Marc Goodfellow

Dynamics-Informed Priors (DIP) for Neural Mass Modelling

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.

Global search metaheuristics for neural mass model calibration

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

Longitudinal assessment of the conversion of mild cognitive impairment into Alzheimer's dementia: Observations and mechanisms from neuropsychological testing and electrophysiology

This study identifies biomarkers for predicting mild cognitive impairment conversion to Alzheimer's dementia using EEG, neuropsychological tests, and mathematical models.

Using Deep Clustering to Improve fMRI Dynamic Functional Connectivity Analysis

This paper evaluates deep clustering methods for improving dynamic functional connectivity analysis in fMRI data, showing that deep autoencoders outperform traditional methods in capturing temporal properties.

A Large-Scale Brain Network Mechanism for Increased Seizure Propensity in Alzheimer's Disease

This study uses EEG and computational modeling to show that AD brains are more prone to seizures due to altered large-scale network structure and increased cortical excitability.

EEG microstate complexity for aiding early diagnosis of Alzheimer's disease

This study uses EEG microstate complexity and spectral measures to improve early diagnosis of Alzheimer's disease with high sensitivity and specificity.

Quantification and selection of ictogenic zones in epilepsy surgery

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