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How Deep Brain Stimulation affects Brain Connectivity in Epilepsy (MSc. Thesis/Semester Research Project)
Responsiveness to neurostimulation (DBS) strongly depends on the target area and to its projections to other brain regions.
Here, we want to study connectivity among subcortical and cortical structures in epileptic patients, determining how these networks might affect the efficacy of stimulation.
Keywords: Neuroscience, ETH, MSc Thesis, Internship, Epilepsy, DBS, EEG, Cognition, Data Analysis, Matlab, Signal Processing, Neuroengineering, Deep Brain Stimulation, Brain Stimulation, Brain Computer Interaction, Neurological Disorder, Master Thesis, Thesis, Neural Engineering, Brain Connectivity, Machine Learning, Signal Processing
Approximately 20 millions of epileptic patients do not respond to medical treatment, and surgical resection or laser ablation of the epileptic zone is not always a feasible solution (i.e. multi-focal epilepsy or adverse effects for executive functions).
Alternative solutions, such as invasive (Deep Brain Stimulation) and non-invasive (Trans-cranial Stimulation) stimulation of brain structures are being proposed and studied extensively as a mean to modulate pathological brain circuits while providing a good safety profile.
While neurostimulation might prove extremely useful in some patients, reducing significantly seizure frequency, in other patients the responsiveness is low and the efficacy of the therapy questionable.
The goal of the project is to analyze available neural data of epileptic patients that have been implanted with a DBS electrode in the anterior nucleus of the thalamus in the framework of exploring neural correlates of responsiveness to the therapy.
Both non-invasive (EEG) and invasive data will be considered in a joint analysis, with the final aim of gathering deeper insights into the properties of pathological brain circuits in epilepsy.
Approximately 20 millions of epileptic patients do not respond to medical treatment, and surgical resection or laser ablation of the epileptic zone is not always a feasible solution (i.e. multi-focal epilepsy or adverse effects for executive functions).
Alternative solutions, such as invasive (Deep Brain Stimulation) and non-invasive (Trans-cranial Stimulation) stimulation of brain structures are being proposed and studied extensively as a mean to modulate pathological brain circuits while providing a good safety profile.
While neurostimulation might prove extremely useful in some patients, reducing significantly seizure frequency, in other patients the responsiveness is low and the efficacy of the therapy questionable. The goal of the project is to analyze available neural data of epileptic patients that have been implanted with a DBS electrode in the anterior nucleus of the thalamus in the framework of exploring neural correlates of responsiveness to the therapy.
Both non-invasive (EEG) and invasive data will be considered in a joint analysis, with the final aim of gathering deeper insights into the properties of pathological brain circuits in epilepsy.
The goal of the project is to process and analyze neural data in epileptic patients, recorded both cortically and subcortically with the aim of gaining a deeper understanding of epileptic networks.
The project is in collaboration with the Swiss Epilepsy Center of Zürich, and suitable for a Master Thesis or a semester project/internship.
Proven experience with Data Analysis/Signal Processing and knowledge in programming (Matlab/Python) is strongly recommended. Previous experience with neural data (i.e. Multi-Unit Activity, Local Field Potential) is a plus.
The goal of the project is to process and analyze neural data in epileptic patients, recorded both cortically and subcortically with the aim of gaining a deeper understanding of epileptic networks.
The project is in collaboration with the Swiss Epilepsy Center of Zürich, and suitable for a Master Thesis or a semester project/internship.
Proven experience with Data Analysis/Signal Processing and knowledge in programming (Matlab/Python) is strongly recommended. Previous experience with neural data (i.e. Multi-Unit Activity, Local Field Potential) is a plus.