Distinct patterns of brain oscillations underlie two basic parameters of human maze learning

Cognitive decline limits compensatory resource allocation within the aged swallowing network

Cognitive decline has been postulated to predispose to presbyphagia but the neurophysiological basis of this interaction is unclear. To investigate the role of cognition for compensatory resource allocation within the swallowing network and behavioral swallowing performance in dual-task cognitive and motor interference in ageing, volunteers ≥ 70 years of age without preexisting diseases causing dysphagia were investigated using Flexible Endoscopic Evaluation of Swallowing (FEES) including a cognitive and motor dual-task paradigm and a Montreal Cognitive Assessment. The neural correlates of swallowing during dual-task were characterized using magnetoencephalography. Results were related to cognitive function. Sixty-three participants (77.7 ± 6.1 years) underwent FEES, of which 40 additionally underwent MEG. Both cognitive and motor dual-tasks interfered with swallowing function resulting in an increase in pharyngeal residue and premature bolus spillage. The extent of swallowing deterioration ("dual-task cost") was associated with cognitive decline (cognitive dual-task: Spearman's rho =  - 0.39, p = 0.002; motor dual-task: Spearman's rho =  - 0.25, p = 0.046). When challenged with dual-tasking participants with regular cognition showed compensatory stronger and broader brain activation in cortical pre- and supplementary motor planning areas as well as in frontal executive regions within the cortical swallowing network (p = 0.004) compared to participants with cognitive deficits. They also performed better in the competing cognitive and motor dual-task and showed fewer incorrect responses (p = 0.028). Oropharyngeal swallowing involves cognitive cortical processing. Cognitive decline seems to limit the capacity for compensatory resource allocation within the swallowing network. This may lead to deterioration in both swallowing function and concurrent cognitive-motor performance in challenging dual-task situations.

Single-trial characterization of frontal theta and parietal alpha oscillatory episodes during spatial navigation in humans

Theoretical proposals and empirical findings both highlight the relevance of theta brain oscillations in human spatial navigation. However, whilst the general assumption is that the relevant theta band activity is purely oscillatory, most empirical studies fail to disentangle oscillatory episodes from wide band activity. In addition, experimental approaches often rely on averaged activity across trials and subjects, disregarding moment-to-moment fluctuations in theta activity, contingent on key aspects of the task. Here, we used novel oscillation detection approaches to investigate the dynamics of theta and alpha episodes in human subjects performing a spatial navigation task in a virtual reality environment, resolved at single-trial level. The results suggest that bouts of frontal theta oscillatory activity are related to task difficulty and access to previously encoded information, across different timescales. Parietal alpha episodes, instead, seem to anticipate successful navigational decisions and could be related to shifts in internal attention.

Spontaneous pain dynamics characterized by stochasticity in neural recordings of awake humans with chronic pain

ABSTRACT

Chronic pain is characterized by spontaneous fluctuations in pain intensity, a phenomenon that remains poorly understood. The aim of this study is to elucidate the neural mechanisms underlying pain fluctuations in patients with chronic pain undergoing deep brain stimulation surgery. We recorded local field potentials (LFPs) from pain-processing hub structures, including the ventral posteromedial nucleus of the thalamus, subgenual cingulate cortex, and periventricular and periaqueductal gray, while patients continuously reported their pain levels. Using novel auto-mutual information metrics to analyze LFP stochastic patterns, we found that pain intensity correlated with both increased regularity of spike-like events and greater past-dependency of neural oscillations in the 4- to 15-Hz frequency band. In addition, during periods of higher pain states, we observed enhanced functional connectivity between the examined hub structures and the prefrontal cortex, suggesting a more focused flow of pain-related information within the pain circuit. By characterizing the dynamic nature of pain fluctuations, this study bridges the gap in understanding moment-to-moment pain variations and their underlying neural mechanisms, paving the way for improved chronic pain management strategies.

Spatiotemporal EEG dynamics of the sleep onset process in preadolescence

BACKGROUND

During preadolescence the sleep electroencephalography undergoes massive qualitative and quantitative modifications. Despite these relevant age-related peculiarities, the specific EEG pattern of the wake-sleep transition in preadolescence has not been exhaustively described.

METHODS

The aim of the present study is to characterize regional and temporal electrophysiological features of the sleep onset (SO) process in a group of 23 preadolescents (9-14 years) and to compare the topographical pattern of slow wave activity and delta/beta ratio of preadolescents with the EEG pattern of young adults.

RESULTS

Results showed in preadolescence the same dynamics known for adults, but with peculiarities in the delta and beta activity, likely associated with developmental cerebral modifications: the delta power showed a widespread increase during the SO with central maxima, and the lower bins of the beta activity showed a power increase after SO. Compared to adults, preadolescents during the SO exhibited higher delta power only in the slowest bins of the band: before SO slow delta activity was higher in prefrontal, frontal and occipital areas in preadolescents, and, after SO the younger group had higher slow delta activity in occipital areas. In preadolescents delta/beta ratio was higher in more posterior areas both before and after the wake-sleep transition and, after SO, preadolescents showed also a lower delta/beta ratio in frontal areas, compared to adults.

CONCLUSION

Results point to a general higher homeostatic drive for the developing areas, consistently with plastic-related maturational modifications, that physiologically occur during preadolescence.

High-Frequency Local Field Potential Oscillations for Pigeons in Effective Turning

Flexible turning behavior endows Homing Pigeons (Columba livia domestica) with high adaptability and intelligence in long-distance flight, foraging, hazard avoidance, and social interactions. The present study recorded the activity pattern of their local field potential (LFP) oscillations and explored the relationship between different bands of oscillations and turning behaviors in the formatio reticularis medialis mesencephali (FRM). The results showed that the C (13-60 Hz) and D (61-130 Hz) bands derived from FRM nuclei oscillated significantly in active turning, while the D and E (131-200 Hz) bands oscillated significantly in passive turning. Additionally, compared with lower-frequency stimulation (40 Hz and 60 Hz), 80 Hz stimulation can effectively activate the turning function of FRM nuclei. Electrical stimulation elicited stronger oscillations of neural activity, which strengthened the pigeons' turning locomotion willingness, showing an enhanced neural activation effect. These findings suggest that different band oscillations play different roles in the turning behavior; in particular, higher-frequency oscillations (D and E bands) enhance the turning behavior. These findings will help us decode the complex relationship between bird brains and behaviors and are expected to facilitate the development of neuromodulation techniques for animal robotics.

Sharp detection of oscillation packets in rich time-frequency representations of neural signals

Brain oscillations most often occur in bursts, called oscillation packets, which span a finite extent in time and frequency. Recent studies have shown that these packets portray a much more dynamic picture of synchronization and transient communication between sites than previously thought. To understand their nature and statistical properties, techniques are needed to objectively detect oscillation packets and to quantify their temporal and frequency extent, as well as their magnitude. There are various methods to detect bursts of oscillations. The simplest ones divide the signal into band limited sub-components, quantifying the strength of the resulting components. These methods cannot by themselves cope with broadband transients that look like genuine oscillations when restricted to a narrow band. The most successful detection methods rely on time-frequency representations, which can readily show broadband transients and harmonics. However, the performance of such methods is conditioned by the ability of the representation to localize packets simultaneously in time and frequency, and by the capabilities of packet detection techniques, whose current state of the art is limited to extraction of bounding boxes. Here, we focus on the second problem, introducing two detection methods that use concepts derived from clustering and topographic prominence. These methods are able to delineate the packets' precise contour in the time-frequency plane. We validate the new approaches using both synthetic and real data recorded in humans and animals and rely on a super-resolution time-frequency representation, namely the superlets, as input to the detection algorithms. In addition, we define robust tests for benchmarking and compare the new methods to previous techniques. Results indicate that the two methods we introduce shine in low signal-to-noise ratio conditions, where they only miss a fraction of packets undetected by previous methods. Finally, algorithms that delineate precisely the border of spectral features and their subcomponents offer far more valuable information than simple rectangular bounding boxes (time and frequency span) and can provide a solid foundation to investigate neural oscillations' dynamics.

Dynamic Changes in Local Activity and Network Interactions among the Anterior Cingulate, Amygdala, and Cerebellum during Associative Learning

Communication between the cerebellum and forebrain structures is necessary for motor learning and has been implicated in a variety of cognitive functions. The exact nature of cerebellar-forebrain interactions supporting behavior and cognition is not known. We examined how local and network activity support learning by simultaneously recording neural activity in the cerebellum, amygdala, and anterior cingulate cortex while male and female rats were trained in trace eyeblink conditioning. Initially, the cerebellum and forebrain signal the contingency between external stimuli through increases in theta power and synchrony. Neuronal activity driving expression of the learned response was observed in the cerebellum and became evident in the anterior cingulate and amygdala as learning progressed. Aligning neural activity to the training stimuli or learned response provided a way to differentiate between learning-related activity driven by different mechanisms. Stimulus and response-related increases in theta power and coherence were observed across all three areas throughout learning. However, increases in slow gamma power and coherence were only observed when oscillations were aligned to the cerebellum-driven learned response. Percentage of learned responses, learning-related local activity, and slow gamma communication from cerebellum to forebrain all progressively increased during training. The relatively fast frequency of slow gamma provides an ideal mechanism for the cerebellum to communicate learned temporal information to the forebrain. This cerebellar response-aligned slow gamma then provides enrichment of behavior-specific temporal information to local neuronal activity in the forebrain. These dynamic network interactions likely support a wide range of behaviors and cognitive tasks that require coordination between the forebrain and cerebellum.SIGNIFICANCE STATEMENT This study presents new evidence for how dynamic learning-related changes in single neurons and neural oscillations in a cerebellar-forebrain network support associative motor learning. The current results provide an integrated mechanism for how bidirectional communication between the cerebellum and forebrain represents important external events and internal neural drive. This bidirectional communication between the cerebellum and forebrain likely supports a wide range of behaviors and cognitive tasks that require temporal precision.

Handedness effects on imagery of dominant- versus non-dominant-hand movements: An electroencephalographic investigation

Mental representations of our bodies are thought to influence how we interact with our surroundings. We can examine these mental representations through motor imagery, the imagination of movement using scalp EEG recordings. The visual modality of motor imagery emphasises 'seeing' the imagined movement and is associated with increased activity in the alpha rhythm (8-14 Hz) measured over the occipital regions. The kinaesthetic modality emphasises 'feeling' the movement and is associated with decreased activity in the mu rhythm (8-14 Hz) measured over the sensorimotor cortices. These two modalities can be engaged in isolation or together. We recorded EEG activity while 37 participants (17 left-hand dominant) completed an objective hand motor imagery task. Left-handers exhibited significant activity differences between occipital and motor regions only during imagery of right-hand (non-dominant-hand) movements. This difference was primarily driven by less oscillatory activity in the mu rhythm, which may reflect a shift in imagery strategy wherein participants placed more effort into generating the kinaesthetic sensations of non-dominant-hand imagery. Spatial features of 8-14 Hz activity generated from principal component analysis (PCA) provide further support for a strategy shift. Right-handers also exhibited significant differences between alpha and mu activity during imagery of non-dominant movements. However, this difference was not primarily driven by either rhythm, and no differences were observed in the group's PCA results. Together, these findings indicate that individuals imagine movement differently when it involves their dominant versus non-dominant hand, and left-handers may be more flexible in their motor imagery strategies.

A guide towards optimal detection of transient oscillatory bursts with unknown parameters

Objectives. Recent event-based analyses of transient neural activities have characterized the oscillatory bursts as a neural signature that bridges dynamic neural states to cognition and behaviors. Following this insight, our study aimed to (1) compare the efficacy of common burst detection algorithms under varying signal-to-noise ratios and event durations using synthetic signals and (2) establish a strategic guideline for selecting the optimal algorithm for real datasets with undefined properties.Approach.We tested the robustness of burst detection algorithms using a simulation dataset comprising bursts of multiple frequencies. To systematically assess their performance, we used a metric called 'detection confidence', quantifying classification accuracy and temporal precision in a balanced manner. Given that burst properties in empirical data are often unknown in advance, we then proposed a selection rule to identify an optimal algorithm for a given dataset and validated its application on local field potentials of basolateral amygdala recorded from male mice (n=8) exposed to a natural threat.Main Results.Our simulation-based evaluation demonstrated that burst detection is contingent upon event duration, whereas accurately pinpointing burst onsets is more susceptible to noise level. For real data, the algorithm chosen based on the selection rule exhibited superior detection and temporal accuracy, although its statistical significance differed across frequency bands. Notably, the algorithm chosen by human visual screening differed from the one recommended by the rule, implying a potential misalignment between human priors and mathematical assumptions of the algorithms.Significance.Therefore, our findings underscore that the precise detection of transient bursts is fundamentally influenced by the chosen algorithm. The proposed algorithm-selection rule suggests a potentially viable solution, while also emphasizing the inherent limitations originating from algorithmic design and volatile performances across datasets. Consequently, this study cautions against relying solely on heuristic-based approaches, advocating for a careful algorithm selection in burst detection studies.

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

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

Episodic memory formation in unrestricted viewing

The brain systems of episodic memory and oculomotor control are tightly linked, suggesting a crucial role of eye movements in memory. But little is known about the neural mechanisms of memory formation across eye movements in unrestricted viewing behavior. Here, we leverage simultaneous eye tracking and EEG recording to examine episodic memory formation in free viewing. Participants memorized multi-element events while their EEG and eye movements were concurrently recorded. Each event comprised elements from three categories (face, object, place), with two exemplars from each category, in different locations on the screen. A subsequent associative memory test assessed participants' memory for the between-category associations that specified each event. We used a deconvolution approach to overcome the problem of overlapping EEG responses to sequential saccades in free viewing. Brain activity was time-locked to the fixation onsets, and we examined EEG power in the theta and alpha frequency bands, the putative oscillatory correlates of episodic encoding mechanisms. Three modulations of fixation-related EEG predicted high subsequent memory performance: (1) theta increase at fixations after between-category gaze transitions, (2) theta and alpha increase at fixations after within-element gaze transitions, (3) alpha decrease at fixations after between-exemplar gaze transitions. Thus, event encoding with unrestricted viewing behavior was characterized by three neural mechanisms, manifested in fixation-locked theta and alpha EEG activity that rapidly turned on and off during the unfolding eye movement sequences. These three distinct neural mechanisms may be the essential building blocks that subserve the buildup of coherent episodic memories during unrestricted viewing behavior.

Hippocampal Theta and Episodic Memory

Computational models of rodent physiology implicate hippocampal theta as a key modulator of learning and memory (Buzsáki and Moser, 2013; Lisman and Jensen, 2013), yet human hippocampal recordings have shown divergent theta correlates of memory formation. Herweg et al. (2020) suggest that decreases in memory-related broadband power mask narrowband theta increases. Their survey also notes that the theta oscillations appear most prominently in contrasts that isolate memory retrieval processes and when aggregating signals across large brain regions. We evaluate these hypotheses by analyzing human hippocampal recordings captured as 162 neurosurgical patients (n = 86 female) performed a free recall task. Using the Irregular-Resampling Auto-Spectral Analysis (IRASA) to separate broad and narrowband components of the field potential, we show that (1) broadband and narrowband components of theta exhibit opposite effects, with broadband signals decreasing and narrowband theta increasing during successful encoding; (2) whereas low-frequency theta oscillations increase before successful recall, higher-frequency theta and alpha oscillations decrease, masking the positive effect of theta when aggregating across the full band; and (3) the effects of theta on memory encoding and retrieval do not differ between reference schemes that accentuate local signals (bipolar) and those that aggregate signals globally (whole-brain average). In line with computational models that ascribe a fundamental role for hippocampal theta in memory, our large-scale study of human hippocampal recordings shows that 3-4 Hz theta oscillations reliably increase during successful memory encoding and before spontaneous recall of previously studied items.SIGNIFICANCE STATEMENT Analyzing recordings from 162 participants, we resolve a long-standing question regarding the role of hippocampal theta oscillations in the formation and retrieval of episodic memories. We show that broadband spectral changes confound estimates of narrowband theta activity, thereby accounting for inconsistent results in the literature. After accounting for broadband effects, we find that increased theta activity marks successful encoding and retrieval of episodic memories, supporting rodent models that ascribe a key role for hippocampal theta in memory function.

Robust estimation of 1/f activity improves oscillatory burst detection

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

Putative rhythms in attentional switching can be explained by aperiodic temporal structure

The neural and perceptual effects of attention were traditionally assumed to be sustained over time, but recent work suggests that covert attention rhythmically switches between objects at 3-8 Hz. Here I use simulations to demonstrate that the analysis approaches commonly used to test for rhythmic oscillations generate false positives in the presence of aperiodic temporal structure. I then propose two alternative analyses that are better able to discriminate between periodic and aperiodic structure in time series. Finally, I apply these alternative analyses to published datasets and find no evidence for behavioural rhythms in attentional switching after accounting for aperiodic temporal structure. The techniques presented here will help clarify the periodic and aperiodic dynamics of perception and of cognition more broadly.

Clinical determinants and neural correlates of presbyphagia in community-dwelling older adults

Background

“Presbyphagia” refers to characteristic age-related changes in the complex neuromuscular swallowing mechanism. It has been hypothesized that cumulative impairments in multiple domains affect functional reserve of swallowing with age, but the multifactorial etiology and postulated compensatory strategies of the brain are incompletely understood. This study investigates presbyphagia and its neural correlates, focusing on the clinical determinants associated with adaptive neuroplasticity.

Materials and methods

64 subjects over 70 years of age free of typical diseases explaining dysphagia received comprehensive workup including flexible endoscopic evaluation of swallowing (FEES), magnetoencephalography (MEG) during swallowing and pharyngeal stimulation, volumetry of swallowing muscles, laboratory analyzes, and assessment of hand-grip-strength, nutritional status, frailty, olfaction, cognition and mental health. Neural MEG activation was compared between participants with and without presbyphagia in FEES, and associated clinical influencing factors were analyzed. Presbyphagia was defined as the presence of oropharyngeal swallowing alterations e.g., penetration, aspiration, pharyngeal residue pooling or premature bolus spillage into the piriform sinus and/or laryngeal vestibule.

Results

32 of 64 participants showed swallowing alterations, mainly characterized by pharyngeal residue, whereas the airway was rarely compromised. In the MEG analysis, participants with presbyphagia activated an increased cortical sensorimotor network during swallowing. As major clinical determinant, participants with swallowing alterations exhibited reduced pharyngeal sensation. Presbyphagia was an independent predictor of a reduced nutritional status in a linear regression model.

Conclusions

Swallowing alterations frequently occur in otherwise healthy older adults and are associated with decreased nutritional status. Increased sensorimotor cortical activation may constitute a compensation attempt to uphold swallowing function due to sensory decline. Further studies are needed to clarify whether the swallowing alterations observed can be considered physiological per se or whether the concept of presbyphagia may need to be extended to a theory with a continuous transition between presbyphagia and dysphagia.

Detection and analysis of cortical beta bursts in developmental EEG data

Developmental EEG research often involves analyzing signals within various frequency bands, based on the assumption that these signals represent oscillatory neural activity. However, growing evidence suggests that certain frequency bands are dominated by transient burst events in single trials rather than sustained oscillations. This is especially true for the beta band, with adult 'beta burst' timing a better predictor of motor behavior than slow changes in average beta amplitude. No developmental research thus far has looked at beta bursts, with techniques used to investigate frequency-specific activity structure rarely even applied to such data. Therefore, we aimed to: i) provide a tutorial for developmental EEG researchers on the application of methods for evaluating the rhythmic versus transient nature of frequency-specific activity; and ii) use these techniques to investigate the existence of sensorimotor beta bursts in infants. We found that beta activity in 12-month-olds did occur in bursts, however differences were also revealed in terms of duration, amplitude, and rate during grasping compared to adults. Application of the techniques illustrated here will be critical for clarifying the functional roles of frequency-specific activity across early development, including the role of beta activity in motor processing and its contribution to differing developmental motor trajectories.

Spectral Pattern Similarity Analysis: Tutorial and Application in Developmental Cognitive Neuroscience

The human brain encodes information in neural activation patterns. While standard approaches to analyzing neural data focus on brain (de-)activation (e.g., regarding the location, timing, or magnitude of neural responses), multivariate neural pattern similarity analyses target the informational content represented by neural activity. In adults, a number of representational properties have been identified that are linked to cognitive performance, in particular the stability, distinctiveness, and specificity of neural patterns. However, although growing cognitive abilities across childhood suggest advancements in representational quality, developmental studies still rarely utilize information-based pattern similarity approaches, especially in electroencephalography (EEG) research. Here, we provide a comprehensive methodological introduction and step-by-step tutorial for pattern similarity analysis of spectral (frequency-resolved) EEG data including a publicly available pipeline and sample dataset with data from children and adults. We discuss computation of single-subject pattern similarities and their statistical comparison at the within-person to the between-group level as well as the illustration and interpretation of the results. This tutorial targets both novice and more experienced EEG researchers and aims to facilitate the usage of spectral pattern similarity analyses, making these methodologies more readily accessible for (developmental) cognitive neuroscientists.

Ongoing neural oscillations influence behavior and sensory representations by suppressing neuronal excitability

The ability to process and respond to external input is critical for adaptive behavior. Why, then, do neural and behavioral responses vary across repeated presentations of the same sensory input? Ongoing fluctuations of neuronal excitability are currently hypothesized to underlie the trial-by-trial variability in sensory processing. To test this, we capitalized on intracranial electrophysiology in neurosurgical patients performing an auditory discrimination task with visual cues: specifically, we examined the interaction between prestimulus alpha oscillations, excitability, task performance, and decoded neural stimulus representations. We found that strong prestimulus oscillations in the alpha+ band (i.e., alpha and neighboring frequencies), rather than the aperiodic signal, correlated with a low excitability state, indexed by reduced broadband high-frequency activity. This state was related to slower reaction times and reduced neural stimulus encoding strength. We propose that the alpha+ rhythm modulates excitability, thereby resulting in variability in behavior and sensory representations despite identical input.

Visual Recognition Is Heralded by Shifts in Local Field Potential Oscillations and Inhibitory Networks in Primary Visual Cortex

Learning to recognize and filter familiar, irrelevant sensory stimuli eases the computational burden on the cerebral cortex. Inhibition is a candidate mechanism in this filtration process, and oscillations in the cortical local field potential (LFP) serve as markers of the engagement of different inhibitory neurons. We show here that LFP oscillatory activity in visual cortex is profoundly altered as male and female mice learn to recognize an oriented grating stimulus-low-frequency (∼15 Hz peak) power sharply increases, whereas high-frequency (∼65 Hz peak) power decreases. These changes report recognition of the familiar pattern as they disappear when the stimulus is rotated to a novel orientation. Two-photon imaging of neuronal activity reveals that parvalbumin-expressing inhibitory neurons disengage with familiar stimuli and reactivate to novelty, whereas somatostatin-expressing inhibitory neurons show opposing activity patterns. We propose a model in which the balance of two interacting interneuron circuits shifts as novel stimuli become familiar.SIGNIFICANCE STATEMENT Habituation, familiarity, and novelty detection are fundamental cognitive processes that enable organisms to adaptively filter meaningless stimuli and focus attention on potentially important elements of their environment. We have shown that this process can be studied fruitfully in the mouse primary visual cortex by using simple grating stimuli for which novelty and familiarity are defined by orientation and by measuring stimulus-evoked and continuous local field potentials. Altered event-related and spontaneous potentials, and deficient habituation, are well-documented features of several neurodevelopmental psychiatric disorders. The paradigm described here will be valuable to interrogate the origins of these signals and the meaning of their disruption more deeply.

Rapid coordination of effective learning by the human hippocampus

Although the human hippocampus is necessary for long-term memory, controversial findings suggest that it may also support short-term memory in the service of guiding effective behaviors during learning. We tested the counterintuitive theory that the hippocampus contributes to long-term memory through remarkably short-term processing, as reflected in eye movements during scene encoding. While viewing scenes for the first time, short-term retrieval operative within the episode over only hundreds of milliseconds was indicated by a specific eye-movement pattern, which was effective in that it enhanced spatiotemporal memory formation. This viewing pattern was predicted by hippocampal theta oscillations recorded from depth electrodes and by shifts toward top-down influence of hippocampal theta on activity within visual perception and attention networks. The hippocampus thus supports short-term memory processing that coordinates behavior in the service of effective spatiotemporal learning.

Mu oscillations and motor imagery performance: A reflection of intra-individual success, not inter-individual ability

Mu oscillations (8-13 Hz), recorded over the human motor cortex, have been shown to consistently suppress during both the imagination and performance of movements; however, its functional significance in the imagery process is currently unclear. Here we examined human electroencephalographic (EEG) oscillations in the context of motor imagery performance as measured by imagery success within participants and imagery ability between participants. We recorded continuous EEG activity while participants performed the Test of Ability in Movement Imagery (TAMI), an objective test of motor imagery task. Results demonstrated that mu oscillatory activity significantly decreased during successful as compared to unsuccessful imagery trials. However, the extent of reduction in mu oscillations did not correlate with overall imagery ability as measured by the total TAMI score. These findings provide further support for the involvement of mu oscillations in indexing motor imagery performance and suggest that mu oscillations may reflect important processes related to imagery accuracy, processes likely related to those underlying overt motor production and motor understanding.

Serial memory for landmarks encountered during route navigation

The present study demonstrates similarities between route learning and classical tests of serial order memory. Here, we investigated serial memory for landmarks in a route learning task, in younger and older adults. We analysed data from a route learning task with 12 landmarks. Participants (88 younger and 77 older) learned a route using either a Fixed Learning (3 exposures to the route) or Flexible Learning (repeated exposures until successful navigation was achieved) procedure. Following route learning, participants completed Immediate Free Recall (IFR) and Free Reconstruction of Order (Free RoO) of the landmarks. We show clear acquisition of sequence memory for landmarks for both age groups, with Free RoO producing a bowed serial position curve. IFR produced recency effects but no primacy effects in fixed learning, with recency reduced following flexible learning for both age groups. Younger adults displayed a primacy bias for the first item recalled in both learning conditions, as did the older adults in the flexible learning condition. In contrast, older adults displayed a recency bias in the fixed learning condition. Evidence of contiguity in IFR was present only for younger adults in the flexible learning condition. Findings are broadly consistent with results from typical short-term list learning procedures and support the universality of sequence learning effects, which we demonstrate are generalisable to a navigation context.

Thalamocortical excitability modulation guides human perception under uncertainty

Knowledge about the relevance of environmental features can guide stimulus processing. However, it remains unclear how processing is adjusted when feature relevance is uncertain. We hypothesized that (a) heightened uncertainty would shift cortical networks from a rhythmic, selective processing-oriented state toward an asynchronous ("excited") state that boosts sensitivity to all stimulus features, and that (b) the thalamus provides a subcortical nexus for such uncertainty-related shifts. Here, we had young adults attend to varying numbers of task-relevant features during EEG and fMRI acquisition to test these hypotheses. Behavioral modeling and electrophysiological signatures revealed that greater uncertainty lowered the rate of evidence accumulation for individual stimulus features, shifted the cortex from a rhythmic to an asynchronous/excited regime, and heightened neuromodulatory arousal. Crucially, this unified constellation of within-person effects was dominantly reflected in the uncertainty-driven upregulation of thalamic activity. We argue that neuromodulatory processes involving the thalamus play a central role in how the brain modulates neural excitability in the face of momentary uncertainty.

A Fuzzy Shell for Developing an Interpretable BCI Based on the Spatiotemporal Dynamics of the Evoked Oscillations

Researchers in neuroscience computing experience difficulties when they try to carry out neuroanalysis in practice or when they need to design an explainable brain-computer interface (BCI) with quick setup and minimal training phase. There is a need of interpretable computational intelligence techniques and new brain states decoding for more understandable interpretation of the sensory, cognitive, and motor brain processing. We propose a general-purpose fuzzy software system shell for developing a custom EEG BCI system. It relies on the bursts of the ongoing EEG frequency power synchronization/desynchronization at scalp level and supports quick BCI setup by linguistic features, ad hoc fuzzy membership construction, explainable IF-THEN rules, and the concept of the Internet of Things (IoT), which makes the BCI system device and service independent. It has a potential for designing both passive and event-related BCIs with options for visual representation at scalp-source level in response to time. The feasibility of the proposed system has been proven by real experiments and bursts for β and γ frequency power have been detected in real time in response to evoked visuospatial selective attention. The presence of the proposed new brain state decoding can be used as a feasible metric for interpretation of the spatiotemporal dynamics of the passive or evoked neural oscillations.

Boundary-anchored neural mechanisms of location-encoding for self and others

Everyday tasks in social settings require humans to encode neural representations of not only their own spatial location, but also the location of other individuals within an environment. At present, the vast majority of what is known about neural representations of space for self and others stems from research in rodents and other non-human animals^1-3. However, it is largely unknown how the human brain represents the location of others, and how aspects of human cognition may affect these location-encoding mechanisms. To address these questions, we examined individuals with chronically implanted electrodes while they carried out real-world spatial navigation and observation tasks. We report boundary-anchored neural representations in the medial temporal lobe that are modulated by one's own as well as another individual's spatial location. These representations depend on one's momentary cognitive state, and are strengthened when encoding of location is of higher behavioural relevance. Together, these results provide evidence for a common encoding mechanism in the human brain that represents the location of oneself and others in shared environments, and shed new light on the neural mechanisms that underlie spatial navigation and awareness of others in real-world scenarios.

Predicting stroke severity with a 3-min recording from the Muse portable EEG system for rapid diagnosis of stroke

In this study, we demonstrated the use of low-cost portable electroencephalography (EEG) as a method for prehospital stroke diagnosis. We used a portable EEG system to record data from 25 participants, 16 had acute ischemic stroke events, and compared the results to age-matched controls that included stroke mimics. Delta/alpha ratio (DAR), (delta + theta)/(alpha + beta) ratio (DBATR) and pairwise-derived Brain Symmetry Index (pdBSI) were investigated, as well as head movement using the on-board accelerometer and gyroscope. We then used machine learning to distinguish between different subgroups. DAR and DBATR increased in ischemic stroke patients with increasing stroke severity (p = 0.0021, partial η² = 0.293; p = 0.01, partial η² = 0.234). Also, pdBSI decreased in low frequencies and increased in high frequencies in patients who had a stroke (p = 0.036, partial η² = 0.177). Using classification trees, we were able to distinguish moderate to severe stroke patients and from minor stroke and controls, with a 63% sensitivity, 86% specificity and accuracy of 76%. There are significant differences in DAR, DBATR, and pdBSI between patients with ischemic stroke when compared to controls, and these effects scale with severity. We have shown the utility of a low-cost portable EEG system to aid in patient triage and diagnosis as an early detection tool.

Oscillatory Bursting as a Mechanism for Temporal Coupling and Information Coding

Even the simplest cognitive processes involve interactions between cortical regions. To study these processes, we usually rely on averaging across several repetitions of a task or across long segments of data to reach a statistically valid conclusion. Neuronal oscillations reflect synchronized excitability fluctuations in ensembles of neurons and can be observed in electrophysiological recordings in the presence or absence of an external stimulus. Oscillatory brain activity has been viewed as sustained increase in power at specific frequency bands. However, this perspective has been challenged in recent years by the notion that oscillations may occur as transient burst-like events that occur in individual trials and may only appear as sustained activity when multiple trials are averaged together. In this review, we examine the idea that oscillatory activity can manifest as a transient burst as well as a sustained increase in power. We discuss the technical challenges involved in the detection and characterization of transient events at the single trial level, the mechanisms that might generate them and the features that can be extracted from these events to study single-trial dynamics of neuronal ensemble activity.

A comprehensive assessment of Brain Computer Interfaces: Recent trends and challenges

BACKGROUND

An uninterrupted channel of communication and control between the human brain and electronic processing units has led to an increased use of Brain Computer Interfaces (BCIs). This article attempts to present an all-encompassing review on BCI and the scientific advancements associated with it. The ultimate goal of this review is to provide a general overview of the BCI technology and to shed light on different aspects of BCIs. This review also underscores the applications, practical challenges and opportunities associated with BCI technology, which can be used to accelerate future developments in this field.

METHODS

This review is based on a systematic literature search for tracking down the relevant research annals and proceedings. Using a methodical search strategy, the search was carried out across major technical databases. The retrieved records were screened for their relevance and a total of 369 research chronicles were engulfed in this review based on the inclusion criteria.

RESULTS

This review describes the present scenario and recent advancements in BCI technology. It also identifies several application areas of BCI technology. This comprehensive review provides evidence that, while we are getting ever closer, significant challenges still exist for the development of BCIs that can seamlessly integrate with the user's biological system.

CONCLUSION

The findings of this review confirm the importance of BCI technology in various applications. It is concluded that BCI technology, still in its sprouting phase, requires significant explorations for further development.

An electrophysiological marker of arousal level in humans

Deep non-rapid eye movement sleep (NREM) and general anesthesia with propofol are prominent states of reduced arousal linked to the occurrence of synchronized oscillations in the electroencephalogram (EEG). Although rapid eye movement (REM) sleep is also associated with diminished arousal levels, it is characterized by a desynchronized, 'wake-like' EEG. This observation implies that reduced arousal states are not necessarily only defined by synchronous oscillatory activity. Using intracranial and surface EEG recordings in four independent data sets, we demonstrate that the 1/f spectral slope of the electrophysiological power spectrum, which reflects the non-oscillatory, scale-free component of neural activity, delineates wakefulness from propofol anesthesia, NREM and REM sleep. Critically, the spectral slope discriminates wakefulness from REM sleep solely based on the neurophysiological brain state. Taken together, our findings describe a common electrophysiological marker that tracks states of reduced arousal, including different sleep stages as well as anesthesia in humans.

The electroencephalographic features of the sleep onset process and their experimental manipulation with sleep deprivation and transcranial electrical stimulation protocols

The sleep onset (SO) process is characterized by gradual electroencephalographic (EEG) changes. The interest for the possibility to manipulate the electrophysiological pattern of the wake-sleep transition is recently growing. This review aims to describe the EEG modifications of the SO process in healthy humans and the evidence about their experimental manipulation. We provide an overview of the electrophysiological changes during the wake-sleep transition, considering several methods to study the EEG signals. We then describe the impact of sleep deprivation (SD) on the electrophysiology of SO. Finally, we discuss the evidence about the possibility to modulate the local EEG activity through transcranial current stimulation protocols with the aim to promote, hinder, or manipulate the electrophysiological mechanisms of the wake-sleep transition. The reviewed findings highlight the local nature of the EEG processes during the SO and their intensification and speedup after SD. The evidence about the possibility to non-invasively affect the EEG pattern of the wake-sleep transition may have important implications for clinical conditions that would benefit from its prevention or promotion.

Frequency-based Dissociation of Symbolic and Nonsymbolic Numerical Processing during Numerical Comparison

Recent evidence suggests that during numerical calculation, symbolic and nonsymbolic processing are functionally distinct operations. Nevertheless, both roughly recruit the same brain areas (spatially overlapping networks in the parietal cortex) and happen at the same time (roughly 250 msec poststimulus onset). We tested the hypothesis that symbolic and nonsymbolic processing are segregated by means of functionally relevant networks in different frequency ranges: high gamma (above 50 Hz) for symbolic processing and lower beta (12–17 Hz) for nonsymbolic processing. EEG signals were quantified as participants compared either symbolic numbers or nonsymbolic quantities. Larger EEG gamma-band power was observed for more difficult symbolic comparisons (ratio of 0.8 between the two numbers) than for easier comparisons (ratio of 0.2) over frontocentral regions. Similarly, beta-band power was larger for more difficult nonsymbolic comparisons than for easier ones over parietal areas. These results confirm the existence of a functional dissociation in EEG oscillatory dynamics during numerical processing that is compatible with the notion of distinct linguistic processing of symbolic numbers and approximation of nonsymbolic numerical information.

Standard multiscale entropy reflects neural dynamics at mismatched temporal scales: What's signal irregularity got to do with it?

Multiscale Entropy (MSE) is used to characterize the temporal irregularity of neural time series patterns. Due to its' presumed sensitivity to non-linear signal characteristics, MSE is typically considered a complementary measure of brain dynamics to signal variance and spectral power. However, the divergence between these measures is often unclear in application. Furthermore, it is commonly assumed (yet sparingly verified) that entropy estimated at specific time scales reflects signal irregularity at those precise time scales of brain function. We argue that such assumptions are not tenable. Using simulated and empirical electroencephalogram (EEG) data from 47 younger and 52 older adults, we indicate strong and previously underappreciated associations between MSE and spectral power, and highlight how these links preclude traditional interpretations of MSE time scales. Specifically, we show that the typical definition of temporal patterns via "similarity bounds" biases coarse MSE scales-that are thought to reflect slow dynamics-by high-frequency dynamics. Moreover, we demonstrate that entropy at fine time scales-presumed to indicate fast dynamics-is highly sensitive to broadband spectral power, a measure dominated by low-frequency contributions. Jointly, these issues produce counterintuitive reflections of frequency-specific content on MSE time scales. We emphasize the resulting inferential problems in a conceptual replication of cross-sectional age differences at rest, in which scale-specific entropy age effects could be explained by spectral power differences at mismatched temporal scales. Furthermore, we demonstrate how such problems may be alleviated, resulting in the indication of scale-specific age differences in rhythmic irregularity. By controlling for narrowband contributions, we indicate that spontaneous alpha rhythms during eyes open rest transiently reduce broadband signal irregularity. Finally, we recommend best practices that may better permit a valid estimation and interpretation of neural signal irregularity at time scales of interest.

Individual Features in the Typology of the Nervous System and the Brain Activity Dynamics of Freestyle Wrestlers Exposed to a Strong Physical Activity (A Pilot Study)

Nowadays, knowledge of psychophysiological features, particularly on the nervous system’s characteristics, is essential in the sporting context, particularly for freestyle wrestling. The study aimed to investigate the peculiarities of the wrestlers’ nervous system—on the individual and electrophysiological levels in two functional states—in calm wakefulness and during intense physical fatigue. Psychological (Well-being, Activity, Mood; Spielberger–Hanin; Leonhard’s questionnaires), as well as electrophysiological techniques (dynamics of the dominant and average frequencies of the main electroencephalogram (EEG) spectra—theta, alpha, low and high-frequency beta rhythms), were used in the study. It was shown that athletes were mainly characterized by the hyperthymic type of character accentuation and a low frequency of theta rhythm in a calm wakefulness state. After the acute physical load, wrestlers with high hyperthymia showed a moderate increase in theta, whereas other athletes showed a decrease in this parameter. Regardless of the level of hyperthymic accentuation, all wrestlers were characterized by an increase in the frequency of alpha rhythm after exercises in the left hemisphere. These results suggest the existence of a particular functional system in freestyle wrestlers, which allows the body’s regulatory systems to be adapted for the effective implementation of sports activity.

Theta Oscillations in Human Memory

Theta frequency (4-8 Hz) fluctuations of the local field potential have long been implicated in learning and memory. Human studies of episodic memory, however, have provided mixed evidence for theta's role in successful learning and remembering. Re-evaluating these conflicting findings leads us to conclude that: (i) successful memory is associated both with increased narrow-band theta oscillations and a broad-band tilt of the power spectrum; (ii) theta oscillations specifically support associative memory, whereas the spectral tilt reflects a general index of activation; and (iii) different cognitive contrasts (generalized versus specific to memory), recording techniques (invasive versus noninvasive), and referencing schemes (local versus global) alter the balance between the two phenomena to make one or the other more easily detectable.

Primate somatosensory cortical neurons are entrained to both spontaneous and peripherally evoked spindle oscillations

Recurrent thalamocortical circuits produce a number of rhythms critical to brain function. In slow-wave sleep, spindles (7-16 Hz) are a prominent spontaneous oscillation generated by thalamic circuits and triggered by cortical slow waves. In wakefulness and under anesthesia, brief peripheral sensory stimuli can evoke 10-Hz reverberations due potentially to similar thalamic mechanisms. Functionally, sleep spindles and peripherally evoked spindles may play a role in memory consolidation and perception, respectively. Yet, rarely have the circuits involved in these two rhythms been compared in the same animals and never in primates. Here, we investigated the entrainment of primary somatosensory cortex (S1) neurons to both rhythms in ketamine-sedated macaques. First, we compared spontaneous spindles in sedation and natural sleep to validate the model. Then, we quantified entrainment with spike-field coherence and phase-locking statistics. We found that S1 neurons entrained to spontaneous sleep spindles were also entrained to the evoked spindles, although entrainment strength and phase systematically differed. Our results indicate that the spindle oscillations triggered by top-down spontaneous cortical activity and bottom-up peripheral input share a common cortical substrate.NEW & NOTEWORTHY Brief sensory stimuli evoke 10-Hz oscillations in thalamocortical neuronal activity and in perceptual thresholds. The mechanisms underlying this evoked rhythm are not well understood but are thought to be similar to those generating sleep spindles. We directly compared the entrainment of cortical neurons to both spontaneous spindles and peripherally evoked oscillations in sedated monkeys. We found that the entrainment strengths to each rhythm were positively correlated, although with differing entrainment phases, implying involvement of similar networks.

Determinants of Brain Rhythm Burst Statistics

Brain rhythms recorded in vivo, such as gamma oscillations, are notoriously variable both in amplitude and frequency. They are characterized by transient epochs of higher amplitude known as bursts. It has been suggested that, despite their short-life and random occurrence, bursts in gamma and other rhythms can efficiently contribute to working memory or communication tasks. Abnormalities in bursts have also been associated with e.g. motor and psychiatric disorders. It is thus crucial to understand how single cell and connectivity parameters influence burst statistics and the corresponding brain states. To address this problem, we consider a generic stochastic recurrent network of Pyramidal Interneuron Network Gamma (PING) type. Using the stochastic averaging method, we derive dynamics for the phase and envelope of the amplitude process, and find that they depend on only two meta-parameters that combine all the model parameters. This allows us to identify an optimal parameter regime of healthy variability with similar statistics to those seen in vivo; in this regime, oscillations and bursts are supported by synaptic noise. The probability density for the rhythm’s envelope as well as the mean burst duration are then derived using first passage time analysis. Our analysis enables us to link burst attributes, such as duration and frequency content, to system parameters. Our general approach can be extended to different frequency bands, network topologies and extra populations. It provides the much needed insight into the biophysical determinants of rhythm burst statistics, and into what needs to be changed to correct rhythms with pathological statistics.

Does data cleaning improve brain state classification?

BACKGROUND

Neuroscientists routinely seek to identify and remove noisy or artifactual observations from their data. They do so with the belief that removing such data improves power to detect relations between neural activity and behavior, which are often subtle and can be overwhelmed by noise. Whereas standard methods can exclude certain well-defined noise sources (e.g., 50/60 Hz electrical noise), in many situations there is not a clear difference between noise and signals so it is not obvious how to separate the two. Here we ask whether methods routinely used to "clean" human electrophysiological recordings lead to greater power to detect brain-behavior relations.

NEW METHOD

This, to the authors' knowledge, is the first large-scale simultaneous evaluation of multiple commonly used methods for removing noise from intracranial EEG recordings.

RESULTS

We find that several commonly used data cleaning methods (automated methods based on statistical signal properties and manual methods based on expert review) do not increase the power to detect univariate and multivariate electrophysiological biomarkers of successful episodic memory encoding, a well-characterized broadband pattern of neural activity observed across the brain.

COMPARISON WITH EXISTING METHODS

Researchers may be more likely to increase statistical power to detect physiological phenomena of interest by allocating resources away from cleaning noisy data and toward collecting more within-patient observations.

CONCLUSIONS

These findings highlight the challenge of partitioning signal and noise in the analysis of brain-behavior relations, and suggest increasing sample size and numbers of observations, rather than data cleaning, as the best approach to improving statistical power.

Advances in Understanding the Relationship between Sleep and Attention Deficit-Hyperactivity Disorder (ADHD)

Starting from the consolidated relationship between sleep and cognition, we reviewed the available literature on the association between Attention Deficit-Hyperactivity Disorder (ADHD) and sleep. This review analyzes the macrostructural and microstructural sleep features, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses criteria (PRISMA). We included the polysomnographic studies published in the last 15 years. The results of macrostructural parameters are mixed. Almost half of the 18 selected investigations did not find differences between sleep architecture of children with ADHD and controls. Five studies observed that children with ADHD show a longer Rapid Eye Movement (REM) sleep duration than controls. Eight studies included microstructural measures. Remarkable alterations in sleep microstructure of ADHD are related to slow wave activity (SWA) and theta oscillations, respectively, during Non-REM (NREM) and REM sleep. Specifically, some studies found higher SWA in the ADHD group than controls. Similarly, higher theta activity appears to be detrimental for memory performance and inhibitory control in ADHD. These patterns could be interpreted as a maturational delay in ADHD. Also, the increased amount of these activities would be consistent with the hypothesis that the poor sleep could imply a chronic sleep deprivation in children with ADHD, which in turn could affect their cognitive functioning.

Neural Pattern Similarity Differentially Relates to Memory Performance in Younger and Older Adults

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.SIGNIFICANCE STATEMENT Declining memory abilities are one of the most evident limitations for humans when growing older. Despite recent advances of our understanding of how the brain represents and stores information in distributed activation patterns, little is known about how the quality of information representation changes during aging and thus affects memory performance. We investigated how the similarity between neural representations relates to subsequent memory in younger and older adults. We present novel evidence that the interaction of pattern similarity and memory performance differs between age groups: Older adults benefited from higher similarity during early encoding, whereas young adults benefited from lower similarity between early and later encoding. These results provide insights into the nature of memory and age-related memory deficits.

Oscillatory EEG Activity During REM Sleep in Elderly People Predicts Subsequent Dream Recall After Awakenings

Several findings underlined that the electrophysiological (EEG) background of the last segment of sleep before awakenings may predict the presence/absence of dream recall (DR) in young subjects. However, little is known about the EEG correlates of DR in elderly people. Only an investigation found differences between recall and non-recall conditions during NREM sleep EEG in older adults, while-surprisingly-no EEG predictor of DR was found for what concerns REM sleep. Considering REM sleep as a privileged scenario to produce mental sleep activity related to cognitive processes, our study aimed to investigate whether specific EEG topography and frequency changes during REM sleep in elderly people may predict a subsequent recall of mental sleep activity. Twenty-one healthy older volunteers (mean age 69.2 ± 6.07 SD) and 20 young adults (mean age 23.4 ± 2.76 SD) were recorded for one night from 19 scalp derivations. Dreams were collected upon morning awakenings from REM sleep. EEG signals of the last 5 min were analyzed by the Better OSCillation algorithm to detect the peaks of oscillatory activity in both groups. Statistical comparisons revealed that older as well as young individuals recall their dream experience when the last segment of REM sleep is characterized by frontal theta oscillations. No Recall (Recall vs. Non-Recall) × Age (Young vs. Older) interaction was found. This result replicated the previous evidence in healthy young subjects, as shown in within- and between-subjects design. The findings are completely original for older individuals, demonstrating that theta oscillations are crucial for the retrieval of dreaming also in this population. Furthermore, our results did not confirm a greater presence of the theta activity in healthy aging. Conversely, we found a greater amount of rhythmic theta and alpha activity in young than older participants. It is worth noting that the theta oscillations detected are related to cognitive functioning. We emphasize the notion that the oscillatory theta activity should be distinguished from the non-rhythmic theta activity identified in relation to other phenomena such as (a) sleepiness and hypoarousal conditions during the waking state and (b) cortical slowing, considered as an EEG alteration in clinical samples.

Prefrontal Dynamics Associated with Efficient Detours and Shortcuts: A Combined Functional Magnetic Resonance Imaging and Magnetoencenphalography Study

Central to the concept of the “cognitive map” is that it confers behavioral flexibility, allowing animals to take efficient detours, exploit shortcuts, and avoid alluring, but unhelpful, paths. The neural underpinnings of such naturalistic and flexible behavior remain unclear. In two neuroimaging experiments, we tested human participants on their ability to navigate to a set of goal locations in a virtual desert island riven by lava, which occasionally spread to block selected paths (necessitating detours) or receded to open new paths (affording real shortcuts or false shortcuts to be avoided). Detours activated a network of frontal regions compared with shortcuts. Activity in the right dorsolateral PFC specifically increased when participants encountered tempting false shortcuts that led along suboptimal paths that needed to be differentiated from real shortcuts. We also report modulation in event-related fields and theta power in these situations, providing insight to the temporal evolution of response to encountering detours and shortcuts. These results help inform current models as to how the brain supports navigation and planning in dynamic environments.

Cognitive effort decreases beta, alpha, and theta coherence and ends afterdischarges in human brain

OBJECTIVE

Mental activation has been reported to modify the occurrence of epileptiform activity. We studied its effect on afterdischarges.

METHOD

In 15 patients with implanted electrodes we presented cognitive tasks when afterdischarges occurred. We developed a wavelet cross-coherence function to analyze the electrocorticography before and after the tasks and compared findings when cognitive tasks did or did not result in afterdischarge termination. Six patients returned for functional MRI (fMRI) testing, using similar tasks.

RESULTS

Cognitive tasks often could terminate afterdischarges when direct abortive stimulation could not. Wavelet cross-coherence analysis showed that, when afterdischarges stopped, there was decreased coherence throughout the brain in the 7.13-22.53 Hz frequency ranges (p values 0.008-0.034). This occurred a) regardless of whether an area activated on fMRI and b) regardless of whether there were afterdischarges in the area.

CONCLUSIONS

It is known that cognitive tasks can alter localized or network synchronization. Our results show that they can change activity throughout the brain. These changes in turn can terminate localized epileptiform activity.

SIGNIFICANCE

Cognitive tasks result in diffuse brain changes that can modify focal brain activity. Combined with a seizure detection device, cognitive activation might provide a non-invasive method of terminating or modifying seizures.

Assessment of changes in neural activity during acquisition of spatial knowledge using EEG signal classification

OBJECTIVE

This study explored the classification of electroencephalography (EEG) signals to assess changes in neural activity as individuals performed a training task in a virtual environment simulator. Commonly, task behavior and perception are used to assess a trainee's ability to perform a task, however, changes in cognition are not usually measured and could be important to provide a true indication of an individual's level of knowledge or skill.

APPROACH

In this study, 15 participants acquired spatial knowledge via 60 navigation trials (divided into ten blocks) in a novel virtual environment. Time performance, perceived certainty, and EEG signal data were collected.

MAIN RESULTS

A significant increase in alpha power and classification accuracy of EEG data from block 1 against blocks 2-10 was observed and stabilized after block 7, while time performance and perceived certainty measures improved and stabilized after block 5 and 6, respectively.

SIGNIFICANCE

Results suggest that changes in neural activity, which may reflect an increase in cognitive efficiency, could provide additional insight beyond time performance and perceived certainty.

The Spatiotemporal Pattern of the Human Electroencephalogram at Sleep Onset After a Period of Prolonged Wakefulness

During the sleep onset (SO) process, the human electroencephalogram (EEG) is characterized by an orchestrated pattern of spatiotemporal changes. Sleep deprivation (SD) strongly affects both wake and sleep EEG, but a description of the topographical EEG power spectra and oscillatory activity during the wake-sleep transition after a period of prolonged wakefulness is still missing. The increased homeostatic sleep pressure should induce an earlier onset of sleep-related EEG oscillations. The aim of the present study was to assess the spatiotemporal EEG pattern at SO following SD. A dataset of a previous study was analyzed. We assessed the spatiotemporal EEG changes (19 cortical derivations) during the SO (5 min before vs. 5 min after the first epoch of Stage 2) of a recovery night after 40 h of SD in 39 healthy subjects, analyzing the EEG power spectra (fast Fourier transform) and the oscillatory activity [better oscillation (BOSC) detection method]. The spatiotemporal pattern of the EEG power spectra mostly confirmed the changes previously observed during the wake-sleep transition at baseline. The comparison between baseline and recovery showed a wide increase of the post- vs. pre-SO ratio during the recovery night in the frequency bins ≤10 Hz. We found a predominant alpha oscillatory rhythm in the pre-SO period, while after SO the theta oscillatory activity was prevalent. The oscillatory peaks showed a generalized increase in all frequency bands from delta to sigma with different predominance, while beta activity increased only in the fronto-central midline derivations. Overall, the analysis of the EEG power replicated the topographical pattern observed during a baseline night of sleep but with a stronger intensity of the SO-induced changes in the frequencies ≤10 Hz, and the detection of the rhythmic activity showed the rise of several oscillations at SO after SD that was not observed during the wake-sleep transition at baseline (e.g., alpha and frontal theta in correspondence of their frequency peaks). Beyond confirming the local nature of the EEG pattern at SO, our results show that SD has an impact on the spatiotemporal modulation of cortical activity during the falling-asleep process, inducing the earlier emergence of sleep-related EEG oscillations.

Spatial Representations in the Human Brain

While extensive research on the neurophysiology of spatial memory has been carried out in rodents, memory research in humans had traditionally focused on more abstract, language-based tasks. Recent studies have begun to address this gap using virtual navigation tasks in combination with electrophysiological recordings in humans. These studies suggest that the human medial temporal lobe (MTL) is equipped with a population of place and grid cells similar to that previously observed in the rodent brain. Furthermore, theta oscillations have been linked to spatial navigation and, more specifically, to the encoding and retrieval of spatial information. While some studies suggest a single navigational theta rhythm which is of lower frequency in humans than rodents, other studies advocate for the existence of two functionally distinct delta-theta frequency bands involved in both spatial and episodic memory. Despite the general consensus between rodent and human electrophysiology, behavioral work in humans does not unequivocally support the use of a metric Euclidean map for navigation. Formal models of navigational behavior, which specifically consider the spatial scale of the environment and complementary learning mechanisms, may help to better understand different navigational strategies and their neurophysiological mechanisms. Finally, the functional overlap of spatial and declarative memory in the MTL calls for a unified theory of MTL function. Such a theory will critically rely upon linking task-related phenomena at multiple temporal and spatial scales. Understanding how single cell responses relate to ongoing theta oscillations during both the encoding and retrieval of spatial and non-spatial associations appears to be key toward developing a more mechanistic understanding of memory processes in the MTL.

Electrophysiological correlates of encoding processes in a full-report visual working memory paradigm

Why are some visual stimuli remembered, whereas others are forgotten? A limitation of recognition paradigms is that they measure aggregate behavioral performance and/or neural responses to all stimuli presented in a visual working memory (VWM) array. To address this limitation, we paired an electroencephalography (EEG) frequency-tagging technique with two full-report VWM paradigms. This permitted the tracking of individual stimuli as well as the aggregate response. We recorded high-density EEG (256 channel) while participants viewed four shape stimuli, each flickering at a different frequency. At retrieval, participants either recalled the location of all stimuli in any order (simultaneous full report) or were cued to report the item in a particular location over multiple screen displays (sequential full report). The individual frequency tag amplitudes evoked for correctly recalled items were significantly larger than the amplitudes of subsequently forgotten stimuli, regardless of retrieval task. An induced-power analysis examined the aggregate neural correlates of VWM encoding as a function of items correctly recalled. We found increased induced power across a large number of electrodes in the theta, alpha, and beta frequency bands when more items were successfully recalled. This effect was more robust for sequential full report, suggesting that retrieval demands can influence encoding processes. These data are consistent with a model in which encoding-related resources are directed to a subset of items, rather than a model in which resources are allocated evenly across the array. These data extend previous work using recognition paradigms and stress the importance of encoding in determining later VWM retrieval success.

Randomized trial of transcranial direct current stimulation for poststroke dysphagia

OBJECTIVE

We evaluated whether transcranial direct current stimulation (tDCS) is able to enhance dysphagia rehabilitation following stroke. Besides relating clinical effects with neuroplastic changes in cortical swallowing processing, we aimed to identify factors influencing treatment success.

METHODS

In this double-blind, randomized study, 60 acute dysphagic stroke patients received contralesional anodal (1mA, 20 minutes) or sham tDCS on 4 consecutive days. Swallowing function was thoroughly assessed before and after the intervention using the validated Fiberoptic Endoscopic Dysphagia Severity Scale (FEDSS) and clinical assessment. In 10 patients, swallowing-related brain activation was recorded applying magnetoencephalography before and after the intervention. Voxel-based statistical lesion pattern analysis was also performed.

RESULTS

Study groups did not differ according to demographic data, stroke characteristics, or baseline dysphagia severity. Patients treated with tDCS showed greater improvement in FEDSS than the sham group (1.3 vs 0.4 points, mean difference = 0.9, 95% confidence interval [CI] = 0.4-1.4, p < 0.0005). Functional recovery was accompanied by a significant increase of activation (p < 0.05) in the contralesional swallowing network after real but not sham tDCS. Regarding predictors of treatment success, for every hour earlier that treatment was initiated, there was greater improvement on the FEDSS (adjusted odds ratio = 0.99, 95% CI = 0.98-1.00, p < 0.05) in multivariate analysis. Stroke location in the right insula and operculum was indicative of worse response to tDCS (p < 0.05).

INTERPRETATION

Application of tDCS over the contralesional swallowing motor cortex supports swallowing network reorganization, thereby leading to faster rehabilitation of acute poststroke dysphagia. Early treatment initiation seems beneficial. tDCS may be less effective in right-hemispheric insulo-opercular stroke. Ann Neurol 2018;83:328-340.