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EVOKED PHASE COHERENCE AS A BIOMARKER FOR ADAPTIVE NEUROMODULATION IN RAT MODEL OF PARKINSON'S DISEASE
Umeå University, Faculty of Social Sciences, Department of Psychology. (Per Petersson)
2023 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Neuromodulation, such as spinal cord stimulation (SCS) and deep brain stimulation (DBS), has been shown to modulate pathophysiological brain activity and provide symptomatic therapy for several neurological disorders, including Parkinson’s Disease. The effectiveness of this therapy could likely be further improved by neuromodulation that is adaptive, delivering stimulation more selectively, by monitoring a biomarker in recorded brain signals, which indicates the presence of a pathological state. In the treatment of Parkinson’s Disease, the most commonly proposed solutions for adaptive neuromodulation are relying on excessive beta-band oscillatory activity as a biomarker, which is however often highly variable between patients during movement and in conjunction with neuromodulatory treatment, such as levodopa. These limitations hinder broader use of this biomarker and prompts further research for alternative solutions. In this work, we instead present the use of a novel feature of evoked electrophysiological activity, which utilizes the inter-trial phase coherence between stimulation pulses, to classify parkinsonian brain states in 6-OHDA lesioned rats. We developed a method, which relates to the rate of decay in inter-tral phase coherence, evoked by single SCS or DBS pulses, that is able to statistically separate experimental conditions recorded from a dopaminergic depleted hemisphere from conditions a non-depleted hemisphere, while also being able to separate conditions with levodopa treatment from conditions without treatment. For animals undergoing SCS we can classify phase decay measurements from pharmacologically treated or untreated parkinsonian states, using a Bayesian model, with a high accuracy and strong classifier performance for a single channel (AUC 0.85 – 0.99) in the motor cortex and striatum. In ongoing experiments, similar implementation of adaptive DBS is being evaluated. Our results support the implementation of our feature in a protocol aimed at performing closed-loop neuromodulation in the 6-OHDA rat model of Parkinon’s Disease, that can serve as the basis for further studies. 

Abstract [sv]

Neuromodulering, såsom ryggmärgsstimulering (SCS) och djup hjärnstimulering (DBS), har visat sig kunna modulera patofysiologisk hjärnaktivitet och ge symtomatisk behandling av flera neurologiska sjukdomar, inklusive Parkinsons sjukdom. Effekten av denna behandling skulle sannolikt kunna förbättras ytterligare genom neuromodulering som är adaptiv och ger stimulering mer selektivt, genom övervakning av en biomarkör i registrerade hjärnsignaler, som indikerar förekomsten av ett patologiskt tillstånd. Vid behandling av Parkinsons sjukdom förlitar sig de vanligaste lösningarna för adaptiv neuromodulering på överdriven beta-bands oscillatorisk aktivitet som en biomarkör som dock ofta är mycket varierande mellan patienter, under rörelse och i samband med behandling så som levodopa. Dessa begränsningar hindrar en bredare användning av denna biomarkör och ytterligare forskning krävs för att hitta alternativa lösningar. I detta arbete presenterar vi istället en ny egenskap hos väckt elektrofysiologisk aktivitet, som utnyttjar faskoherens mellan stimuleringspulser för att klassificera parkinsonistiska hjärntillstånd hos 6-OHDA-lesionerade råttor. Vi har utvecklat en metod som relaterar till avklingningshastigheten i faskoherens, framkallad av enstaka SCS- eller DBS-pulser, som kan statistiskt särskilja de experimentella tillstånden i en dopaminergiskt utarmad hemisfär från liknande tillstånd, fast i en icke utarmad hemisfär. Den kan även statistiskt särskilja tillstånd med levodopabehandling från tillstånd utan behandling. För djur som genomgår SCS kan vi klassificera fasförfallsmätningar från farmakologiskt behandlade eller obehandlade parkinsontillstånd, med hjälp av en Bayesiansk modell, med hög noggrannhet och stark klassificeringsprestanda för en enda kanal (AUC 0,85 - 0,99) i motorcortex och striatum. I pågående experiment utvärderas en liknande implementering av adaptiv DBS. Våra resultat stöder implementeringen av vår funktion i ett protokoll som syftar till att utföra sluten neuromodulering i 6-OHDA-råttmodellen för Parkinons sjukdom, som kan tjäna som grund för ytterligare studier.

Place, publisher, year, edition, pages
2023. , p. 47
Keywords [en]
Adaptive, Closed Loop, Neuromodulation, Parkinson’s Disease, Rat model, 6-OHDA, ITC, Phase Coherence, Bayesian Model, Classifier, Spinal Cord Stimulation, Deep Brain Stimulation.
Keywords [sv]
Adaptiv, Closed Loop, Neuromodulation, Parkinson’s Sjukdom, Råttmodell, 6-OHDA, ITC, Faskoherens, Bayesiansk Modell, Klassificerare, Ryggradsstimulering, Djup Hjärnstimulering.
National Category
Other Medical Biotechnology
Identifiers
URN: urn:nbn:se:umu:diva-216106OAI: oai:DiVA.org:umu-216106DiVA, id: diva2:1809068
Educational program
Master's Programme in Cognitive Science
Presentation
2023-03-16, Online, 23:07 (Swedish)
Supervisors
Examiners
Available from: 2023-11-06 Created: 2023-11-01 Last updated: 2023-11-06Bibliographically approved

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