Biology subjects

Cook, M. J.

Publications and source records attributed to Cook, M. J..

9 recordsLinked to original sources

bioRxiv2026

A Neural Mass Modelling Framework for Evaluating EEG Source Localisation of Seizure Activity

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.

neuroscience↗

bioRxiv2025

Structural Eigenmodes of the Brain to Improve the Source Localisation of EEG: Application to Epileptiform Activity

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 localisation 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, we presented a simple framework for incorporating different forms of structural brain eigenmodes to constrain the source localisation problem in epilepsy. We found that geometric eigenmodes were 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.

neuroscience↗

bioRxiv2024

Reduced Synaptic Heterogeneity in a Tetanus ToxinModel of Epilepsy: Insights from ComputationalModeling

A neural mass model was used to assess connectivity strength across diverse populations by fitting the model to background EEG data obtained from a Tetanus Toxin rat model of epilepsy. Our findings reveal a notable decline in the variability of estimated parameters when using EEG data recorded from rats in the Tetanus Toxin group compared with the control group. A detailed comparison of standard deviations in estimated parameters between day 1 and day 20 recordings, coinciding with a heightened number of seizures, underscores the impact of Tetanus Toxin on diminishing synaptic strength variability across recordings. This study supports electrophysiological studies suggesting that epileptogenesis induces a reduction in biophysical heterogeneity, potentially leading to an increase in network synchrony associated with epilepsy. Furthermore, our computational model establishes a foundation for future explorations of the implications of this diminished variability.

neuroscience↗

bioRxiv2024

Neural Dynamics and Seizure Correlations: Insights from Neural Mass Models in a Tetanus Toxin Rat Model of Epilepsy

This study focuses on the use of a neural mass model to investigate potential relationships between functional connectivity and seizure frequency in epilepsy. We fitted a three-layer neural mass model of a cortical column to intracranial EEG (iEEG) data from a Tetanus Toxin rat model of epilepsy, which also included responses to periodic electrical stimulation. Our results show that some of the connectivity weights between different neural populations correlate significantly with the number of seizures each day, offering valuable insights into the dynamics of neural circuits during epileptogenesis. We also simulated single-pulse electrical stimulation of the neuronal populations to observe their responses after the connectivity weights were optimized to fit background (non-seizure) EEG data. The recovery time, defined as the time from stimulation until the membrane potential returns to baseline, was measured as a representation of the critical slowing down phenomenon observed in nonlinear systems operating near a bifurcation boundary. The results revealed that recovery times in the responses of the computational model fitted to the EEG data were longer during 5 min periods preceding seizures compared to 1 hr before seizures in four out of six rats. Analysis of the iEEG recorded in response to electrical stimulation revealed results similar to the computational model in four out of six rats. This study supports the potential use of this computational model as a model-based biomarker for seizure prediction when direct electrical stimulation to the brain is not feasible.

neuroscience↗

bioRxiv2022

Inhibitory stabilized network behaviour in a balanced neural mass model of a cortical column

Strong inhibitory recurrent connections can reduce the tendency for a neural network to become unstable. This is known as inhibitory stabilization; networks that are unstable in the absence of strong inhibitory feedback because of their unstable excitatory recurrent connections are known as Inhibition Stabilized Networks (ISNs). One of the characteristics of ISNs is their "paradoxical response", where perturbing the inhibitory neurons with additional excitatory input results in a decrease in their activity after a temporal delay instead of increasing their activity. Here, we develop a model of populations of neurons across different layers of cortex. Within each layer, there is one population of inhibitory neurons and one population of excitatory neurons. The connectivity weights across different populations in the model are derived from a synaptic physiology database provided by the Allen Institute. The model shows a gradient of excitation-inhibition balance across different layers in the cortex, where superficial layers are more inhibitory dominated compared to deeper layers. To investigate the presence of ISNs across different layers, we measured the membrane potentials of neural populations in the model after perturbing inhibitory populations. The results show that layer 2/3 in the model does not operate in the ISN regime but layers 4 and 5 do operate in the ISN regime. These results accord with neurophysiological findings that explored the presence of ISNs across different layers in the cortex. The results show that there may be a systematic macroscopic gradient of inhibitory stabilization across different layers in the cortex that depends on the level of excitation-inhibition balance, and that the strength of the paradoxical response increases as the model moves closer to bifurcation points. Author summaryStrong feedback inhibition prevents neural networks from becoming unstable. Inhibition Stabilized Networks (ISNs) have strong inhibitory connections combined with high levels of unstable excitatory recurrent connections. In the absence of strong inhibitory feedback, ISNs become unstable. ISNs demonstrate a paradoxical effect: perturbing inhibitory neurons in an ISN by increasing their excitatory input results in a decrease in their activity after a temporal delay instead of increasing their activity. Here, we developed a neural mass model of a cortical column based on neurophysiological data. The model shows a gradual change in inhibitory stabilization across different layers in the cortex where layer 2/3 is less inhibitory stabilized and shows no paradoxical effect in contrast to layer 4 and layer 5, which operate in the ISN regime and show paradoxical responses to perturbation.

neuroscience↗

bioRxiv2022

Circadian distribution of epileptiform discharges in epilepsy: candidate mechanisms of variability

Epilepsy is a serious neurological disorder characterised by a tendency to have recurrent, spontaneous, seizures. Classically, seizures are assumed to occur at random. However, recent research has uncovered underlying rhythms both in seizures and in key signatures of epilepsy - so-called interictal epileptiform activity - with timescales that vary from hours and days through to months. Understanding the physiological mechanisms that determine these rhythmic patterns of epileptiform discharges remains an open question. Many people with epilepsy identify precipitants of their seizures, the most common of which include stress, sleep deprivation and fatigue. To quantify the impact of these physiological factors, we analysed 24-hour EEG recordings from a cohort of 107 people with idiopathic generalized epilepsy. We found two subgroups with distinct distributions of epileptiform discharges: one with highest incidence during sleep and the other during day-time. We interrogated these data using a mathematical model that describes the transitions between background and epileptiform activity in large-scale brain networks. This model was extended to include a time-dependent forcing term, where the excitability of nodes within the network could be modulated by other factors. We calibrated this forcing term using independently-collected human cortisol (the primary stress-responsive hormone characterised by circadian and ultradian patterns of secretion) data and sleep-staged EEG from healthy human participants. We found that either the dynamics of cortisol or sleep stage transition, or a combination of both, could explain most of the observed distributions of epileptiform discharges. Our findings provide conceptual evidence for the existence of underlying physiological drivers of rhythms of epileptiform discharges. These findings should motivate future research to explore these mechanisms in carefully designed experiments using animal models or people with epilepsy. Author summary65 million people have epilepsy worldwide. Many of these people report specific triggers that make their seizures (the primary symptom of epilepsy) more likely. Here, we use a mathematical model to understand the relationship between possible triggers and rhythms in epileptiform activity observed across the day. The mathematical model describes the activity of connected brain regions, and how the excitability of these regions can change in response to different stimuli. Based on data collected from people with idiopathic generalized epilepsy, we identify transitions between sleep stages and variation in concentration of the stress-hormone cortisol as candidate factors that influence how likely it is for epileptiform activity to occur. By including those factors into the model, we show they can explain most of the daily variability. More broadly, our approach provides a framework for better understanding what factors drive the occurrence of epileptiform activity and offers the potential to suggest experiments that can validate model predictions.

neuroscience↗

bioRxiv2020

Spatiotemporal patterns of high-frequency activity (80-170 Hz) in long-term intracranial EEG

ObjectiveTo assess the variability in the rates and locations of high-frequency activity (HFA) and epileptiform spikes after electrode implantation, and to examine the long-term patterns of HFA using ambulatory intracranial EEG (iEEG) recordings. MethodsContinuous iEEG recordings obtained over an average of 1.4 years from 15 patients with drug-resistant focal epilepsy were used in this study. HFA was defined as high-frequency events with amplitudes clearly larger than the background, which was automatically detected using a custom algorithm. High-frequency oscillations (HFOs) were also visually annotated by three neurologists in randomly sampled segments of the total data. The automatically detected HFA was compared with the visually marked HFOs. The variations of HFA rates were compared with spikes and seizures on patient-specific and electrode-specific bases. ResultsHFA was a more general event that encompassed HFOs manually annotated by different reviewers. HFA and spike rates had high amounts of intra- and inter-patient variability. The rates and locations of HFA and spikes took up to weeks to stabilize after electrode implantation in some patients. Both HFA and spike rates showed strong circadian rhythms in all patients and some also showed multiday cycles. Furthermore, the circadian patterns of HFA and spike rates had patient-specific correlations with seizures, which tended to vary across electrodes. ConclusionsAnalysis of HFA and epileptiform spikes should account for post-implantation variability. Like seizures, HFA and epileptiform spikes show circadian rhythms. However, the circadian profiles can vary spatially within patients and their correlations to seizures are patient-specific.

neuroscience↗

bioRxiv2019

Critical slowing as a biomarker for seizure susceptibility

The human brain has the capacity to rapidly change state, and in epilepsy these state changes can be catastrophic, resulting in loss of consciousness, injury and even death. Theoretical interpretations considering the brain as a dynamical system would suggest that prior to a seizure recorded brain signals may exhibit critical slowing, a warning signal preceding many critical transitions in dynamical systems. Using long-term intracranial electroencephalography (iEEG) recordings from fourteen patients with focal epilepsy, we found key signatures of critical slowing prior to seizures. Signals related to a critically slowing process fluctuated over temporally long scales (hours to days), longer than would be detectable in standard clinical evaluation settings. Seizure risk was associated with a combination of these signals together with epileptiform discharges. These results provide strong validation of theoretical models and demonstrate that critical slowing is a reliable indicator that could be used in seizure forecasting algorithms.

neuroscience↗

bioRxiv2018

Metagenomic 16S rRNA gene sequencing survey of Borrelia species in Irish samples of Ixodes ricinus ticks

Lyme borreliosis is a systemic infection caused by tick-borne pathogenic borreliae of the Borrelia burgdorferi sensu lato complex or of the more heterogeneous relapsing fever borrelia group. Clinical distinction of the infections due to different borrelia species is difficult. Accurate knowledge of the prevalence and the species of borreliae in the infected ticks in the endemic areas is valuable for formulating appropriate guidelines for proper management of this infectious disease. The purpose of this research was to design a readily implementable protocol to detect the divergent species of borreliae known to exist in Europe, using Irish samples of Ixodes ricinus ticks as the subject for study. Questing I. ricinus nymph samples were taken at six localities within Ireland. The crude DNA of each dried tick was extracted by hot NH4OH and used to initiate a same-nested PCR with a pair of borrelial genus-specific primers to amplify a highly conserved 357/358 bp segment of the 16S rRNA gene for detection and as the template for Sanger sequencing. To distinguish B. garinii from B. burgdorferi and to discriminate the various strains of B. garinii, a second 282 bp segment of the 16S rRNA gene was amplified for Sanger sequencing. A signature segment of the DNA sequence excised from the computer-generated electropherogram was submitted to the GenBank for BLAST alignment analysis. A 100% ID match with the unique reference sequence in the GenBank was required for the molecular diagnosis of the borrelial species or strain. We found the overall rate of borrelial infection in the Irish tick population to be 5%, with a range from 2% to 12% depending on the locations of tick collection. At least 3 species, namely B. garinii, B. valaisiana and B. miyamotoi, are infecting the ticks collected in Ireland. The isolates of B. garinii were confirmed to be strain BgVir, strain Bernie or strain T25. Since antigens for diagnostic serology tests may be species- or even strain-specific, expanded surveillance of the species and strains of the borreliae among human-biting ticks in Ireland is needed to ensure that the antigens used for the serology tests do contain the epitopes matching the antibodies elicited by the borrelial species and strains in the ticks cohabitating in the same environment.

microbiology↗