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Highlights
• A big dataset reveals age-related alterations in EEG biomarkers and cognition.
• Prominent decline of individual alpha peak frequency primarily in temporal lobes.
• A positive association between individual alpha peak frequency and working memory.
• Absence of age-related alpha power decline when controlling for 1/f decay of the PSD.
• Alpha power is negatively associated with the speed of processing in elderly sample.
Abstract
While many structural and biochemical changes in the brain have previously been associated with older age, findings concerning functional properties of neuronal networks, as reflected in their electrophysiological signatures, remain rather controversial. These discrepancies might arise due to several reasons, including diverse factors determining general spectral slowing in the alpha frequency range as well as amplitude mixing between the rhythmic and non-rhythmic parameters. We used a large dataset (N = 1703, mean age 70) to comprehensively investigate age-related alterations in multiple EEG biomarkers taking into account rhythmic and non-rhythmic activity and their individual contributions to cognitive performance. While we found strong evidence for an individual alpha peak frequency (IAF) decline in older age, we did not observe a significant relationship between theta power and age while controlling for IAF. Not only did IAF decline with age, but it was also positively associated with interference resolution in a working memory task primarily in the right and left temporal lobes suggesting its functional role in information sampling. Critically, we did not detect a significant relationship between alpha power and age when controlling for the 1/f spectral slope, while the latter one showed age-related alterations. These findings thus suggest that the entanglement of IAF slowing and power in the theta frequency range, as well as 1/f slope and alpha power measures, might explain inconsistencies reported previously in the literature. Finally, despite the absence of age-related alterations, alpha power was negatively associated with the speed of processing in the right frontal lobe while 1/f slope showed no consistent relationship to cognitive performance. Our results thus demonstrate that multiple electrophysiological features, as well as their interplay, should be considered for the comprehensive assessment of association between age, neuronal activity, and cognitive performance.
Most current models assume that the perceptual and cognitive processes of visual word recognition and reading operate upon neuronally coded domain-general low-level visual representations – typically oriented line representations. We here demonstrate, consistent with neurophysiological theories of Bayesian-like predictive neural computations, that prior visual knowledge of words may be utilized to ‘explain away’ redundant and highly expected parts of the visual percept. Subsequent processing stages, accordingly, operate upon an optimized representation of the visual input, the orthographic prediction error, highlighting only the visual information relevant for word identification. We show that this optimized representation is related to orthographic word characteristics, accounts for word recognition behavior, and is processed early in the visual processing stream, i.e., in V4 and before 200 ms after word-onset. Based on these findings, we propose that prior visual-orthographic knowledge is used to optimize the representation of visually presented words, which in turn allows for highly efficient reading processes.
EEG microstate periodicity explained by rotating phase patterns of resting-state alpha oscillations
(2020)
Spatio-temporal patterns in electroencephalography (EEG) can be described by microstate analysis, a discrete approximation of the continuous electric field patterns produced by the cerebral cortex. Resting-state EEG microstates are largely determined by alpha frequencies (8-12 Hz) and we recently demonstrated that microstates occur periodically with twice the alpha frequency.
To understand the origin of microstate periodicity, we analyzed the analytic amplitude and the analytic phase of resting-state alpha oscillations independently. In continuous EEG data we found rotating phase patterns organized around a small number of phase singularities which varied in number and location. The spatial rotation of phase patterns occurred with the underlying alpha frequency. Phase rotors coincided with periodic microstate motifs involving the four canonical microstate maps. The analytic amplitude showed no oscillatory behaviour and was almost static across time intervals of 1-2 alpha cycles, resulting in the global pattern of a standing wave.
In n=23 healthy adults, time-lagged mutual information analysis of microstate sequences derived from amplitude and phase signals of awake eyes-closed EEG records showed that only the phase component contributed to the periodicity of microstate sequences. Phase sequences showed mutual information peaks at multiples of 50 ms and the group average had a main peak at 100 ms (10 Hz), whereas amplitude sequences had a slow and monotonous information decay. This result was confirmed by an independent approach combining temporal principal component analysis (tPCA) and autocorrelation analysis.
We reproduced our observations in a generic model of EEG oscillations composed of coupled non-linear oscillators (Stuart-Landau model). Phase-amplitude dynamics similar to experimental EEG occurred when the oscillators underwent a supercritical Hopf bifurcation, a common feature of many computational models of the alpha rhythm.
These findings explain our previous description of periodic microstate recurrence and its relation to the time scale of alpha oscillations. Moreover, our results corroborate the predictions of computational models and connect experimentally observed EEG patterns to properties of critical oscillator networks.
Dreams and psychosis share several important features regarding symptoms and underlying neurobiology, which is helpful in constructing a testable model of, for example, schizophrenia and delirium. The purpose of the present communication is to discuss two major concepts in dreaming and psychosis that have received much attention in the recent literature: insight and dissociation. Both phenomena are considered functions of higher order consciousness because they involve metacognition in the form of reflective thought and attempted control of negative emotional impact. Insight in dreams is a core criterion for lucid dreams. Lucid dreams are usually accompanied by attempts to control the dream plot and dissociative elements akin to depersonalization and derealization. These concepts are also relevant in psychotic illness. Whereas insightfulness can be considered innocuous in lucid dreaming and even advantageous in psychosis, the concept of dissociation is still unresolved. The present review compares correlates and functions of insight and dissociation in lucid dreaming and psychosis. This is helpful in understanding the two concepts with regard to psychological function as well as neurophysiology.
Intention attribution in children and adolescents with autism spectrum disorder: an EEG study
(2021)
The ability to infer intentions from observed behavior and predict actions based on this inference, known as intention attribution (IA), has been hypothesized to be impaired in individuals with autism spectrum disorder (ASD). The underlying neural processes, however, have not been conclusively determined. The aim of this study was to examine the neural signature of IA in children and adolescents with ASD, and to elucidate potential links to contextual updating processes using electroencephalography. Results did not indicate that IA or early contextual updating was impaired in ASD. However, there was evidence of aberrant processing of expectation violations in ASD, particularly if the expectation was based on IA. Results are discussed within the context of impaired predictive coding in ASD.
Transcranial alternating-current stimulation (tACS) in the frequency range of 1–100 Hz has come to be used routinely in electroencephalogram (EEG) studies of brain function through entrainment of neuronal oscillations. It turned out, however, to be highly non-trivial to remove the strong stimulation signal, including its harmonic and non-harmonic distortions, as well as various induced higher-order artifacts from the EEG data recorded during the stimulation. In this paper, we discuss some of the problems encountered and present methodological approaches aimed at overcoming them. To illustrate the mechanisms of artifact induction and the proposed removal strategies, we use data obtained with the help of a schematic demonstrator setup as well as human-subject data.
Analyzing non-invasive recordings of electroencephalography (EEG) and magnetoencephalography (MEG) directly in sensor space, using the signal from individual sensors, is a convenient and standard way of working with this type of data. However, volume conduction introduces considerable challenges for sensor space analysis. While the general idea of signal mixing due to volume conduction in EEG/MEG is recognized, the implications have not yet been clearly exemplified. Here, we illustrate how different types of activity overlap on the level of individual sensors. We show spatial mixing in the context of alpha rhythms, which are known to have generators in different areas of the brain. Using simulations with a realistic 3D head model and lead field and data analysis of a large resting-state EEG dataset, we show that electrode signals can be differentially affected by spatial mixing by computing a sensor complexity measure. While prominent occipital alpha rhythms result in less heterogeneous spatial mixing on posterior electrodes, central electrodes show a diversity of rhythms present. This makes the individual contributions, such as the sensorimotor mu-rhythm and temporal alpha rhythms, hard to disentangle from the dominant occipital alpha. Additionally, we show how strong occipital rhythms can contribute the majority of activity to frontal channels, potentially compromising analyses that are solely conducted in sensor space. We also outline specific consequences of signal mixing for frequently used assessment of power, power ratios and connectivity profiles in basic research and for neurofeedback application. With this work, we hope to illustrate the effects of volume conduction in a concrete way, such that the provided practical illustrations may be of use to EEG researchers to in order to evaluate whether sensor space is an appropriate choice for their topic of investigation.
During meditation, practitioners are required to center their attention on a specific object for extended periods of time. When their thoughts get diverted, they learn to quickly disengage from the distracter. We hypothesized that learning to respond to the dual demand of engaging attention on specific objects and disengaging quickly from distracters enhances the efficiency by which meditation practitioners can allocate attention. We tested this hypothesis in a global-to-local task while measuring electroencephalographic activity from a group of eight highly trained Buddhist monks and nuns and a group of eight age and education matched controls with no previous meditation experience. Specifically, we investigated the effect of attentional training on the global precedence effect, i.e., faster detection of targets on a global than on a local level. We expected to find a reduced global precedence effect in meditation practitioners but not in controls, reflecting that meditators can more quickly disengage their attention from the dominant global level. Analysis of reaction times confirmed this prediction. To investigate the underlying changes in brain activity and their time course, we analyzed event-related potentials. Meditators showed an enhanced ability to select the respective target level, as reflected by enhanced processing of target level information. In contrast with control group, which showed a local target selection effect only in the P1 and a global target selection effect in the P3 component, meditators showed effects of local information processing in the P1, N2, and P3 and of global processing for the N1, N2, and P3. Thus, meditators seem to display enhanced depth of processing. In addition, meditation altered the uptake of information such that meditators selected target level information earlier in the processing sequence than controls. In a longitudinal experiment, we could replicate the behavioral effects, suggesting that meditation modulates attention already after a 4-day meditation retreat. Together, these results suggest that practicing meditation enhances the speed with which attention can be allocated and relocated, thus increasing the depth of information processing and reducing response latency.
Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, fMRI research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Raven’s Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) regarding their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (r ∼ 0.20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the default-mode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, N = 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits.
Neural pattern similarity differentially relates to memory performance in younger and older adults
(2019)
Age-related memory decline is associated with changes in neural functioning, but little is known about how aging affects the quality of information representation in the brain. Whereas a long-standing hypothesis of the aging literature links cognitive impairments to less distinct neural representations in old age (“neural dedifferentiation”), memory studies have shown that overlapping neural representations of different studied items are beneficial for memory performance. In an electroencephalography (EEG) study, we addressed the question whether distinctiveness or similarity between patterns of neural activity supports memory differentially in younger and older adults. We analyzed between-item neural pattern similarity in 50 younger (19–27 years old) and 63 older (63–75 years old) male and female human adults who repeatedly studied and recalled scene–word associations using a mnemonic imagery strategy. We compared the similarity of spatiotemporal EEG frequency patterns during initial encoding in relation to subsequent recall performance. The within-person association between memory success and pattern similarity differed between age groups: For older adults, better memory performance was linked to higher similarity early in the encoding trials, whereas young adults benefited from lower similarity between earlier and later periods during encoding, which might reflect their better success in forming unique memorable mental images of the joint picture–word pairs. Our results advance the understanding of the representational properties that give rise to subsequent memory, as well as how these properties may change in the course of aging.