Shaping functional architecture by oscillatory alpha activity: gating by inhibition

Effects of one session of theta or high alpha neurofeedback on EEG activity and working memory

Neurofeedback techniques provide participants immediate feedback on neuronal signals, enabling them to modulate their brain activity. This technique holds promise to unveil brain-behavior relationship and offers opportunities for neuroenhancement. Establishing causal relationships between modulated brain activity and behavioral improvements requires rigorous experimental designs, including appropriate control groups and large samples. Our primary objective was to examine whether a single neurofeedback session, designed to enhance working memory through the modulation of theta or high-alpha frequencies, elicits specific changes in electrophysiological and cognitive outcomes. Additionally, we explored predictors of successful neuromodulation. A total of 101 healthy adults were assigned to groups trained to increase frontal theta, parietal high alpha, or random frequencies (active control group). We measured resting-state EEG, working memory performance, and self-reported psychological states before and after one neurofeedback session. Although our analyses revealed improvements in electrophysiological and behavioral outcomes, these gains were not specific to the experimental groups. An increase in the frequency targeted by the training has been observed for the theta and high alpha groups, but training designed to increase randomly selected frequencies appears to induce more generalized neuromodulation compared with targeting a specific frequency. Among all the predictors of neuromodulation examined, resting theta and high alpha amplitudes predicted specifically the increase of those frequencies during the training. These results highlight the challenge of integrating a control group based on enhancing randomly selected frequency bands and suggest potential avenues for optimizing interventions (e.g., by including a control group trained in both up- and down-regulation).

Mindfulness meditation is associated with global EEG spectral changes in theta, alpha, and beta amplitudes

Mindfulness meditation is linked to a broad range of psychological and physical health benefits, potentially mediated by changes in neural oscillations. This study explored changes in neural oscillations associated with both immediate and regular mindfulness meditation practice. Electroencephalographic (EEG) data were collected from 40 healthy young adults (Mage = 20.8, 24 females) during eyes-closed resting and mindfulness meditation states in two separate recording sessions, six weeks apart. Participants were novice meditators, and following the first recording session, were randomly assigned to either a daily mindfulness meditation practice or classical music listening as an active control, which they completed until the second recording session. Traditional bands of delta (1.0-3.5 Hz), theta (4.0-7.5 Hz), alpha (8.0-13.0 Hz), beta (13.5-30.0 Hz), and gamma (30.5-45.0 Hz) were used to explore changes in global EEG spectral amplitude. A significant increase in theta between sessions was observed in both groups and states. Alpha decreased significantly during meditation compared with rest, and a three-way interaction indicated a smaller reduction during meditation between sessions in the mindfulness group. There was a similar interaction in beta, which remained stable between sessions during both rest and meditation in the mindfulness group while varying in the classical music listening group. No significant effects were observed in global delta or gamma amplitudes. These findings suggest that changes in neural oscillations associated with breath-focused mindfulness meditation may be related to processes underlying attention and awareness. Further research is necessary to consolidate these findings, particularly in relation to the associated health benefits.

A "Conscious" Loss of Balance: Directing Attention to Movement Can Impair the Cortical Response to Postural Perturbations

"Trying too hard" can interfere with skilled movement, such as sports and music playing. Postural control can similarly suffer when conscious attention is directed toward it ("conscious movement processing"; CMP). However, the neural mechanisms through which CMP influences balance remain poorly understood. We explored the effects of CMP on electroencephalographic (EEG) perturbation-evoked cortical responses and subsequent balance performance. Twenty healthy young adults (age = 25.1 ± 5 years; 10 males and 10 females) stood on a force plate-embedded moveable platform while mobile EEG was recorded. Participants completed two blocks of 50 discrete perturbations, containing an even mix of slower (186 mm/s peak velocity) and faster (225 mm/s peak velocity) perturbations. One block was performed under conditions of CMP (i.e., instructions to consciously control balance), while the other was performed under "Control" conditions with no additional instructions. For both slow and fast perturbations, CMP resulted in significantly smaller cortical N1 signals (a perturbation-evoked potential localized to the supplementary motor area) and lower sensorimotor beta EEG activity 200-400 ms postperturbation. Significantly greater peak velocities of the center of pressure (i.e., greater postural instability) were also observed during the CMP condition. Our findings provide the first evidence that disruptions to postural control during CMP may be a consequence of insufficient cortical activation relevant for balance (i.e., insufficient cortical N1 responses followed by enhanced beta suppression). We propose that conscious attempts to minimize postural instability through CMP acts as a cognitive dual-task that dampens the sensitivity of the sensorimotor system for future losses of balance.

Different sustained and induced alpha oscillations emerge in the human auditory cortex during sound processing

Alpha oscillations in the auditory cortex have been associated with attention and the suppression of irrelevant information. However, their anatomical organization and interaction with other neural processes remain unclear. Do alpha oscillations function as a local mechanism within most neural sources to regulate their internal excitation/inhibition balance, or do they belong to separated inhibitory sources gating information across the auditory network? To address this question, we acquired intracerebral electrophysiological recordings from epilepsy patients during rest and tones listening. Thanks to independent component analysis, we disentangled the different neural sources and labeled them as "oscillatory" if they presented strong alpha oscillations at rest, and/or "evoked" if they displayed a significant evoked response to the stimulation. Our results show that 1) sources are condition-specific and segregated in the auditory cortex, 2) both sources have a high-gamma response followed by an induced alpha suppression, 3) only oscillatory sources present a sustained alpha suppression during all the stimulation period. We hypothesize that there are two different alpha oscillations in the auditory cortex: an induced bottom-up response indicating a selective engagement of the primary cortex to process the stimuli, and a sustained suppression reflecting a general disinhibited state of the network to process sensory information.

Analgesic effects of high-frequency rTMS on pain anticipation and perception

Previous studies suggest that pain perception is greatly shaped by anticipation, with M1 and DLPFC involved in this process. We hypothesized that high-frequency rTMS targeting these regions could alter pain anticipation and thereby reduce pain perception. In a double-blind, sham-controlled study, healthy participants received 10 Hz rTMS to M1, DLPFC, or a sham treatment. Assessments were conducted before, immediately after, and 60 min after stimulation, including laser-evoked potentials, pain ratings, and anticipatory EEG. M1-rTMS immediately reduced laser-evoked P2 amplitude, increased sensorimotor high-frequency α-oscillation power, and accelerated peak alpha frequency in the midfrontal region during pain anticipation. In contrast, DLPFC-rTMS reduced the N2-P2 complex and pain ratings 60 min post-stimulation, an effect associated with prolonged microstate C duration during pain anticipation-a microstate linked to default mode network activity. Thus, M1-rTMS immediately modulates anticipatory α-oscillations and laser-evoked potentials, while DLPFC-rTMS induces delayed analgesic effects partially by modulating default mode network activity.

Neural dynamics of shifting attention between perception and working-memory contents

In everyday tasks, our focus of attention shifts seamlessly between contents in the sensory environment and internal memory representations. Yet, research has mainly considered external and internal attention in isolation. We used magnetoencephalography to compare the neural dynamics of shifting attention to visual contents within vs. between the external and internal domains. Participants performed a combined perception and working-memory task in which two sequential cues guided attention to upcoming (external) or memorized (internal) sensory information. Critically, the second cue could redirect attention to visual content within the same or alternative domain as the first cue. Multivariate decoding unveiled distinct patterns of human brain activity when shifting attention within vs. between domains. Brain activity distinguishing within- from between-domain shifts was broadly distributed and highly dynamic. Intriguingly, crossing domains did not invoke an additional stage prior to shifting attention. Alpha lateralization, a canonical marker of shifting spatial attention, showed no delay when cues redirected attention to the same vs. alternative domain. Instead, evidence suggested that neural states associated with a given domain linger and influence subsequent shifts of attention within vs. between domains. Our findings provide critical insights into the neural dynamics that govern attentional shifts between perception and working memory.

Compensatory mechanisms amidst demyelinating disorders: insights into cognitive preservation

Demyelination disrupts the transmission of electrical signals in the brain and affects neurodevelopment in children with disorders such as multiple sclerosis and myelin oligodendrocyte glycoprotein-associated disorders. Although cognitive impairments are prevalent in these conditions, some children maintain cognitive function despite substantial structural injury. These findings raise an important question: in addition to the degenerative process, do compensatory neural mechanisms exist to mitigate the effects of myelin loss? We propose that a multi-dimensional approach integrating multiple neuroimaging modalities, including diffusion tensor imaging, magnetoencephalography and eye-tracking, is key to investigating this question. We examine the structural and functional connectivity of the default mode and executive control networks due to their significant roles in supporting higher-order cognitive processes. As cognitive proxies, we examine saccade reaction times and direction errors during an interleaved pro- (eye movement towards a target) and anti-saccade (eye movement away from a target) task. 28 typically developing children, 18 children with multiple sclerosis and 14 children with myelin oligodendrocyte glycoprotein-associated disorders between 5 and 18.9 years old were scanned at the Hospital for Sick Children. Tractography of diffusion MRI data examined structural connectivity. Intracellular and extracellular microstructural parameters were extracted using a white matter tract integrity model to provide specific inferences on myelin and axon structure. Magnetoencephalography scanning was conducted to examine functional connectivity. Within groups, participants had longer saccade reaction times and greater direction errors on the anti- versus pro-saccade task; there were no group differences on either task. Despite similar behavioural performance, children with demyelinating disorders had significant structural compromise and lower bilateral high gamma, higher left-hemisphere theta and higher right-hemisphere alpha synchrony relative to typically developing children. Children diagnosed with multiple sclerosis had greater structural compromise relative to children with myelin oligodendrocyte glycoprotein-associated disorders; there were no group differences in neural synchrony. For both patient groups, increased disease disability predicted greater structural compromise, which predicted longer saccade reaction times and greater direction errors on both tasks. Structural compromise also predicted increased functional connectivity, highlighting potential adaptive functional reorganisation in response to structural compromise. In turn, increased functional connectivity predicted faster saccade reaction times and fewer direction errors. These findings suggest that increased functional connectivity, indicated by increased alpha and theta synchrony, may be necessary to compensate for structural compromise and preserve cognitive abilities. Further understanding these compensatory neural mechanisms could pave the way for the development of targeted therapeutic interventions aimed at enhancing these mechanisms, ultimately improving cognitive outcomes for affected individuals.

Testing the impact of hatha yoga on task switching: a randomized controlled trial

Switching attention between or within tasks is part of the implementation and maintenance of executive control processes and plays an indispensable role in our daily lives: It allows us to perform on distinct tasks and with variable objects, enabling us to adapt to and respond in dynamically changing environments. Here, we tested if yoga could benefit switching of attention between distinct objects of one’s focus (e.g., through practicing switching between one’s own body, feelings, and different postures) in particular and executive control in general. We therefore conducted a randomized controlled trial with 98 participants and a waitlisted control group. In the intervention group, healthy yoga novices practiced Hatha yoga 3x a week, for 8 weeks. We conducted two experiments: A purely behavioral task investigating changes in behavioral costs during switching between attentional control sets (74 participants analyzed), and a modality-switching task focusing on electrophysiology (EEG data of 47 participants analyzed). At the electrophysiological level, frequency-tagging indicated no interventional effect on participants’ ability to switch between the auditory and visual modalities. However, increases in task-related frontocentral theta activity, resulting from the intervention, indicated an ability to increasingly deploy executive resources to the prioritized task when needed. At the behavioral level, our intervention resulted in more efficient holding of target representations in working memory, indicated by decreased mixing costs. Again, however, intervention effects on switching costs were missing. We, thus, conclude that Hatha yoga has a positive influence on executive control, potentially through improvements in working memory rather than directly on switching.

Clinical trial registration

clinicaltrials.gov, identifier [NCT05232422].

Local and interareal alpha and low-beta band oscillation dynamics underlie the bilateral field advantage in visual working memory

Visual working memory has a limited maximum capacity, which can be larger if stimuli are presented bilaterally vs. unilaterally. However, the neuronal mechanisms underlying this bilateral field advantage are not known. Visual working memory capacity is predicted by oscillatory delay-period activity, specifically, by a decrease in alpha (8 to 12 Hz) band amplitudes in posterior brain regions reflecting attentional deployment and related shifts in excitation, as well as a concurrent increase of prefrontal oscillation amplitudes and interareal synchronization in multiple frequencies reflecting active maintenance of information. Here, we asked whether posterior alpha suppression or prefrontal oscillation enhancement explains the bilateral field advantage. We recorded brain activity with high-density electroencephalography, while subjects (n = 26, 14 males) performed a visual working memory task with uni- and bilateral visual stimuli. The bilateral field advantage was associated with early suppression of low-alpha (6 to 10 Hz) and alpha-beta (10 to 17 Hz) band amplitudes, and a subsequent alpha-beta amplitude increase, which, along with a concurrent load-dependent interareal synchronization in the high-alpha band (10 to 15 Hz), correlated with hit rates and reaction times and thus predicted higher maximum capacities in bilateral than unilateral visual working memory. These results demonstrate that the electrophysiological basis of the bilateral field advantage in visual working memory is both in the changes in attentional deployment and enhanced interareal integration.

Differential neural mechanisms underlie cortical gating of visual spatial attention mediated by alpha-band oscillations

Selective attention relies on neural mechanisms that facilitate processing of behaviorally relevant sensory information while suppressing irrelevant information, consistently linked to alpha-band oscillations in human M/EEG studies. We analyzed cortical alpha responses from intracranial electrodes implanted in eight epilepsy patients, who performed a visual spatial attention task. Electrocorticographic data revealed a spatiotemporal dissociation between attention-modulated alpha desynchronization, associated with the enhancement of sensory processing, and alpha synchronization, associated with the suppression of sensory processing, during the cue-target interval. Dorsal intraparietal areas contralateral to the attended hemifield primarily exhibited a delayed and sustained alpha desynchronization, while ventrolateral extrastriatal areas ipsilateral to the attended hemifield primarily exhibited an earlier and sustained alpha synchronization. Analyses of cross-frequency coupling between alpha phase and broadband high-frequency activity (HFA) further revealed cross-frequency interactions along the visual hierarchy contralateral to the attended locations. Directionality analyses indicate that alpha phase in early and extrastriatal visual areas modulated HFA power in downstream visual areas, thus potentially facilitating the feedforward processing of an upcoming, spatially predictable target. In contrast, in areas ipsilateral to the attended locations, HFA power modulated local alpha phase in early and extrastriatal visual areas, with suppressed interareal interactions, potentially attenuating the processing of distractors. Our findings reveal divergent alpha-mediated neural mechanisms underlying target enhancement and distractor suppression during the deployment of spatial attention, reflecting enhanced functional connectivity at attended locations, while suppressed functional connectivity at unattended locations. The collective dynamics of these alpha-mediated neural mechanisms play complementary roles in the efficient gating of sensory information.

Hierarchical Organization of Visual Feature Attention Control

Attention can be deployed in anticipation of visual stimuli based on features such as their color or direction of motion. This anticipatory feature-based attention involves top-down neural control signals from the frontoparietal network that bias visual cortex to enhance the processing of attended information and suppress distraction. So, for example, anticipatory attention control can enable effective selection based on stimulus color while ignoring distracting information about stimulus motion. But as well, anticipatory attention can be focused more narrowly, for example, to select specific colors or motion directions that define task-relevant events and objects. One important question that remains open is whether anticipatory attention control first biases broad feature dimensions such as color versus motion before biasing the specific feature attributes (e.g., blue vs. green). To investigate this, we recorded EEG activity during a task where participants were cued to either attend to a color (blue or green) or a motion direction (up or down) on a trial-by-trial basis. Applying multivariate decoding approaches to the EEG alpha band (8-12 Hz) activity during the attention control period (cue-target interval), we observed significant decoding for both the attended dimensions (color vs. motion) and specific feature attributes (blue vs. green; up vs. down). Importantly, the temporal onset of the dimension-level biasing (color vs. motion) preceded that of the attribute-level biasing (e.g., blue vs. green). These findings demonstrate that the top-down control of feature-based attention proceeds in a hierarchical fashion, first biasing the broad feature dimension, and then narrowing to the specific feature attribute.

Significance Statement

During voluntary feature-based attention, electrophysiological and neuroimaging studies have highlighted the role of anticipatory (top-down) biasing of the sensory cortex in enhancing the selection of attended stimulus attributes, but little is known about how this is achieved. In particular, it is not clear whether attending to an attribute such as a color (blue vs. green) or motion direction (up vs. down) first biases all neural structures coding that dimension (color/motion) before biasing the specific attribute, or if the top-down signals directly bias only the attended attribute. Using EEG and multivariate decoding, we report that top-down attention control follows a hierarchical organization: first, the broader attended feature dimension is biased, which is followed by the biasing of the specific feature attribute.

Probing Neurophysiological Processes Related to Self-Referential Processing to Predict Improvement in Adolescents With Depression Receiving Cognitive Behavioral Therapy

BACKGROUND

Cognitive behavioral therapy (CBT) is a gold-standard approach for treating major depressive disorder in adolescents. However, nearly half of adolescents receiving CBT do not improve. To personalize treatment, it is essential to identify objective markers that predict treatment responsiveness. To address this aim, we investigated neurophysiological processes related to self-referential processing that predicted CBT response among female adolescents with depression.

METHODS

At baseline, female adolescents ages 13 to 18 years (N = 80) completed a comprehensive clinical assessment, and a self-referential encoding task was administered while electroencephalographic data were recorded. Baseline electroencephalographic data were utilized to identify oscillatory differences between healthy adolescents (n = 42) and adolescents with depression (n = 38). Following the baseline assessment, adolescents with depression received up to 12 weeks of CBT. Baseline differences in electroencephalographic oscillations between healthy adolescents and those with depression were used to guide CBT prediction analysis. Cluster-based event-related spectral perturbation analysis was used to probe theta and alpha event-related synchronization (ERS)/event-related desynchronization (ERD) response to negative and positive words.

RESULTS

Baseline analyses showed that, relative to the healthy adolescents, adolescents with depression exhibited higher levels of frontal theta ERS and greater posterior alpha ERD. Multilevel modeling identified primary neural pretreatment predictors of treatment response: greater theta ERS in the right prefrontal cortex after the onset of negative words and lower alpha ERD in both the right prefrontal cortex and posterior cingulate cortex. ERS and ERD associations with treatment response remained significant, with baseline depressive and anxiety symptoms included as covariates in all analyses.

CONCLUSIONS

Consistent with prior research, results highlighted that relative to healthy adolescents, adolescents with depression are characterized by prominent theta synchronization and alpha desynchronization over the prefrontal cortex and posterior cingulate cortex, respectively. Cluster-based event-related spectral perturbation analysis also identified key mechanisms underlying depression-related self-referential processing that predicted improved symptoms during the course of CBT. Ultimately, a better characterization of the neural underpinnings of adolescent depression and its treatment may lead to more personalized interventions.

Regional and interregional functional and structural brain abnormalities in neuropathic pain

Neuropathic pain is a severe form of chronic pain due to a lesion or disease of the somatosensory nervous system. Here we provide an overview of the neuroimaging approaches that can be used to assess brain abnormalities in a chronic pain condition, with particular focus on people with neuropathic pain and then summarize the findings of studies that applied these methodologies to study neuropathic pain. First, we review the most commonly used approaches to examine grey and white matter abnormalities using magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) and then review functional neuroimaging techniques to measure regional activity and inter-regional communication using functional MRI, electroencephalography (EEG) and magnetoencephalography (MEG). In neuropathic pain the most prominent structural abnormalities have been found to be in the primary somatosensory cortex, insula, anterior cingulate cortex and thalamus, with differences in volume directionality linked to neuropathic pain symptomology. Functional connectivity findings related to treatment outcome point to a potential clinical utility. Some prominent abnormalities in neuropathic pain identified with EEG and MEG throughout the dynamic pain connectome are slowing of alpha activity and higher regional oscillatory activity in the theta and alpha band, lower low beta and higher high beta band power. Finally, connectivity and coupling findings placed into context how regional abnormalities impact the networks and pathways of the dynamic pain connectome. Overall, functional and structural neuroimaging have the potential to identify predictive biomarkers that can be used to guide development of personalized pain management of neuropathic pain.

New Vistas for the Relationship between Empathy and Political Ideology

The study of ideological asymmetries in empathy has consistently yielded inconclusive findings. Yet, until recently these inconsistencies relied exclusively on self-reports, which are known to be prone to biases and inaccuracies when evaluating empathy levels. Very recently, we reported ideological asymmetries in cognitive-affective empathy while relying on neuroimaging for the first time to address this question. In the present investigation which sampled a large cohort of human individuals from two distant countries and neuroimaging sites, we re-examine this question, but this time from the perspective of empathy to physical pain. The results are unambiguous at the neural and behavioral levels and showcase no asymmetry. This finding raises a novel premise: the question of whether empathy is ideologically asymmetrical depends on the targeted component of empathy (e.g., physical pain vs cognitive-affective) and requires explicit but also unobtrusive techniques for the measure of empathy. Moreover, the findings shed new light on another line of research investigating ideological (a)symmetries in physiological responses to vicarious pain, disgust, and threat.

Oscillatory activity in bilateral prefrontal cortices is altered by distractor strength during working memory processing

Working memory (WM) enables the temporary storage of limited information and is a central component of higher order cognitive function. Irrelevant and/or distracting information can have a negative impact on WM processing and suppressing such incoming stimuli is critical to maintaining adequate performance. However, the neural mechanisms and dynamics underlying such distractor inhibition remain poorly understood. In the current study, we enrolled 46 healthy adults (Mage: 27.92, Nfemale: 28) who completed a Sternberg type WM task with high- and low-distractor conditions during magnetoencephalography (MEG). MEG data were transformed into the time-frequency domain and significant task-related oscillatory responses were imaged to identify the underlying anatomical areas. Whole-brain paired t-tests, with cluster-based permutation testing for multiple comparisons correction, were performed to assess differences between the low- and high-distractor conditions for each oscillatory response. Across conditions, we found strong alpha and beta oscillations (i.e., decreases relative to baseline) and increases in theta power throughout the encoding and maintenance periods. Whole-brain contrasts revealed significantly stronger alpha and beta oscillations in bilateral prefrontal regions during maintenance in high- compared to low-distractor trials, with the stronger beta oscillations being centered on the left dorsolateral prefrontal cortex and right inferior frontal gyrus, while those for alpha being within the right anterior prefrontal cortices and the right middle frontal gyrus. These findings suggest that alpha and beta oscillations in the bilateral prefrontal cortices play a major role in the inhibition of distracting information during WM maintenance. Our results also contribute to prior research on cognitive control and functional inhibition, in which prefrontal regions have been widely implicated.

Multimodal neuroimaging of hierarchical cognitive control

Cognitive control enables us to translate our knowledge into actions, allowing us to flexibly adjust our behavior, according to environmental contexts, our internal goals, and future plans. Multimodal neuroimaging and neurostimulation techniques have proven essential for advancing our understanding of how cognitive control emerges from the coordination of distributed neuronal activities in the brain. In this review, we examine the literature on multimodal studies of cognitive control. We explore how these studies provide converging evidence for a novel, multiplexed model of cognitive control, in which neural oscillations support different levels of control processing along a functionally hierarchical organization of distinct frontoparietal networks.

Transcranial temporal interference stimulation (tTIS) influences event-related alpha activity during mental rotation

Non-invasive brain stimulation techniques offer therapeutic potential for neurological and psychiatric disorders. However, current methods are often limited in their stimulation depth. The novel transcranial temporal interference stimulation (tTIS) aims to overcome this limitation by non-invasively targeting deeper brain regions. In this study, we aimed to evaluate the efficacy of tTIS in modulating alpha activity during a mental rotation task. The effects of tTIS were compared with transcranial alternating current stimulation (tACS) and a sham control. Participants were randomly assigned to a tTIS, tACS, or sham group. They performed alternating blocks of resting and mental rotation tasks before, during, and after stimulation. During the stimulation blocks, participants received 20 min of stimulation adjusted to their individual alpha frequency (IAF). We assessed shifts in resting state alpha power, event-related desynchronization (ERD) of alpha activity during mental rotation, as well as resulting improvements in behavioral performance. Our results indicate tTIS and tACS to be effective in modulating cortical alpha activity during mental rotation, leading to an increase in ERD from pre- to poststimulation as well as compared to sham stimulation. However, this increase in ERD was not correlated with enhanced mental rotation performance, and resting state alpha power remained unchanged. Our findings underscore the complex nature of tTIS and tACS efficacy, indicating that stimulation effects are more observable during active cognitive tasks, while their impacts are less pronounced on resting neuronal systems.

Aberrant connectivity of the lateralized readiness system in non-syndromic congenital mirror movements

OBJECTIVES

Non-syndromic CMM has a complex phenotype. Abnormal corpus callosum and corticospinal tract processes are suggested mechanisms of the mirror movements. To further explore behavioural and neural phenotype(s) the present study tests the hypothesis that the response readiness network comprising supplementary motor area (SMA) and connections with motor cortex (M1) functions abnormally in CMM.

METHODS

Twelve participants with (non-syndromic) CMM and a control group (n = 28) were tested on a probabilistic Go-NoGo task while electroencephalography (EEG) was recorded to assess possible group differences in lateralized readiness of voluntary hand movements together with measures of SMA-M1 functional connectivity.

RESULTS

The CMM group demonstrated delayed lateralized readiness and stronger functional connectivity between left-brain SMA-M1 regions. Connectivity strength was correlated with measures of behavioural performance but not with extent of mirroring.

CONCLUSIONS

Abnormalities in brain processes upstream of movement output likely reflect neurocompensation as a result of lifelong experience with mirroring in CMM.

SIGNIFICANCE

These findings extend the known neural abnormalities in CMM to include brain networks upstream from those involved in motor output and raise the question of whether neurocompensatory plasticity might be involved.

Visual Processing by Hierarchical and Dynamic Multiplexing

The complexity of natural environments requires highly flexible mechanisms for adaptive processing of single and multiple stimuli. Neuronal oscillations could be an ideal candidate for implementing such flexibility in neural systems. Here, we present a framework for structuring attention-guided processing of complex visual scenes in humans, based on multiplexing and phase coding schemes. Importantly, we suggest that the dynamic fluctuations of excitability vary rapidly in terms of magnitude, frequency and wave-form over time, i.e., they are not necessarily sinusoidal or sustained oscillations. Different elements of single objects would be processed within a single cycle (burst) of alpha activity (7-14 Hz), allowing for the formation of coherent object representations while separating multiple objects across multiple cycles. Each element of an object would be processed separately in time-expressed as different gamma band bursts (>30 Hz)-along the alpha phase. Since the processing capacity per alpha cycle is limited, an inverse relationship between object resolution and size of attentional spotlight ensures independence of the proposed mechanism from absolute object complexity. Frequency and wave-shape of those fluctuations would depend on the nature of the object that is processed and on cognitive demands. Multiple objects would further be organized along the phase of slower fluctuations (e.g., theta), potentially driven by saccades. Complex scene processing, involving covert attention and eye movements, would therefore be associated with multiple frequency changes in the alpha and lower frequency range. This framework embraces the idea of a hierarchical organization of visual processing, independent of environmental temporal dynamics.

When Maturation is Not Linear: Brain Oscillatory Activity in the Process of Aging as Measured by Electrophysiology

Changes in brain oscillatory activity are commonly used as biomarkers both in cognitive neuroscience and in neuropsychiatric conditions. However, little is known about how its profile changes across maturation. Here we use regression models to characterize magnetoencephalography power changes within classical frequency bands in a sample of 792 healthy participants, covering the range 13 to 80 years old. Our findings unveil complex, non-linear power trajectories that defy the traditional linear paradigm, with notable cortical region variations. Interestingly, slow wave activity increases correlate with improved cognitive performance throughout life and larger gray matter volume in the elderly. Conversely, fast wave activity diminishes in adulthood. Elevated low-frequency activity during aging, traditionally seen as compensatory, may also signify neural deterioration. This dual interpretation, highlighted by our study, reveals the intricate dynamics between brain oscillations, cognitive performance, and aging. It advances our understanding of neurodevelopment and aging by emphasizing the regional specificity and complexity of brain rhythm changes, with implications for cognitive and structural integrity.

Fatigue in Multiple Sclerosis: A Resting-State EEG Microstate Study

Fatigue affects approximately 80% of people with Multiple Sclerosis (PwMS) and can impact several domains of daily life. However, the neural underpinnings of fatigue in MS are still not completely clear. The aim of our study was to investigate the spontaneous large-scale networks functioning associated with fatigue in PwMS using the EEG microstate approach with a spectral decomposition. Forty-three relapsing-remitting MS patients and twenty-four healthy controls (HCs) were recruited. All participants underwent an administration of Modified Fatigue Impact scale (MFIS) and a 15-min resting-state high-density EEG recording. We compared the microstates of healthy subjects, fatigued (F-MS) and non-fatigued (nF-MS) patients with MS; correlations with clinical and behavioral fatigue scores were also analyzed. Microstates analysis showed six templates across groups and frequencies. We found that in the F-MS emerged a significant decrease of microstate F, associated to the salience network, in the broadband and in the beta band. Moreover, the microstate B, associated to the visual network, showed a significant increase in fatigued patients than healthy subjects in broadband and beta bands. The multiple linear regression showed that the high cognitive fatigue was predicted by both an increase and decrease, respectively, in delta band microstate B and beta band microstate F. On the other hand, higher physical fatigue was predicted with lower occurrence microstate F in beta band. The current findings suggest that in MS the higher level of fatigue might be related to a maladaptive functioning of the salience and visual network.

Age-related differences in memory encoding and retrieval during referential processing: A time-frequency analysis

We investigated how lexical form similarity of referential candidates and ambiguity of following pronouns impact the encoding and retrieval of words from memory during sentence processing in younger and older adults. Critical sentences included two noun phrases (henceforth NPs) that were either phonologically and orthographically similar (Jason and Jacob/Jade) or dissimilar (Jason and Matt/Hannah), followed by a pronoun (e.g., he) that was either ambiguous or unambiguous (depending on the genders of the preceding NPs). We analyzed brain activity time-locked to the onsets of the second NP (NP2) and the pronoun to investigate the encoding and the retrieval of the NPs, respectively. During encoding NP2, older adults exhibited greater alpha power when NP1 had the same-gender, whereas younger adults showed no such effect, suggesting an increased need for inhibition for older adults during encoding. Moreover, although both groups exhibited an increase in alpha power for similar NPs, only younger adults exhibited a theta power increase, suggesting similarity-induced inhibition for both groups, but an additional maintenance cost only for younger adults. During retrieval (i.e., on the pronoun), we found that both pronominal ambiguity and form similarity resulted in greater theta power for younger adults, suggesting full pronominal processing and therefore more difficult retrieval, but smaller theta/alpha power for older adults, suggesting good-enough processing and therefore easier retrieval. Together with complementary behavioral results, our findings suggest that older adults resort to good-enough referential processing when the retrieval of relevant representations is cognitively demanding. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Neurophysiology of Effortful Listening: Decoupling Motivational Modulation from Task Demands

In demanding listening situations, a listener's motivational state may affect their cognitive investment. Here, we aim to delineate how domain-specific sensory processing, domain-general neural alpha power, and pupil size as a proxy for cognitive investment encode influences of motivational state under demanding listening. Participants (male and female) performed an auditory gap-detection task while the pupil size and the magnetoencephalogram were simultaneously recorded. Task demand and a listener's motivational state were orthogonally manipulated through changes in gap duration and monetary-reward prospect, respectively. Whereas task difficulty impaired performance, reward prospect enhanced it. The pupil size reliably indicated the modulatory impact of an individual's motivational state. At the neural level, the motivational state did not affect auditory sensory processing directly but impacted attentional postprocessing of an auditory event as reflected in the late evoked-response field and alpha-power change. Both pregap pupil dilation and higher parietal alpha power predicted better performance at the single-trial level. The current data support a framework wherein the motivational state acts as an attentional top-down neural means of postprocessing the auditory input in challenging listening situations.

Distinct functions for beta and alpha bursts in gating of human working memory

Multiple neural mechanisms underlying gating to working memory have been proposed with divergent results obtained in human and animal studies. Previous findings from non-human primates suggest prefrontal beta frequency bursts as a correlate of transient inhibition during selective encoding. Human studies instead suggest a similar role for sensory alpha power fluctuations. To cast light on these discrepancies we employed a sequential working memory task with distractors for human participants. In particular, we examined their whole-brain electrophysiological activity in both alpha and beta bands with the same single-trial burst analysis earlier performed on non-human primates. Our results reconcile earlier findings by demonstrating that both alpha and beta bursts in humans correlate with the filtering and control of memory items, but with region and task-specific differences between the two rhythms. Occipital beta burst patterns were selectively modulated during the transition from sensory processing to memory retention whereas prefrontal and parietal beta bursts tracked sequence order and were proactively upregulated prior to upcoming target encoding. Occipital alpha bursts instead increased during the actual presentation of unwanted sensory stimuli. Source reconstruction additionally suggested the involvement of striatal and thalamic alpha and beta. Thus, specific whole-brain burst patterns correlate with different aspects of working memory control.

Propofol-mediated loss of consciousness disrupts predictive routing and local field phase modulation of neural activity

Predictive coding is a fundamental function of the cortex. The predictive routing model proposes a neurophysiological implementation for predictive coding. Predictions are fed back from the deep-layer cortex via alpha/beta (8 to 30 Hz) oscillations. They inhibit the gamma (40 to 100 Hz) and spiking that feed sensory inputs forward. Unpredicted inputs arrive in circuits unprepared by alpha/beta, resulting in enhanced gamma and spiking. To test the predictive routing model and its role in consciousness, we collected data from intracranial recordings of macaque monkeys during passive presentation of auditory oddballs before and after propofol-mediated loss of consciousness (LOC). In line with the predictive routing model, alpha/beta oscillations in the awake state served to inhibit the processing of predictable stimuli. Propofol-mediated LOC eliminated alpha/beta modulation by a predictable stimulus in the sensory cortex and alpha/beta coherence between sensory and frontal areas. As a result, oddball stimuli evoked enhanced gamma power, late period (>200 ms from stimulus onset) spiking, and superficial layer sinks in the sensory cortex. LOC also resulted in diminished decodability of pattern-level prediction error signals in the higher-order cortex. Therefore, the auditory cortex was in a disinhibited state during propofol-mediated LOC. However, despite these enhanced feedforward responses in the auditory cortex, there was a loss of differential spiking to oddballs in the higher-order cortex. This may be a consequence of a loss of within-area and interareal spike-field coupling in the alpha/beta and gamma frequency bands. These results provide strong constraints for current theories of consciousness.

The strength of anticipated distractors shapes EEG alpha and theta oscillations in a Working Memory task

Working Memory (WM) requires maintenance of task-relevant information and suppression of task-irrelevant/distracting information. Alpha and theta oscillations have been extensively investigated in relation to WM. However, studies that examine both theta and alpha bands in relation to distractors, encompassing not only power modulation but also connectivity modulation, remain scarce. Here, we depicted, at the EEG-source level, the increase in power and connectivity in theta and alpha bands induced by strong relative to weak distractors during a visual Sternberg-like WM task involving the encoding of verbal items. During retention, a strong or weak distractor was presented, predictable in time and nature. Analysis focused on the encoding and retention phases before distractor presentation. Theta and alpha power were computed in cortical regions of interest, and connectivity networks estimated via spectral Granger causality and synthetized using in/out degree indices. The following modulations were observed for strong vs. weak distractors. In theta band during encoding, the power in frontal regions increased, together with frontal-to-frontal and bottom-up occipital-to-temporal-to-frontal connectivity; even during retention, bottom-up theta connectivity increased. In alpha band during retention, but not during encoding, the power in temporal-occipital regions increased, together with top-down frontal-to-occipital and temporal-to-occipital connectivity. From our results, we postulate a proactive cooperation between theta and alpha mechanisms: the first would mediate enhancement of target representation both during encoding and retention, and the second would mediate increased inhibition of sensory areas during retention only, to suppress the processing of imminent distractor without interfering with the processing of ongoing target stimulus during encoding.

Pre-Stimulus Activity of Left and Right TPJ in Linguistic Predictive Processing: A MEG Study

BACKGROUND

The left and right temporoparietal junctions (TPJs) are two brain areas involved in several brain networks, largely studied for their diverse roles, from attentional orientation to theory of mind and, recently, predictive processing. In predictive processing, one crucial concept is prior precision, that is, the reliability of the predictions of incoming stimuli. This has been linked with modulations of alpha power as measured with electrophysiological techniques, but TPJs have seldom been studied in this framework.

METHODS

The present article investigates, using magnetoencephalography, whether spontaneous oscillations in pre-stimulus alpha power in the left and right TPJs can modulate brain responses during a linguistic task that requires predictive processing in literal and non-literal sentences.

RESULTS

Overall, results show that pre-stimulus alpha power in the rTPJ was associated with post-stimulus responses only in the left superior temporal gyrus, while lTPJ pre-stimulus alpha power was associated with post-stimulus activity in Broca's area, left middle temporal gyrus, and left superior temporal gyrus.

CONCLUSIONS

We conclude that both the right and left TPJs have a role in linguistic prediction, involving a network of core language regions, with differences across brain areas and linguistic conditions that can be parsimoniously explained in the context of predictive processing.

Neural correlates of listening to nonnative-accented speech in multi-talker background noise

We examined the neural correlates underlying the semantic processing of native- and nonnative-accented sentences, presented in quiet or embedded in multi-talker noise. Implementing a semantic violation paradigm, 36 English monolingual young adults listened to American-accented (native) and Chinese-accented (nonnative) English sentences with or without semantic anomalies, presented in quiet or embedded in multi-talker noise, while EEG was recorded. After hearing each sentence, participants verbally repeated the sentence, which was coded and scored as an offline comprehension accuracy measure. In line with earlier behavioral studies, the negative impact of background noise on sentence repetition accuracy was higher for nonnative-accented than for native-accented sentences. At the neural level, the N400 effect for semantic anomaly was larger for native-accented than for nonnative-accented sentences, and was also larger for sentences presented in quiet than in noise, indicating impaired lexical-semantic access when listening to nonnative-accented speech or sentences embedded in noise. No semantic N400 effect was observed for nonnative-accented sentences presented in noise. Furthermore, the frequency of neural oscillations in the alpha frequency band (an index of online cognitive listening effort) was higher when listening to sentences in noise versus in quiet, but no difference was observed across the accent conditions. Semantic anomalies presented in background noise also elicited higher theta activity, whereas processing nonnative-accented anomalies was associated with decreased theta activity. Taken together, we found that listening to nonnative accents or background noise is associated with processing challenges during online semantic access, leading to decreased comprehension accuracy. However, the underlying cognitive mechanism (e.g., associated listening efforts) might manifest differently across accented speech processing and speech in noise processing.

Cardiac-Sympathetic Contractility and Neural Alpha-Band Power: Cross-Modal Collaboration during Approach-Avoidance Conflict

As evidence mounts that the cardiac-sympathetic nervous system reacts to challenging cognitive settings, we ask if these responses are epiphenomenal companions or if there is evidence suggesting a more intertwined role of this system with cognitive function. Healthy male and female human participants performed an approach-avoidance paradigm, trading off monetary reward for painful electric shock, while we recorded simultaneous electroencephalographic and cardiac-sympathetic signals. Participants were reward sensitive but also experienced approach-avoidance "conflict" when the subjective appeal of the reward was near equivalent to the revulsion of the cost. Drift-diffusion model parameters suggested that participants managed conflict in part by integrating larger volumes of evidence into choices (wider decision boundaries). Late alpha-band (neural) dynamics were consistent with widening decision boundaries serving to combat reward sensitivity and spread attention more fairly to all dimensions of available information. Independently, wider boundaries were also associated with cardiac "contractility" (an index of sympathetically mediated positive inotropy). We also saw evidence of conflict-specific "collaboration" between the neural and cardiac-sympathetic signals. In states of high conflict, the alignment (i.e., product) of alpha dynamics and contractility were associated with a further widening of the boundary, independent of either signal's singular association. Cross-trial coherence analyses provided additional evidence that the autonomic systems controlling cardiac-sympathetics might influence the assessment of information streams during conflict by disrupting or overriding reward processing. We conclude that cardiac-sympathetic control might play a critical role, in collaboration with cognitive processes, during the approach-avoidance conflict in humans.

Challenges and Approaches in the Study of Neural Entrainment

When exposed to rhythmic stimulation, the human brain displays rhythmic activity across sensory modalities and regions. Given the ubiquity of this phenomenon, how sensory rhythms are transformed into neural rhythms remains surprisingly inconclusive. An influential model posits that endogenous oscillations entrain to external rhythms, thereby encoding environmental dynamics and shaping perception. However, research on neural entrainment faces multiple challenges, from ambiguous definitions to methodological difficulties when endogenous oscillations need to be identified and disentangled from other stimulus-related mechanisms that can lead to similar phase-locked responses. Yet, recent years have seen novel approaches to overcome these challenges, including computational modeling, insights from dynamical systems theory, sophisticated stimulus designs, and study of neuropsychological impairments. This review outlines key challenges in neural entrainment research, delineates state-of-the-art approaches, and integrates findings from human and animal neurophysiology to provide a broad perspective on the usefulness, validity, and constraints of oscillatory models in brain-environment interaction.

Similarity of brain activity patterns during learning and subsequent resting state predicts memory consolidation

Spontaneous reactivation of brain activity from learning to a subsequent off-line period has been implicated as a neural mechanism underlying memory consolidation. However, similarities in brain activity may also emerge as a result of individual, trait-like characteristics. Here, we introduced a novel approach for analyzing continuous electroencephalography (EEG) data to investigate learning-induced changes as well as trait-like characteristics in brain activity underlying memory consolidation. Thirty-one healthy young adults performed a learning task, and their performance was retested after a short (∼1 h) delay. Consolidation of two distinct types of information (serial-order and probability) embedded in the task were tested to reveal similarities in functional networks that uniquely predict the changes in the respective memory performance. EEG was recorded during learning and pre- and post-learning rest periods. To investigate brain activity associated with consolidation, we quantified similarities in EEG functional connectivity between learning and pre-learning rest (baseline similarity) and learning and post-learning rest (post-learning similarity). While comparable patterns of these two could indicate trait-like similarities, changes from baseline to post-learning similarity could indicate learning-induced changes, possibly spontaneous reactivation. Higher learning-induced changes in alpha frequency connectivity (8.5-9.5 Hz) were associated with better consolidation of serial-order information, particularly for long-range connections across central and parietal sites. The consolidation of probability information was associated with learning-induced changes in delta frequency connectivity (2.5-3 Hz) specifically for more local, short-range connections. Furthermore, there was a substantial overlap between the baseline and post-learning similarities and their associations with consolidation performance, suggesting robust (trait-like) differences in functional connectivity networks underlying memory processes.

Age-related differences in how the shape of alpha and beta oscillations change during reaction time tasks

While the shape of cortical oscillations is increasingly recognised to be physiologically and functionally informative, its relevance to the aging motor system has not been established. We therefore examined the shape of alpha and beta band oscillations recorded at rest, as well as during performance of simple and go/no-go reaction time tasks, in 33 young (23.3 ± 2.9 years, 27 females) and 27 older (60.0 ± 5.2 years, 23 females) adults. The shape of individual oscillatory cycles was characterised using a recently developed pipeline involving empirical mode decomposition, before being decomposed into waveform motifs using principal component analysis. This revealed four principal components that were uniquely influenced by task and/or age. These described specific dimensions of shape and tended to be modulated during the reaction phase of each task. Our results suggest that although oscillation shape is task-dependent, the nature of this effect is altered by advancing age, possibly reflecting alterations in cortical activity. These outcomes demonstrate the utility of this approach for understanding the neurophysiological effects of ageing.

Task difficulty of visually guided gait modifications involves differences in central drive to spinal motor neurons

Walking in natural environments requires visually guided modifications, which can be more challenging when involving sideways steps rather than longer steps. This exploratory study investigated whether these two types of modifications involve different changes in the central drive to spinal motor neurons of leg muscles. Fifteen adults [age: 36 ± 6 (SD) years] walked on a treadmill (4 km/h) while observing a screen displaying the real-time position of their toes. At the beginning of the swing phase, a visual target appeared in front (forward) or medial-lateral (sideways) of the ground contact in random step cycles (approximately every third step). We measured three-dimensional kinematics and electromyographic activity from leg muscles bilaterally. Intermuscular coherence was calculated in the alpha (5-15 Hz), beta (15-30 Hz), and gamma bands (30-45 Hz) approximately 230 ms before and after ground contact in control and target steps. Results showed that adjustments toward sideways targets were associated with significantly higher error, lower foot lift, and higher cocontraction between antagonist ankle muscles. Movements toward sideways targets were associated with larger beta-band soleus (SOL): medial gastrocnemius (MG) coherence and a more narrow and larger peak of synchronization in the cumulant density before ground contact. In contrast, movements toward forward targets showed no significant differences in coherence or synchronization compared with control steps. Larger SOL:MG beta-band coherence and short-term synchronization were observed during sideways, but not forward, gait modifications. This suggests that visually guided gait modifications may involve differences in the central drive to spinal ankle motor neurons dependent on the level of task difficulty.NEW & NOTEWORTHY This exploratory study suggests a specific and temporally restricted increase of central (likely corticospinal) drive to ankle muscles in relation to visually guided gait modifications. The findings indicate that a high level of visual attention to control the position of the ankle joint precisely before ground contact may involve increased central drive to ankle muscles. These findings are important for understanding the neural mechanisms underlying visually guided gait and may help develop rehabilitation interventions.

Neural correlates of motor learning: Network communication versus local oscillations

Learning new motor skills through training, also termed motor learning, is central for everyday life. Current training strategies recommend intensive task-repetitions aimed at inducing local activation of motor areas, associated with changes in oscillation amplitudes ("event-related power") during training. More recently, another neural mechanism was suggested to influence motor learning: modulation of functional connectivity (FC), that is, how much spatially separated brain regions communicate with each other before and during training. The goal of the present study was to compare the impact of these two neural processing types on motor learning. We measured EEG before, during, and after a finger-tapping task (FTT) in 20 healthy subjects. The results showed that training gain, long-term expertise (i.e., average motor performance), and consolidation were all predicted by whole-brain alpha- and beta-band FC at motor areas, striatum, and mediotemporal lobe (MTL). Local power changes during training did not predict any dependent variable. Thus, network dynamics seem more crucial than local activity for motor sequence learning, and training techniques should attempt to facilitate network interactions rather than local cortical activation.

Self-selected versus imposed running intensity and the acute effects on mood, cognition, and (a)periodic brain activity

The beneficial psychological effects of exercise might be explained by self-determination theory and autonomy. However, the underlying neurophysiological mechanisms are even less elucidated. Previously neglected, aperiodic (1/f) brain activity is suggested to indicate enhanced cortical inhibition when the slope is steeper. This is thought to be associated with an increased cognitive performance. Therefore, we hypothesize that running with a self-selected intensity and thus given autonomy leads to stronger neural inhibition accompanied by psychological improvements. Twenty-nine runners performed two 30-min runs. First, they chose their individual feel-good intensity (self-selected run; SR). After a 4-weeks washout, the same speed was blindly prescribed (imposed run; IR). Acute effects on mood (Feeling Scale, Felt Arousal Scale, MoodMeter®), cognition (d2-R, digit span test) and electrocortical activity (slope, offset, 1/f-corrected alpha and low beta band) were analyzed before and after the runs. Both runs had an equal physical workload and improved mood in the Felt Arousal Scale, but not in the Feeling Scale or MoodMeter®. Cognitive performance improved after both runs in the d2-R, while it remained stable in the digit span test after SR, but decreased after IR. After running, the aperiodic slope was steeper, and the offset was reduced. Alpha activity increased after SR only, while low beta activity decreased after both conditions. The aperiodic features partially correlated with mood and cognition. SR was not clearly superior regarding psychological effects. Reduced aperiodic brain activity indicates enhanced neural inhibition after both runs. The 1/f-corrected alpha band may emphasize a different neural processing between both runs.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-024-10084-2.

Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks

Understanding cognitive processes in the brain demands sophisticated models capable of replicating neural dynamics at large scales. We present a physiologically inspired speech recognition architecture, compatible and scalable with deep learning frameworks, and demonstrate that end-to-end gradient descent training leads to the emergence of neural oscillations in the central spiking neural network. Significant cross-frequency couplings, indicative of these oscillations, are measured within and across network layers during speech processing, whereas no such interactions are observed when handling background noise inputs. Furthermore, our findings highlight the crucial inhibitory role of feedback mechanisms, such as spike frequency adaptation and recurrent connections, in regulating and synchronizing neural activity to improve recognition performance. Overall, on top of developing our understanding of synchronization phenomena notably observed in the human auditory pathway, our architecture exhibits dynamic and efficient information processing, with relevance to neuromorphic technology.

EEG spectral attractors identify a geometric core of brain dynamics

Multidimensional reconstruction of brain attractors from electroencephalography (EEG) data enables the analysis of geometric complexity and interactions between signals in state space. Utilizing resting-state data from young and older adults, we characterize periodic (traditional frequency bands) and aperiodic (broadband exponent) attractors according to their geometric complexity and shared dynamical signatures, which we refer to as a geometric cross-parameter coupling. Alpha and aperiodic attractors are the least complex, and their global shapes are shared among all other frequency bands, affording alpha and aperiodic greater predictive power. Older adults show lower geometric complexity but greater coupling, resulting from dedifferentiation of gamma activity. The form and content of resting-state thoughts were further associated with the complexity of attractor dynamics. These findings support a process-developmental perspective on the brain's dynamic core, whereby more complex information differentiates out of an integrative and global geometric core.

Opposing neural processing modes alternate rhythmically during sustained auditory attention

During continuous tasks, humans show spontaneous fluctuations in performance, putatively caused by varying attentional resources allocated to process external information. If neural resources are used to process other, presumably "internal" information, sensory input can be missed and explain an apparent dichotomy of "internal" versus "external" attention. In the current study, we extract presumed neural signatures of these attentional modes in human electroencephalography (EEG): neural entrainment and α-oscillations (~10-Hz), linked to the processing and suppression of sensory information, respectively. We test whether they exhibit structured fluctuations over time, while listeners attend to an ecologically relevant stimulus, like speech, and complete a task that requires full and continuous attention. Results show an antagonistic relation between neural entrainment to speech and spontaneous α-oscillations in two distinct brain networks-one specialized in the processing of external information, the other reminiscent of the dorsal attention network. These opposing neural modes undergo slow, periodic fluctuations around ~0.07 Hz and are related to the detection of auditory targets. Our study might have tapped into a general attentional mechanism that is conserved across species and has important implications for situations in which sustained attention to sensory information is critical.

An overview of the effects and mechanisms of transcranial stimulation frequency on motor learning

Many studies over the recent decades have attempted the modulation of motor learning using brain stimulation. Alternating currents allow for researchers not only to electrically stimulate the brain, but to further investigate the effects of specific frequencies, in and beyond the context of their endogenous associations. Transcranial alternating current stimulation (tACS) has therefore been used during motor learning to modulate aspects of acquisition, consolidation and performance of a learned motor skill. Despite numerous reviews on the effects of tACS, and its role in motor learning, there are few studies which synthesize the numerous frequencies and their respective theoretical mechanisms as they relate to motor and perceptual processes. Here we provide a short overview of the main stimulation frequencies used in motor learning modulation (e.g., alpha, beta, and gamma), and discuss the effect and proposed mechanisms of these studies. We summarize with the current state of the field, the effectiveness and variability in motor learning modulation, and novel mechanistic proposals from other fields.

Reliable but multi-dimensional cognitive demand in operating partially automated vehicles: implications for real-world automation research

The reliability of cognitive demand measures in controlled laboratory settings is well-documented; however, limited research has directly established their stability under real-life and high-stakes conditions, such as operating automated technology on actual highways. Partially automated vehicles have advanced to become an everyday mode of transportation, and research on driving these advanced vehicles requires reliable tools for evaluating the cognitive demand on motorists to sustain optimal engagement in the driving process. This study examined the reliability of five cognitive demand measures, while participants operated partially automated vehicles on real roads across four occasions. Seventy-one participants (aged 18-64 years) drove on actual highways while their heart rate, heart rate variability, electroencephalogram (EEG) alpha power, and behavioral performance on the Detection Response Task were measured simultaneously. Findings revealed that EEG alpha power had excellent test-retest reliability, heart rate and its variability were good, and Detection Response Task reaction time and hit-rate had moderate reliabilities. Thus, the current study addresses concerns regarding the reliability of these measures in assessing cognitive demand in real-world automation research, as acceptable test-retest reliabilities were found across all measures for drivers across occasions. Despite the high reliability of each measure, low intercorrelations among measures were observed, and internal consistency was better when cognitive demand was estimated as a multi-factorial construct. This suggests that they tap into different aspects of cognitive demand while operating automation in real life. The findings highlight that a combination of psychophysiological and behavioral methods can reliably capture multi-faceted cognitive demand in real-world automation research.

Antagonistic behavior of brain networks mediated by low-frequency oscillations: electrophysiological dynamics during internal-external attention switching

Antagonistic activity of brain networks likely plays a fundamental role in how the brain optimizes its performance by efficient allocation of computational resources. A prominent example involves externally/internally oriented attention tasks, implicating two anticorrelated, intrinsic brain networks: the default mode network (DMN) and the dorsal attention network (DAN). To elucidate electrophysiological underpinnings and causal interplay during attention switching, we recorded intracranial EEG (iEEG) from 25 epilepsy patients with electrode contacts localized in the DMN and DAN. We show antagonistic network dynamics of activation-related changes in high-frequency (> 50 Hz) and low-frequency (< 30 Hz) power. The temporal profile of information flow between the networks estimated by functional connectivity suggests that the activated network inhibits the other one, gating its activity by increasing the amplitude of the low-frequency oscillations. Insights about inter-network communication may have profound implications for various brain disorders in which these dynamics are compromised.

Novel cyclic homogeneous oscillation detection method for high accuracy and specific characterization of neural dynamics

Determining the presence and frequency of neural oscillations is essential to understanding dynamic brain function. Traditional methods that detect peaks over 1/f noise within the power spectrum fail to distinguish between the fundamental frequency and harmonics of often highly non-sinusoidal neural oscillations. To overcome this limitation, we define fundamental criteria that characterize neural oscillations and introduce the cyclic homogeneous oscillation (CHO) detection method. We implemented these criteria based on an autocorrelation approach to determine an oscillation's fundamental frequency. We evaluated CHO by verifying its performance on simulated non-sinusoidal oscillatory bursts and validated its ability to determine the fundamental frequency of neural oscillations in electrocorticographic (ECoG), electroencephalographic (EEG), and stereoelectroencephalographic (SEEG) signals recorded from 27 human subjects. Our results demonstrate that CHO outperforms conventional techniques in accurately detecting oscillations. 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.

Distraction impact of concurrent conversation on event-related potential based brain-computer interfaces

Objective.This study investigates the impact of conversation on the performance of visual event-related potential (ERP)-based brain-computer interfaces (BCIs), considering distractions in real life environment. The research aims to understand how cognitive distractions from speaking and listening activities affect ERP-BCI performance.Approach.The experiment employs a dual-task paradigm where participants control a smart light using visual ERP-BCIs while simultaneously conducting speaking or listening tasks.Main results.The findings reveal that speaking notably degrades BCI accuracy and the amplitude of ERP components, while increases the latency variability of ERP components and occipital alpha power. In contrast, listening and simple syllable repetition tasks have a lesser impact on these variables. The results suggest that speaking activity significantly distracts visual attentional processes critical for BCI operationSignificance. This study highlights the need to take distractions by daily conversation into account of the design and implementation of ERP-BCIs.

Working Memory Workload When Making Complex Decisions: A Behavioral and EEG Study

Working memory (WM) is crucial for adequate performance execution in effective decision-making, enabling individuals to identify patterns and link information by focusing on current and past situations. This work explored behavioral and electrophysiological (EEG) WM correlates through a novel decision-making task, based on real-life situations, assessing WM workload related to contextual variables. A total of 24 participants performed three task phases (encoding, retrieval, and metacognition) while their EEG activity (delta, theta, alpha, and beta frequency bands) was continuously recorded. From the three phases, three main behavioral indices were computed: Efficiency in complex Decision-making, Tolerance of Decisional Complexity, and Metacognition of Difficulties. Results showed the central role of alpha and beta bands during encoding and retrieval: decreased alpha/beta activity in temporoparietal areas during encoding might indicate activation of regions related to verbal WM performance and a load-related effect, while decreased alpha activity in the same areas and increased beta activity over posterior areas during retrieval might indicate, respectively, active information processing and focused attention. Evidence from correlational analysis between the three indices and EEG bands are also discussed. Integration of behavioral and metacognitive data gathered through this novel task and their interrelation with EEG correlates during task performance proves useful to assess WM workload during complex managerial decision-making.

Spatial prediction modulates the rhythm of attentional sampling

Recent studies demonstrate that behavioral performance during visual spatial attention fluctuates at theta (4 to 8 Hz) and alpha (8 to 16 Hz) frequencies, linked to phase-amplitude coupling of neural oscillations within the visual and attentional system depending on task demands. To investigate the influence of prior spatial prediction, we employed an adaptive discrimination task with variable cue-target onset asynchronies (300 to 1,300 ms) and different cue validity (100% & 50%). We recorded electroencephalography concurrently and adopted adaptive electroencephalography data analytical methods, namely, Holo-Holo-Hilbert spectral analysis and Holo-Hilbert cross-frequency phase clustering. Our findings indicate that response precision for near-threshold Landolt rings fluctuates at the theta band (4 Hz) under certain predictions and at alpha & beta bands (15 & 19 Hz) with uncertain predictions. Furthermore, spatial prediction strengthens theta-alpha modulations at parietal-occipital areas, frontal theta/parietal-occipital alpha phase-amplitude coupling, and within frontal theta-alpha phase-amplitude coupling. Notably, during the pretarget period, beta-modulated gamma oscillations in parietal-occipital areas predict response precision under uncertain prediction, while frontal theta/parietal-occipital alpha phase-amplitude coupling predicts response precision in spatially certain conditions. In conclusion, our study highlights the critical role of spatial prediction in attentional sampling rhythms with both behavioral and electroencephalography evidence.

Evaluating aperiodic and periodic neural activity as markers of listening effort in speech perception

Listening effort (LE) is critical to understanding speech perception in acoustically challenging environments. EEG alpha power has emerged as a potential neural correlate of LE. However, the magnitude and direction of the relationship between acoustic challenge and alpha power has been inconsistent in the literature. In the current study, a secondary data analysis of Silcox and Payne (2021), we examine the broadband 1/f-like exponent and offset of the EEG power spectrum as measures of aperiodic neural activity during effortful speech perception and the influence of this aperiodic activity on reliable estimation of periodic (i.e., alpha) neural activity. EEG was continuously recorded during sentence listening and the broadband (1-40 Hz) EEG power spectrum was computed for each participant for quiet and noise trials separately. Using the specparam algorithm, we decomposed the power spectrum into both aperiodic and periodic components and found that broadband aperiodic activity was sensitive to background noise during speech perception and additionally impacted the measurement of noise-induced changes on alpha oscillations. We discuss the implications of these results for the LE and neural speech processing literatures.

Aperiodic Activity Indexes Neural Hyperexcitability in Generalized Epilepsy

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

Electrophysiological and Cognitive Changes in Hard Coal Miners Associated with Working Underground

Miners working underground face some risk factors that affect the nervous system-such as high noise, dark environment, chronic stress, and exposure to toxic gases. However, it is not known whether these risk factors affect the cognition of miners. In this study, the cognitive changes of miners were examined through event-related oscillations via electroencephalogram (EEG). Twenty underground miners and control groups, equal to each other in age, education level, and working duration, participated in this study. Neuropsychological tests were applied to all participants to examine their cognitive characteristics. Then, 20-channel EEG was recorded for electrophysiological changes during visual oddball paradigm. Event-related power spectrum and phase locking were analyzed in delta (0.5-3.5), theta (4-7), and alpha (8-13 Hz) frequency bands. It was determined that the delta responses that emerged during the target stimulus differed between the two groups in terms of phase locking (p < 0.05). Considering event-related alpha responses, a statistical difference was found regarding power spectrum and phase locking (p < 0.05). Moreover, the alpha power spectrum in the miners was found to be negatively statistically correlated with working duration (p < 0.05). This study determined that the event-related electrophysiological responses of the miners were negatively affected depending on the working conditions. In addition, neuropsychological assessment determined miners had deficiencies in learning and memory skills and many other cognitive functions such as attention, behavioral inhibition, and visual perception.

A tutorial on fitting joint models of M/EEG and behavior to understand cognition

We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document.