Brain States Research Articles

EEGminer: discovering interpretable features of brain activity with learnable filters

Objective. The patterns of brain activity associated with different brain processes can be used to identify different brain states and make behavioural predictions. However, the relevant features are not readily apparent and accessible. Our aim is to design a system for learning informative latent representations from multichannel recordings of ongoing EEG activity. Approach: We propose a novel differentiable decoding pipeline consisting of learnable filters and a pre-determined feature extraction module. Specifically, we introduce filters parameterized by generalized Gaussian functions that offer a smooth derivative for stable end-to-end model training and allow for learning interpretable features. For the feature module, we use signal magnitude and functional connectivity estimates. Main results. We demonstrate the utility of our model on a new EEG dataset of unprecedented size (i.e. 721 subjects), where we identify consistent trends of music perception and related individual differences. Furthermore, we train and apply our model in two additional datasets, specifically for emotion recognition on SEED and workload classification on simultaneous task EEG workload. The discovered features align well with previous neuroscience studies and offer new insights, such as marked differences in the functional connectivity profile between left and right temporal areas during music listening. This agrees with the specialisation of the temporal lobes regarding music perception proposed in the literature. Significance. The proposed method offers strong interpretability of learned features while reaching similar levels of accuracy achieved by black box deep learning models. This improved trustworthiness may promote the use of deep learning models in real world applications. The model code is available at https://github.com/SMLudwig/EEGminer/.

Group ICA of wide-field calcium imaging data reveals the retrosplenial cortex as a major contributor to cortical activity during anesthesia.

Large-scale cortical dynamics play a crucial role in many cognitive functions such as goal-directed behaviors, motor learning and sensory processing. It is well established that brain states including wakefulness, sleep, and anesthesia modulate neuronal firing and synchronization both within and across different brain regions. However, how the brain state affects cortical activity at the mesoscale level is less understood. This work aimed to identify the cortical regions engaged in different brain states. To this end, we employed group ICA (Independent Component Analysis) to wide-field imaging recordings of cortical activity in mice during different anesthesia levels and the awake state. Thanks to this approach we identified independent components (ICs) representing elements of the cortical networks that are common across subjects under decreasing levels of anesthesia toward the awake state. We found that ICs related to the retrosplenial cortices exhibited a pronounced dependence on brain state, being most prevalent in deeper anesthesia levels and diminishing during the transition to the awake state. Analyzing the occurrence of the ICs we found that activity in deeper anesthesia states was characterized by a strong correlation between the retrosplenial components and this correlation decreases when transitioning toward wakefulness. Overall these results indicate that during deeper anesthesia states coactivation of the posterior-medial cortices is predominant over other connectivity patterns, whereas a richer repertoire of dynamics is expressed in lighter anesthesia levels and the awake state.

Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine

The study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests new methods for learning and identifying diagnostic biomarkers using three prominent deep learning neural network models: deep BiLSTM, reservoir SNN, and NeuCube. EEG data from datasets related to epilepsy, migraine, and healthy subjects are employed. Results reveal that BiLSTM hidden neurons capture biological significance, while reservoir SNN activities and NeuCube spiking dynamics identify EEG channels as diagnostic biomarkers. BiLSTM and reservoir SNN achieve 90 and 85% classification accuracy, while NeuCube achieves 97%, all methods pinpointing potential biomarkers like T6, F7, C4, and F8. The research bears implications for refining online EEG classification, analysis, and early brain state diagnosis, enhancing AI models with interpretability and discovery. The proposed techniques hold promise for streamlined brain-computer interfaces and clinical applications, representing a significant advancement in pattern discovery across the three most popular neural network methods for addressing a crucial problem. Further research is planned to study how early can these diagnostic biomarkers predict an onset of brain states.

The dynamics of functional brain network segregation in feedback-driven learning

Prior evidence suggests that increasingly efficient task performance in human learning is associated with large scale brain network dynamics. However, the specific nature of this general relationship has remained unclear. Here, we characterize performance improvement during feedback-driven stimulus-response (S-R) learning by learning rate as well as S-R habit strength and test whether and how these two behavioral measures are associated with a functional brain state transition from a more integrated to a more segregated brain state across learning. Capitalizing on two separate fMRI studies using similar but not identical experimental designs, we demonstrate for both studies that a higher learning rate is associated with a more rapid brain network segregation. By contrast, S-R habit strength is not reliably related to changes in brain network segregation. Overall, our current study results highlight the utility of dynamic functional brain state analysis. From a broader perspective taking into account previous study results, our findings align with a framework that conceptualizes brain network segregation as a general feature of processing efficiency not only in feedback-driven learning as in the present study but also in other types of learning and in other task domains.

Re-awakening the brain: Forcing transitions in disorders of consciousness by external in silico perturbation.

A fundamental challenge in neuroscience is accurately defining brain states and predicting how and where to perturb the brain to force a transition. Here, we investigated resting-state fMRI data of patients suffering from disorders of consciousness (DoC) after coma (minimally conscious and unresponsive wakefulness states) and healthy controls. We applied model-free and model-based approaches to help elucidate the underlying brain mechanisms of patients with DoC. The model-free approach allowed us to characterize brain states in DoC and healthy controls as a probabilistic metastable substate (PMS) space. The PMS of each group was defined by a repertoire of unique patterns (i.e., metastable substates) with different probabilities of occurrence. In the model-based approach, we adjusted the PMS of each DoC group to a causal whole-brain model. This allowed us to explore optimal strategies for promoting transitions by applying off-line in silico probing. Furthermore, this approach enabled us to evaluate the impact of local perturbations in terms of their global effects and sensitivity to stimulation, which is a model-based biomarker providing a deeper understanding of the mechanisms underlying DoC. Our results show that transitions were obtained in a synchronous protocol, in which the somatomotor network, thalamus, precuneus and insula were the most sensitive areas to perturbation. This motivates further work to continue understanding brain function and treatments of disorders of consciousness.

Threat, safety, safeness and social safeness 30 years on: Fundamental dimensions and distinctions for mental health and well-being.

In 1993, the British Journal of Clinical Psychology published my paper titled 'Defence and safety: Their function in social behaviour and psychopathology'. The paper highlightsthat to understand people's sensitivity to threat, we also need to understand their ability to identify what is safe. This paper offers an update on these concepts, highlighting distinctions that were implicit but not clearly defined at the time. Hence, the paper seeks to clarify distinctions between: (i) threat detection and response, (ii) safety and safety seeking, (iii) safeness and (iv) their social and non-social functions and forms. Threat detection and response are to prevent or minimize harm (e.g., run from a predator or fire). Safety checking relates to monitoring for the absence and avoidance of threat, while safety seeking links to the destination of the defensive behaviour (e.g., running home). Safety seeking also relates to maintaining vigilance to the appearance of potential harms and doing things believed to avoid harm. Threat-defending and safety checking and seeking are regulated primarily through evolved threat processing systems that monitor the nature, presence, controllability and/or absence of threat (e.g., amygdala and sympathetic nervous system). Safeness uses different monitoring systems via different psychophysiological systems (e.g., prefrontal cortex, parasympathetic system) for the presence of internal and external resources that support threat-coping, risk-taking, resource exploration. Creating brain states that recruit safeness processing can impact how standard evidence-based therapies (e.g., exposure, distress tolerance and reappraisal) are experienced and produce long-term change.

MEET: A Multi-Band EEG Transformer for Brain States Decoding.

Electroencephalography (EEG) is among the most widely used and inexpensive neuroimaging techniques. Compared to the CNN or RNN based models, Transformer can better capture the temporal information in EEG signals and focus more on global features of the brain's functional activities. Importantly, according to the multiscale nature of EEG signals, it is crucial to consider the multi-band concept into the design of EEG Transformer architecture. We propose a novel Multi-band EEG Transformer (MEET) to represent and analyze the multiscale temporal time series of human brain EEG signals. MEET mainly includes three parts: 1) transform the EEG signals into multi-band images, and preserve the 3D spatial information between electrodes; 2) design a Band Attention Block to compute the attention maps of the stacked multi-band images and infer the fused feature maps; 3) apply the Temporal Self-Attention and Spatial Self-Attention modules to extract the spatiotemporal features for the characterization and differentiation of multi-frame dynamic brain states. The experimental results show that: 1) MEET outperforms state-of-the-art methods on multiple open EEG datasets (SEED, SEED-IV, WM) for brain states classification; 2) MEET demonstrates that 5-bands fusion is the best integration strategy; and 3) MEET identifies interpretable brain attention regions. MEET is an interpretable and universal model based on the multiband-multiscale characteristics of EEG. The innovative combination of band attention and temporal/spatial self-attention mechanisms in MEET achieves promising data-driven learning of the temporal dependencies and spatial relationships of EEG signals across the entire brain in a holistic and comprehensive fashion.

Analysis of Amplitude Modulation of EEG Based on Holo-Hilbert Spectrum Analysis During General Anesthesia.

The study aims to investigate the relationship between amplitude modulation (AM) of EEG and anesthesia depth during general anesthesia. In this study, Holo-Hilbert spectrum analysis (HHSA) was used to decompose the multichannel EEG signals of 15 patients to obtain the spatial distribution of AM in the brain. Subsequently, HHSA was applied to the prefrontal EEG (Fp1) obtained during general anesthesia surgery in 15 and 34 patients, and the α-θ and α-δ regions of feature (ROFs) were defined in Holo-Hilbert spectrum (HHS) and three features were derived to quantify AM in ROFs. During anesthetized phase, an anteriorization of the spatial distribution of AMs of α-carrier in brain was observed, as well as AMs of α-θ and α-δ in the EEG of Fp1. The total power ([Formula: see text]), mean carrier frequency ([Formula: see text]) and mean amplitude frequency ([Formula: see text]) of AMs changed during different anesthesia states. HHSA can effectively analyze the cross-frequency coupling of EEG during anesthesia and the AM features may be applied to anesthesia monitoring. The study provides a new perspective for the characterization of brain states during general anesthesia, which is of great significance for exploring new features of anesthesia monitoring.

466. Transient Divergences in Movie-Driven Brain State Dynamics in Schizophrenia are Characterized by High Social-Cognitive Load

466. Transient Divergences in Movie-Driven Brain State Dynamics in Schizophrenia are Characterized by High Social-Cognitive Load

Investigating Brain State Engagement Variability in Individuals With Opioid Use Disorder Using Naturalistic and Task-Based fMRI Data

Investigating Brain State Engagement Variability in Individuals With Opioid Use Disorder Using Naturalistic and Task-Based fMRI Data

500. Characterizing Brain States From Resting State fMRI at a Fine Temporal Scale by Learning Personalized Functional Networks of Single Time Points

500. Characterizing Brain States From Resting State fMRI at a Fine Temporal Scale by Learning Personalized Functional Networks of Single Time Points

Acute stress augments obstructive apneas and related O2-desaturations in urethane-anesthetised rats via disruption of cholinergic modulation

Obstructive sleep apnea (OSA) is a multifactorial respiratory disorder affecting nearly 1 billon of adults worldwide. Anomalies in respiratory control during sleep contribute to airway obstructions and recurrent desaturations; however, the origins of the problem are not well known. Stress disrupts brain function and increases the risk of OSA co-morbidities, including hypertension and obesity. Because, hypothalamic structures regulating the stress response also project to medullary areas regulating cardiorespiratory function and airway motor control, we hypothesised that exposure to a single stress session is suffcient to induce cardiorespiratory disturbances associated with OSA. Experiments were performed in urethane-anesthetised rats; this approach allows sleep-like brain state alternations with significant reductions in upper airway (genioglossus; GG), but not diaphragmatic (DIA), muscle tone during the transition from NREM-like to REM-like states. Adult male rats were either subjected to a 90 min session of immobilisation stress (n = 21) or handled briefly by the experimenter (n = 18). 24h later, rats were anesthetised (urethane, 1.6 g/kg; i.v.) and instrumented to monitor DIA and GG EMG. O2 saturation was measured by pulse oximetry (S pO2). Blood pressure was measured by an arterial catheter. Rats were placed in a supine position and respiratory airflow was measure by a temperature probe near the nares. Obstructive apneas were identified by the absence of airflow in the presence of DIA activity with a S pO2 drop greater than 3%. Stress did not affect distribution of sleep-like states or the state-related changes in phasic GG activity. However, stress augmented the incidence of obstructive apneas (0.5 vs 2.2 events/h; p = 0.05) and doubled the severity of desaturations associated with apneas (-6% vs -12% drop; p = 0.03). Pre-treatment with atropine (0.5 mg/kg) reduced obstructive apneas (drug effect: p = 0.04). Atropine blocked apnea-related O2 desaturations in stressed but not controls (apnea x stress x atropine: p = 0.04). Atropine treatment augmented GG activity; this effect was greater in NREM-like state, especially in stressed rats (atropine x state x stress: p = 0.05). We conclude that, in urethane anesthetised rats, stress disrupts cardiorespiratory control in ways that favor significant obstructive apneas. Disruption of cholinergic modulation of GG activity and cardiovascular function likely contributes to this process. These data point to an important factor in the onset of OSA. While the frequency of apneas was modest we propose that recurrent exposure to stressors or more significant events will worsen the condition over time. Supported by the Canadian Institutes of Health Research and the Foundation of the Québec Heart & Lung Institute. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Augmented Recognition of Distracted Driving State Based on Electrophysiological Analysis of Brain Network.

In this study, we propose an electrophysiological analysis-based brain network method for the augmented recognition of different types of distractions during driving. Driver distractions, such as cognitive processing and visual disruptions during driving, lead to distinct alterations in the electroencephalogram (EEG) signals and the extracted brain networks. We designed and conducted a simulated experiment comprising 4 distracted driving subtasks. Three connectivity indices, including both linear and nonlinear synchronization measures, were chosen to construct the brain network. By computing connectivity strengths and topological features, we explored the potential relationship between brain network configurations and states of driver distraction. Statistical analysis of network features indicates substantial differences between normal and distracted states, suggesting a reconfiguration of the brain network under distracted conditions. Different brain network features and their combinations are fed into varied machine learning classifiers to recognize the distracted driving states. The results indicate that XGBoost demonstrates superior adaptability, outperforming other classifiers across all selected network features. For individual networks, features constructed using synchronization likelihood (SL) achieved the highest accuracy in distinguishing between cognitive and visual distraction. The optimal feature set from 3 network combinations achieves an accuracy of 95.1% for binary classification and 88.3% for ternary classification of normal, cognitively distracted, and visually distracted driving states. The proposed method could accomplish the augmented recognition of distracted driving states and may serve as a valuable tool for further optimizing driver assistance systems with distraction control strategies, as well as a reference for future research on the brain-computer interface in autonomous driving.

Expert-level sleep staging using an electrocardiography-only feed-forward neural network

Reliable classification of sleep stages is crucial in sleep medicine and neuroscience research for providing valuable insights, diagnoses, and understanding of brain states. The current gold standard method for sleep stage classification is polysomnography (PSG). Unfortunately, PSG is an expensive and cumbersome process involving numerous electrodes, often conducted in an unfamiliar clinic and annotated by a professional. Although commercial devices like smartwatches track sleep, their performance is well below PSG. To address these disadvantages, we present a feed-forward neural network that achieves gold-standard levels of agreement using only a single lead of electrocardiography (ECG) data. Specifically, the median five-stage Cohen's kappa is 0.725 on a large, diverse dataset of 5 to 90-year-old subjects. Comparisons with a comprehensive meta-analysis of between-human inter-rater agreement confirm the non-inferior performance of our model. Finally, we developed a novel loss function to align the training objective with Cohen's kappa. Our method offers an inexpensive, automated, and convenient alternative for sleep stage classification—further enhanced by a real-time scoring option. Cardiosomnography, or a sleep study conducted with ECG only, could take expert-level sleep studies outside the confines of clinics and laboratories and into realistic settings. This advancement democratizes access to high-quality sleep studies, considerably enhancing the field of sleep medicine and neuroscience. It makes less-expensive, higher-quality studies accessible to a broader community, enabling improved sleep research and more personalized, accessible sleep-related healthcare interventions.

Classification of Sleep Quality and Aging as a Function of Brain Complexity: A Multiband Non-Linear EEG Analysis.

Understanding and classifying brain states as a function of sleep quality and age has important implications for developing lifestyle-based interventions involving sleep hygiene. Current studies use an algorithm that captures non-linear features of brain complexity to differentiate awake electroencephalography (EEG) states, as a function of age and sleep quality. Fifty-eight participants were assessed using the Pittsburgh Sleep Quality Inventory (PSQI) and awake resting state EEG. Groups were formed based on age and sleep quality (younger adults n = 24, mean age = 24.7 years, SD = 3.43, good sleepers n = 11; older adults n = 34, mean age = 72.87; SD = 4.18, good sleepers n = 9). Ten non-linear features were extracted from multiband EEG analysis to feed several classifiers followed by a leave-one-out cross-validation. Brain state complexity accurately predicted (i) age in good sleepers, with 75% mean accuracy (across all channels) for lower frequencies (alpha, theta, and delta) and 95% accuracy at specific channels (temporal, parietal); and (ii) sleep quality in older groups with moderate accuracy (70 and 72%) across sub-bands with some regions showing greater differences. It also differentiated younger good sleepers from older poor sleepers with 85% mean accuracy across all sub-bands, and 92% at specific channels. Lower accuracy levels (<50%) were achieved in predicting sleep quality in younger adults. The algorithm discriminated older vs. younger groups excellently and could be used to explore intragroup differences in older adults to predict sleep intervention efficiency depending on their brain complexity.

Quantifying local field potential dynamics with amplitude and frequency stability between ON and OFF medication and stimulation in Parkinson's disease

Neural oscillations are critical to understanding the synchronisation of neural activities and their relevance to neurological disorders. For instance, the amplitude of beta oscillations in the subthalamic nucleus has gained extensive attention, as it has been found to correlate with medication status and the therapeutic effects of continuous deep brain stimulation in people with Parkinson's disease. However, the frequency stability of subthalamic nucleus beta oscillations, which has been suggested to be associated with dopaminergic information in brain states, has not been well explored. Moreover, the administration of medicine can have inverse effects on changes in frequency and amplitude. In this study, we proposed a method based on the stationary wavelet transform to quantify the amplitude and frequency stability of subthalamic nucleus beta oscillations and evaluated the method using simulation and real data for Parkinson's disease patients. The results suggest that the amplitude and frequency stability quantification has enhanced sensitivity in distinguishing pathological conditions in Parkinson's disease patients. Our quantification shows the benefit of combining frequency stability information with amplitude and provides a new potential feedback signal for adaptive deep brain stimulation.

Functional MRI brain state occupancy in the presence of cerebral small vessel disease—a pre-registered replication analysis of the Hamburg City Health Study

Abstract We aimed to replicate recent findings on the association between the extent of cerebral small vessel disease (cSVD), functional brain network dedifferentiation, and cognitive impairment. We analyzed demographic, imaging, and behavioral data from the prospective population-based Hamburg City Health Study. Using a fully prespecified analysis pipeline, we estimated discrete brain states from structural and resting-state functional magnetic resonance imaging (MRI). In a multiverse analysis, we varied brain parcellations and functional MRI confound regression strategies. The severity of cSVD was operationalized as the volume of white matter hyperintensities of presumed vascular origin. Processing speed and executive dysfunction were quantified using the Trail Making Test (TMT). We hypothesized a) that a greater volume of supratentorial white matter hyperintensities would be associated with less time spent in functional MRI-derived brain states of high fractional occupancy; and b) that less time spent in these high-occupancy brain states associated with a longer time to completion in part B of the TMT. High-occupancy brain states were characterized by activation or suppression of the default mode network. Every 5.1-fold increase in WMH volume was associated with a 0.94-fold reduction in the odds of occupying DMN-related brain states (P = 5.01×10−8). Every 5% increase in time spent in high-occupancy brain states was associated with a 0.98-fold reduction in the TMT-B completion time (P = 0.0116). Findings were robust across most brain parcellations and confound regression strategies. In conclusion, we successfully replicated previous findings on the association between cSVD, functional brain occupancy, and cognition in an independent sample. The data provide further evidence for a functional network dedifferentiation hypothesis of cSVD-related cognitive impairment. Further research is required to elucidate the mechanisms underlying these associations.

A large-scale c-Fos brain mapping study on extinction of cocaine-primed reinstatement.

Individuals with cocaine addiction can experience many craving episodes and subsequent relapses, which represents the main obstacle to recovery. Craving is often favored when abstinent individuals ingest a small dose of cocaine, encounter cues associated with drug use or are exposed to stressors. Using a cocaine-primed reinstatement model in rat, we recently showed that cocaine-conditioned interoceptive cues can be extinguished with repeated cocaine priming in the absence of drug reinforcement, a phenomenon we called extinction of cocaine priming. Here, we applied a large-scale c-Fos brain mapping approach following extinction of cocaine priming in male rats to identify brain regions implicated in processing the conditioned interoceptive stimuli of cocaine priming. We found that cocaine-primed reinstatement is associated with increased c-Fos expression in key brain regions (e.g., dorsal and ventral striatum, several prefrontal areas and insular cortex), while its extinction mostly disengages them. Moreover, while reinstatement behavior was correlated with insular and accumbal activation, extinction of cocaine priming implicated parts of the ventral pallidum, the mediodorsal thalamus and the median raphe. These brain patterns of activation and inhibition suggest that after repeated priming, interoceptive signals lose their conditioned discriminative properties and that action-outcome associations systems are mobilized in search for new contingencies, a brain state that may predispose to rapid relapse.

Acetylcholine Engages Distinct Amygdala Microcircuits to Gate Internal Theta Rhythm.

Acetylcholine (ACh) is released from basal forebrain cholinergic neurons in response to salient stimuli and engages brain states supporting attention and memory. These high ACh states are associated with theta oscillations, which synchronize neuronal ensembles. Theta oscillations in the basolateral amygdala (BLA) in both humans and rodents have been shown to underlie emotional memory, yet their mechanism remains unclear. Here, using brain slice electrophysiology in male and female mice, we show large ACh stimuli evoke prolonged theta oscillations in BLA local field potentials that depend upon M3 muscarinic receptor activation of cholecystokinin (CCK) interneurons (INs) without the need for external glutamate signaling. Somatostatin (SOM) INs inhibit CCK INs and are themselves inhibited by ACh, providing a functional SOM→CCK IN circuit connection gating BLA theta. Parvalbumin (PV) INs, which can drive BLA oscillations in baseline states, are not involved in the generation of ACh-induced theta, highlighting that ACh induces a cellular switch in the control of BLA oscillatory activity and establishes an internally BLA-driven theta oscillation through CCK INs. Theta activity is more readily evoked in BLA over the cortex or hippocampus, suggesting preferential activation of the BLA during high ACh states. These data reveal a SOM→CCK IN circuit in the BLA that gates internal theta oscillations and suggest a mechanism by which salient stimuli acting through ACh switch the BLA into a network state enabling emotional memory.

Coronavirus impact on insomnia - research based on social media and scientific publications

GT outcomes related to covid and insomnia are reviewed. Peaks in requests are noted during disease outbreaks. A machine-expert analysis of the vocabulary of tweets showed that in November 2021, users mainly complained of feeling unwell, insomnia, and headaches. In November 2022, problems related to the brain became important topics of discussion. Users actively discussed sleep problems, often using emoji. Comparison of the vocabulary of scientific articles and tweets showed a significant similarity of the topics discussed. Twitter users often complain about poor sleep and insomnia due to current covid, or a previous illness (long-term consequences of covid). At the same time, problems associated with the brain and neuropsychological state are often noted. At the same time, these topics are poorly represented in the scientific literature. Consequently, Twitter users may be exposed to distorted information, which may exacerbate their psychological stress.