Biomedical Engineering - Theses
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ItemPerturbation-based biomarkers outperform passive ones as indicators for changes in cortical excitability and seizure transitions( 2023-06)Epilepsy is a neurological disorder that affects patients differently and manifests as spontaneous recurrent seizures. It is a prevalent condition that affects about 50 million people globally, but its exact aetiology and pathophysiology often remain elusive. Functional imaging techniques allow us to observe alterations in brain activity during seizures. For instance, EEG data reveals hyper-excitable and hyper-synchronised neuronal firing in the brain. This thesis explores the potential of using biomarkers to track cortical excitability and detect state transitions in epileptic models. Nine biomarkers were developed based on theoretical concepts such as Critical Slowing Down (CSD) and signal processing methods. The performances of these biomarkers were evaluated in neural mass models, animal models of epilepsy and human data. In this thesis, we first employed two mesoscopic neural mass models to study state transitions in a controlled way. Through simulation and perturbation, we found that biomarkers can anticipate state transitions before they occur. They are effective when a system undergoes a critical transition, but less so when the system jumps between multiple stable states due to stochastic noise. Overall, active biomarkers with perturbations outperform passive biomarkers in terms of accuracy and robustness. We also investigated how biomarkers and perturbations can be used to identify state transitions using experimental data. We contrasted active and passive biomarkers on various time scales: pre-seizure scale, circadian cycle and lifespan scale in animal data. We found that active biomarkers with perturbations are superior to passive biomarkers in monitoring state transitions and giving early warnings before seizures. Moreover, by examining two datasets of human epileptic transitions, we further assess the possibility of adopting biomarkers in clinical studies. It is observed that changes in biomarkers differ depending on both patients and seizures. We propose that seizure transitions are not only patient-dependent but also seizure-dependent. Through this thesis, we have shown that biomarkers can detect the underlying changes that precede a seizure by measuring cortical excitability with perturbations. By applying a small perturbation, it is possible to probe changes in brain states by measuring the response to perturbation, or cortical excitability. Understanding changes in cortical response to perturbation during brain state transitions may yield important insights into brain disorders. The methods employed in this project are anticipated to be applicable to clinical settings for seizure forecasting and epilepsy management.