Mark Philip Richardson

This study examines how two antiseizure medications affect aperiodic and periodic EEG activity in healthy volunteers.

This paper introduces DIP-DCM, a method that improves parameter estimation in neural mass models by using dynamics-informed priors, showing better performance in predicting neuroimaging data and capturing disease mechanisms.

This paper evaluates evolutionary search metaheuristics and approximate Bayesian computation for calibrating neural mass models to EEG data, finding ESMs more efficient and accurate.

The paper examines associations between epilepsy-related polygenic risk scores and brain morphology in children, finding PRS-related cortical thinning linked to structural patterns in TLE-HS patients.

This study shows that including zero-phase-delay connectivity improves the reliability and prognostic ability of functional connectivity metrics in neural populations.

This study examines age-related changes in grey and white matter in TLE patients across the adult lifespan, finding accelerated aging effects after age 55.

This study identifies genetic loci associated with impulsivity in juvenile myoclonic epilepsy and links them to synaptic assembly genes and SLCO5A1 function.

This study identifies divergent patterns of atypical cortical asymmetry and regional atrophy in temporal lobe epilepsy using MRI data from a large multi-site cohort.

This study links structural network changes in epilepsy syndromes to risk gene expression patterns using ENIGMA data.

The paper shows that EI imbalance during seizures disrupts whole-brain critical dynamics by reorganizing neural networks, leading to chaotic and less stable brain states.

The paper identifies microglial activation as a factor contributing to cortical thinning in epilepsy by integrating neuroimaging and gene expression data.

This study identifies white matter microstructural differences across epilepsy syndromes using diffusion MRI data from a large multicenter cohort.

This study identifies network associations with atrophy patterns in epilepsy using neuroimaging and connectome analysis across 1,021 patients and 1,564 controls.

The paper introduces three measures to predict seizure onset patterns using interictal brain network properties from scalp EEG data.

This paper uses computational models to identify optimal resection strategies for epilepsy by quantifying ictogenic zones in brain networks.