عنوان

Developing Non-Linear and Adaptive Neuronal Synchrony and Connectivity Analysis to Personalize Closed-Loop Dbs Therapy for Treating Epilepsy

Number

TLpq2282535467

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Developing Non-Linear and Adaptive Neuronal Synchrony and Connectivity Analysis to Personalize Closed-Loop Dbs Therapy for Treating Epilepsy

General Material Designation

[Thesis]

First Statement of Responsibility

Farahmand, Sina

Subsequent Statement of Responsibility

Mogul, David J.

Name of Publisher, Distributor, etc.

Illinois Institute of Technology

Date of Publication, Distribution, etc.

2019

Specific Material Designation and Extent of Item

117

Dissertation or thesis details and type of degree

Ph.D.

Body granting the degree

Illinois Institute of Technology

Text preceding or following the note

2019

Text of Note

Epilepsy disease afflicts more than seventy million people worldwide. In approximately one third of the cases, antiepileptic medications fail to control seizures. Over the last few decades, electrical stimulation of the brain has been evaluated as a potential alternative to treat surgically and medically refractory epilepsy patients. Despite some successes, most of the devices using this protocol operate based on pre-determined stimulation parameters (e.g. frequency and location of stimulation) that have little or no relationship to the individuals' underlying brain dynamics, which we hypothesize may explain their low clinical efficacy in preventing or terminating seizures. In this study, a non-linear adaptive neuronal synchrony and connectivity analysis was developed in order to extract stimulation parameters from endogenous, multi-site brain dynamics of epilepsy patients. A non-linear analytical methodology was proposed to assess phase-synchrony dynamics in epilepsy patients as seizures evolve. This study revealed a desynchronization around seizure onset. However, the synchrony level started to increase gradually towards seizure end and reached its maximum at seizure termination. This results reveal that hyper-synchronization of the epileptic network may be a critical self-regulatory mechanism by which the brain terminates seizures. In the other phase of this study, a non-linear adaptive phase-connectivity analysis was developed in order to extract frequency and locations of stimulation that match the synchronized network dynamics at seizure termination. Matching these parameters to the endogenous brain dynamics of epilepsy patients as seizure naturally terminates may not only terminate seizures prior to their development, but it may also lead to a personalized deep brain stimulation (DBS) therapy with higher clinical efficacy.

Bioengineering

Neurosciences

Farahmand, Sina

Mogul, David J.

Electronic name

p

[Thesis]

276903

a

Y