediction. The present study extends upon existing neurocognitive models of language and prediction more generally, by illuminating the flexible and subject-specific nature of predictive processing.

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

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.

Data leakage in deep learning studies of translational EEG

A growing number of studies apply deep neural networks (DNNs) to recordings of human electroencephalography (EEG) to identify a range of disorders. In many studies, EEG recordings are split into segments, and each segment is randomly assigned to the training or test set. As a consequence, data from individual subjects appears in both the training and the test set. Could high test-set accuracy reflect data leakage from subject-specific patterns in the data, rather than patterns that identify a disease? We address this question by testing the performance of DNN classifiers using segment-based holdout (in which segments from one subject can appear in both the training and test set), and comparing this to their performance using subject-based holdout (where all segments from one subject appear exclusively in either the training set or the test set). In two datasets (one classifying Alzheimer's disease, and the other classifying epileptic seizures), we find that performance on previously-unseen subjects is strongly overestimated when models are trained using segment-based holdout. Finally, we survey the literature and find that the majority of translational DNN-EEG studies use segment-based holdout. Most published DNN-EEG studies may dramatically overestimate their classification performance on new subjects.

Long-term HD-tDCS modulates dynamic changes of brain activity on patients with disorders of consciousness: A resting-state EEG study

OBJECTIVE

High-definition transcranial direct current stimulation (HD-tDCS) has been an effective neurostimulation method in the treatment of disorders of consciousness (DOC). However, the effects and mechanism of HD-tDCS are still unclear.

METHODS

This study recruited 8 DOC patients and applied 20-min sessions of 2 mA HD-tDCS (central anode electrode at Pz) for 14 consecutive days. We record DOC patients' EEG data and Coma Recovery Scale-Revised (CRS-R) values at four time point: baseline (T0), after 1 day's and 7,14 days' parietal HD-tDCS treatment (T1, T2, T3). Power spectral density (PSD), relative power (RP), spectral entropy and spectral exponent were calculated to evaluate the EEG dynamic changes of DOC patients during long-term parietal HD-tDCS. At last, we calculated the correlation between changes of EEG features and changes of CRS-R values.

RESULT

After 1 day's parietal HD-tDCS, DOC patients' CRS-R value had not changed (8.25 ± 1.91). HD-tDCS improved DOC patients' CRS-R value at T2 (9.75 ± 1.91, p < 0.05) and at T3 (11.38 ± 2.77, p < 0.05), compared with that at T0 (8.25 ± 1.91). As the treatment time increased, the EEG PSD decayed more slowly. Specifically, the delta frequency band RP decreased, while the alpha, beta, and gamma frequency bands RP increased. EEG oscillation characteristics changed but not significant at T1 (p > 0.05), and showed significant changes at T2 and T3 (p < 0.05). The spectral entropy continuously increased and the spectral exponent continuously decreased from T0 to T3. Specifically, the spectral entropy and spectral exponent of the parietal and occipital regions were significantly higher at T2 and T3 than that at T0 (p < 0.05). In addition, The changes in EEG features of the parietal and occipital lobes were correlated with changes in CRS-R value, especially between T2 and T0.

CONCLUSION

Long-term parietal HD-tDCS can improve the consciousness level and brain activity in DOC patients. Resting-state EEG can evaluate the dynamic changes of brain activity in DOC patients during HD-tDCS. EEG oscillation and non-oscillatory activity might be used to explain the mechanism of HD-tDCS on DOC patients.

Using oscillatory and aperiodic neural activity features for identifying idle state in SSVEP-based BCIs reduces false triggers

Objective.In existing studies, rhythmic (oscillatory) components were used as main features to identify brain states, such as control and idle states, while non-rhythmic (aperiodic) components were ignored. Recent studies have shown that aperiodic (1/f) activity is functionally related to cognitive processes. It is not clear if aperiodic activity can distinguish brain states in asynchronous brain-computer interfaces (BCIs) to reduce false triggers. In this paper, we propose an asynchronous method based on the fusion of oscillatory and aperiodic features for steady-state visual evoked potential-based BCIs.Approach.The proposed method first evaluates the oscillatory and aperiodic components of control and idle states using irregular-resampling auto-spectral analysis. Oscillatory features are then extracted using the spectral power of fundamental, second-harmonic, and third-harmonic frequencies of the oscillatory component, and aperiodic features are extracted using the slope and intercept of the first-order polynomial of the spectral fit of the aperiodic component under a log-logarithmic axis. The process produces two types of feature pools (oscillatory, aperiodic features). Next, feature selection (dimensionality reduction) is applied to the feature pools by Bonferroni correctedp-values from two-way analysis of variance. Last, these spatial-specific statistically significant features are used as input for classification to identify the idle state.Mainresults.On a 7-target dataset from 15 subjects, the mix of oscillatory and aperiodic features achieved an average accuracy of 88.39% compared to 83.53% when using oscillatory features alone (4.86% improvement). The results demonstrated that the proposed idle state recognition method achieved enhanced performance by incorporating aperiodic features.Significance.Our results demonstrated that (1) aperiodic features were effective in recognizing idle states and (2) fusing features of oscillatory and aperiodic components enhanced classification performance by 4.86% compared to oscillatory features alone.

Early development of electrophysiological activity: Contribution of periodic and aperiodic components of the EEG signal

Brain function rapidly changes in the first 2 years of life. In the last decades, resting-state EEG has been widely used to explore those changes. Previous studies have focused on the relative power of the signal in established frequency bands (i.e., theta, alpha, and beta). However, EEG power is a mixture of a 1/f-like background power (aperiodic) in combination with narrow peaks that appear over that curve (periodic activity, e.g., alpha peak). Therefore, it is possible that relative power captures both, aperiodic and periodic brain activity, contributing to changes in electrophysiological activity observed in infancy. For this reason, we explored the early developmental trajectory of the relative power in theta, alpha, and beta frequency bands from infancy to toddlerhood and compared it with changes in periodic activity in a longitudinal study with three waves at age 6, 9, and 16 to 18 months. Finally, we tested the contribution of periodic activity and aperiodic components of the EEG to age changes in relative power. We found that relative power and periodic activity trajectories differed in this period in all the frequency bands but alpha. Furthermore, aperiodic EEG activity flattened between 6 and 18 months. More importantly, only alpha relative power was exclusively related to periodic activity, whereas aperiodic components of the signal significantly contributed to the relative power of activity in theta and beta bands. Thus, relative power in these frequencies is influenced by developmental changes of the aperiodic activity, which should be considered for future studies.

Neural fingerprinting on MEG time series using MiniRocket

Neural fingerprinting is the identification of individuals in a cohort based on neuroimaging recordings of brain activity. In magneto- and electroencephalography (M/EEG), it is common practice to use second-order statistical measures, such as correlation or connectivity matrices, when neural fingerprinting is performed. These measures or features typically require coupling between signal channels and often ignore the individual temporal dynamics. In this study, we show that, following recent advances in multivariate time series classification, such as the development of the RandOm Convolutional KErnel Transformation (ROCKET) classifier, it is possible to perform classification directly on short time segments from MEG resting-state recordings with remarkably high classification accuracies. In a cohort of 124 subjects, it was possible to assign windows of time series of 1 s in duration to the correct subject with above 99% accuracy. The achieved accuracies are vastly superior to those of previous methods while simultaneously requiring considerably shorter time segments.

Maternal hair cortisol predicts periodic and aperiodic infant frontal EEG activity longitudinally across infancy

Maternal stress is known to be an important factor in shaping child development, yet the complex pattern of associations between stress and infant brain development remains understudied. To better understand the nuanced relations between maternal stress and infant neurodevelopment, research investigating longitudinal relations between maternal chronic physiological stress and infant brain function is warranted. In this study, we leveraged longitudinal data to disentangle between- from within-person associations of maternal hair cortisol and frontal electroencephalography (EEG) power at three time points across infancy at 3, 9, and 15 months. We analyzed both aperiodic power spectral density (PSD) slope and traditional periodic frequency band activity. On the within-person level, maternal hair cortisol was associated with a flattening of frontal PSD slope and an increase in relative frontal beta. However, on the between-person level, higher maternal hair cortisol was associated with steeper frontal PSD slope, increased relative frontal theta, and decreased relative frontal beta. The within-person findings may reflect an adaptive neural response to relative shifts in maternal stress levels, while the between-person results demonstrate the potentially detrimental effects of chronically elevated maternal stress. This analysis offers a novel, quantitative insight into the relations between maternal physiological stress and infant cortical function.

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.

Periodic and Aperiodic EEG Features as Potential Markers of Developmental Dyslexia

Developmental Dyslexia (DD) is a neurobiological condition affecting the ability to read fluently and/or accurately. Analyzing resting-state electroencephalographic (EEG) activity in DD may provide a deeper characterization of the underlying pathophysiology and possible biomarkers. So far, studies investigating resting-state activity in DD provided limited evidence and did not consider the aperiodic component of the power spectrum. In the present study, adults with (n = 26) and without DD (n = 31) underwent a reading skills assessment and resting-state EEG to investigate potential alterations in aperiodic activity, their impact on the periodic counterpart and reading performance. In parieto-occipital channels, DD participants showed a significantly different aperiodic activity as indexed by a flatter and lower power spectrum. These aperiodic measures were significantly related to text reading time, suggesting a link with individual differences in reading difficulties. In the beta band, the DD group showed significantly decreased aperiodic-adjusted power compared to typical readers, which was significantly correlated to word reading accuracy. Overall, here we provide evidence showing alterations of the endogenous aperiodic activity in DD participants consistently with the increased neural noise hypothesis. In addition, we confirm alterations of endogenous beta rhythms, which are discussed in terms of their potential link with magnocellular-dorsal stream deficit.

Stability, change, and reliable individual differences in electroencephalography measures: a lifespan perspective on progress and opportunities

Electroencephalographic (EEG) methods have great potential to serve both basic and clinical science approaches to understand individual differences in human neural function. Importantly, the psychometric properties of EEG data, such as internal consistency and test-retest reliability, constrain their ability to differentiate individuals successfully. Rapid and recent technological and computational advancements in EEG research make it timely to revisit the topic of psychometric reliability in the context of individual difference analyses. Moreover, pediatric and clinical samples provide some of the most salient and urgent opportunities to apply individual difference approaches, but the changes these populations experience over time also provide unique challenges from a psychometric perspective. Here we take a developmental neuroscience perspective to consider progress and new opportunities for parsing the reliability and stability of individual differences in EEG measurements across the lifespan. We first conceptually map the different profiles of measurement reliability expected for different types of individual difference analyses over the lifespan. Next, we summarize and evaluate the state of the field's empirical knowledge and need for testing measurement reliability, both internal consistency and test-retest reliability, across EEG measures of power, event-related potentials, nonlinearity, and functional connectivity across ages. Finally, we highlight how standardized pre-processing software for EEG denoising and empirical metrics of individual data quality may be used to further improve EEG-based individual differences research moving forward. We also include recommendations and resources throughout that individual researchers can implement to improve the utility and reproducibility of individual differences analyses with EEG across the lifespan.

Task matters: Individual MEG signatures from naturalistic and neurophysiological brain states

The discovery that human brain connectivity data can be used as a "fingerprint" to identify a given individual from a population, has become a burgeoning research area in the neuroscience field. Recent studies have identified the possibility to extract these brain signatures from the temporal rich dynamics of resting-state magneto encephalography (MEG) recordings. Nevertheless, it is still uncertain to what extent MEG signatures can serve as an indicator of human identifiability during task-related conduct. Here, using MEG data from naturalistic and neurophysiological tasks, we show that identification improves in tasks relative to resting-state, providing compelling evidence for a task dependent axis of MEG signatures. Notably, improvements in identifiability were more prominent in strictly controlled tasks. Lastly, the brain regions contributing most towards individual identification were also modified when engaged in task activities. We hope that this investigation advances our understanding of the driving factors behind brain identification from MEG signals.

Estimated EEG functional connectivity and aperiodic component induced by vagal nerve stimulation in patients with drug-resistant epilepsy

Background

Vagal nerve stimulation (VNS) improves seizure frequency and quality of life in patients with drug-resistant epilepsy (DRE), although the exact mechanism is not fully understood. Previous studies have evaluated the effect of VNS on functional connectivity using the phase lag index (PLI), but none has analyzed its effect on EEG aperiodic parameters (offset and exponent), which are highly conserved and related to physiological functions.

Objective

This study aimed to evaluate the effect of VNS on PLI and aperiodic parameters and infer whether these changes correlate with clinical responses in subjects with DRE.

Materials and methods

PLI, exponent, and offset were derived for each epoch (and each frequency band for PLI), on scalp-derived 64-channel EEG traces of 10 subjects with DRE, recorded before and 1 year after VNS. PLI, exponent, and offset were compared before and after VNS for each patient on a global basis, individual scalp regions, and channels and separately in responders and non-responders. A correlation analysis was performed between global changes in PLI and aperiodic parameters and clinical response.

Results

PLI (global and regional) decreased after VNS for gamma and delta bands and increased for an alpha band in responders, but it was not modified in non-responders. Aperiodic parameters after VNS showed an opposite trend in responders vs. non-responders: both were reduced in responders after VNS, but they were increased in non-responders. Changes in aperiodic parameters correlated with the clinical response.

Conclusion

This study explored the action of VNS therapy from a new perspective and identified EEG aperiodic parameters as a new and promising method to analyze the efficacy of neuromodulation.

Scale-free and oscillatory spectral measures of sleep stages in humans

Power spectra of sleep electroencephalograms (EEG) comprise two main components: a decaying power-law corresponding to the aperiodic neural background activity, and spectral peaks present due to neural oscillations. “Traditional” band-based spectral methods ignore this fundamental structure of the EEG spectra and thus are susceptible to misrepresenting the underlying phenomena. A fitting method that attempts to separate and parameterize the aperiodic and periodic spectral components called “fitting oscillations and one over f” (FOOOF) was applied to a set of annotated whole-night sleep EEG recordings of 251 subjects from a wide age range (4–69 years). Most of the extracted parameters exhibited sleep stage sensitivity; significant main effects and interactions of sleep stage, age, sex, and brain region were found. The spectral slope (describing the steepness of the aperiodic component) showed especially large and consistent variability between sleep stages (and low variability between subjects), making it a candidate indicator of sleep states. The limitations and arisen problems of the FOOOF method are also discussed, possible solutions for some of them are suggested.

Eyes-closed versus eyes-open differences in spontaneous neural dynamics during development

BACKGROUND

Assessing brain activity during rest has become a widely used approach in developmental neuroscience. Extant literature has measured resting brain activity both during eyes-open and eyes-closed conditions, but the difference between these conditions has not yet been well characterized. Studies, limited to fMRI and EEG, have suggested that eyes-open versus -closed conditions may differentially impact neural activity, especially in visual cortices.

METHODS

Spontaneous cortical activity was recorded using MEG from 108 typically developing youth (9-15 years-old; 55 female) during separate sessions of eyes-open and eyes-closed rest. MEG source images were computed, and the strength of spontaneous neural activity was estimated in the canonical delta, theta, alpha, beta, and gamma bands, respectively. Power spectral density maps for eyes-open were subtracted from eyes-closed rest, and then submitted to vertex-wise regression models to identify spatially specific differences between conditions and as a function of age and sex.

RESULTS

Relative alpha power was weaker in the eyes-open compared to -closed condition, but otherwise eyes-open was stronger in all frequency bands, with differences concentrated in the occipital cortex. Relative theta power became stronger in the eyes-open compared to the eyes-closed condition with increasing age in frontal cortex. No differences were observed between males and females.

CONCLUSIONS

The differences in relative power from eyes-closed to -open conditions are consistent with changes observed in task-based visual sensory responses. Age differences occurred in relatively late developing frontal regions, consistent with canonical attention regions, suggesting that these differences could be reflective of developmental changes in attention processes during puberty. Taken together, resting-state paradigms using eyes-open versus -closed produce distinct results and, in fact, can help pinpoint sensory related brain activity.

Human electromagnetic and haemodynamic networks systematically converge in unimodal cortex and diverge in transmodal cortex

Whole-brain neural communication is typically estimated from statistical associations among electromagnetic or haemodynamic time-series. The relationship between functional network architectures recovered from these 2 types of neural activity remains unknown. Here, we map electromagnetic networks (measured using magnetoencephalography (MEG)) to haemodynamic networks (measured using functional magnetic resonance imaging (fMRI)). We find that the relationship between the 2 modalities is regionally heterogeneous and systematically follows the cortical hierarchy, with close correspondence in unimodal cortex and poor correspondence in transmodal cortex. Comparison with the BigBrain histological atlas reveals that electromagnetic-haemodynamic coupling is driven by laminar differentiation and neuron density, suggesting that the mapping between the 2 modalities can be explained by cytoarchitectural variation. Importantly, haemodynamic connectivity cannot be explained by electromagnetic activity in a single frequency band, but rather arises from the mixing of multiple neurophysiological rhythms. Correspondence between the two is largely driven by MEG functional connectivity at the beta (15 to 29 Hz) frequency band. Collectively, these findings demonstrate highly organized but only partly overlapping patterns of connectivity in MEG and fMRI functional networks, opening fundamentally new avenues for studying the relationship between cortical microarchitecture and multimodal connectivity patterns.

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.

Robustness of individualized inferences from longitudinal resting state EEG dynamics

Tracking how individual human brains change over extended timescales is crucial to clinical scenarios ranging from stroke recovery to healthy aging. The use of resting state (RS) activity for tracking is a promising possibility. However, it is unresolved how a person's RS activity over time can be decoded to distinguish neurophysiological changes from confounding cognitive variability. Here, we develop a method to screen RS activity changes for these confounding effects by formulating it as a problem of change classification. We demonstrate a novel solution to change classification by linking individual-specific change to inter-individual differences. Individual RS-EEG was acquired over five consecutive days including task states devised to simulate the effects of inter-day cognitive variation. As inter-individual differences are shaped by neurophysiological differences, the inter-individual differences in RS activity on one day were analyzed (using machine learning) to identify distinctive configurations in each individual's RS activity. Using this configuration as a decision-rule, an individual could be re-identified from 2-second samples of the instantaneous oscillatory power spectrum acquired on a different day both from RS and confounded-RS with a limited loss in accuracy. Importantly, the low loss in accuracy in cross-day vs same-day classification was achieved with classifiers that combined information from multiple frequency bands at channels across the scalp (with a concentration at characteristic fronto-central and occipital zones). Taken together, these findings support the technical feasibility of screening RS activity for confounding effects and the suitability of longitudinal RS for robust individualized inferences about neurophysiological change in health and disease.

Electroencephalogram aperiodic power spectral slope can be reliably measured and predicts ADHD risk in early development

The aperiodic exponent of the electroencephalogram (EEG) power spectrum has received growing attention as a physiological marker of neurodevelopmental psychopathology, including attention-deficit/hyperactivity disorder (ADHD). However, its use as a marker of ADHD risk across development, and particularly in very young children, is limited by unknown reliability, difficulty in aligning canonical band-based measures across development periods, and unclear effects of treatment in later development. Here, we investigate the internal consistency of the aperiodic EEG power spectrum slope and its association with ADHD risk in both infants (n = 69, 1-month-old) and adolescents (n = 262, ages 11-17 years). Results confirm good to excellent internal consistency in infancy and adolescence. In infancy, a larger aperiodic exponent was associated with greater family history of ADHD. In contrast, in adolescence, ADHD diagnosis was associated with a smaller aperiodic exponent, but only in children with ADHD who had not received stimulant medication treatment. Results suggest that disruptions in cortical development associated with ADHD risk may be detectable shortly after birth via this approach. Together, findings imply a dynamic developmental shift in which the developmentally normative flattening of the EEG power spectrum is exaggerated in ADHD, potentially reflecting imbalances in cortical excitation and inhibition that could contribute to long-lasting differences in brain connectivity.

Connectomics of human electrophysiology

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

Stimulus-induced narrowband gamma oscillations are test–retest reliable in human EEG

Visual stimulus-induced gamma oscillations in electroencephalogram (EEG) recordings have been recently shown to be compromised in subjects with preclinical Alzheimer’s Disease (AD), suggesting that gamma could be an inexpensive biomarker for AD diagnosis provided its characteristics remain consistent across multiple recordings. Previous magnetoencephalography studies in young subjects have reported consistent gamma power over recordings separated by a few weeks to months. Here, we assessed the consistency of stimulus-induced slow (20–35 Hz) and fast gamma (36–66 Hz) oscillations in subjects (n = 40) (age: 50–88 years) in EEG recordings separated by a year, and tested the consistency in the magnitude of gamma power, its temporal evolution and spectral profile. Gamma had distinct spectral/temporal characteristics across subjects, which remained consistent across recordings (average intraclass correlation of ~0.7). Alpha (8–12 Hz) and steady-state-visually evoked-potentials were also reliable. We further tested how EEG features can be used to identify 2 recordings as belonging to the same versus different subjects and found high classifier performance (AUC of ~0.89), with temporal evolution of slow gamma and spectral profile being most informative. These results suggest that EEG gamma oscillations are reliable across sessions separated over long durations and can also be a potential tool for subject identification.

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.

Exploring MEG brain fingerprints: Evaluation, pitfalls, and interpretations

Individual characterization of subjects based on their functional connectome (FC), termed "FC fingerprinting", has become a highly sought-after goal in contemporary neuroscience research. Recent functional magnetic resonance imaging (fMRI) studies have demonstrated unique characterization and accurate identification of individuals as an accomplished task. However, FC fingerprinting in magnetoencephalography (MEG) data is still widely unexplored. Here, we study resting-state MEG data from the Human Connectome Project to assess the MEG FC fingerprinting and its relationship with several factors including amplitude- and phase-coupling functional connectivity measures, spatial leakage correction, frequency bands, and behavioral significance. To this end, we first employ two identification scoring methods, differential identifiability and success rate, to provide quantitative fingerprint scores for each FC measurement. Secondly, we explore the edgewise and nodal MEG fingerprinting patterns across the different frequency bands (delta, theta, alpha, beta, and gamma). Finally, we investigate the cross-modality fingerprinting patterns obtained from MEG and fMRI recordings from the same subjects. We assess the behavioral significance of FC across connectivity measures and imaging modalities using partial least square correlation analyses. Our results suggest that fingerprinting performance is heavily dependent on the functional connectivity measure, frequency band, identification scoring method, and spatial leakage correction. We report higher MEG fingerprinting performances in phase-coupling methods, central frequency bands (alpha and beta), and in the visual, frontoparietal, dorsal-attention, and default-mode networks. Furthermore, cross-modality comparisons reveal a certain degree of spatial concordance in fingerprinting patterns between the MEG and fMRI data, especially in the visual system. Finally, the multivariate correlation analyses show that MEG connectomes have strong behavioral significance, which however depends on the considered connectivity measure and temporal scale. This comprehensive, albeit preliminary investigation of MEG connectome test-retest identifiability offers a first characterization of MEG fingerprinting in relation to different methodological and electrophysiological factors and contributes to the understanding of fingerprinting cross-modal relationships. We hope that this first investigation will contribute to setting the grounds for MEG connectome identification.

Extracting Robust Biomarkers From Multichannel EEG Time Series Using Nonlinear Dimensionality Reduction Applied to Ordinal Pattern Statistics and Spectral Quantities

In this study, ordinal pattern analysis and classical frequency-based EEG analysis methods are used to differentiate between EEGs of different age groups as well as individuals. As characteristic features, functional connectivity as well as single-channel measures in both the time and frequency domain are considered. We compare the separation power of each feature set after nonlinear dimensionality reduction using t-distributed stochastic neighbor embedding and demonstrate that ordinal pattern-based measures yield results comparable to frequency-based measures applied to preprocessed data, and outperform them if applied to raw data. Our analysis yields no significant differences in performance between single-channel features and functional connectivity features regarding the question of age group separation.

EEG Fingerprints under Naturalistic Viewing Using a Portable Device

The electroencephalogram (EEG) has been proven to be a promising technique for personal identification and verification. Recently, the aperiodic component of the power spectrum was shown to outperform other commonly used EEG features. Beyond that, EEG characteristics may capture relevant features related to emotional states. In this work, we aim to understand if the aperiodic component of the power spectrum, as shown for resting-state experimental paradigms, is able to capture EEG-based subject-specific features in a naturalistic stimuli scenario. In order to answer this question, we performed an analysis using two freely available datasets containing EEG recordings from participants during viewing of film clips that aim to trigger different emotional states. Our study confirms that the aperiodic components of the power spectrum, as evaluated in terms of offset and exponent parameters, are able to detect subject-specific features extracted from the scalp EEG. In particular, our results show that the performance of the system was significantly higher for the film clip scenario if compared with resting-state, thus suggesting that under naturalistic stimuli it is even easier to identify a subject. As a consequence, we suggest a paradigm shift, from task-based or resting-state to naturalistic stimuli, when assessing the performance of EEG-based biometric systems.

Predictive modeling of neurobehavioral state and trait variation across development

A key goal of human neurodevelopmental research is to map neural and behavioral trajectories across both health and disease. A growing number of developmental consortia have begun to address this gap by providing open access to cross-sectional and longitudinal 'big data' repositories. However, it remains challenging to develop models that enable prediction of both within-subject and between-subject neurodevelopmental variation. Here, we present a conceptual and analytical perspective of two essential ingredients for mapping neurodevelopmental trajectories: state and trait components of variance. We focus on mapping variation across a range of neural and behavioral measurements and consider concurrent alterations of state and trait variation across development. We present a quantitative framework for combining both state- and trait-specific sources of neurobehavioral variation across development. Specifically, we argue that non-linear mixed growth models that leverage state and trait components of variance and consider environmental factors are necessary to comprehensively map brain-behavior relationships. We discuss this framework in the context of mapping language neurodevelopmental changes in early childhood, with an emphasis on measures of functional connectivity and their reliability for establishing robust neurobehavioral relationships. The ultimate goal is to statistically unravel developmental trajectories of neurobehavioral relationships that involve a combination of individual differences and age-related changes.