Biology subjects

Richardson, M. P.

Publications and source records attributed to Richardson, M. P..

6 recordsLinked to original sources

bioRxiv2025

Investigating the Influence of Anti-Seizure Medications on Aperiodic EEG Activity

ObjectiveElectroencephalography (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. MethodsResting 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 Welchs method. Aperiodic parameters were estimated using the specparam algorithm, and periodic activity was quantified after subtraction of the aperiodic component. ResultsIn the eyes-open condition, lamotrigine significantly reduced both aperiodic offset and exponent relative to placebo, consistent with a flattening of the aperiodic spectrum, whereas levetiracetam reduced the exponent without significantly altering the offset. Neither drug significantly altered aperiodic exponent or offset during eyes-closed. Lamotrigine reduced corrected theta power in both conditions and alpha power during eyes-open, whereas levetiracetam increased beta power in both conditions and reduced gamma power during eyes-closed. ConclusionsBoth lamotrigine and levetiracetam altered aperiodic and periodic EEG activity, with aperiodic effects observed only during eyes-open. SignificanceAperiodic EEG measures may provide a non-invasive approach for characterising ASM-related neurophysiological effects and may help elucidate how distinct pharmacological mechanisms influence large-scale cortical activity.

neuroscience↗

bioRxiv2025

Dynamics-Informed Priors (DIP) for Neural Mass Modelling

Neural Mass Models (NMMs) are important mathematical tools for inferring hidden neural mechanisms that generate healthy and pathological brain activities. A critical step in the inference process is parameter estimation, which calibrates NMMs based on measured neuroimaging data. While parameter estimation can be conducted via various approaches, one of the most influential methods is Dynamic Causal Modelling (DCM). DCM adopts a Bayesian inference approach that relies on, and is sensitive to, the specification of prior parameter distributions reflecting a priori hypotheses about the causes of data. However, most parameters of NMMs encode neuronal properties that are not directly measurable. For this reason, in the absence of sufficient empirical data and well-founded prior beliefs, inference becomes increasingly susceptible to bias. Therefore, it was imperative to establish a comprehensive strategy for mapping model parameters to data. This study proposes a computational extension of DCM, named DCM with dynamics-informed priors (DIP-DCM), which adopts a genetic algorithm (GA) to map parameter values to model dynamics. Optimal sub-regions of the parameter space were subsequently selected and translated into groups of parameter priors for DCM. DIP-DCM was compared to the standard DCM inference and to the standalone GA, using two independent neuroimaging datasets. Results indicated that DIP-DCM models were the best predictors of data and captured key mechanistic signatures of psychiatric disease and pharmacological interventions. Overall, DIP-DCM addressed degeneracy, handled local minima, and explored diverse parameter regimes following trajectories informed directly by model dynamics and data. This study suggests that DIP-DCM is an advantageous route to parameter estimation when information is limited, enabling a data-driven derivation of parameter priors - or hypotheses - in exploratory studies, across different biological contexts and datasets.

systems biology↗

bioRxiv2025

Global search metaheuristics for neural mass model calibration

Neural mass models (NMMs) are often used to help understand the circuitry that underpins observed brain dynamics in basic and clinical research. A key step is to fuse models with data so that model parameter values can be inferred for a given data set--a process called model fitting or model calibration. This can shed light on putative physiological mechanisms underlying the observed signals. Calibration is notoriously challenging in biology since models are often non-identifiable, high-dimensional, and nonlinear. Established methods such as dynamic causal modelling (DCM) circumvent some of these issues, for example, by incorporating prior information and employing fast local search methods in the space of feasible parameter values ("parameter space"). However, it is pertinent to better understand the potential limitations of these methods so that we can increase our confidence in the use of models to interpret brain activity, and to develop new approaches as required. Here we use tools from dynamical systems theory to illustrate some of the complexities of model calibration in an archetypal NMM. We use this information to motivate the use of calibration methods that work across large regions of parameter space, rather than focusing on informative priors or localised search methods. We subsequently evaluate the performance of approximate Bayesian computation (ABC) and evolutionary search metaheuristics (ESMs) for mapping feasible sets of parameters for which an NMM can recreate electroen-cephalographic recordings during an eyes-closed resting state. Our results demonstrate the superiority of ESMs in terms of computational efficiency and accuracy. Furthermore, we elucidate potential reasons why ESMs are able to perform better than ABC, i.e. that they are less susceptible to biases induced by the complexity of underlying cost landscapes. These results highlight the importance of incorporating ESMs in future efforts to model brain dynamics.

neuroscience↗

bioRxiv2021

Single-cell Networks Reorganise to Facilitate Whole-brain Supercritical Dynamics During Epileptic Seizures

Excitation-inhibition (EI) balance may be required for the organisation of brain dynamics to a phase transition, criticality, which confers computational benefits. Brain pathology associated with EI imbalance may therefore occur due to a deviation from criticality. However, evidence linking critical dynamics with EI imbalance-induced pathology is lacking. Here, we studied the effect of EI imbalance-induced epileptic seizures on brain dynamics, using in vivo whole-brain 2-photon imaging of GCaMP6s larval zebrafish at single-neuron resolution. We demonstrate the importance of EI balance for criticality, with EI imbalance causing a loss of whole-brain critical statistics. Using network models we show that a reorganisation of network topology drives this loss of criticality. Seizure dynamics match theoretical predictions for networks driven away from a phase transition into disorder, with the emergence of chaos and a loss of network-mediated separation, dynamic range and metastability. These results demonstrate that EI imbalance drives a pathological deviation from criticality.

neuroscience↗

bioRxiv2019

Quantification and selection of ictogenic zones in epilepsy surgery

Network models of brain dynamics provide valuable insight into the healthy functioning of the brain and how this breaks down in disease. A pertinent example is the use of network models to understand seizure generation (ictogenesis) in epilepsy. Recently, computational models have emerged to aid our understanding of seizures and to predict the outcome of surgical perturbations to brain networks. Such approaches provide the opportunity to quantify the effect of removing regions of tissue from brain networks and thereby search for the optimal resection strategy.\n\nHere, we use computational models to elucidate how sets of nodes contribute to the ictogenicity of networks. In small networks we fully elucidate the ictogenicity of all possible sets of nodes and demonstrate that the distribution of ictogenicity across sets depends on network topology. However, the full elucidation is a combinatorial problem that becomes intractable for large networks. Therefore, we develop a global optimisation approach to search for minimal sets of nodes that contribute significantly to ictogenesis. We demonstrate the potential applicability of these methods in practice by identifying optimal sets of nodes to resect in networks derived from 20 individuals who underwent resective surgery for epilepsy.

neuroscience↗

bioRxiv2019

Dynamic network properties of the interictal brain determine whether seizures appear focal or generalised

ObjectiveCurrent explanatory concepts suggest seizures emerge from ongoing dynamics of brain networks. It is unclear how brain network properties determine focal or generalised seizure onset, or how network properties can be described in a clinically-useful manner. Understanding network properties would cast light on seizure-generating mechanisms and allow to quantify in the clinic the extent to which a seizure is focal or generalised.\n\nMethods68 people with epilepsy and 38 healthy controls underwent 19 channel scalp EEG recording. Functional brain networks were estimated in each subject using phase-locking between EEG channels in the 6-9Hz band from segments of 20s without interictal discharges. Simplified brain dynamics were simulated using a computer model. We introduce three concepts: Critical Coupling (Cc), the ability of a network to generate seizures; Onset Index (OI), the tendency of a region to generate seizures; and Participation Index (PI), the tendency of a region to become involved in seizures.\n\nResultsCc was lower in both patient groups compared with controls. OI and PI were more variable in focal-onset than generalised-onset cases. No regions showed higher OI and PI in generalised-onset cases than in healthy controls; in focal cases, the regions with highest OI and PI corresponded to the side of seizure onset.\n\nConclusionsProperties of interictal functional networks from scalp EEG can be estimated using a computer model and used to predict seizure likelihood and onset patterns. Our framework, consisting of three clinically-meaningful measures, could be implemented in the clinic to quantify the diagnosis and seizure onset pattern.

neuroscience↗