Neurophysiological Signals Research Articles

Concordant neurophysiological signatures of cognitive control in humans and rats.

Progress towards understanding neural mechanisms in humans relevant to psychiatric conditions has been hindered by a lack of translationally-relevant cognitive tasks for laboratory animals. Accordingly, there is a critical need to develop parallel neurophysiological assessments of domains of cognition, such as cognitive control, in humans and laboratory animals. To address this, we developed a touchscreen-based cognitive (Eriksen Flanker) task in rats and used its key characteristics to construct a novel human version, with similar testing parameters and endpoints across species. We obtained continuous electroencephalogram (EEG) recordings, including local field potentials in rats, and compared electrophysiological signatures locked to stimulus onset and responses across species. We also assessed whether behavioral or physiological task effects were modulated by modafinil, which enhances aspects of cognitive function in humans. In both species, the task elicited expected flanker interference effects (reduced accuracy) during high-conflict trials. Across homologous neuroanatomical loci, stimulus-locked increases in theta power during high-conflict trials as well as error-related negative potentials were observed. These endpoints were not affected by modafinil in either species. Despite some species-specific patterns, our findings demonstrate the feasibility of a rat Flanker task as well as cross-species behavioral and neurophysiological similarities, which may enable novel insights into the neural correlates of healthy and aberrant behavior and provide mechanistic insights relevant to treatment.

Sleep abnormalities in individuals at clinical high risk for psychosis

Youth at clinical high risk (CHR) represent a unique population enriched for precursors of major psychiatric disorders. Sleep disturbances are consistently reported in CHR individuals. However, there is a dearth of studies investigating quantifiable objective measures of sleep dysfunction in CHR youth. In this study, sleep high density (hd)-EEG recordings were collected in twenty-two CHR and twenty healthy control (HC) subjects. Sleep architecture parameters, as well as sleep EEG power spectra in five frequency bands, were computed and compared between CHR and HC groups during non-rapid eye movement (NREM) sleep. Furthermore, correlation analyses between sleep EEG power spectra, sleep architecture parameters, and clinical symptoms, assessed with the scale of prodromal symptoms (SOPS), were conducted in CHR participants. Our results show that CHR individuals had more wakefulness after sleep onset (WASO) compared to HC participants. CHR also showed a higher NREM sleep gamma EEG power, which was observed in a large fronto-parieto-occipital area, relative to HC. Additionally, higher NREM gamma activity in lateral fronto-occipital regions was associated with more WASO, and increased NREM gamma power in medial fronto/parietal areas correlated with worse SOPS negative symptoms. Altogether, these findings suggest that topographically specific increases in EEG gamma activity during NREM sleep represent neurophysiological signatures underlying some of the objectively assessed sleep disturbances and clinical symptoms of CHR individuals.

Clinical Diagnosis of Psychiatry Based on Electroencephalography

At present, neurophysiological signals used for neuro feedback are EEG (Electroencephalogram), functional magnetic resonance imaging. Among them, the acquisition of EEG signals has the advantages of non-invasive way with low cost. It has been widely used in brain-machine interface technology in recent years. Important progress has been made in rehabilitation and environmental control. However, neural feedback and brainmachine interface technology are completely similar in signal acquisition, signal feature extraction, and pattern classification. Therefore, the related research results of brain-machine interface can be used to closely cooperate with clinical needs to research and develop neural feedback technology based on EEG. Based on neurophysiology and brain-machine interface technology, this paper develops a neural feedback training system based on the acquisition and analysis of human EEG signals. Aiming at the autonomous rhythm components in the EEG signal, such as sensorimotor rhythm and alpha rhythm, the characteristic parameters are extracted through real-time EEG signal processing to generate feedback information, and the subject is self-regulated and trained from a physiological-psychological perspective by providing adjuvant treatment, a practical and stable treatment platform for the clinic.

Marked point process representation of oscillatory dynamics underlying working memory

Objective. Computational models of neural activity at the meso-scale suggest the involvement of discrete oscillatory bursts as constructs of cognitive processing during behavioral tasks. Classical signal processing techniques that attempt to infer neural correlates of behavior from meso-scale activity employ spectral representations of the signal, exploiting power spectral density techniques and time–frequency (T–F) energy distributions to capture band power features. However, such analyses demand more specialized methods that incorporate explicitly the concepts of neurophysiological signal generation and time resolution in the tens of milliseconds. This paper focuses on working memory (WM), a complex cognitive process involved in encoding, storing and retrieving sensory information, which has been shown to be characterized by oscillatory bursts in the beta and gamma band. Employing a generative model for oscillatory dynamics, we present a marked point process (MPP) representation of bursts during memory creation and readout. We show that the markers of the point process quantify specific neural correlates of WM. Approach. We demonstrate our results on field potentials recorded from the prelimbic and secondary motor cortices of three rats while performing a WM task. The generative model for single channel, band-passed traces of field potentials characterizes with high-resolution, the timings and amplitudes of transient neuromodulations in the high gamma (80–150 Hz, γ) and beta (10–30 Hz, β) bands as an MPP. We use standard hypothesis testing methods on the MPP features to check for significance in encoding of task variables, sensory stimulus and executive control while comparing encoding capabilities of our model with other T–F methods. Main Results. Firstly, the advantages of an MPP approach in deciphering encoding mechanisms at the meso-scale is demonstrated. Secondly, the nature of state encoding by neuromodulatory events is determined. Third, we demonstrate the necessity of a higher time resolution alternative to conventionally employed T–F methods. Finally, our results underscore the novelty in interpreting oscillatory dynamics encompassed by the marked features of the point process. Significance. An MPP representation of meso-scale activity not just enables a rich, high-resolution parameter space for analysis but also presents a novel tool for diverse neural applications.

Electrophysiological investigation of reward anticipation and outcome evaluation during slot machine play

Slot machines are a popular form of gambling, offering a tractable way to experimentally model reward processes. This study used a 3-reel slot paradigm to assess psychologically distinct phases of reward processing, reflecting anticipation, and early- and late-stage outcome processing. EEG measures of winning, nearly missing (a losing outcome revealed at the final, third reel), and “totally” missing (a losing outcome revealed earlier, at the second reel) were collected from healthy adults (n=54). Condition effects were evaluated in: i) event-related potential (ERP) components reflecting anticipatory attention (stimulus preceding negativity, SPN) and outcome processing (reward positivity, RewP and late-positive potential, LPP) and ii) total power and phase synchrony of theta and delta band oscillations. Behaviorally, trial initiation was fastest after a near miss outcome and slowest after a winning outcome. As expected, a significant SPN was observed for possible wins (AA) vs. total misses (AB), consistent with reward anticipation. Larger win (AAA) vs. near miss (AAB) amplitudes were observed for the RewP; LPP amplitudes were largest for wins (AAA), intermediate for near misses (AAB), and smallest for total misses (ABC), reflecting significant early (RewP) and late-stage (LPP) outcome processing effects. There was an effect of reel position on the RewP, with larger amplitude in the final reel (AAA-AAB) relative to the 2nd-reel locked difference waves (AA-AB). Across all outcomes, near misses elicited the largest and most phase-synchronized theta responses, while wins elicited larger and more phase-synchronized delta responses than total misses, with delta band measures not distinguishing between near misses and wins. . Phase locking measures contrasting win vs. near miss delta and theta synchronization, within time windows corresponding to ERP measurements, covaried with RewP, but not SPN or LPP, amplitude. Lastly, EEG measures showed differential relationships with age and self-reported consummatory pleasure. In the context of slot machine play, where reward anticipation and attainment place minimal demands on effort and skill, ERP and time-frequency methods capture distinct neurophysiological signatures of reward anticipation and outcome processing.

A study on decomposing EEGs during sleep into frequency components and revealing spindles using Kalman filter

Sleep scoring in psychiatry is one of the important issues for classification of diseases, investigation of sleep patterns and also appropriate treatment practices. Sleep specialists usually perform manual sleep stage scoring by visually examining the patient's neurophysiological signals collected in sleep laboratories. This is not only very difficult but also very time consuming and easing to make mistake task. Sleep spindles are characteristic electroencephalogram (EEG) signatures of the sleep of stage-2 non-rapid eye movement. Spindles taking place in sleep regulation and cognitive functions may represent biomarkers of a neuropsychiatric disease. Sleep spindles, which are considered to have an important role in enhancing sleep duration, are characteristic waveforms seen in the non-REM sleep phase. The detection and analysis of the waveforms of the spindles, as well as determining the time and duration of the spindles, are very important to fully understand the process of sleep. However, as the spindles have transient properties compared to background EEG signals, it becomes difficult to analyze and distinguish them visually. There are automatic spindle detection methods (ASDM). But there is no consensus on the usage of these automated methods since they are not robust especially in different characteristics of the spindles. In this study, raw sleep EEG data is taken as a time-series data and it is used without any pre-processing (without filtering). Adaptive extended Kalman filter (AEKF), a recursive estimation method, is used to separate this time series data into trend, cycle and drift components. In the zones where the psychiatrist or an expert in sleep scoring specifies the occurrence of the spindles, the trend and cycle values obtained by using AEKF are also indicating the occurrences of the spindles very clearly in the same zones. Also, the estimation of the cycle gives some information about the starting point of the spindle. Just by viewing both raw data and trend and cycle components, the expert can easily recognize whether there is a spindle or not. Therefore, the main contribution of this article is to suggest a more effective methodology for detecting spindles. The estimation results support the methodology used in this article. Such a finding also shows that the nonlinear state-space model and the AEKF can be properly applied in similar frameworks. In particular, this study separates EEG signals into trend and cycle components, where the methodology allows for system parameters and the components to be estimated simultaneously. Since the methodology allows for the parameters to vary over time, observing the time patterns of the estimated parameters have the potential to reveal further information about the sleep process.

NeuroKit2: A Python toolbox for neurophysiological signal processing.

NeuroKit2 is an open-source, community-driven, and user-centered Python package for neurophysiological signal processing. It provides a comprehensive suite of processing routines for a variety of bodily signals (e.g., ECG, PPG, EDA, EMG, RSP). These processing routines include high-level functions that enable data processing in a few lines of code using validated pipelines, which we illustrate in two examples covering the most typical scenarios, such as an event-related paradigm and an interval-related analysis. The package also includes tools for specific processing steps such as rate extraction and filtering methods, offering a trade-off between high-level convenience and fine-tuned control. Its goal is to improve transparency and reproducibility in neurophysiological research, as well as foster exploration and innovation. Its design philosophy is centred on user-experience and accessibility to both novice and advanced users.

A Neurophysiological Pattern as a Precursor of Work-Related Musculoskeletal Disorders Using EEG Combined with EMG.

We aimed to determine the neurophysiological pattern that is associated with the development of musculoskeletal pain that is induced by biomechanical constraints. Twelve (12) young healthy volunteers (two females) performed two experimental realistic manual tasks for 30 min each: (1) with the high risk of musculoskeletal pain development and (2) with low risk for pain development. During the tasks, synchronized electroencephalographic (EEG) and electromyography (EMG) signals data were collected, as well as pain scores. Subsequently, two main variables were computed from neurophysiological signals: (1) cortical inhibition as Task-Related Power Increase (TRPI) in beta EEG frequency band (β.TRPI) and (2) muscle variability as Coefficient of Variation (CoV) from EMG signals. A strong effect size was observed for pain measurement under the high risk condition during the last 5 min of the task execution; with muscle fatigue, because the CoV has decreased below 18%. An increase in cortical inhibition (β.TRPI >50%) was observed after the 5th min of the task in both experimental conditions. These results suggest the following neurophysiological pattern—β.TRPI ≥ 50% and CoV ≤ 18%—as a possible indicator to monitor the development of musculoskeletal pain in the shoulder in the context of repeated and prolonged exposure to manual tasks.

I'm a believer: Illusory self-generated touch elicits sensory attenuation and somatosensory evoked potentials similar to the real self-touch

Sensory attenuation (i.e., the phenomenon whereby self-produced sensations are perceived as less intense compared to externally occurring ones) is among the neurocognitive processes that help distinguishing ourselves from others. It is thought to be rooted in the motor system (e.g., related to motor intention and prediction), while the role of body awareness, which necessarily accompanies any voluntary movement, in this phenomenon is largely unknown. To fill this gap, here we compared the perceived intensity, somatosensory evoked potentials, and alpha-band desynchronization for self-generated, other-generated, and embodied-fake-hand-generated somatosensory stimuli. We showed that sensory attenuation triggered by the own hand and by the embodied fake hand had the same behavioral and neurophysiological signatures (reduced subjective intensity, reduced of N140 and P200 SEP components and post-stimulus alpha-band desynchronization). Therefore, signals subserving body ownership influenced attenuation of somatosensory stimuli, possibly in a postdictive manner. This indicates that body ownership is crucial for distinguishing the source of the perceived sensations.

A dimension reduction technique applied to regression on high dimension, low sample size neurophysiological data sets

BackgroundA common problem in neurophysiological signal processing is the extraction of meaningful information from high dimension, low sample size data (HDLSS). We present RoLDSIS (regression on low-dimension spanned input space), a regression technique based on dimensionality reduction that constrains the solution to the subspace spanned by the available observations. This avoids regularization parameters in the regression procedure, as needed in shrinkage regression methods.ResultsWe applied RoLDSIS to the EEG data collected in a phonemic identification experiment. In the experiment, morphed syllables in the continuum /da/–/ta/ were presented as acoustic stimuli to the participants and the event-related potentials (ERP) were recorded and then represented as a set of features in the time-frequency domain via the discrete wavelet transform. Each set of stimuli was chosen from a preliminary identification task executed by the participant. Physical and psychophysical attributes were associated to each stimulus. RoLDSIS was then used to infer the neurophysiological axes, in the feature space, associated with each attribute. We show that these axes can be reliably estimated and that their separation is correlated with the individual strength of phonemic categorization. The results provided by RoLDSIS are interpretable in the time-frequency domain and may be used to infer the neurophysiological correlates of phonemic categorization. A comparison with commonly used regularized regression techniques was carried out by cross-validation.ConclusionThe prediction errors obtained by RoLDSIS are comparable to those obtained with Ridge Regression and smaller than those obtained with LASSO and SPLS. However, RoLDSIS achieves this without the need for cross-validation, a procedure that requires the extraction of a large amount of observations from the data and, consequently, a decreased signal-to-noise ratio when averaging trials. We show that, even though RoLDSIS is a simple technique, it is suitable for the processing and interpretation of neurophysiological signals.

The Effect of Short-Term Mindfulness Training on the Emotional State of College Students with Different Levels of Mindfulness: Evidence from Neurophysiological Signals

Objectives: The objective of this study was to explore the effects of short-term mindfulness training on the emotional state of individuals with different levels of mindfulness, especially from the perspective of autonomic neurophysiological mechanism. Methods: 52 college students were divided into higher or lower dispositional mindfulness groups. Then they underwent the measurement periods of baseline, short-term mindfulness training and recovery. During these periods, wrist-worn wearable devices were used for recording their autonomic nervous system activities, including heart rate (HR), galvanic skin response (GSR) and pulse rate variability (PRV). The Positive and Negative affect Scale (PANAS) and the State Anxiety Inventory (S-AI) were used before and after the experiment. Results: The results showed that in both groups, instead of positive emotion, negative emotions and state anxiety reduced significantly after the training. In terms of physiological signals, in lower-dispositional group, the HR of the recovery period were significantly lower than that of the other two periods, and the PRV of the recovery period were significantly higher. In higher-dispositional group, the GSR of recovery period was significantly higher than that of the other two periods. Conclusions: The results suggest that short-term mindfulness training works mainly by reducing negative emotions rather than increasing positive ones. Our findings also provide neurophysiological evidence for a better emotional intervention effect of short-term mindfulness training on individuals with lower dispositional mindfulness.

Enhancing EEG-Based Classification of Depression Patients Using Spatial Information.

Depression has become a leading mental disorder worldwide. Evidence has shown that subjects with depression exhibit different spatial responses in neurophysiological signals from the healthy controls when they are exposed to positive and negative stimuli. We proposed an effective electroencephalogram-based detection method for depression classification using spatial information. A face-in-the-crowd task, including positive and negative emotional facial expressions, was presented to 30 participants, including 16 depression patients and 14 healthy controls. Differential entropy and the genetic algorithm were used for feature extraction and selection, and a support vector machine was used for classification. A task-related common spatial pattern (TCSP) was proposed to enhance the spatial differences before the feature extraction. We achieved a leave-one-subject-out cross-validation classification result of 84% and 85.7% for positive and negative stimuli, respectively, using TCSP, which is statistically significantly higher than 81.7% and 83.2%, respectively, acquired without the TCSP (p < 0.05). We also evaluated the classification performance using individual frequency bands and found that the contribution of the gamma band was predominant. In addition, we evaluated different classifiers, including k-nearest neighbor and logistic regression, which showed similar trends in the improvement of classification by employing TCSP. The results show that our proposed method, employing spatial information, significantly improves the accuracy of classifying depression patients.

Position-related neurovascular injuries detected by intraoperative monitoring

Background: Poor patient positioning during surgeries can result in vascular and peripheral nerve injuries. The purpose of this study was to analyze the various etiologic factors related to positioning detected by intraoperative monitoring (IOM) and make recommendations for prevention of comorbidities. Materials and Methods: The data for a total of 4450 consecutively monitored patients who underwent orthopedic and neurosurgical procedures were retrospectively reviewed. Patients with signal changes related to positioning detected by IOM were analyzed for position, modalities, timing of interventions, duration of surgery, and etiologic factors. Deficit and non-deficit groups were further compared using Wilcoxon rank sum test. Results: Intraoperative evidence of impending neurologic injury was seen 1.1% times, most frequently due to malpositioning of upper extremities (57%) in prone position (55%). Shoulder tape, bootstrap, thigh/hip pads, sitting and lower extremity malpositioning were other etiologic factors. Appropriate intervention was performed within 3min 66% of the time. Four patients developed new postoperative deficits: brachial plexopathy (n = 3) and bilateral sciatic neuropathy (n = 1). The deficit group had longer duration of surgery (P = 0.031), and neurophysiological (NP) signal changes persisted for an increased time interval (P = 0.0084) when compared to the non-deficit group. Conclusion: Prolonged duration of surgery and persistence of NP signal changes can increase the risk of potential neurovascular injury. Intraoperative neurovascular injuries due to positioning can occur in various settings. Early recognition of signal changes during monitoring and immediate intervention is likely to prevent neurological deficits.

Joint recording of EEG and audio signals in hyperscanning and pseudo-hyperscanning experiments

Hyperscanning is an emerging technique that allows for the study of brain similarities between interacting individuals. This methodology has powerful implications for understanding the neural basis of joint actions, such as conversation; however, it also demands precise time-locking between the different brain recordings and sensory stimulation. Such precise timing, nevertheless, is often difficult to achieve. Recording auditory stimuli jointly with the ongoing high temporal resolution neurophysiological signal presents an effective way to control timing asynchronies offline between the digital trigger sent by the stimulation program and the actual onset of the auditory stimulus delivered to participants via speakers/headphones. This configuration is particularly challenging in hyperscanning setups due to the general increased complexity of the methodology. In other designs using the related technique of pseudo-hyperscanning, combined brain-auditory recordings are also a highly desirable feature, since reliable offline synchronization can be performed by using the shared audio signal. Here, we describe two hardware configurations wherein the real-time delivered auditory stimulus is recorded jointly with ongoing electroencephalographic (EEG) recordings. Specifically, we describe and provide customized implementations for joint EEG-audio recording in hyperscanning and pseudo-hyperscanning paradigms using hardware and software from Brain Products GmbH.•Joint EEG-audio recording configuration for hyperscanning and pseudo-hyperscanning paradigms.•Near zero-latency playback of auditory signal captured by a microphone.•Precise alignment between EEG and auditory stimulation.

Deep Learning Analysis for Estimating Sleep Syndrome Detection Utilizing the Twin Convolutional Model FTC2

Manual sleep stage scoring is frequently performed by sleep specialists by visually evaluating the patient's neurophysiological signals acquired in sleep laboratories. This is a difficult, time-consuming, laborious process. Because of the limits of human sleep stage scoring, there is a greater need for creating Automatic Sleep Stage Classification (ASSC) systems. Sleep stage categorization is the process of distinguishing the distinct stages of sleep is an important step in assisting physicians in the diagnosis treatment of associated sleep disorders. In this research, we offer a unique method a practical strategy to predicting early onsets of sleep disorders, such as restless leg syndrome insomnia, using the Twin Convolutional Model FTC2, based on an algorithm composed of two modules. To provide localised time-frequency information, 30 second long epochs of EEG recordings are subjected to a Fast Fourier Transform, a deep convolutional LSTM neural network is trained for sleep stage categorization. Automating sleep stages detection from EEG data offers a great potential to tackling sleep irregularities on a daily basis. Thereby, a novel approach for sleep stage classification is pro- posed which combines the best of signal processing statistics. In this study, we used the PhysioNet Sleep European Data Format (EDF) Database. The code evaluation showed impressive results, reaching accuracy of 90.43, precision of 77.76, recall of 93,32, F1-score of 89.12 with the final mean false error loss 0.09. All the source code is availlable at https://github.com/timothy102/eeg.

Deep Learning Analysis for Estimating Sleep Syndrome Detection Utilizing the Twin Convolutional Model FTC2

Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient's neurophysiological signals collected at sleep labs. This is a difficult, tedious and a time-consuming task. The limitations of manual sleep stage scor- ing have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diag- nosis and treatment of related sleep disorders. In this paper, we propose a novel method and a practical approach to predicting early onsets of sleep syndromes utilizing the Twin Convolutional Model FTC2, including restless leg syndrome, insomnia, based on an algorithm which is comprised of two modules. A Fast Fourier Transform is applied to 30 seconds long epochs of EEG recordings to provide localized time-frequency information, and a deep convolutional LSTM neural network is trained for sleep stage classification. Automating sleep stages detection from EEG data offers a great potential to tackling sleep irregularities on a daily basis. Thereby, a novel approach for sleep stage classification is pro- posed which combines the best of signal processing and statistics. In this study, we used the PhysioNet Sleep European Data Format (EDF) Database. The code evaluation showed impressive results, reaching accuracy of 90.43, precision of 77.76, recall of 93,32, F1-score of 89.12 with the final mean false error loss 0.09. All the source code is availlable at https://github.com/timothy102/eeg.

Behavioral and neurophysiological signatures of interoceptive enhancements following vagus nerve stimulation.

An accruing body of research has shown that interoception (the sensing of signals from the body's internal milieu) relies on both a direct route (afforded by the vagus nerve) and a secondary route (supported by somatosensory mechanisms). However, no study has causally tested the differential role of these pathways, let alone via direct stimulation. To bridge this gap, we tested whether multidimensional signatures of interoception are modulated by noninvasive vagus nerve stimulation (nVNS). Sixty‐three participants were divided into an nVNS and a sham‐stimulation group. Before and after stimulation, both groups performed a validated heartbeat detection (HBD) task including a genuinely interoceptive condition (monitoring one's own heartbeat) and a control exteroceptive condition (tracking an aurally presented heartbeat). Electroencephalographic signals were obtained during both conditions to examine modulations of the heartbeat‐evoked potential (HEP). Moreover, before and after stimulation, participants were asked to complete a somatosensory heartbeat localization task. Results from the interoceptive condition revealed that, after treatment, only the nVNS group exhibited improved performance and greater HEP modulations. No behavioral differences were found for the exteroceptive control condition, which was nonetheless associated with significant HEP modulations. Finally, no between‐group differences were observed regarding the localization of the heartbeat sensations or relevant cardiodynamic variables (heart rate and or heart rate variability). Taken together, these results constitute unprecedented evidence that the vagus nerve plays a direct role in neurovisceral integration during interoception. This finding can constrain mechanistic models of the domain while informing a promising transdiagnostic agenda for interoceptive impairments across neuropsychiatric conditions.

Neurocomputational mechanisms of prior-informed perceptual decision-making in humans.

To interact successfully with diverse sensory environments, we must adapt our decision processes to account for time constraints and prior probabilities. The full set of decision-process parameters that undergo such flexible adaptation has proven to be difficult to establish using simplified models that are based on behaviour alone. Here, we utilize well-characterized human neurophysiological signatures of decision formation to construct and constrain a build-to-threshold decision model with multiple build-up (evidence accumulation and urgency) and delay components (pre- and post-decisional). The model indicates that all of these components were adapted in distinct ways and, in several instances, fundamentally differ from the conclusions of conventional diffusion modelling. The neurally informed model outcomes were corroborated by independent neural decision signal observations that were not used in the model's construction. These findings highlight the breadth of decision-process parameters that are amenable to strategic adjustment and the value in leveraging neurophysiological measurements to quantify these adjustments.

Dysfunctional frontal activation of mismatch negativity in panic disorder: A magnetoencephalographic study

BackgroundMismatch negativity (MMN) or its magnetic counterpart (MMNm) is a neurophysiological signal to reflect the automatic change-detection ability. However, MMN studies in patients with panic disorder (PD) showed contrasting results using electroencephalographic (EEG) recordings. The present study attempted to overcome the limitations of EEG methodology by means of a whole-head magnetoencephalography (MEG) combined with the depth-weighted minimum norm estimate method to conduct an in-depth investigation on the MMNm at the cortical level in patients with PD. MethodsWe recruited 22 healthy controls (HC) and 20 patients with PD to perform auditory oddball paradigm during MEG recordings. The cortical MMNm amplitudes and latencies in the superior temporal gyrus, inferior parietal lobule, and inferior frontal gyrus (IFG) were compared between the HC and PD groups. The correlations between MMNm responses and clinical measurement were also examined. ResultsCompared with the HC group, the PD group demonstrated significantly reduced MMNm amplitudes in the IFG. Furthermore, higher trait scores of the State-Trait Anxiety Inventory were associated with lower MMNm amplitudes of the right IFG among patients with PD. LimitationsGeneralization of the current results to other settings or samples should be made cautiously due to the use of different medication regimens and presence of comorbidities in our patients. ConclusionsOur data suggest dysfunctional pre-attentive change-detection ability in patients with PD, particularly in the IFG.

Signature of consciousness in brain-wide synchronization patterns of monkey and human fMRI signals

During the sleep-wake cycle, the brain undergoes profound dynamical changes, which manifest subjectively as transitions between conscious experience and unconsciousness. Yet, neurophysiological signatures that can objectively distinguish different consciousness states based are scarce. Here, we show that differences in the level of brain-wide signals can reliably distinguish different stages of sleep and anesthesia from the awake state in human and monkey fMRI resting state data. Moreover, a whole-brain computational model can faithfully reproduce changes in global synchronization and other metrics such as functional connectivity, structure-function relationship, integration and segregation across vigilance states. We demonstrate that the awake brain is close to a Hopf bifurcation, which naturally coincides with the emergence of globally correlated fMRI signals. Furthermore, simulating lesions of individual brain areas highlights the importance of connectivity hubs in the posterior brain and subcortical nuclei for maintaining the model in the awake state, as predicted by graph-theoretical analyses of structural data.