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Despite the distortion of speech signals caused by unavoidable noise in daily life, our ability to comprehend speech in noisy environments is relatively stable. However, the neural mechanisms underlying reliable speech-in-noise comprehension remain to be elucidated. The present study investigated the neural tracking of acoustic and semantic speech information during noisy naturalistic speech comprehension. Participants listened to narrative audios mixed with spectrally matched stationary noise at three signal-to-ratio (SNR) levels (no noise, 3 dB, −3 dB), and 60-channel electroencephalography (EEG) signals were recorded. A temporal response function (TRF) method was employed to derive event-related-like responses to the continuous speech stream at both the acoustic and the semantic levels. Whereas the amplitude envelope of the naturalistic speech was taken as the acoustic feature, word entropy and word surprisal were extracted via the natural language processing method as two semantic features. Theta-band frontocentral TRF responses to the acoustic feature were observed at around 400 ms following speech fluctuation onset over all three SNR levels, and the response latency was more delayed with increasing noise. Delta-band frontal TRF responses to the semantic feature of word entropy were observed at around 200 to 600 ms leading to speech fluctuation onset over all three SNR levels. The response latency became more leading with increasing noise and was correlated with the perceived speech intelligibility. While the following responses to speech acoustics were consistent with previous studies, our study revealed the robustness of leading responses to speech semantics, which suggests a possible predictive mechanism at the semantic level for maintaining reliable speech comprehension in noisy environments.HighlightsLeading responses were observed in the semantic-level neural tracking across all noise levels, with more leading latency for increasing noises.Following responses were observed in the acoustic-level neural tracking, with more delayed latency for increasing noises.Semantic-level neural tracking is correlated with perceived intelligibility.Distinct frequency bands were involved in speech semantic and acoustic processing.

The parkinsonian gait disorder and freezing of gait are therapeutically demanding symptoms with considerable impact on quality of life. The aim of this study was to assess the role of subthalamic and nigral neurons in the parkinsonian gait control using intraoperative microelectrode recordings of basal ganglia neurons during a supine stepping task. Twelve male patients (56 ± 7 years) suffering from moderate idiopathic Parkinson’s disease (disease duration 10 ± 3 years, Hoehn and Yahr stage 2), undergoing awake neurosurgery for deep brain stimulation, participated in the study. After 10 s resting, stepping at self-paced speed for 35 s was followed by short intervals of stepping in response to random ‘start’ and ‘stop’ cues. Single- and multi-unit activity was analysed offline in relation to different aspects of the stepping task (attentional ‘start’ and ‘stop’ cues, heel strikes, stepping irregularities) in terms of firing frequency, firing pattern and oscillatory activity. Subthalamic nucleus and substantia nigra neurons responded to different aspects of the stepping task. Of the subthalamic nucleus neurons, 24% exhibited movement-related activity modulation as an increase of the firing rate, suggesting a predominant role of the subthalamic nucleus in motor aspects of the task, while 8% of subthalamic nucleus neurons showed a modulation in response to the attentional cues. In contrast, responsive substantia nigra neurons showed activity changes exclusively associated with attentional aspects of the stepping task (15%). The firing pattern of subthalamic nucleus neurons revealed gait-related firing regularization and a drop of beta oscillations during the stepping performance. During freezing episodes instead, there was a rise of beta oscillatory activity. This study shows for the first time specific, task-related subthalamic nucleus and substantia nigra single-unit activity changes during gait-like movements in humans with differential roles in motor and attentional control of gait. The emergence of perturbed firing patterns in the subthalamic nucleus indicates a disrupted information transfer within the gait network, resulting in freezing of gait.

BackgroundCovert visuo-spatial attention is marked by the anticipatory lateralization of neuronal alpha activity in the posterior parietal cortex. Previous applications of transcranial alternating current stimulation (tACS) at the alpha frequency, however, were inconclusive regarding the causal contribution of oscillatory activity during visuo-spatial attention.ObjectiveAttentional shifts of behavior and electroencephalography (EEG) after-effects were assessed in a cued visuo-spatial attention paradigm. We hypothesized that parietal alpha-tACS facilitates attention in the ipsilateral visual hemifield. Furthermore, we assumed that modulations of behavior and neurophysiology are related to individual electric field simulations.MethodsWe applied personalized tACS at alpha and gamma frequencies to elucidate the role of oscillatory neuronal activity for visuo-spatial attention. Personalized tACS montages were algorithmically optimized to target individual left and right parietal regions that were defined by an EEG localizer.ResultsBehavioral performance in the left hemifield was specifically increased by alpha-tACS compared to gamma-tACS targeting the left parietal cortex. This hemisphere-specific effect was observed despite the symmetry of simulated electric fields. In addition, visual event-related potential (ERP) amplitudes showed a reduced lateralization over posterior sites induced by left alpha-tACS. Neuronal sources of this effect were localized in the left premotor cortex. Interestingly, accuracy modulations induced by left parietal alpha-tACS were directly related to electric field magnitudes in the left premotor cortex.ConclusionOverall, results corroborate the notion that alpha lateralization plays a causal role in covert visuo-spatial attention and indicate an increased susceptibility of parietal and premotor brain regions of the left dorsal attention network to subtle tACS-neuromodulation.

In human-robot collaboration scenarios with shared workspaces, a highly desired performance boost is offset by high requirements for human safety, limiting speed and torque of the robot drives to levels which cannot harm the human body. Especially for complex tasks with flexible human behavior, it becomes vital to maintain safe working distances and coordinate tasks efficiently. An established approach in this regard is reactive servo in response to the current human pose. However, such an approach does not exploit expectations of the human's behavior and can therefore fail to react to fast human motions in time. To adapt the robot's behavior as soon as possible, predicting human intention early becomes a factor which is vital but hard to achieve. Here, we employ a recently developed type of brain-computer interface (BCI) which can detect the focus of the human's overt attention as a predictor for impending action. In contrast to other types of BCI, direct projection of stimuli onto the workspace facilitates a seamless integration in workflows. Moreover, we demonstrate how the signal-to-noise ratio of the brain response can be used to adjust the velocity of the robot movements to the vigilance or alertness level of the human. Analyzing this adaptive system with respect to performance and safety margins in a physical robot experiment, we found the proposed method could improve both collaboration efficiency and safety distance.

When we pay attention to someone, do we focus only on the sound they make, the word they use, or do we form a mental space shared with the speaker we want to pay attention to? Some would argue that the human language is no other than a simple signal, but others claim that human beings understand each other not only by relying on the words that have been said but also formed a shared ground in the specific conversation. This debate was raised early, but the conclusion remains vague. Our study aimed to investigate how attention modulates the neural coupling between the speaker and the listener in a cocktail party paradigm. The temporal response function (TRF) method was employed to reveal how the listener was coupled to the speaker at the neural level. The results showed that the neural coupling between the listener and the attended speaker peaked 5 seconds before speech onset at the delta band over the left frontal region, and was correlated with speech comprehension performance. In contrast, the attentional processing of speech acoustics and semantics occurred primarily at a later stage after speech onset and was not significantly correlated with comprehension performance. These findings suggest that our human brain might have adopted a predictive mechanism to achieve speaker-listener neural coupling for successful speech comprehension.Three key pointsListener’s EEG signals coupled to the speaker’s 5 s before the speech onset, which revealed a “beyond the stimulus” attentional modulation.Speaker-listener attentional coupling is correlated to the listener’s comprehension performance, but the speech-listener’s coupling didn’t.The implementation of temporal response function methods and the neural language methods yielded novel perspectives to the analysis of the inter-brain studies.

Dynamic coupling of neural signals is a hallmark of brain networks, but its potential relevance is still debated. Does coupling play a causal role for network functions, or is it just a by-product of structural connectivity or other physiological processes? With intervention techniques that have become available, experiments seem within reach that may provide answers to this long-standing question.

Comprehending speech in noise is an essential cognitive skill for verbal communication. However, it remains unclear how our brain adapts to the noisy environment to achieve comprehension. The present study investigated the neural mechanisms of speech comprehension in noise using an functional near-infrared spectroscopy-based inter-brain approach. A group of speakers was invited to tell real-life stories. The recorded speech audios were added with meaningless white noise at four signal-to-noise levels and then played to listeners. Results showed that speaker–listener neural couplings of listener’s left inferior frontal gyri (IFG), that is, sensorimotor system, and right middle temporal gyri (MTG), angular gyri (AG), that is, auditory system, were significantly higher in listening conditions than in the baseline. More importantly, the correlation between neural coupling of listener’s left IFG and the comprehension performance gradually became more positive with increasing noise level, indicating an adaptive role of sensorimotor system in noisy speech comprehension; however, the top behavioral correlations for the coupling of listener’s right MTG and AG were only obtained in mild noise conditions, indicating a different and less robust mechanism. To sum up, speaker–listener coupling analysis provides added value and new sight to understand the neural mechanism of speech-in-noise comprehension.

In classical representation-oriented approaches of social cognition, agents are thought to interact with conspecifics based on their capacity to develop a “theory-of-mind”, i.e., to generate complex models of the intentions, beliefs, and personalities of their interaction partners. In this framework, the primary mode of interaction with the social environment is that of a detached observer who theorizes and produces inferences about other participants. In contrast, this Research Topic seeks to turn the spotlight on the grounding of social cognition in dynamic sensorimotor and informational coupling of agents, in human-human as well as human-robot interaction settings. According to this view, interaction dynamics hold substantial clues to the mechanism of social understanding and its disturbances (as for example observed in autism spectrum disorders). The argument is that high-level social deficits may be rooted in the impaired capacity for entraining and sustaining sensorimotor and informational coupling. Beyond novel insights into the mechanisms of functional and dysfunctional social behavior, the investigation of basic sensorimotor interaction patterns may help the development of socially competent robot technology. Tapping into the same logic, robotic agents sensitive to interpersonal sensorimotor contingencies should have an advantage over technology that does not consider this key aspect of human interaction. This Research Topic provides an interdisciplinary overview of trends and recent developments in conceptual, methodological and basic research, as well as applications of sensorimotor approaches in social cognitive science, neuroscience, and robotic research. One of the key questions is how concepts and methods from social cognitive and neuroscience transfer to human-robot interaction. The authors acknowledge support by the EU through project socSMCs (H2020-641321).

While the increasingly globalized world has brought more and more demands for non-native language communication, the prevalence of background noise in everyday life poses a great challenge to non-native speech comprehension. The present study employed an interbrain approach based on functional near-infrared spectroscopy (fNIRS) to explore how people adapt to comprehend non-native speech information in noise. A group of Korean participants who acquired Chinese as their non-native language was invited to listen to Chinese narratives at 4 noise levels (no noise, 2 dB, −6 dB, and − 9 dB). These narratives were real-life stories spoken by native Chinese speakers. Processing of the non-native speech was associated with significant fNIRS-based listener–speaker neural couplings mainly over the right hemisphere at both the listener’s and the speaker’s sides. More importantly, the neural couplings from the listener’s right superior temporal gyrus, the right middle temporal gyrus, as well as the right postcentral gyrus were found to be positively correlated with their individual comprehension performance at the strongest noise level (−9 dB). These results provide interbrain evidence in support of the right-lateralized mechanism for non-native speech processing and suggest that both an auditory-based and a sensorimotor-based mechanism contributed to the non-native speech-in-noise comprehension.

BackgroundAnaesthesia and surgery can lead to cognitive decline, especially in the elderly. However, to date, the neurophysiological underpinnings of perioperative cognitive decline remain unknown.MethodsWe included male patients, who were 60 years or older scheduled for elective radical prostatectomy under general anaesthesia. We obtained neuropsychological (NP) tests as well as a visual match-to-sample working memory (WM) task with concomitant 62-channel scalp electroencephalography (EEG) before and after surgery.ResultsA total number of 26 patients completed neuropsychological assessments and EEG pre- and postoperatively. Behavioural performance declined in the neuropsychological assessment after anaesthesia (total recall; t-tests: t25 = -3.25, Bonferroni-corrected p = 0.015 d = -0.902), while WM performance showed a dissociation between match and mis-match accuracy (rmANOVA: match*session F1,25 = 3.866, p = 0.060). Distinct EEG signatures tracked behavioural performance: Better performance in the NP assessment was correlated with an increase of non-oscillatory (aperiodic) activity, reflecting increased cortical activity (cluster permutation tests: total recall r = 0.66, p = 0.029, learning slope r = 0.66, p = 0.015), while WM accuracy was tracked by distinct temporally-structured oscillatory theta/alpha (7 – 9 Hz), low beta (14 – 18 Hz) and high beta/gamma (34 – 38 Hz) activity (cluster permutation tests: matches: p < 0.001, mis-matches: p = 0.022).ConclusionsOscillatory and non-oscillatory (aperiodic) activity in perioperative scalp EEG recordings track distinct features of perioperative cognition. Aperiodic activity provides a novel electrophysiological biomarker to identify patients at risk for developing perioperative neurocognitive decline.

BackgroundFreezing of gait (FoG) is a disabling burden for Parkinson’s disease (PD) patients with poor response to conventional therapies. Combined deep brain stimulation of the subthalamic nucleus and substantia nigra (STN+SN DBS) moved into focus as a potential therapeutic option to treat the parkinsonian gait disorder and refractory FoG. The mechanisms of action of DBS within the cortical-subcortical-basal ganglia network on gait, particularly at the cortical level, remain unclear.MethodsTwelve patients with idiopathic PD and chronically-implanted DBS electrodes were assessed on their regular dopaminergic medication in a standardized stepping in place paradigm. Patients executed the task with DBS switched off (STIM OFF), conventional STN DBS and combined STN+SN DBS and were compared to healthy matched controls. Simultaneous high-density EEG and kinematic measurements were recorded during resting-state, effective stepping, and freezing episodes.ResultsClinically, STN+SN DBS was superior to conventional STN DBS in improving temporal stepping variability of the more affected leg. During resting-state and effective stepping, the cortical activity of PD patients in STIM OFF was characterized by excessive over-synchronization in the theta (4–8 Hz), alpha (9–13 Hz), and high-beta (21–30 Hz) band compared to healthy controls. Both active DBS settings similarly decreased resting-state alpha power and reduced pathologically enhanced high-beta activity during resting-state and effective stepping compared to STIM OFF. Freezing episodes during STN DBS and STN+SN DBS showed spectrally and spatially distinct cortical activity patterns when compared to effective stepping. During STN DBS, FoG was associated with an increase in cortical alpha and low-beta activity over central cortical areas, while with STN+SN DBS, an increase in high-beta was prominent over more frontal areas.ConclusionsSTN+SN DBS improved temporal aspects of parkinsonian gait impairment compared to conventional STN DBS and differentially affected cortical oscillatory patterns during regular locomotion and freezing suggesting a potential modulatory effect on dysfunctional cortical-subcortical communication in PD.

The spatially-coded SSVEP BCI employs the retinotopic map in the human visual pathway to infer the gaze direction of the operator relative to a flicker stimulus inducing steady-state visual evoked potentials (SSVEPs) in the brain. It has been shown that with this method, up to 16 channels can be encoded using only a single flicker stimulus. Another advantage over conventional frequency-coded SSVEP BCIs, in which channels are encoded by different combinations of frequencies and phases, is that the operator does not have to gaze directly at flickering lights. This can reduce visual fatigue and improve user comfort. Whereas the frequency of the SSVEP response is well predictable, which has enabled the development of frequency-coded SSVEP BCIs which do not require training data, the spatial distribution of the SSVEP response over the scalp differs much more between different people. This requires collecting a substantial amount of training data before the spatially-coded BCI could be put into operation.In this study we address this issue by combining the spatially-coded BCI with a feedback channel which the operator uses to flag classification errors, and which allows the system to accumulate valid training data while the BCI is used to solve a spatial navigation task.Starting from the minimal number of samples required by the classification method, the approach achieved an average accuracy of 69 ± 15 %, corresponding to an ITR of 31 ± 17 bits/min, in solving the task for the first time. This accuracy improved to 87 ± 9 % (ITR: 54 ± 14 bits/min) after completing the task 2 more times. Further we show that participants with a stable SSVEP topography over repeated stimulation enable the BCI to achieve higher accuracies.Compared to a similar system with separate training and application phases, the time to achieve the same output is reduced by more than 50 %.Evaluating the approach in 17 participants suggests that the performance of the spatially-coded BCI with a minimal set of training samples is sufficient to be operational, and that performance keeps improving in the course of its application.

Transcranial electric stimulation (tES) induces electric fields that propagate in the brain and depend on individual anatomies. The interaction between the electric fields and individual anatomies may contribute to the heterogenous results that are commonly observed across tES studies in humans. Targeted tES is able to account for some of these individual factors by adapting the electric field to the stimulation target. Here, the effect of individually targeted tES on simulated intracranial electric fields was evaluated in head models of twenty-one participants using the finite-element method (FEM). For all participants, two individually targeted tES montages were compared to a fixed stimulation montage that was not individually optimized. For a simulated parietal stimulation target with three different orientations, individual current densities showed varying intensities near the lower limit at which physiological efficacy of electric fields can be assumed. However, targeting algorithms were able to control different electric field properties, by either maximizing the target current densities or by increasing the specificity of electric fields with respect to target location and orientation. Electric fields were constrained by individual anatomical properties, but still showed considerable variation for the given parietal stimulation target across participants. Thus, we present findings of inter-individual variability within the same cortical region to complement recent studies that showed large variation across cortical regions in a single FEM head model. Our results support the usage of individual targeting for enhancing the efficacy of tES and for elucidating the underlying mechanisms of tES. At the same time, residual variability in electric fields is suggested to be utilized for the explanation of individual differences in the tES outcome.

Significance Prevalence and temporal dynamics of transient oscillations in the beta frequency band (15 to 35 Hz), referred to as β bursts, are correlated with motor performance. Disturbance of these activities is a candidate mechanism for motor impairment in Parkinson’s disease (PD), where the excessively long bursts correlate with symptom severity and are reduced by pharmacological and surgical treatments. Here we describe the changes in action potential firing that take place across multiple nodes of the cortical and basal ganglia circuit as these transient oscillations evolve. These analyses provide fresh insights into the network dynamics of β bursts that can guide novel strategies to interfere with their generation and maintenance in PD., Beta frequency oscillations (15 to 35 Hz) in cortical and basal ganglia circuits become abnormally synchronized in Parkinson’s disease (PD). How excessive beta oscillations emerge in these circuits is unclear. We addressed this issue by defining the firing properties of basal ganglia neurons around the emergence of cortical beta bursts (β bursts), transient (50 to 350 ms) increases in the beta amplitude of cortical signals. In PD patients, the phase locking of background spiking activity in the subthalamic nucleus (STN) to frontal electroencephalograms preceded the onset and followed the temporal profile of cortical β bursts, with conditions of synchronization consistent within and across bursts. Neuronal ensemble recordings in multiple basal ganglia structures of parkinsonian rats revealed that these dynamics were recapitulated in STN, but also in external globus pallidus and striatum. The onset of consistent phase-locking conditions was preceded by abrupt phase slips between cortical and basal ganglia ensemble signals. Single-unit recordings demonstrated that ensemble-level properties of synchronization were not underlain by changes in firing rate but, rather, by the timing of action potentials in relation to cortical oscillation phase. Notably, the preferred angle of phase-locked action potential firing in each basal ganglia structure was shifted during burst initiation, then maintained stable phase relations during the burst. Subthalamic, pallidal, and striatal neurons engaged and disengaged with cortical β bursts to different extents and timings. The temporal evolution of cortical and basal ganglia synchronization is cell type-selective, which could be key for the generation/ maintenance of excessive beta oscillations in parkinsonism.

Objective: We present a new type of brain-computer interface (BCI) that utilizes the retinotopic mapping of motion-onset visual evoked potentials (mVEP) to accomplish four control channels using a single motion stimulus. Methods: Participants selected a BCI command by fixating one of four target locations around a centrally presented visual motion stimulus. A template-matching method was employed to recognize the users’ intention by decoding the position of the motion stimulus in the peripheral visual field, and classification performances were evaluated in an offline manner. The motion stimulus eccentricity between the targets and the visual motion stimulus varied among 5.1°, 6.7°, 9.8°, and 13.0°. Results: Distinct N200 spatial patterns were elicited when participants directed attention overtly to the target locations. A four-class classification accuracy of 72.2 ± 5.05% was achieved with a distance of 5.1° visual angle between the targets and the visual motion stimulus. The classification accuracies decreased with increasing motion stimulus eccentricities but remained separable well above the chance level at 13.0° (47.3 ± 8.54%). Conclusion: Our results support the feasibility of a single-stimulus, multitarget mVEP BCI. Significance: The proposed system can simplify the visual stimulation of mVEP BCIs, improve user experience and pave the way for simple yet efficient BCI communication.

The spatially-coded SSVEP BCI exploits changes in the topography of the steady-state visual evoked response to visual flicker stimulation in the extrafoveal field of view. In contrast to frequency-coded SSVEP BCIs, the operator does not gaze into any flickering lights; therefore, this paradigm can reduce visual fatigue. Other advantages include high classification accuracies and a simplified stimulation setup. Previous studies of the paradigm used stimulation intervals of a fixed duration. For frequency-coded SSVEP BCIs, it has been shown that dynamically adjusting the trial duration can increase the system’s information transfer rate (ITR). We therefore investigated whether a similar increase could be achieved for spatially-coded BCIs by applying dynamic stopping methods. To this end we introduced a new stopping criterion which combines the likelihood of the classification result and its stability across larger data windows. Whereas the BCI achieved an average ITR of 28.4±6.4 bits/min with fixed intervals, dynamic intervals increased the performance to 81.1±44.4 bits/min. Users were able to maintain performance up to 60 minutes of continuous operation. We suggest that the dynamic response time might have worked as a kind of temporal feedback which allowed operators to optimize their brain signals and compensate fatigue.

Transcranial alternating current stimulation (tACS), applied to two brain sites with different phase lags, has been shown to modulate stimulation-outlasting functional EEG connectivity between the targeted regions. Given the lack of knowledge on mechanisms of tACS aftereffects, it is difficult to further enhance effect sizes and reduce variability in experiments. In this computational study, we tested if spike-timing-dependent plasticity (STDP) can explain stimulation-outlasting connectivity modulation by dual-site tACS and explored the effects of tACS parameter choices. Two populations of spiking neurons were coupled with synapses subject to STDP, and results were validated via a re-analysis of EEG data. Our simulations showed stimulation-outlasting connectivity changes between in- and anti-phase tACS, dependent on both tACS frequency and synaptic conduction delays. Importantly, both a simple network entraining to a wide range of tACS frequencies as well as a more realistic network that spontaneously oscillated at alpha frequency predicted that the largest effects would occur for short conduction delays between the stimulated regions. This finding agreed with experimental EEG connectivity modulation by 10 Hz tACS, showing a clear negative correlation of tACS effects with estimated conduction delays between regions. In conclusion, STDP can explain connectivity aftereffects of dual-site tACS. However, not all combinations of tACS frequency and application sites are expected to effectively modulate connectivity via STDP. We therefore suggest using appropriate computational models and/or EEG analysis for planning and interpretation of dual-site tACS studies relying on aftereffects.HighlightsNetwork model with STDP explains EEG connectivity change after dual-site tACSEffects are predicted to depend on tACS frequency and conduction delaysEEG data confirm dependence on conduction delays between regionsModel can be used to estimate and maximize experimental effects

Mice display signs of fear when neurons that express cFos during fear conditioning are artificially reactivated. This finding gave rise to the notion that cFos marks neurons that encode specific memories. Here we show that cFos expression patterns in the mouse dentate gyrus (DG) change dramatically from day to day in a water maze spatial learning paradigm, regardless of training level. Optogenetic inhibition of neurons that expressed cFos on the first training day affected performance days later, suggesting that these neurons continue to be important for spatial memory recall. The mechanism preventing repeated cFos expression in DG granule cells involves accumulation of ΔFosB, a long-lived splice variant of FosB. CA1 neurons, in contrast, repeatedly expressed cFos. Thus, cFos-expressing granule cells may encode new features being added to the internal representation during the last training session. This form of timestamping is thought to be required for the formation of episodic memories.

Transgenic cFos reporter mice are used to identify and manipulate neurons that store contextual information during fear learning. It is not clear, however, how spatial information acquired over several training days is integrated in the hippocampus. Using a water maze task, we observed that cFos expression patterns in the dentate gyrus are temporally unstable and shift daily. Surprisingly, cFos patterns did not get more stable with increasing spatial memory precision. Despite the fact that cFos was no longer expressed, optogenetic inhibition of neurons that expressed cFos on the first training day affected performance days later. Triggered by training, ΔFosB accumulates and provides a negative feedback mechanism that makes the cFos ensemble in the dentate gyrus dependent on the history of activity. Shifting cFos expression to a different set of granule cells every day may aid the formation of episodic memories.

This study provides an analysis of the cytoarchitecture, myeloarchitecture, and chemoarchitecture of the diencephalon (dorsal thalamus, ventral thalamus, and epithalamus) of the banded mongoose (Mungos mungo) and domestic ferret (Mustela putorius furo). Using architectural and immunohistochemical stains, we observe that the nuclear organization of the diencephalon is very similar in the two species, and similar to that reported in other carnivores, such as the domestic cat and dog. The same complement of putatively homologous nuclei were identified in both species, with only one variance, that being the presence of the perireticular nucleus in the domestic ferret, that was not observed in the banded mongoose. The chemoarchitecture was also mostly consistent between species, although there were a number of minor variations across a range of nuclei in the density of structures expressing the calcium-binding proteins parvalbumin, calbindin, and calretinin. Thus, despite almost 53 million years since these two species of carnivores shared a common ancestor, strong phylogenetic constraints appear to limit the potential for adaptive evolutionary plasticity within the carnivore order. Apart from the presence of the perireticular nucleus, the most notable difference between the species studied was the physical inversion of the dorsal lateral geniculate nucleus, as well as the lateral posterior and pulvinar nuclei in the domestic ferret compared to the banded mongoose and other carnivores, although this inversion appears to be a feature of the Mustelidae family. While no functional sequelae are suggested, this inversion is likely to result from the altricial birth of Mustelidae species.

Employing cyto-, myelo-, and chemoarchitectural staining techniques, we analyzed the structure of the hippocampal formation in the banded mongoose and domestic ferret, species belonging to the two carnivoran superfamilies, which have had independent evolutionary trajectories for the past 55 million years. Our observations indicate that, despite the time since sharing a last common ancestor, these species show extensive similarities. The four major portions of the hippocampal formation (cornu Ammonis, dentate gyrus, subicular complex, and entorhinal cortex) were readily observed, contained the same internal subdivisions, and maintained the topological relationships of these subdivisions that could be considered typically mammalian. In addition, adult hippocampal neurogenesis was observed in both species, occurring at a rate similar to that observed in other mammals. Despite the overall similarities, several differences to each other, and to other mammalian species, were observed. We could not find evidence for the presence of the CA2 and CA4 fields of the cornu Ammonis region. In the banded mongoose the dentate gyrus appears to be comprised of up to seven lamina, through the sublamination of the molecular and granule cell layers, which is not observed in the domestic ferret. In addition, numerous subtle variations in chemoarchitecture between the two species were observed. These differences may contribute to an overall variation in the functionality of the hippocampal formation between the species, and in comparison to other mammalian species. These similarities and variations are important to understanding to what extent phylogenetic affinities and constraints affect potential adaptive evolutionary plasticity of the hippocampal formation.

The current study provides an analysis of the cytoarchitecture, myeloarchitecture, and chemoarchitecture of the amygdaloid body of the banded mongoose (Mungos mungo) and domestic ferret (Mustela putorius furo). Using architectural and immunohistochemical stains, we observe that the organization of the nuclear and cortical portions of the amygdaloid complex is very similar in both species. The one major difference is the presence of a cortex-amygdala transition zone observed in the domestic ferret that is absent in the banded mongoose. In addition, the chemoarchitecture is, for the most part, quite similar in the two species, but several variances, such as differing densities of neurons expressing the calcium-binding proteins in specific nuclei are noted. Despite this, certain aspects of the chemoarchitecture, such as the cholinergic innervation of the magnocellular division of the basal nuclear cluster and the presence of doublecortin expressing neurons in the shell division of the accessory basal nuclear cluster, appear to be consistent features of the Eutherian mammal amygdala. The domestic ferret presented with an overall lower myelin density throughout the amygdaloid body than the banded mongoose, a feature that may reflect artificial selection in the process of domestication for increased juvenile-like behavior in the adult domestic ferret, such as a muted fear response. The shared, but temporally distant, ancestry of the banded mongoose and domestic ferret allows us to generate observations relevant to understanding the relative influence that phylogenetic constraints, adaptive evolutionary plasticity, and the domestication process may play in the organization and chemoarchitecture of the amygdaloid body.

Optogenetic manipulation of neuronal activity has become an indispensable experimental strategy in neuroscience research. A large repertoire of excitatory and inhibitory tools allows precise activation or inhibition of genetically targetable neuronal populations. However, an optogenetic tool for reliable bidirectional control of neuronal activity allowing both up- and downregulation of the same neurons in a single experiment is still missing. Here we report BiPOLES, an optogenetic tool for potent excitation and inhibition of the same population of neurons with light of two different colors. BiPOLES consists of an inhibitory, blue-light-sensitive anion-conducting channelrhodopsin fused to an excitatory, red-light-sensitive cation-conducting channelrhodopsin in a single, trafficking-optimized tandem protein. BiPOLES enables multiple new applications including potent dual-color spiking and silencing of the same neurons in vivo and dual-color optogenetic control of two independent neuronal populations.

A large variety of methods exist to estimate brain coupling in the frequency domain from electrophysiological data measured, e.g., by EEG and MEG. Those data are to reasonable approximation, though certainly not perfectly, Gaussian distributed. This work is based on the well-known fact that for Gaussian distributed data, the cross-spectrum completely determines all statistical properties. In particular, for an infinite number of data, all normalized coupling measures at a given frequency are a function of complex coherency. However, it is largely unknown what the functional relations are. We here present those functional relations for six different measures: the weighted phase lag index, the phase lag index, the absolute value and imaginary part of the phase locking value (PLV), power envelope correlation, and power envelope correlation with correction for artifacts of volume conduction. With the exception of PLV, the final results are simple closed form formulas. In an excursion we also discuss differences between short time Fourier transformation and Hilbert transformation for estimations in the frequency domain. We tested in simulations of linear and non-linear dynamical systems and for empirical resting state EEG on sensor level to what extent a model, namely the respective function of coherency, can explain the observed couplings. For empirical data we found that for measures of phase-phase coupling deviations from the model are in general minor, while power envelope correlations systematically deviate from the model for all frequencies. For power envelope correlation with correction for artifacts of volume conduction the model cannot explain the observed couplings at all. We also analyzed power envelope correlation as a function of time and frequency in an event related experiment using a stroop reaction task and found significant event related deviations mostly in the alpha range.

Influential accounts postulate distinct roles of the catecholamine and acetylcholine neuromodulatory systems in cognition and behavior. But previous work found similar effects of these modulators on the response properties of individual cortical neurons. Here, we report a double dissociation between catecholamine and acetylcholine effects at the level of cortex-wide network interactions in humans. A pharmacological boost of catecholamine levels increased cortex-wide interactions during a visual task, but not rest. Conversely, an acetylcholine-boost decreased correlations during rest, but not task. Cortical circuit modeling explained this dissociation by differential changes in two circuit properties: the local excitation-inhibition balance (more strongly altered by catecholamines) and intracortical transmission (more strongly reduced by acetylcholine). The inferred catecholaminergic mechanism also predicted increased behavioral exploration, which we confirmed in human behavior during both a perceptual and value-based choice task. In sum, we identified specific circuit mechanisms for shaping cortex-wide network interactions and behavior by key neuromodulatory systems.

Working memory (WM) refers to the temporary retention and manipulation of information, and its capacity is highly susceptible to training. Yet, the neural mechanisms that allow for increased performance under demanding conditions are not fully understood. We expected that post-training efficiency in WM performance modulates neural processing during high load tasks. We tested this hypothesis, using electroencephalography (EEG) (N = 39), by comparing source space spectral power of healthy adults performing low and high load auditory WM tasks. Prior to the assessment, participants either underwent a modality-specific auditory WM training, or a modality-irrelevant tactile WM training, or were not trained (active control). After a modality-specific training participants showed higher behavioral performance, compared to the control. EEG data analysis revealed general effects of WM load, across all training groups, in the theta-, alpha-, and beta-frequency bands. With increased load theta-band power increased over frontal, and decreased over parietal areas. Centro-parietal alpha-band power and central beta-band power decreased with load. Interestingly, in the high load condition a tendency towards reduced beta-band power in the right medial temporal lobe was observed in the modality-specific WM training group compared to the modality-irrelevant and active control groups. Our finding that WM processing during the high load condition changed after modality-specific WM training, showing reduced beta-band activity in voice-selective regions, possibly indicates a more efficient maintenance of task-relevant stimuli. The general load effects suggest that WM performance at high load demands involves complementary mechanisms, combining a strengthening of task-relevant and a suppression of task-irrelevant processing.

Congenital blindness has been shown to result in behavioral adaptation and neuronal reorganization, but the underlying neuronal mechanisms are largely unknown. Brain rhythms are characteristic for anatomically defined brain regions and provide a putative mechanistic link to cognitive processes. In a novel approach, using magnetoencephalography resting state data of congenitally blind and sighted humans, deprivation-related changes in spectral profiles were mapped to the cortex using clustering and classification procedures. Altered spectral profiles in visual areas suggest changes in visual alpha-gamma band inhibitory-excitatory circuits. Remarkably, spectral profiles were also altered in auditory and right frontal areas showing increased power in theta-to-beta frequency bands in blind compared with sighted individuals, possibly related to adaptive auditory and higher cognitive processing. Moreover, occipital alpha correlated with microstructural white matter properties extending bilaterally across posterior parts of the brain. We provide evidence that visual deprivation selectively modulates spectral profiles, possibly reflecting structural and functional adaptation.

The functional relevance of crossmodal activation (e.g. auditory activation of occipital brain regions) in congenitally blind individuals is still not fully understood. The present study tested whether the occipital cortex of blind individuals is integrated into a challenged functional network. A working memory (WM) training over four sessions was implemented. Congenitally blind and matched sighted participants were adaptively trained with an n-back task employing either voices (auditory training) or tactile stimuli (tactile training). In addition, a minimally demanding 1-back task served as an active control condition. Power and functional connectivity of EEG activity evolving during the maintenance period of an auditory 2-back task were analyzed, run prior to and after the WM training. Modality-specific (following auditory training) and modality-independent WM training effects (following both auditory and tactile training) were assessed. Improvements in auditory WM were observed in all groups, and blind and sighted individuals did not differ in training gains. Auditory and tactile training of sighted participants led, relative to the active control group, to an increase in fronto-parietal theta-band power, suggesting a training-induced strengthening of the existing modality-independent WM network. No power effects were observed in the blind. Rather, after auditory training the blind showed a decrease in theta-band connectivity between central, parietal, and occipital electrodes compared to the blind tactile training and active control groups. Furthermore, in the blind auditory training increased beta-band connectivity between fronto-parietal, central and occipital electrodes. In the congenitally blind, these findings suggest a stronger integration of occipital areas into the auditory WM network.

Cortical single neuron activity and local field potential patterns change at different depths of general anesthesia. Here, we investigate the associated network level changes of functional connectivity. We recorded ongoing electrocorticographic (ECoG) activity from temporo-parieto-occipital cortex of 6 ferrets at various levels of isoflurane/nitrous oxide anesthesia and determined functional connectivity by computing amplitude envelope correlations. Through hierarchical clustering, we derived typical connectivity patterns corresponding to light, intermediate and deep anesthesia. Generally, amplitude correlation strength increased strongly with depth of anesthesia across all cortical areas and frequency bands. This was accompanied, at the deepest level, by the emergence of burst-suppression activity in the ECoG signal and a change of the spectrum of the amplitude envelope. Normalization of functional connectivity to the distribution of correlation coefficients showed that the topographical patterns remained similar across depths of anesthesia, reflecting the functional association of the underlying cortical areas. Thus, while strength and temporal properties of amplitude co-modulation vary depending on the activity of local neural circuits, their network-level interaction pattern is presumably most strongly determined by the underlying structural connectivity.

Pathological synchronisation of beta frequency (12–35 Hz) oscillations between the subthalamic nucleus (STN) and cerebral cortex is thought to contribute to motor impairment in Parkinson's disease (PD). For this cortico-subthalamic oscillatory drive to be mechanistically important, it must influence the firing of STN neurons and, consequently, their downstream targets. Here, we examined the dynamics of synchronisation between STN LFPs and units with multiple cortical areas, measured using frontal ECoG, midline EEG and lateral EEG, during rest and movement. STN neurons lagged cortical signals recorded over midline (over premotor cortices) and frontal (over prefrontal cortices) with stable time delays, consistent with strong corticosubthalamic drive, and many neurons maintained these dynamics during movement. In contrast, most STN neurons desynchronised from lateral EEG signals (over primary motor cortices) during movement and those that did not had altered phase relations to the cortical signals. The strength of synchronisation between STN units and midline EEG in the high beta range (25–35 Hz) correlated positively with the severity of akinetic-rigid motor symptoms across patients. Together, these results suggest that sustained synchronisation of STN neurons to premotor-cortical beta oscillations play an important role in disrupting the normal coding of movement in PD., Highlights • Multi-channel EEG with coincident STN single unit and local field potential recordings • Variable time delays between beta oscillations in different cortical areas and STN neurons. • Frontal/premotor cortical areas have most stable oscillatory synchronisation with STN neurons. • Correlation between cortico-subthalamic beta-frequency synchronisation and clinical scores in PD.

Interhemispheric auditory connectivity via the corpus callosum has been demonstrated to be important for normal speech processing. According to the callosal relay model, directed information flow from the right to the left auditory cortex has been suggested, but this has not yet been proven. For this purpose, 33 healthy participants were investigated with 64-channel EEG while performing the dichotic listening task in which two different consonant–vowel syllables were presented simultaneously to the left (LE) and right ear (RE). eLORETA source estimation was used to investigate the functional (lagged phase synchronization/LPS) and effective (isolated effective coherence/ICoh) connectivity between right and left primary (PAC) and secondary auditory cortices (SAC) in the gamma-band (30–100 Hz) during right and left ear reports. The major finding was a significantly increased effective connectivity in the gamma-band from the right to the left SAC during conscious perception of LE stimuli. In addition, effective and functional connectivity was significantly enhanced during LE as compared to RE reports. These findings give novel insight into transcallosal information transfer during auditory perception by showing that LE performance requires causal interhemispheric inputs from the right to the left auditory cortices, and that this interaction is mediated by synchronized gamma-band oscillations.

Compared to the different brain signals used in brain–computer interface (BCI) paradigms, the s teady-state visually evoked potential (SSVEP) features a high signal to noise ratio, enabling reliable and fast classification of neural activity patterns without extensive training requirements. In this paper, we present methods to further increase the information transfer rates (ITRs) of SSVEP-based BCIs. Starting with stimulus parameter optimizations methods, we develop an improved approach for the use of Canonical correlation analysis and analyze properties of the SSVEP when the user fixates a target and during transitions between targets. These transitions show a negative effect on the system's ITR which we trace back to delays and dead times of the SSVEP. Using two classifier types adapted to continuous and transient SSVEPs and two control modes (fast feedback and fast input), we present a simulated online BCI implementation which addresses the challenges introduced by transient SSVEPs. The resulting system reaches an average ITR of 181 Bits/min and peak ITR values of up to 295 Bits/min for individual users.

Facial expressions attract attention due to their motivational significance. Previous work focused on attentional biases towards threat-related, fearful faces, although healthy participants tend to avoid mild threat. Growing evidence suggests that neuronal gamma (30Hz) and alpha-band activity (8-12Hz) play an important role in attentional selection, but it is unknown if such oscillatory activity is involved in the guidance of attention through facial expressions. Thus, in this magnetoencephalography (MEG) study we investigated whether attention is shifted towards or away from fearful faces and characterized the underlying neuronal activity in these frequency ranges in forty-four healthy volunteers. We employed a covert spatial attention task using neutral and fearful faces as task-irrelevant distractors and emotionally neutral Gabor patches as targets. Participants had to indicate the tilt direction of the target. Analysis of the neuronal data was restricted to the responses to target Gabor patches. We performed statistical analysis at the sensor level and used subsequent source reconstruction to localize the observed effects. Spatially selective attention effects in the alpha and gamma band were revealed in parieto-occipital regions. We observed an attentional cost of processing the face distractors, as reflected in lower task performance on targets with short stimulus onset asynchrony (SOA150ms) between faces and targets. On the neuronal level, attentional orienting to face distractors led to enhanced gamma band activity in bilateral occipital and parietal regions, when fearful faces were presented in the same hemifield as targets, but only in short SOA trials. Our findings provide evidence that both top-down and bottom-up attentional biases are reflected in parieto-occipital gamma-band activity.

The phase of neural oscillatory signals aligns to the predicted onset of upcoming stimulation. Whether such phase alignments represent phase resets of underlying neural oscillations or just rhythmically evoked activity, and whether they can be observed in a rhythm-free visual context, however, remains unclear. Here, we recorded the magnetoencephalogram while participants were engaged in a temporal prediction task, judging the visual or tactile reappearance of a uniformly moving stimulus. The prediction conditions were contrasted with a control condition to dissociate phase adjustments of neural oscillations from stimulus-driven activity. We observed stronger delta band inter-trial phase consistency (ITPC) in a network of sensory, parietal and frontal brain areas, but no power increase reflecting stimulus-driven or prediction-related evoked activity. Delta ITPC further correlated with prediction performance in the cerebellum and visual cortex. Our results provide evidence that phase alignments of low-frequency neural oscillations underlie temporal predictions in a non-rhythmic visual and crossmodal context.

Background Oscillatory phase has been proposed as a key parameter defining the spatiotemporal structure of neural activity. To enhance our understanding of brain rhythms and improve clinical outcomes in pathological conditions, modulation of neural activity by transcranial alternating current stimulation (tACS) emerged as a promising approach. However, the phase-specificity of tACS effects in humans is still critically debated. Objective Here, we investigated the phase-specificity of tACS on visually evoked steady state responses (SSRs) in 24 healthy human participants. Methods We used an intermittent electrical stimulation protocol and assessed the influence of tACS on SSR amplitude in the interval immediately following tACS. A neural network model served to validate the plausibility of experimental findings. Results We observed a modulation of SSR amplitudes dependent on the phase shift between flicker and tACS. The tACS effect size was negatively correlated with the strength of flicker-evoked activity. Supported by simulations, data suggest that strong network synchronization limits further neuromodulation by tACS. Neural sources of phase-specific effects were localized in the parieto-occipital cortex within flicker-entrained regions. Importantly, the optimal phase shift between flicker and tACS associated with strongest SSRs was correlated with SSR phase delays in the tACS target region. Conclusions Overall, our data provide electrophysiological evidence for phase-specific modulations of rhythmic brain activity by tACS in humans. As the optimal timing of tACS application was dependent on cortical SSR phase delays, our data suggest that tACS effects were not mediated by retinal co-stimulation. These findings highlight the potential of tACS for controlled, phase-specific modulations of neural activity.

Whereas the fundamental role of the body in social cognition seems to be generally accepted, elucidating the bodily mechanisms associated with non-verbal communication and cooperation between two or more persons is still a challenging endeavor. In this article we propose a fresh approach for investigating the function of the autonomic nervous system that is reflected in parameters of heart rate variability, respiration, and electrodermal activity in a social setting. We analyzed autonomic parameters of dyads solving a target-tracking task together with the partner or individually. A machine classifier was trained to predict the subjects' rating of performance and collaboration either from tracking error data or from the set of autonomic parameters. When subjects collaborated, this classifier could predict the subjective performance ratings better from the autonomic response than from the objective performance of the subjects. However, when they solved the task individually, predictability from autonomic parameters dropped to the level of objective performance, indicating that subjects were more rational in rating their performance in this condition. Moreover, the model captured general knowledge about the population that allows it to predict the performance ratings of an unseen subject significantly better than chance. Our results suggest that, in particular in situations that require collaboration with others, evaluation of performance is shaped by the bodily processes that are quantified by autonomic parameters. Therefore, subjective performance assessments appear to be modulated not only by the output of a rational or discriminative system that tracks the objective performance but to a significant extent also by interoceptive processes.

While human speech comprehension is thought to be an active process that involves top-down predictions, it remains unclear how predictive information is used to prepare for the processing of upcoming speech information. We aimed to identify the neural signatures of the preparatory processing of upcoming speech. Participants selectively attended to one of two competing naturalistic, narrative speech streams, and a temporal response function (TRF) method was applied to derive event-related-like neural responses from electroencephalographic data. The phase responses to the attended speech at the delta band (1–4 Hz) were correlated with the comprehension performance of individual participants, with a latency of − 200–0 ms relative to the onset of speech amplitude envelope fluctuations over the fronto-central and left-lateralized parietal electrodes. The phase responses to the attended speech at the alpha band also correlated with comprehension performance but with a latency of 650–980 ms post-onset over the fronto-central electrodes. Distinct neural signatures were found for the attentional modulation, taking the form of TRF-based amplitude responses at a latency of 240–320 ms post-onset over the left-lateralized fronto-central and occipital electrodes. Our findings reveal how the brain gets prepared to process an upcoming speech in a continuous, naturalistic speech context.

Background Deep brain stimulation (DBS) within the pallidum represents an effective and well-established treatment for isolated dystonia. However, clinical outcome after surgery may be variable with limited response in 10–25% of patients. The effect of lead location on clinical improvement is still under debate. Objective To identify stimulated brain regions associated with the most beneficial clinical outcome in dystonia patients. Methods 18 patients with cervical and generalized dystonia with chronic DBS of the internal pallidum were investigated. Patients were grouped according to their clinical improvement into responders, intermediate responders and non-responders. Magnetic resonance and computed tomography images were co-registered, and the volume of tissue activated (VTA) with respect to the pallidum of individual patients was analysed. Results VTAs in responders (n = 11), intermediate responders (n = 3) and non-responders (n = 4) intersected with the posterior internal (GPi) and external (GPe) pallidum and the subpallidal area. VTA heat maps showed an almost complete overlap of VTAs of responders, intermediate and non-responders. VTA coverage of the GPi was not higher in responders. In contrast, VTAs of intermediate and non-responders covered the GPi to a significantly larger extent in the left hemisphere (p Conclusions DBS of ventral parts of the posterior GPi, GPe and the adjacent subpallidal area containing pallidothalamic output projections resulted in favourable clinical effects. Of note, non-responders were also stimulated within the same area. This suggests that factors other than mere lead location (e.g., clinical phenotype, genetic background) have determined clinical outcome in the present cohort.

Learning and memorizing sequences of events is an important function of the human brain and the basis for forming expectations and making predictions. Learning is facilitated by repeating a sequence several times, causing rhythmic appearance of the individual sequence elements. This observation invites to consider the resulting multitude of rhythms as a spectral ‘fingerprint’ which characterizes the respective sequence. Here we explore the implications of this perspective by developing a neurobiologically plausible computational model which captures this ‘fingerprint’ by attuning an ensemble of neural oscillators. In our model, this attuning process is based on a number of oscillatory phenomena that have been observed in electrophysiological recordings of brain activity like synchronization, phase locking and reset as well as cross-frequency coupling. We compare the learning properties of the model with behavioral results from a study in human participants and observe good agreement of the errors for different levels of complexity of the sequence to be memorized. Finally, we suggest an extension of the model for processing sequences that extend over several sensory modalities.