Levin Kuhlmann

Background: Psilocybin is a serotonergic psychedelic that provides a pharmacological tool for examining neural activity associated with altered states of consciousness. Existing studies report reduced power (4-30 Hz) in posterior regions and altered functional connectivity (FC) at rest, but have typically involved small samples and coarse anatomical parcellations. Additionally, perceptual engagement has been shown to attenuate psilocybin-related whole-brain FC changes. However, how naturalistic context modifies psilocybin’s frequency-specific electrophysiological effects remains incompletely characterised. Methods: We analysed the largest psilocybin electroencephalography (EEG) dataset to date (n=56), with matched baseline recordings acquired before psilocybin administration. EEG was recorded during movie viewing, eyes-closed rest, guided meditation, and music listening. Cortical source activity was estimated using minimum-norm estimation and analysed across six canonical frequency bands (1-80 Hz). Spectral power, alpha peak frequency (APF), and magnitude-squared coherence within and between seven functional networks were quantified. Psilocybin effects were assessed using permutation testing with FDR correction, and linear mixed-effects models separated the contributions of drug, context, and their interaction to alpha-band connectivity. Results: At rest, psilocybin decreased alpha (8-13 Hz) power, increased gamma-range power, and elevated APF. Alpha-band FC decreased within and between higher-order networks implicated in top-down control. Relative to eyes-closed rest, psilocybin-context interactions showed that structured sensory environments attenuated or reversed psilocybin-related alpha-band connectivity changes. Conclusions: These findings identify alpha-band activity as a key electrophysiological correlate of psilocybin-related changes in cortical communication. Structured sensory input reshapes psilocybin-related cortical dynamics such as alpha-band connectivity across large-scale cortical networks, with the strength and direction of this modulation differing by context.

While general anaesthesia typically induces unconsciousness, some patients retain the capacity to respond behaviourally to noxious stimulation. Reduced frontal alpha (8-12 Hz) power has been proposed as a marker of arousal, but its clinical reliability remains inconsistent. This exploratory study aimed to identify multichannel EEG spectral and connectivity markers associated with intraoperative behavioural responsiveness around noxious stimulation. Sixty-four-channel EEG was recorded intraoperatively from seven patients undergoing microlaryngoscopy under propofol anaesthesia with analgesia. Responsiveness was assessed via reactions to verbal commands. Alpha-band spectral features of responders and non-responders were compared pre- and post-noxious stimulation using descriptive statistics. Linear mixed models evaluated the relationship between channel-wise spectral power, and the factors of noxious stimulation and responsiveness. Event-related synchronization/desynchronization and connectivity via weighted Symbolic Mutual Information (wSMI) were analysed across frequency bands and response categories, with correction for multiple comparisons. Alpha power and global coherence showed minimal changes following noxious stimulation across patients. In contrast, volitional responses to noxious stimulation were associated with increased high-frequency power in sensory-motor and auditory cortices. These responses showed event-related synchronisation in left-central channels, whereas incoherent movements were marked by desynchronisation in the same areas. Theta-band activity further differentiated response types: cognitive responses showed suppression, while incoherent movements showed enhancement, particularly over the same regions. Cognitive responses were associated with increased global whole-brain integration, especially in the theta-band linking motor, auditory, and premotor cortices. Incoherent movements, by contrast, were associated with reductions in global brain connectivity. None of these patients reported postoperative awareness with recall. These preliminary findings suggest that localised high-frequency power increases and enhanced theta-band connectivity may reflect "connected consciousness," in which patients retain the capacity to process sensory input despite anaesthesia. As this state may precede awareness with recall, particularly during noxious stimulation, its detection remains a key clinical challenge, underscoring the potential of these multichannel EEG features as markers. Despite limitations in sample size and variability in anaesthetic and patient factors, the surgical setting enhances the clinical relevance of these findings. However, further validation is required before clinical application for intraoperative monitoring and optimisation of analgesia prior to noxious stimulation.

Objective. This study addresses the challenge of objectively evaluating electroencephalography and magnetoencephalography (EEG/MEG) source localisation algorithms in the absence of an experimentally verifiable ground truth, specifically for the characterisation of ictal dynamics. Approach. A simulation framework is presented for generating biologically plausible ictal dynamics and their corresponding EEG signals to enable systematic benchmarking of source imaging approaches. Cortical seizure initiation and propagation were simulated using network-coupled neural mass (Epileptor) models and combined with realistic forward models of the human head to produce macroscopic, electrophysiological data with known ground truth under varying conditions. Main results. Using this dataset, we evaluated the established MN-family of source localisation methods across idealised and realistic scenarios. Existing approaches achieved reasonable spatial accuracy under high-density, noise-free conditions, but performance degraded substantially with reduced sensor coverage and added noise. This degradation was driven primarily by failures to recover source polarity, even when spatial localisation remained relatively accurate. Significance. These results suggest that current methods may be sufficient for identifying epileptogenic regions or tracking regional recruitment but highlight polarity reconstruction as a key limitation for studies of seizure dynamics and network organisation. The proposed framework provides a reproducible and biologically grounded testbed for the development and evaluation of electrophysiological source localisation techniques.

Recently, automated Interictal Epileptiform Discharge (IED) detection has attracted significant attention as a challenging predictive data analysis task aimed at improving early epilepsy diagnosis. Automated IED detection simplifies visual inspection and assists clinicians in identifying crucial IED waveform patterns in Electroencephalographic (EEG) brain activities. However, IEDs are vastly outnumbered by non-IED or background data, directly training on such data leads to a detrimental impact on model performance. Moreover, most existing methods lack high precision when tested on cross-institution datasets and this will lead to time wasted by the neurologist having to look at predicted IEDs that are not IEDs. To address these issues, we propose a novel approach that employs probabilistic diffusion models for data augmentation alongside Area Under the Precision–Recall Curve (AUPRC) maximization methods. This approach effectively addresses data scarcity and imbalance by combining real and synthesized IEDs to fully balance the training dataset, resulting in a 4.5% increase in precision and a 0.7% improvement in the F1-score during within-data evaluation. Additionally, it proves to be robust and generalizable, as evidenced by a 40.04% and 18.74% enhancement in precision and F1-score when applied to cross-data evaluation using data from another hospital.

A fundamental view of neuroscience is that, in addition to neuronal activity, the structure of the brain constrains and explains brain function. An alluring formalism in computational neuroscience has been the generation of structural eigenmodes of neural activity from a matrix representing the anatomy of the brain. Traditionally, brain connectomics has been the gold standard for the coupling between structure and function. However, it has recently been suggested that simpler brain geometry can provide more explanatory power in fMRI. An adjacent modality is the source localization problem of EEG, which aims to identify the underlying generators of EEG recordings. The underdetermined nature of the problem requires sufficient constraints to produce realistic and unique solutions of source activity. In this work, a simple framework for incorporating different forms of structural brain eigenmodes to constrain the source localization problem in epilepsy is presented. Geometric eigenmodes were found to be able to reconstruct the spread of a seizure through the brain slightly better than connectome eigenmodes, and both types of structural modes significantly outperformed commonly used approaches.

Electroencephalography and magnetoencephalography (EEG/MEG) provide non-invasive measurements of large-scale neural activity but do not directly reveal the underlying cortical sources, motivating the use of source localisation algorithms. However, objective evaluation of these methods remains challenging due to the absence of an experimentally verifiable ground truth. This study presents a simulation framework for generating biologically plausible ictal dynamics and their corresponding EEG signals to enable systematic benchmarking of source imaging approaches. Cortical seizure initiation and propagation were simulated using network-coupled neural mass (Epileptor) models, and combined with realistic forward models of the human head to produce macroscopic, electrophysiological data with known ground truth under varying conditions. Using this dataset, we evaluated established source localisation methods across idealised and realistic scenarios. Existing approaches achieved reasonable spatial accuracy under high-density, noise-free conditions; however, performance degraded substantially with reduced sensor coverage and added noise. This degradation was driven primarily by failures to recover source polarity, even when spatial localisation remained relatively accurate. These results suggest that current methods may be sufficient for identifying epileptogenic regions or tracking regional recruitment, but highlight polarity reconstruction as a key limitation for studies of seizure dynamics and network organisation. The proposed framework provides a reproducible and biologically grounded testbed for the development and evaluation of electrophysiological source localisation techniques.

Objective: Forecasting epileptic seizures is a difficult task. Studies of seizure prediction have investigated many different EEG features, but none of them have been useful enough to be applied in clinical practice beyond trials. Moreover, most of these features have been applied to short‐term intracranial EEG (iEEG) recordings, limiting the possibility of reliable statistical evaluation. This paper proposes a machine learning algorithm to forecast an epileptic seizure 2‐4 mins before seizure. This allows patients to seek help, or stimulation devices to work. Methods: This paper investigates a large subset of features from the past and present to unravel which features and feature analysis methods will yield the best performance on long‐term iEEG recordings (from 14 patients with focal epilepsy) and thus provide the most reliable step toward clinical utility. Specifically, this study implements a multiple long‐time scale cycle feature analysis framework for seizure forecasting that considers the state‐of‐the‐art time series features of critical slowing down (autocorrelation and variance) as well as interictal epileptiform discharge (IED) / spike rate, High Frequency Activity (HFA), seven different univariate features, and three Neural Mass Model (NMM) features based on brain dynamics. Results: Seizure phase histograms of all the features are then analyzed to investigate each feature’s potential for seizure forecasting by evaluating corresponding synchronization indices (SI) on fast (40 minutes to 2 days) and slow (2+ days) wideband time scales. Out of all combinations considered, the overall performance comparison across patients highlights that ‘autocorrelation + variance + NMM + spike rate’ features achieve the highest average AUC of 0.83, showcasing its performance in forecasting seizures. Significance: A model is proposed that has a similar performance compared to the state‐of‐the‐art method, without the need of selecting the best channel prior to model building. Light is also shed on the comparative performance on long‐term recordings of many of the seizure forecasting features considered in the past.

Waking levels of human consciousness are known to be supported by the integrity of complex structures and processes in the brain, yet how they are exactly regulated by neurobiological mechanisms remains uncertain. Here a space-time-resolved inference-based framework is applied to estimate the neurophysiological variables of a whole-cortex model and analyze the neural mechanism correlates of global consciousness by way of a correlation analysis between behavioural and neural variable time-series. Using magnetoencephalography (MEG) data from 15 participants under Xenon-induced anesthesia, interconnected neural mass models (NMMs) were developed and time-evolving regional neurophysiological variables and inter-regional connectivity strengths were inferred from the data. Analyses revealed significant correlations between consciousness levels and inter-regional connectivity, particularly in posterior parietal, occipital, and prefrontal regions. Moreover, results support a parietal, rather than frontal, network backbone to facilitate global consciousness. Regional-level analyses further identified correlates of consciousness within the posterior parietal and occipital regions. Lastly, reductions in consciousness were linked to stabilized cortical dynamics, reflected by changes in the eigenmodes of the system. This framework provides a novel, inference-based approach to investigating consciousness, offering a time-resolved perspective on neural mechanism correlates during altered states.

Interictal epileptiform discharges (IEDs) are reliable biomarkers in electroencephalograms for epilepsy. To automate IED detection, deep learning (DL) has been applied to treat EEG signals as time-series data. However, collecting sufficient IEDs for training DL models is expensive; public and private datasets are often overwhelmed with background activities. To address this issue, generative adversarial networks (GANs) have been used to synthesise IEDs. Previous works using GANs achieved some improvements, but the quality metrics of synthetic samples were not contextualised, resulting in suboptimal model tuning. In this work, we proposed to use density and coverage to evaluate synthetic data and to tune a GAN model. We also utilised soft dynamic time warping (soft-DTW), a differentiable version of DTW, as the reconstruction loss to learn temporal alignments of the IED's waveforms and improve the diversity of synthetic samples. Our proposed approach achieved a 10% improvement in the AUPRC score on the Temple University Events (TUEV) dataset by combining synthetic and real data, demonstrating the potential of this method to enhance the accuracy of IED detection.