Biology subjects

Abdi-Sargezeh, B.

Publications and source records attributed to Abdi-Sargezeh, B..

4 recordsLinked to original sources

bioRxiv2024

Beta burst characteristics and coupling within the sensorimotor cortical-subthalamic nucleus circuit in Parkinson's disease

BackgroundBursts of exaggerated subthalamic nucleus (STN) beta activity contribute to clinical impairments in Parkinsons disease (PD). Few studies have explored the characteristics and coupling of bursts across the sensorimotor cortical-STN circuit. ObjectiveWe sought to (1) establish the characteristics of sensorimotor cortical and STN bursts during naturalistic behaviours, and (2) determine the predictability of STN bursts from motor cortical recordings. MethodsWe analysed 1,478 hours of wirelessly streamed bilateral sensorimotor cortical and STN recordings from 5 PD patients. ResultsSTN bursts were longer than cortical bursts and had shorter inter-burst intervals. Long bursts (>200ms) in both structures displayed temporal overlap (>30%), with an estimated cortico-STN conduction delay of 8ms. Furthermore, approximately 27% of all STN bursts were preceded by a cortical burst. ConclusionCortical beta bursts tend to precede STN beta bursts, with short delays. However, subcortical mechanisms are also likely to contribute to STN burst initiation and propagation.

neuroscience↗

bioRxiv2024

Prediction of pathological subthalamic nucleus beta burst occurrence in Parkinson's disease

The cortico-basal ganglia network in Parkinsons disease (PD) is characterized by the emergence of transient episodes of exaggerated beta frequency oscillatory synchrony known as bursts. Although it is well established that bursts of prolonged duration associate closely with motor impairments, the mechanisms leading to burst initiation remain poorly understood. Crucially, it is unclear whether there are features of basal ganglia activity which reliably predict burst onset. Current adaptive Deep Brain Stimulation (aDBS) approaches can only reactively deliver stimulation following burst detection and are unable to stimulate proactively to prevent burst onset. The discovery of predictive biomarkers could allow for such proactive stimulation, thereby offering potential for improvements in therapeutic efficacy. Here, using deep learning, we show that the timing of subthalamic nucleus (STN) beta bursts can be accurately predicted up to 60 ms prior to onset. Furthermore, we highlight that a dip in the beta amplitude - which is likely to be indicative of a phase reset of oscillatory populations occurring between 80-100 ms prior to burst onset - is a predictive biomarker for burst occurrence. These findings demonstrate proof-of-principle for the feasibility of beta burst prediction for DBS and provide insights into the mechanisms of burst initiation.

neuroscience↗

bioRxiv2023

Predicting Long and Short Duration Beta Bursts from Subthalamic Nucleus Local Field Potential Activity in Parkinson's Disease

Neural activities within the beta frequency range (13-30 Hz) are not stationary, but occur in transient packets known as beta bursts. Parkinsons disease (PD) is characterized by the occurrence of beta bursts of increased duration and amplitude within the cortico-basal ganglia network. The pathophysiological importance of beta bursts is exemplified by the fact that they serve as a clinically useful feedback signal in beta amplitude triggered adaptive Deep Brain Stimulation (aDBS). Prolonged duration beta bursts are closely associated with motor impairments in PD, whilst bursts of shorter duration may have a physiological role. Consequently, we aimed to develop a deep learning-based pipeline capable of predicting long (> 150ms) and short (< 150ms) duration beta bursts from subthalamic nucleus local field potential (LFP) recordings. Our approach achieved promising accuracy values of 87% and 85.2% in two patients implanted with a DBS device that was capable of long-term wireless LFP sensing. Our findings highlight the feasibility of prolonged beta burst prediction and could inform the development of a new type of intelligent DBS approach with the capability of delivering stimulation only during the occurrence of prolonged bursts.

bioengineering↗

bioRxiv2023

Robust estimation of brain stimulation evoked responses using magnetoencephalography

Magnetoencephalography (MEG) recordings are often contaminated by interference that can exceed the amplitude of physiological brain activity by several orders of magnitude. Furthermore, activity of interference sources may leak into the activity of brain signals of interest, resulting in source estimation inaccuracies. This problem is particularly apparent when using MEG to interrogate the effects of brain stimulation on large scale cortical networks. This technical report offers two contributions. Firstly, using phantom MEG recordings we describe an approach for validating the estimation accuracy of brain stimulation evoked responses. Secondly, we propose a novel denoising method for suppressing the leakage of stimulation related signal into recorded brain activity. This approach leverages spatial and temporal domain projectors for signal arising from prespecified anatomical regions of interest. We highlight its advantages compared to the benchmark - spatiotemporal signal space separation (tSSS) - and show that it can more accurately reveal brain stimulation evoked responses.

bioengineering↗