Physiologically based arousal state estimation and dynamics

Background: Monitoring and evaluating dynamic changes in brain states during electroencephalography (EEG) neurofeedback training (NFT) for post-traumatic stress disorder (PTSD) patients remains challenging when using traditional methods. Method: This study proposes a novel Process Noise Dynamic Adaptation-Mesoscale Mesonetwork Network (PNDA-MMNet) model, which improves upon conventional techniques by establishing a discrete linear dynamic model of the NFT process. The model utilizes a mesoscale intermediate network architecture to create a brain state observation matrix, computes the brain state transition matrix, and applies fuzzy rules for dynamic adaptive noise processing. This maximizes the separability between brain state transitions during NFT and resting states. Results: The proposed model achieves a brain state identification accuracy of 0.7428 ± 0.12 (area under the curve, AUC = 0.84), significantly outperforming conventional algorithms. Interpretations of the model indicate that continuous NFT reduces functional connectivity within the motor cortex, with stronger suppression in the right hemisphere compared to the left. Additionally, it reveals decreased activity in the occipital cortex, particularly in the left occipital region, where inhibition increases radially from the midline. Notably, the connectivity between the motor and occipital cortices remains stable throughout the training process. These connectivity changes reflect NFT-induced modulation of cortical activity and are consistent with known neurophysiological patterns in PTSD, highlighting their potential relevance to therapeutic mechanisms. Conclusion: This research introduces a more effective approach for real-time monitoring and evaluation of PTSD patients’ brain states during NFT, offering a quantitative method for assessing treatment efficacy and guiding therapeutic interventions.

Brain state dynamics vary at different spatiotemporal scales with behavior, stimulation, and disease, and may be unobserved (latent). Using a state-space model framework and subspace identification, we estimated spatiotemporally localized, latent state changes associated with the application of transcranial magnetic stimulation (TMS), to assess the effect of stimulation on brain state dynamics. State appeared to be modulated by behavior in a spatially-specific manner and small-amplitude state fluctuations were temporally locked to stimulus presentations. In addition, during and following TMS, an overall, bilateral and spatially nonspecific decrease in brain state was observed. We also estimated brain state changes during seizure evolution (independent of TMS), in focal and generalized seizures, which have very different epileptogenesis and propagation mechanisms, possibly resulting also in distinct spatiotemporal dynamics. Indeed, our preliminary results showed that in focal seizures, temporally localized dynamic state changes occur at least 1 min prior to seizure onset, with a decrease in steady-state followed by an increase which reaches a maximum during the ictal interval. In contrast, no such dynamic pattern was evident in state estimates during generalized seizures.

Understanding the link between cellular processes and brain function remains a key challenge in neuroscience. One crucial aspect is the interplay between specific ion channels and network dynamics. This work reveals a role for h-current, a hyperpolarization-activated cationic current, in shaping cortical slow oscillations. Cortical slow oscillations are generated not only during slow wave sleep and deep anaesthesia, but also in association with disorders of consciousness and brain lesions. Cortical slow oscillations exhibit rhythmic periods of activity (Up states) alternating with silent periods (Down states). By progressively reducing h-current in both cortical slices and in a computational model, we observed Up states transformed into prolonged plateaus of sustained firing, while Down states were also significantly extended. This transformation led to a fivefold reduction in oscillation frequency. In a biophysical recurrent network model, we identified the cellular mechanisms underlying this transformation of network dynamics: an increased neuronal input resistance and membrane time constant, increasing neuronal responsiveness to even weak inputs. A partial block of h-current therefore resulted in a change in brain state. HCN (hyperpolarization-activated cyclic nucleotide-gated) channels, which generate h-current, are known targets for neuromodulation, suggesting potential pathways for dynamic control of brain rhythms. KEY POINTS: We investigated the role of h-current in shaping emergent cortical slow oscillation dynamics, specifically Up and Down states, in cortical slices. Blocking h-current transformed Up states into prolonged plateaus of sustained firing, lasting up to 4s. Down states were also significantly elongated and the oscillatory frequency decreased. A biophysical model of the cortical network replicated these findings and allowed us to explore the underlying mechanisms. An increase in cellular input resistance and time constant led to a rise in network excitability, synaptic responsiveness and firing rates. Our results highlight the significant role of h-current in controlling cortical slow rhythmic patterns, making it a relevant target for neuromodulators regulating brain states.

A key step toward understanding how the brain works is the reliable characterization of directed functional (i.e., causal) connectivity between brain regions, generally during various states of wake and sleep, as well as during task performance. Here we perform a critical analysis of the potential of Granger causality (GC) [1] to confront this challenge. We describe new methodology for rigorously applying GC to steady-state data, validated against analytical calculations, and for the first time, simulated spiking neuron local field potential data. We apply our methods to scalp and intracranial electroencephalographic (EEG) recordings. The concept of GC is to quantify the extent to which the past of one signal Y predicts the future of another signal X. For 'pairwise' GC, one compares predictions based on the past of just × and on the past of both × and Y. For 'conditional' GC, one quantifies the extent to which the past of Y assists in predicting the future of × beyond the extent to which the pasts of × and all other variables excluding Y predict the future of X. These approaches lead to distinct functional connectivity maps. The conditional approach potentially describes better how each system component processes and communicates distinct information; however it is practically more awkward because it requires estimating more parameters. We review the elegant mathematical properties of the linear autoregressive approach to GC that make it an attractive connectivity measure, e.g. its links to information theory and its insensitivity to amplification level [2,3]. We then discuss challenges in the application to electrophysiology data, such as non-stationarity and statistical bias. We present ways to overcome these challenges, involving data segmentation and permutation analysis [4], and modifications to the basic linear autoregression model. We validate our methods in two ways: first using simulated data from models for which true GC values can be analytically derived; second using simulated local field potential data from a large-scale spiking neuron simulator. The latter allows us to confirm, for the first time, that GC analysis can indeed correctly reproduce the underlying causal structure in realistic neural dynamics. We compare and contrast the ability of pairwise and conditional GC approaches to detect significant changes in functional connectivity between wake, sleep and anaesthesia states across recorded scalp and intracranial EEG variables. Analyses are performed in both the time and frequency domains, and we make use of the first ever code for computing conditional GC in the frequency domain. As predicted, pairwise GC is more sensitive to changes in brain state than full conditional GC; however conditional GC performed on just a few simultaneous variables at once can also give meaningful results. In summary, we demonstrate rigorous methodology and new code for GC analysis of steady-state data, and illustrate the utility of GC in exposing the functional neural interactions underlying different brain states.

Understanding the effects of anesthesia on cortical neuronal spiking and information transfer could help illuminate the neuronal basis of the conscious state. Recent investigations suggest that the brain state identified by local field potential spectrum is not stationary but changes spontaneously at a fixed level of anesthetic concentration. How cortical unit activity changes with dynamically transitioning brain states under anesthesia is unclear. Extracellular unit activity was measured with 64-channel silicon microelectrode arrays in cortical layers 5/6 of the primary visual cortex of chronically instrumented, freely moving male rats (n = 7) during stepwise reduction of the anesthetic desflurane (6%, 4%, 2%, and 0%). Unsupervised machine learning applied to multiunit spike patterns revealed five distinct brain states. A novel desynchronized brain state with increased spike rate variability, sample entropy, and EMG activity occurred in 6% desflurane with 40.0% frequency. The other four brain states reflected graded levels of anesthesia. As anesthesia deepened the spike rate of neurons decreased regardless of their spike rate profile at baseline conscious state. Actively firing neurons with wide-spiking pattern showed increased bursting activity along with increased spike timing variability, unit-to-population correlation, and unit-to-unit transfer entropy, despite the overall decrease in transfer entropy. The narrow-spiking neurons showed similar changes but to a lesser degree. These results suggest that (1) anesthetic effect on spike rate is distinct from sleep, (2) synchronously fragmented spiking pattern is a signature of anesthetic-induced unconsciousness, and (3) the paradoxical, desynchronized brain state in deep anesthesia contends the generally presumed monotonic, dose-dependent anesthetic effect on the brain.SIGNIFICANCE STATEMENT Recent studies suggest that spontaneous changes in brain state occur under anesthesia. However, the spiking behavior of cortical neurons associated with such state changes has not been investigated. We found that local brain states defined by multiunit activity had a nonunitary relationship with the current anesthetic level. A paradoxical brain state displaying asynchronous firing pattern and high EMG activity was found unexpectedly in deep anesthesia. In contrast, the synchronous fragmentation of neuronal spiking appeared to be a robust signature of the state of anesthesia. The findings challenge the assumption of monotonic, anesthetic dose-dependent behavior of cortical neuron populations. They enhance the interpretation of neuroscientific data obtained under anesthesia and the understanding of the neuronal basis of anesthetic-induced state of unconsciousness.

During the wake–sleep cycle, consciousness waxes and wanes, and this is thought to be reflected in varying levels of integration between brain areas. Recent studies challenged the notion that consciousness is homogeneously present or absent in a brain state, as exemplified by conscious reports found in otherwise unconscious Non-rapid eye movement (NREM) sleep. Here, we tested if functional connectivity (FC) between neurons varies within brain states in a way compatible with a fluctuating level of consciousness. We examined directed FC between neurons across the wake–sleep cycle in rats, at a scale of a few seconds. We observed that NREM sleep contains epochs in which patterns of inter-areal FC are comparable to those observed in wakefulness and REM sleep, and vice versa. Thus, circuit-level connectivity patterns are not univocally determined by the brain state in which they occur, but could rather reflect other factors such as the fluctuation in the level of consciousness that takes place not only between but also within brain states.

Finding precise signatures of different brain states is a central, unsolved question in neuroscience. We reformulated the problem to quantify the ‘inside out’ balance of intrinsic and extrinsic brain dynamics in brain states. The difference in brain state can be described as differences in the detailed causal interactions found in the underlying intrinsic brain dynamics. We used a thermodynamics framework to quantify the breaking of the detailed balance captured by the level of asymmetry in temporal processing, i.e. the arrow of time. Specifically, the temporal asymmetry was computed by the time-shifted correlation matrices for the forward and reversed time series, reflecting the level of non-reversibility/non-equilibrium. We found precise, distinguishing signatures in terms of the reversibility and hierarchy of large-scale dynamics in three radically different brain states (awake, deep sleep and anaesthesia) in electrocorticography data from non-human primates. Significantly lower levels of reversibility were found in deep sleep and anaesthesia compared to wakefulness. Non-wakeful states also showed a flatter hierarchy, reflecting the diversity of the reversibility across the brain. Overall, this provides signatures of the breaking of detailed balance in different brain states, perhaps reflecting levels of conscious awareness.

Depending on brain's current levels of relaxation, focus, and other mental states, brain will produced variable proportions of brain wave types. Each category of EEG(electroencephalography). Generally people's brain waves will respond to lights and sounds thuding at brain wave frequencies, and it will impact the change in state of brain — this activity called “entrainment.” We can schedule our brain to observe any brain wave experience as we like, by toggling number of pulses into eyes and ears. So here we are going to program various frequencies which are correlated to brain modes like Alfa, Beta, Gamma, Theta, & Delta by implementing Brain wave generator (BWG) using dsPIC DSC. This will be very useful in medical field for psychiatrist and many more.

What is the central question of this study? A transcranial magnetic stimulation (TMS)-induced twitch applied on isolated single breaths over the motor cortex somatotopic representation of the tongue briefly recruits submental muscles and improves airflow dynamics of flow-limited respiratory cycles without arousing sleep apnoea patients. However, the mechanical impact of the TMS-induced twitch applied during consecutive breathing cycles on airflow dynamics remains unknown. What is the main finding and what is its importance? Our results show that application of TMS with the stimulator output set at the sleep submental motor threshold intensity on consecutive respiratory cycles increases inspiratory flow and reduces the turbulent airflow component. These results indicate an improvement of airflow pattern after two single consecutive TMS-induced twitches without arousing sleep apnoea patients. Transcranial magnetic stimulation (TMS)-induced twitches applied on isolated breaths briefly recruit upper airway dilator muscles and improve airflow and inspiratory volume without arousing apnoeic patients from sleep, but the effects of applying such twitches consecutively on airflow dynamics is unknown. The objective of this study was to quantify the effects of five consecutive TMS-induced twitches applied on sleep-induced obstructive hypopnoeic breaths in 10 obstructive sleep apnoea patients. Submental muscle motor threshold (SUBMT) and motor-evoked potential were measured during wakefulness and sleep. The TMS-induced twitches were applied during stable non-rapid eye movement (NREM) sleep, at the beginning of inspiration of consecutive flow-limited respiratory cycles, with the stimulator output set at sleep SUBMT. Maximal inspiratory flow, inspiratory volume, inspiratory time, shifts of electroencephalogram frequency and pulse rate variability were assessed. During sleep, SUBMT increased (wakefulness, 25.3 ± 4.9%; NREM sleep, 27.0 ± 6.2%; P = 0.02). During each series of stimulations there was a rise in maximal inspiratory flow (from 306.7 ± 123.2 to 359.8 ± 154.1 ml s(-1); P = 0.0002) and in inspiratory volume (from 346.1 ± 128.1 to 414.9 ± 171.2 ml; P = 0.02) without differences in thoraco-abdominal efforts and inspiratory time. These responses were observed in the absence of arousals and ceased immediately after TMS interruption. Transcranial magnetic stimulation-induced cortical and/or autonomic arousal was observed in 30.2% of all series of stimulation. Consecutive twitch TMS of submental muscles may lead to arousals in a minority of patients but can be applied on consecutive respiratory cycles during sleep and can significantly improve maximal inspiratory flow and inspiratory volume of flow-limited cycles.

Neural impact of anti-G suits on pilots: Analyzing microstates and functional connectivity.

Accurately identifying and discriminating between different brain states is a major emphasis of functional brain imaging research. Various machine learning techniques play an important role in this regard. However, when working with a small number of study participants, the lack of sufficient data and achieving meaningful classification results remain a challenge. In this study, we employ a classification strategy to explore stress and its impact on spatial activation patterns and brain connectivity caused by the Stroop color-word task (SCWT). To improve our results and increase our dataset, we use data augmentation with a deep convolutional generative adversarial network (DCGAN). The study is carried out at two separate times of day (morning and evening) and involves 21 healthy participants. Additionally, we introduce binaural beats (BBs) stimulation to investigate its potential for stress reduction. The morning session includes a control phase with 10 SCWT trials, whereas the afternoon session is divided into three phases: stress, mitigation (with 16 Hz BB stimulation), and post-mitigation, each with 10 SCWT trials. For a comprehensive evaluation, the acquired fNIRS data are classified using a variety of machine-learning approaches. Linear discriminant analysis (LDA) showed a maximum accuracy of 60%, whereas non-augmented data classified by a convolutional neural network (CNN) provided the highest classification accuracy of 73%. Notably, after augmenting the data with DCGAN, the classification accuracy increases dramatically to 96%. In the time series data, statistically significant differences were noticed in the data before and after BB stimulation, which showed an improvement in the brain state, in line with the classification results. These findings illustrate the ability to detect changes in brain states with high accuracy using fNIRS, underline the need for larger datasets, and demonstrate that data augmentation can significantly help when data are scarce in the case of brain signals.

Compared with wakefulness, neuronal activity during nonrapid eye movement (NREM) sleep is characterized by a decreased ability to integrate information, but also by the reemergence of task-related information patterns. To investigate the mechanisms underlying these seemingly opposing phenomena, we measured directed information flow by computing transfer entropy between neuronal spiking activity in three cortical regions and the hippocampus of rats across brain states. State-dependent information flow was jointly determined by the anatomical distance between neurons and by their functional specialization. We distinguished two regimes, operating at short and long time scales, respectively. From wakefulness to NREM sleep, transfer entropy at short time scales increased for interareal connections between neurons showing behavioral task correlates. Conversely, transfer entropy at long time scales became stronger between nontask modulated neurons and weaker between task-modulated neurons. These results may explain how, during NREM sleep, a global interareal disconnection is compatible with highly specific task-related information transfer.

Do neurotoxic lesions in rostral medullary nuclei induce/accentuate hypoventilation during NREM sleep?

Anaesthesia is a critical procedure for surgeons because it allows them to operate on unconscious and pain-free patients. This paper proposes using CNN-based deep learning models to infer brain state while under anesthesia. Both neuroscience research and clinical situations can help determine how deeply unconscious a patient is during anesthesia. The electroencephalogram (EEG) is a real-time, objective method for detecting anesthetic-induced changes in brain states of arousal and/or cognition. To pre-process the signal, the adaptive median filter is used. The median filter is a signal-filtering method used to remove noise from signals. The feature extraction process is carried out using wavelet decomposition, which allows for perfect signal reconstruction. To classify anesthetized brain states, this paper employs a CNN-based technique.

Error-based learning is one of the basic skill acquisition mechanisms that can be modeled as a perception–action system and investigated based on brain–behavior analysis during skill training. Here, the error-related chain of mental processes is postulated to depend on the skill level leading to a difference in the contextual switching of the brain states on error commission. Therefore, the objective of this paper was to compare error-related brain states, measured with multi-modal portable brain imaging, between experts and novices during the Fundamentals of Laparoscopic Surgery (FLS) “suturing and intracorporeal knot-tying” task (FLS complex task)—the most difficult among the five psychomotor FLS tasks. The multi-modal portable brain imaging combined functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) for brain–behavior analysis in thirteen right-handed novice medical students and nine expert surgeons. The brain state changes were defined by quasi-stable EEG scalp topography (called microstates) changes using 32-channel EEG data acquired at 250 Hz. Six microstate prototypes were identified from the combined EEG data from experts and novices during the FLS complex task that explained 77.14% of the global variance. Analysis of variance (ANOVA) found that the proportion of the total time spent in different microstates during the 10-s error epoch was significantly affected by the skill level (p < 0.01), the microstate type (p < 0.01), and the interaction between the skill level and the microstate type (p < 0.01). Brain activation based on the slower oxyhemoglobin (HbO) changes corresponding to the EEG band power (1–40 Hz) changes were found using the regularized temporally embedded Canonical Correlation Analysis of the simultaneously acquired fNIRS–EEG signals. The HbO signal from the overlying the left inferior frontal gyrus—opercular part, left superior frontal gyrus—medial orbital, left postcentral gyrus, left superior temporal gyrus, right superior frontal gyrus—medial orbital cortical areas showed significant (p < 0.05) difference between experts and novices in the 10-s error epoch. We conclude that the difference in the error-related chain of mental processes was the activation of cognitive top-down attention-related brain areas, including left dorsolateral prefrontal/frontal eye field and left frontopolar brain regions, along with a ‘focusing’ effect of global suppression of hemodynamic activation in the experts, while the novices had a widespread stimulus(error)-driven hemodynamic activation without the ‘focusing’ effect.Supplementary InformationThe online version contains supplementary material available at 10.1186/s40708-022-00179-z.