Mark Philip Richardson

Background/Objectives: Despite the clinical efficacy of cannabidiol (CBD) in treating certain epilepsies, its in vivo neuropharmacological mechanisms remain incompletely understood. This Phase 1 clinical trial aimed to evaluate the acute effects of a single oral dose of highly purified CBD (GWP42003-P) on cortical excitability. Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) and electromyography (TMS-EMG) were utilized as assessment tools. Methods: In a randomized, double-blind, placebo-controlled crossover trial, 15 healthy male participants received a single 1500 mg dose of GWP42003-P or placebo. Cortical activity metrics, including resting and active motor thresholds (RMT, AMT), short intracortical inhibition (SICI), TMS-evoked potentials (TEPs), TMS-related spectral perturbations (TRSP), and inter-trial phase clustering (ITPC), were recorded pre-dose and at 1, 4, and 6 h post-dose. Results: RMT and AMT significantly decreased over time following CBD administration, though without a significant Condition × Time interaction. While primary analyses showed no condition-driven alterations for SICI, TEPs, or TRSP, secondary longitudinal modeling revealed a transient reduction in beta-band desynchronization 1 h post-dose, aligning with peak plasma concentration. The drug condition also exhibited a qualitative, non-significant trend toward increased alpha ITPC and decreased delta ITPC at 4 and 6 h post-dose compared to placebo. Conclusions: A single acute 1500 mg dose of GWP42003-P did not produce statistically significant alterations in widespread cortical excitability in healthy adult males. However, the transient blunting of beta desynchronization at peak concentration may reflect CBD’s distinct, non-classical neuromodulatory profile. Overall, these largely null findings suggest that acute CBD administration lacks a robust, direct modulatory effect on cortical networks. Capturing its precise neuropharmacological mechanisms will likely require future investigations utilizing chronic dosing paradigms or clinical patient populations.

Objective: To evaluate a large language model (LLM) pipeline for extracting structured diagnostic labels and confidence levels from unstructured free-text EEG reports, addressing the barrier of narrative data analysis. Methods: We developed a hierarchical schema classifying reports for four abnormality types using a four-point confidence scale. Two certified EEG technologists annotated the reports independently; one was designated the Reference Annotator (RA) and the other the Second Annotator (SA). We implemented a grammar-constrained Mistral-7B pipeline and evaluated its agreement with the RA alongside the SA and classical NLP baselines using Core Agreement and Certainty-Adjusted Agreement. A portability analysis held the task, labels, output grammar, report identifiers, reference annotations, and endpoints fixed while a MedGemma configuration used its model-declared instruction-turn format. Results: For overall abnormality, Mistral-7B achieved 96% Core Agreement with the RA and the SA achieved 98%. Mistral-7B exceeded the tested low-resource base-lines in most categories and identified rare epileptiform abnormalities for which the baselines produced no or few true positives. Among reports with matching overall-abnormality decisions, Conditional Certainty Agreement was about 80% for Mistral–RA on each evaluated report set and 90% and 94% for SA–RA. The MedGemma configuration showed higher agreement than Mistral-7B in several non-epileptiform categories, but focal epileptiform performance involved a trade-off between missed and additional positive calls. Conclusions: Under the studied report-level conditions, the grammar-constrained pipeline produced structured annotations with high binary agreement to the RA. After adapting the prompt presentation to its model-declared instruction-turn format, the same structured task and evaluation framework transferred to a newer local model. The results support its use as a research annotation method; they do not establish diagnostic ground truth, external validation, or readiness for clinical deployment. Significance: This study demonstrates a locally executable method for converting de-identified EEG narratives into structured research annotations while retaining an ordinal representation of annotator certainty.

We propose a study protocol for routine clinical electroencephalograms (EEGs) from public hospitals, which represents a vast resource for neuroscience research. These non-invasive measures of brain function, paired with rich clinical annotations from large and diverse patient populations, are critical for developing robust artificial intelligence (AI) models and conducting population-level studies. This protocol presents a scalable methodology for curating and harmonizing extensive clinical EEG datasets, encompassing over 40,000 individual studies, to facilitate research applications. Key steps include: (i) integration of raw EEG recordings with corresponding clinical records, including neurological reports, diagnostic codes, and potentially medication data; and (ii) spatial standardization of EEG signals by mapping them to a common brain space defined by functional and anatomical landmarks. The resulting harmonized datasets enable the development of large-scale EEG foundation models, the discovery of novel EEG waveform representations, and the creation of normative “brain charts” for electrophysiological assessment across the lifespan. By enabling standardised, large-scale analyses of real-world clinical EEG data, this protocol supports data-intensive solutions for EEG applications and addresses the challenge of generalising AI models. Our approach promotes the translation of AI tools from research to diverse patient populations, advancing population neuroscience.

Objective: There is a growing synergy between the lines of research on cycles in epilepsy and seizure forecasting. It has been conjectured, for instance, that incorporating information about significant seizure cycles into forecasting algorithms can lead to a better‐than‐chance forecasting performance. However, significance and better‐than‐chance are each typically evaluated against only a single null hypothesis, for example, that forecasts are generated by a Poisson process. We here argue that this should be considered only a first step. Our objective is to demonstrate the importance of testing complementary null hypotheses that represent alternative chance models. Methods: To ensure controlled conditions, we use synthetic data generated from simple mathematical models. Samples drawn from gamma distributions are used to generate sequences of random seizure times and random forecasts. We then determine the strength of cycles as a function of the cycle duration and calculate the sensitivity and fraction of time under alarm obtained for the random forecasting algorithm. In both analyses, we apply numerical, surrogate‐based null‐hypothesis testing methods. In the latter case, this includes a straightforward approach to correcting for multiple testing on nonindependent data. Results: Counterintuitively, the random seizure‐time sequences contain multiple prominent cycles, which are judged highly significant by the Rayleigh test. Moreover, randomly forecasting random seizure times results in a sensitivity of 79% at a fraction of time under alarm of only 42%, clearly outperforming a Poisson‐like predictor. In both cases, however, the flexibility and versatility of surrogate‐based null‐hypothesis tests allow us to successfully reveal that all results can be explained by chance models. Significance: Before reaching conclusions on real cycles in epilepsy, the forecastability of seizures, and genuine capacity of forecasting algorithms, it is essential to test and reject several complementary null hypotheses. Many conclusions might not withstand such rigorous tests, allowing the community to focus on those that do.

Seizure forecasting models require long‐term, high‐quality data collected in real‐life settings using noninvasive or minimally invasive devices, yet, the lack of such systems remains a major barrier to their clinical translation. Here, we aimed to evaluate the signal quality of self‐applied at‐home EEG monitoring using a wearable system in patients with epilepsy to assess its reliability for future seizure forecasting applications. All EEG recordings were reviewed to identify non‐usable data and interictal epileptiform discharges (IEDs). We analyzed power spectral density and the temporal evolution of a signal‐to‐noise ratio, and applied composite quality criteria for each patient based on spectral profile and the proportion of EEG data discarded. Twelve patients with drug‐resistant epilepsy performed self‐applied resting‐state EEG recordings twice daily over a median period of 173.5 days (min. 12, max. 235). Two‐thirds of patients had data of good or moderate quality ( N = 3 and 5, respectively), which remained overall stable over time with cap replacement every 2–3 months. IEDs were found in four patients and were concordant with prior in‐hospital recordings. Self‐applied at‐home EEG monitoring can yield clinically relevant insights and may support future seizure forecasting strategies in selected patients, provided patient adherence and the feasibility of regular maintenance follow‐up are addressed. Plain Language Summary: Twelve people whose epilepsy was hard to control with medication recorded a short brain‐wave test (electroencephalography, or EEG) at home twice a day for several months. In 8 of the 12 people, most recordings were clear enough to use and stayed steady over time, although some EEG caps needed replacement. In four people, the home EEG showed abnormal spikes between seizures that matched earlier hospital EEGs. This suggests that long‐term, self‐recorded EEG at home is possible for some patients and may help clinicians track brain activity outside the hospital.

Objectiv: Electroencephalography (EEG) signals comprise both oscillatory (periodic) and non-oscillatory (aperiodic) components. Aperiodic activity forms the 1/f-like background of the EEG power spectrum and can be characterised by parameters describing the offset and slope (1/f exponent). This study examined the effects of two antiseizure medications (ASMs), which reduce cortical excitability through different mechanisms of action, on aperiodic and periodic EEG activity. Methods: Resting EEG was recorded with eyes open and closed from 13 healthy male volunteers at baseline and two hours after administration of lamotrigine (300 mg), levetiracetam (3000 mg), or placebo. Power spectra were computed using Welch’s method. Aperiodic parameters were estimated using the specparam algorithm, and periodic activity was quantified after subtraction of the aperiodic component. Results: In the eyes-open condition, lamotrigine significantly reduced the aperiodic offset from pre- to post-dose and relative to placebo, and reduced the exponent relative to placebo, indicating a flattening of the spectra. No significant changes in slope or offset were observed during eyes-closed, and levetiracetam produced no significant effects on any aperiodic parameter. Lamotrigine reduced theta power in both conditions, reduced alpha power, and increased gamma power during eyes open. Levetiracetam increased beta power in both conditions. Conclusions: Lamotrigine altered both aperiodic and periodic EEG activity, with aperiodic effects restricted to the eyes-open condition, whereas levetiracetam influenced periodic activity without altering aperiodic parameters. Significance: Aperiodic EEG measures may provide a non-invasive approach for assessing ASM-related neurophysiological effects and improve understanding of how distinct pharmacological mechanisms influence large-scale cortical activity.

Temporal lobe epilepsy (TLE) is commonly associated with mesiotemporal pathology and widespread alterations of gray and white matter structures. Evidence supports a progressive condition, although the temporal evolution of TLE is poorly defined. In this ENIGMA-Epilepsy study, we aim to investigate structural alterations in gray and white matter across the adult lifespan in patients with TLE by charting both gray and white matter changes and explore the covariance of age-related alterations in both compartments. Mega-analysis of parcellated T1-weighted and diffusion MRI data across 18 international sites for patients with TLE was compared against healthy controls. We combined median-age split groupwise comparisons with cross-sectional sliding age-window analyses to explore gray (cortical thickness, subcortical volume) and white matter microstructure (fractional anisotropy, mean diffusivity) age-related changes. Five-year range age windows were constructed from mean z scores of all patients. Covariance analyses examined the coupled correlations of gray and white matter lifespan curves for each region. We studied 769 patients with TLE and 885 healthy controls across an age range of 17-73 years. Robust (p_FDR < 0.05) gray matter thickness/volume decline (d < -0.20) was seen across a broad cortico-subcortical territory, extending beyond the mesiotemporal lobe throughout the adult lifespan in patients with TLE. White matter changes were also widespread across multiple fiber tracts with peak effects in temporolimbic fibers in fractional anisotropy (d < -0.3, p_FDR < 0.05) and mean diffusivity measures (d > 0.3, p_FDR < 0.05). Changes spanned the adult time window and effects exceeded typical aging-related processes in patients at the level of cortical thickness, subcortical volume, and diffusion measures, particularly in patients older than 55 years. Covariance analyses revealed strong associations across multiple white matter tracts, subcortical structures, and cortical regions within and beyond the temporolimbic system. This study highlights that patients with TLE exhibit more pronounced and widespread gray and white matter atrophy across the lifespan. The cross-sectional nature of our study limits definitive conclusions on whether the atrophy shown is progressive but emphasizes the importance of prompt diagnosis and intervention in patients. Collectively, our results motivate future longitudinal studies to clarify consequences of drug-resistant epilepsy.