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Recent research has extensively reported the phenomenon of inter-brain neural coupling between speakers and listeners during speech communication. Yet, the specific speech processes underlying this neural coupling remain elusive. To bridge this gap, this study estimated the correlation between the temporal dynamics of speaker-listener neural coupling with speech features, utilizing two inter-brain datasets accounting for different noise levels and listener's language experiences (native vs. non-native). We first derived time-varying speaker-listener neural coupling, extracted acoustic feature (envelope) and semantic features (entropy and surprisal) from speech, and then explored their correlational relationship. Our findings reveal that in clear conditions, speaker-listener neural coupling correlates with semantic features. However, as noise increases, this correlation is only significant for native listeners. For non-native listeners, neural coupling correlates predominantly with acoustic feature rather than semantic features. These results revealed how speaker-listener neural coupling is associated with the acoustic and semantic features under various scenarios, enriching our understanding of the inter-brain neural mechanisms during natural speech communication. We therefore advocate for more attention on the dynamic nature of speaker-listener neural coupling and its modeling with multilevel speech features., (© The Author(s) 2024. Published by Oxford University Press.)

We introduce a blockwise generalisation of the Antisymmetric Cross-Bicoherence (ACB), a statistical method based on bispectral analysis. The Multi-dimensional ACB (MACB) is an approach that aims at detecting quadratic lagged phase-interactions between vector time series in the frequency domain. Such a coupling can be empirically observed in functional neuroimaging data, e.g., in electro/magnetoencephalographic signals. MACB is invariant under orthogonal trasformations of the data, which makes it independent, e.g., on the choice of the physical coordinate system in the neuro-electromagnetic inverse procedure. In extensive synthetic experiments, we prove that MACB performance is significantly better than that obtained by ACB. Specifically, the shorter the data length, or the higher the dimension of the single data space, the larger the difference between the two methods., (© 2024. The Author(s).)

Background: Borderline personality disorder (BPD) is a severe psychiatric disorder conceptualised as a disorder of emotion regulation. Emotion regulation has been linked to a frontolimbic network comprising the dorsolateral prefrontal cortex and the amygdala, which apparently synchronises its activity via oscillatory coupling in the theta frequency range., Aims: To analyse whether there are distinct differences in theta oscillatory coupling in frontal brain regions between individuals with BPD and matched controls during emotion regulation by cognitive reappraisal., Method: Electroencephalogram (EEG) recordings were performed in 25 women diagnosed with BPD and 25 matched controls during a cognitive reappraisal task in which participants were instructed to downregulate negative emotions evoked by aversive visual stimuli. Between- and within-group time-frequency analyses were conducted to analyse regulation-associated theta activity (3.5-8.5 Hz)., Results: Oscillatory theta activity differed between the participants with BPD and matched controls during cognitive reappraisal. Regulation-associated theta increases were lower in frontal regions in the BPD cohort compared with matched controls. Functional connectivity analysis for regulation-associated changes in the theta frequency band revealed a lower multivariate interaction measure (MIM) increase in frontal brain regions in persons with BPD compared with matched controls., Conclusions: Our findings support the notion of alterations in a frontal theta network in BPD, which may be underlying core symptoms of the disorder such as deficits in emotion regulation. The results add to the growing body of evidence for altered oscillatory brain dynamics in psychiatric populations, which might be investigated as individualised treatment targets using non-invasive stimulation methods.

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 audio recordings 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 latencies were 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 latencies became more leading with increasing noise and decreasing speech comprehension and 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., Competing Interests: Declaration of Competing Interest All the co-authors declare that they have no conflict of interest., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Introduction: One of the most important cognitive functions in our everyday life is the working memory (WM). In several neuropsychiatric diseases such as ADHD or schizophrenia WM deficits can be observed, making it an attractive target for non-invasive brain stimulation methods like transcranial electrical stimulation (tES). However, the literature shows rather heterogeneous results of tES effects on WM performance. fMRI meta-analyses have identified a WM network including frontoparietal brain areas such as the dorsolateral prefrontal cortex (DLPFC) and the posterior parietal cortex (PPC). Neurophysiological studies revealed oscillatory activity in the theta band frequency range to be of crucial functional relevance for WM processes. Based on this, transcranial alternating current stimulation (tACS) in the theta frequency range targeting DLPFC and PPC in a spatially optimized way might further improve effects of tES on WM performance., Methods: Sixteen healthy subjects were stimulated with varying stimulation settings on four different days in a counterbalanced within-subject design. These setups included the application of (1) tACS with a frequency of 5 Hz (theta frequency range) over the left DLPFC and (2) the right superior parietal cortex, (3) transcranial direct current stimulation (tDCS) of the DLPFC and (4) a sham stimulation condition during the online performance of a visual delayed-match-to-sample task with varying working memory load. We introduce a procedure to calculate an optimal tES model revealing optimized high-density setups for the present study for 3 cathodes and 1 anode and stimulation currents of 1.5 mA., Results: A significant interaction effect of stimulation type and load condition on working memory capacity was found. This was reflected by a significant improvement of WM performance in the high load condition during tACS over the left DLPFC compared with sham stimulation, which was not the case for our parietal tACS or tDCS setup., Discussion: Working memory performance can be improved with optimized high-definition tACS with a frequency of 5 Hz over the left DLPFC. The conception of different mechanisms underlying transcranial electrical stimulation with alternating and direct currents is supported by these results. Patients suffering from working memory impairments due to neuropsychiatric diseases might potentially benefit from this brain stimulation approach., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Rauh, Müller, Nolte, Haaf, Mußmann, Steinmann, Mulert and Leicht.)

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., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.)

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., Competing Interests: Conflict of interestsThe authors declare no competing interests., (© The Author(s), under exclusive licence to Springer Nature B.V. 2021.)

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., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Influential theories postulate distinct roles of catecholamines and acetylcholine in cognition and behavior. However, previous physiological work reported similar effects of these neuromodulators on the response properties (specifically, the gain) of individual cortical neurons. Here, we show a double dissociation between the effects of catecholamines and acetylcholine at the level of large-scale interactions between cortical areas in humans. A pharmacological boost of catecholamine levels increased cortex-wide interactions during a visual task, but not rest. An acetylcholine boost decreased interactions 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 increased by catecholamines) and intracortical transmission (more strongly reduced by acetylcholine). The inferred catecholaminergic mechanism also predicted noisier decision-making, which we confirmed for both perceptual and value-based choice behavior. Our work highlights specific circuit mechanisms for shaping cortical network interactions and behavioral variability by key neuromodulatory systems., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

Disturbed functional connectivity is assumed to cause neurocognitive deficits in patients suffering from schizophrenia. A Glutamate N-methyl-D-aspartate receptor (NMDAR) dysfunction has been suggested as a possible mechanism underlying altered connectivity in schizophrenia, especially in the gamma- and theta-frequency range. The present study aimed to investigate the effects of the NMDAR-antagonist ketamine on resting-state power, functional connectivity, and schizophrenia-like psychopathological changes in healthy volunteers. In a placebo-controlled crossover design, 25 healthy subjects were recorded using resting-state 64-channel-electroencephalography (EEG) (eyes closed). The imaginary coherence-based Multivariate Interaction Measure (MIM) was used to measure gamma and theta connectivity across 80 cortical regions. The network-based statistic was applied to identify involved networks under ketamine. Psychopathology was assessed with the Positive and Negative Syndrome Scale (PANSS) and the 5-Dimensional Altered States of Consciousness Rating Scale (5D-ASC). Ketamine caused an increase in all PANSS ( p < 0.001) as well as 5D-ASC scores ( p < 0.01). Significant increases in resting-state gamma and theta power were observed under ketamine compared to placebo ( p < 0.05). The source-space analysis revealed two distinct networks with an increased mean functional gamma- or theta-band connectivity during the ketamine session. The gamma-network consisted of midline regions, the cuneus, the precuneus, and the bilateral posterior cingulate cortices, while the theta-band network involved the Heschl gyrus, midline regions, the insula, and the middle cingulate cortex. The current source density (CSD) within the gamma-band correlated negatively with the PANSS negative symptom score, and the activity within the gamma-band network correlated negatively with the subjective changed meaning of percepts subscale of the 5D-ASC. These results are in line with resting-state patterns seen in people who have schizophrenia and argue for a crucial role of the glutamate system in mediating dysfunctional gamma- and theta-band-connectivity in schizophrenia. Resting-state networks could serve as biomarkers for the response to glutamatergic drugs or drug development efforts within the glutamate system., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Curic, Andreou, Nolte, Steinmann, Thiebes, Polomac, Haaf, Rauh, Leicht and Mulert.)

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., (Copyright © 2020 Nolte, Galindo-Leon, Li, Liu and Engel.)

In this paper we make two contributions to the analysis of brain oscillations with CFC techniques. First, we introduce a new bispectral CFC measure which is selective to couplings between three or more brain sources. This measure can be derived from ordinary cross-bispectra by performing a total-antisymmetrization operation on them. Significant coupling values can then be attributed to at least three interacting signals. This selectivity to the number of sources can be helpful to test hypotheses on the number of brain sources involved in the generation of commonly observed brain oscillations, such as the alpha rhythm. In a second step we present the correct empirical distribution for the coupling measure, which is necessary to properly assess the significance of coupling results. More importantly however, this corrected statistic is not limited to our particular measure, but holds for all complex-valued coupling estimators. We illustrate how the very common misassumption of empirical normality of such estimators can lead to a systematic underestimation of p -values, the breakdown of multiple comparison control procedures and in consequence a drastic inflation of the number of false positives., (Copyright © 2020 Bartz, Andreou and Nolte.)

Imaging studies help us understand the important role of brainstem and midbrain regions in human trigeminal pain processing without solving the question of how these regions actually interact. In the current study, we describe this connectivity and its dynamics during nociception with a novel analytical approach called Partial Similarity (PS). We developed PS specifically to estimate the communication between individual hubs of the network in contrast to the overall communication within that network. Partial Similarity works on trial-to-trial variance of neuronal activity acquired with functional magnetic resonance imaging. It discovers direct communication between two hubs considering the remainder of the network as confounds. A similar method to PS is Representational Similarity, which works with ordinary correlations and does not consider any external influence on the communication between two hubs. Particularly the combination of Representational Similarity and Partial Similarity analysis unravels brainstem dynamics involved in trigeminal pain using the spinal trigeminal nucleus (STN)-the first relay station of peripheral trigeminal input-as a seed region. The combination of both methods can be valuable tools in discovering the network dynamics in fMRI and an important instrument for future insight into the nature of various neurological diseases like primary headaches.

Objectives : Evidence from animal studies suggests that aerobic exercise may promote neuroplasticity and could, therefore, provide therapeutic benefits for neurological diseases such as multiple sclerosis (MS). However, the effects of exercise in human CNS disorders on the topology of brain networks, which might serve as an outcome at the interface between biology and clinical performance, remain poorly understood. Methods : We investigated functional and structural networks in patients with relapsing-remitting MS in a clinical trial of standardized aerobic exercise. Fifty-seven patients were randomly assigned to moderate-intensity exercise for 3 months or a non-exercise control group. We reconstructed functional networks based on resting-state functional magnetic resonance imaging (MRI) and used probabilistic tractography on diffusion-weighted imaging data for structural networks. Results : At baseline, compared to 30 healthy controls, patients exhibited decreased structural connectivity that was most pronounced in hub regions of the brain. Vice versa, functional connectivity was increased in hubs. After 3 months, we observed hub independent increased functional connectivity in the exercise group while the control group presented a loss of functional hub connectivity. On a structural level, the control group remained unchanged, while the exercise group had also increased connectivity. Increased clustering of hubs indicates a better structural integration and internal connectivity at the top of the network hierarchy. Conclusion : Increased functional connectivity of hubs contrasts a loss of structural connectivity in relapsing-remitting MS. Under an exercise condition, a further hub independent increase of functional connectivity seems to translate in higher structural connectivity of the whole brain., (Copyright © 2020 Stellmann, Maarouf, Schulz, Baquet, Pöttgen, Patra, Penner, Gellißen, Ketels, Besson, Ranjeva, Guye, Nolte, Engel, Audoin, Heesen and Gold.)

Cross-frequency coupling (CFC) between neuronal oscillations reflects an integration of spatially and spectrally distributed information in the brain. Here, we propose a novel framework for detecting such interactions in Magneto- and Electroencephalography (MEG/EEG), which we refer to as Nonlinear Interaction Decomposition (NID). In contrast to all previous methods for separation of cross-frequency (CF) sources in the brain, we propose that the extraction of nonlinearly interacting oscillations can be based on the statistical properties of their linear mixtures. The main idea of NID is that nonlinearly coupled brain oscillations can be mixed in such a way that the resulting linear mixture has a non-Gaussian distribution. We evaluate this argument analytically for amplitude-modulated narrow-band oscillations which are either phase-phase or amplitude-amplitude CF coupled. We validated NID extensively with simulated EEG obtained with realistic head modelling. The method extracted nonlinearly interacting components reliably even at SNRs as small as -15 dB. Additionally, we applied NID to the resting-state EEG of 81 subjects to characterize CF phase-phase coupling between alpha and beta oscillations. The extracted sources were located in temporal, parietal and frontal areas, demonstrating the existence of diverse local and distant nonlinear interactions in resting-state EEG data. All codes are available publicly via GitHub., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Detrended fluctuation analysis (DFA) is a popular method to analyze long-range temporal correlations in time series of many different research areas but in particular also for electrophysiological recordings. Using the classical DFA method, the cumulative sum of data are divided into segments, and the variance of these sums is studied as a function of segment length after linearly detrending them in each segment. The starting point of the proposed new method is the observation that the classical method is inherently non-stationary without justification by a corresponding non-stationarity of the data. This leads to unstable estimates of fluctuations to the extent that it is impossible to estimate slopes of the fluctuations other than by fitting a line over a wide range of temporal scales. We here use a modification of the classical method by formulating the detrending as a strictly stationary operation. With this modification the detrended fluctuations can be expressed as a weighted average across the power spectrum of a signal. Most importantly, we can also express the slopes, calculated as analytic derivatives of the fluctuations with respect to the scales, as statistically robust weighted averages across the power spectra. The method is applied to amplitudes of brain oscillations measured with magnetoencephalography in resting state condition. We found for envelopes of the the alpha rhythm that fluctuations as a function of time scales in a double-logarithmic plot differ substantially from a linear relation for time scales below 10 seconds. In particular we will show that model selections fail to determine accurate scaling laws, and that standard parameter settings are likely to yield results depending on signal to noise ratios than on true long range temporal correlations.

Synchronized oscillatory gamma-band activity (30-100Hz) has been suggested to constitute a key mechanism to dynamically orchestrate sensory information integration across multiple spatio-temporal scales. We here tested whether interhemispheric functional connectivity and ensuing auditory perception can selectively be modulated by high-density transcranial alternating current stimulation (HD-tACS). For this purpose, we applied multi-site HD-tACS at 40Hz bilaterally with a phase lag of 180° and recorded a 64-channel EEG to study the oscillatory phase dynamics at the source-space level during a dichotic listening (DL) task in twenty-six healthy participants. In this study, we revealed an oscillatory phase signature at 40Hz which reflects different temporal profiles of the phase asymmetries during left and right ear percept. Here we report that 180°-tACS did not affect the right ear advantage during DL at group level. However, a follow-up analysis revealed that the intrinsic phase asymmetries during sham-tACS determined the directionality of the behavioral modulations: While a shift to left ear percept was associated with augmented interhemispheric asymmetry (closer to 180°), a shift to right ear processing was elicited in subjects with lower asymmetry (closer to 0°). Crucially, the modulation of the interhemispheric network dynamics depended on the deviation of the tACS-induced phase-lag from the intrinsic phase asymmetry. Our characterization of the oscillatory network trends is giving rise to the importance of phase-specific gamma-band coupling during ambiguous auditory perception, and emphasizes the necessity to address the inter-individual variability of phase asymmetries in future studies by tailored stimulation protocols., Competing Interests: The authors have declared that no competing interests exist.

Migraineurs are hypersensitive for most sensory domains like visual, auditory or somatosensory processing even outside of attacks. This behavioral peculiarity is mirrored by findings of cortical hyper-responsivity already in the interictal state. Using repetitive visual stimulation to elicit steady state visually evoked potentials (SSVEP) in 30 interictal episodic migraineurs and 30 controls we show hyper-responsivity of the visual cortex in the migraineurs. Additionally, the occipital regions were remarkably stronger coupled to the temporal, premotor and the anterior cingulate cortex than in headache free controls. These data suggest harmonized oscillations of different cortical areas as a response to visual input which might be driven by the cuneus. Furthermore, the increased coupling is modulated by the current state of the migraine cycle as the coupling was significantly stronger in patients with longer interictal periods., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

The ascending modulatory systems of the brain stem are powerful regulators of global brain state. Disturbances of these systems are implicated in several major neuropsychiatric disorders. Yet, how these systems interact with specific neural computations in the cerebral cortex to shape perception, cognition, and behavior remains poorly understood. Here, we probed into the effect of two such systems, the catecholaminergic (dopaminergic and noradrenergic) and cholinergic systems, on an important aspect of cortical computation: its intrinsic variability. To this end, we combined placebo-controlled pharmacological intervention in humans, recordings of cortical population activity using magnetoencephalography (MEG), and psychophysical measurements of the perception of ambiguous visual input. A low-dose catecholaminergic, but not cholinergic, manipulation altered the rate of spontaneous perceptual fluctuations as well as the temporal structure of "scale-free" population activity of large swaths of the visual and parietal cortices. Computational analyses indicate that both effects were consistent with an increase in excitatory relative to inhibitory activity in the cortical areas underlying visual perceptual inference. We propose that catecholamines regulate the variability of perception and cognition through dynamically changing the cortical excitation-inhibition ratio. The combined readout of fluctuations in perception and cortical activity we established here may prove useful as an efficient and easily accessible marker of altered cortical computation in neuropsychiatric disorders.

Recent studies support the view that cortical sensory, limbic and executive networks and the autonomic nervous system might interact in distinct manners under the influence of acupuncture to modulate pain. We performed a double-blind crossover design study to investigate subjective ratings, EEG and ECG following experimental laser pain under the influence of sham and verum acupuncture in 26 healthy volunteers. We analyzed neuronal oscillations and inter-regional coherence in the gamma band of 128-channel-EEG recordings as well as heart rate variability (HRV) on two experimental days. Pain ratings and pain-induced gamma oscillations together with vagally-mediated power in the high-frequency bandwidth (vmHF) of HRV decreased significantly stronger during verum than sham acupuncture. Gamma oscillations were localized in the prefrontal cortex (PFC), mid-cingulate cortex (MCC), primary somatosensory cortex and insula. Reductions of pain ratings and vmHF-power were significantly correlated with increase of connectivity between the insula and MCC. In contrast, connectivity between left and right PFC and between PFC and insula correlated positively with vmHF-power without a relationship to acupuncture analgesia. Overall, these findings highlight the influence of the insula in integrating activity in limbic-saliency networks with vagally mediated homeostatic control to mediate antinociception under the influence of acupuncture.

Throughout each day, the brain displays transient changes in state, as evidenced by shifts in behavior and vigilance. While the electrophysiological correlates of brain states have been studied for some time, it remains unclear how large-scale cortico-cortical functional connectivity systematically reconfigures across states. Here, we investigate state-dependent shifts in cortical functional connectivity by recording local field potentials (LFPs) during spontaneous behavioral transitions in the ferret using chronically implanted micro-electrocorticographic (µECoG) arrays positioned over occipital, parietal, and temporal cortical regions. To objectively classify brain state, we describe a data-driven approach that projects time-varying LFP spectral properties into brain state space. Distinct brain states displayed markedly different patterns of cross-frequency phase-amplitude coupling and inter-electrode phase synchronization across several LFP frequency bands. The largest across-state differences in functional connectivity were observed between periods of presumed slow-wave and rapid-eye-movement-sleep/active-state, which were characterized by the contrasting phenomena of cortical network fragmentation and global synchronization, respectively. Collectively, our data provide strong evidence that large-scale functional interactions in the brain dynamically reconfigure across behavioral states.

The cerebral cortex continuously undergoes changes in its state, which are manifested in transient modulations of the cortical power spectrum. Cortical state changes also occur at full wakefulness and during rapid cognitive acts, such as perceptual decisions. Previous studies found a global modulation of beta-band (12-30 Hz) activity in human and monkey visual cortex during an elementary visual decision: reporting the appearance or disappearance of salient visual targets surrounded by a distractor. The previous studies disentangled neither the motor action associated with behavioral report nor other secondary processes, such as arousal, from perceptual decision processing per se. Here, we used magnetoencephalography in humans to pinpoint the factors underlying the beta-band modulation. We found that disappearances of a salient target were associated with beta-band suppression, and target reappearances with beta-band enhancement. This was true for both overt behavioral reports (immediate button presses) and silent counting of the perceptual events. This finding indicates that the beta-band modulation was unrelated to the execution of the motor act associated with a behavioral report of the perceptual decision. Further, changes in pupil-linked arousal, fixational eye movements, or gamma-band responses were not necessary for the beta-band modulation. Together, our results suggest that the beta-band modulation was a top-down signal associated with the process of converting graded perceptual signals into a categorical format underlying flexible behavior. This signal may have been fed back from brain regions involved in decision processing to visual cortex, thus enforcing a "decision-consistent" cortical state. SIGNIFICANCE STATEMENT Elementary visual decisions are associated with a rapid state change in visual cortex, indexed by a modulation of neural activity in the beta-frequency range. Such decisions are also followed by other events that might affect the state of visual cortex, including the motor command associated with the report of the decision, an increase in pupil-linked arousal, fixational eye movements, and fluctuations in bottom-up sensory processing. Here, we ruled out the necessity of these events for the beta-band modulation of visual cortex. We propose that the modulation reflects a decision-related state change, which is induced by the conversion of graded perceptual signals into a categorical format underlying behavior. The resulting decision signal may be fed back to visual cortex., (Copyright © 2017 Meindertsma et al.)