Analyzing Local Field Potentials in the healthy basal ganglia under Deep Brain Stimulation

Pedoto, Gilda; Santaniello, Sabato; Montgomery, Erwin B.; Gale, John T.; Fiengo, Giovanni; Glielmo, Luigi; Sarma, Sridevi V.

doi:10.1109/cdc.2010.5717450

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…These results suggest that bursting pattern, that have been studied for a long time in the STN and other basal ganglia (i.e. [21]–[25]), are not uniformly distributed, and confirm the hypothesis that β-oscillatory activity is seen largely within the dorsolateral portion of the STN [4], the same location that seems to provide optimal therapeutic benefit to patient undergoing STN DBS. Therefore, the predominance of tremor and β-band oscillations detected in the dorsal STN, confirms the spatial pattern of neuronal oscillatory frequency distribution within the STN [1][3].…”

Section: Resultssupporting

confidence: 66%

Point process modeling reveals anatomical non-uniform distribution across the Subthalamic Nucleus in Parkinson's disease

Pedoto

Santaniello

Fiengo

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Deep brain stimulation (DBS) is a highly promising therapy for Parkinson’s disease (PD). However, most patients do not get full therapeutic benefit from DBS, due to its critical dependence on electrode location in the Subthalamic Nucleus (STN). For this reason, we believe that the development of a novel surgical tool for DBS placement, i.e., an automated intraoperative closed-loop DBS localization system, is essential. In this paper, we analyze single unit spiking activity of 120 neurons in different STN locations collected from 4 PD patients. Specifically, for each neuron, we estimate a point process model (PPM) of the spiking activity for different depths within the STN by which we are able to detect pathological bursting and oscillations. Our results suggest that these signatures are more prominent in the dorsolateral part of the STN. Therefore, accurately placing the DBS electrode in this target may result in maximal therapeutic benefit with less power effort required by DBS. Furthermore, PPMs might be an effective tool for modeling of the STN neuronal activities as a function of location within the STN, which may pave the way towards developing a closed-loop navigation tool for optimal DBS electrode placement.

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Section: Resultssupporting

confidence: 66%

Point process modeling reveals anatomical non-uniform distribution across the Subthalamic Nucleus in Parkinson's disease

Pedoto

Santaniello

Fiengo

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…Finally, a third body of work has specifically pursued data-driven predictive modeling of the brain's response to neurostimulation. Multiple studies have used linear autoregressive models to fit the neural activity triggered by neurostimulation [28][29][30][31] while others have opted for nonlinear kernel-based autoregressive modeling 32,33 . However, these works have mainly focused on univariate modeling (i.e., modeling the neurostimulation-evoked response at a single measurement source), while modeling the evoked network response has remained more challenging [34][35][36] .…”

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confidence: 99%

Predictive Modeling of Evoked Intracranial EEG Response to Medial Temporal Lobe Stimulation in Patients with Epilepsy

Acharya

Davis

Nozari

2023

Preprint

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Despite over a decade of promising results, closed-loop neurostimulation for the treatment of drugresistant epilepsy (DRE) still relies on manual parameter tuning and yields unpredictably variable outcomes. Fully automated algorithms with predictable outcomes have remained a theoretical possibility, mainly due to a lack of generalizable models of the brain's dynamic network response to stimulation under varying parameters. In this work, we study predictive dynamical models of human intracranial EEG (iEEG) response under parametrically-rich neurostimulation and develop models that are predictively accurate and biologically interpretable. Using data from n = 10 subjects, we show that evoked iEEG dynamics are best explained by stimulation-triggered switched-linear models with approximately 300ms of causal historical dependence. These models are highly consistent across stimulation amplitudes and frequencies, including STIM OFF durations, which allows for learning a single generalizable model from abundant STIM OFF and limited STIM ON data. Despite significant heterogeneity among subjects, we observed a consistent distance-dependent pattern whereby stimulation impacts the actuation site and nearby regions (≤ 20mm) directly with little or no network mediation, reaches medium-distance regions (20 ~ 100mm) indirectly through network interactions and hardly reaches more distal areas (≥ 100mm). Peak involvement of network interactions occurs at about 60-80mm from the stimulation site. Due to their predictive accuracy and mechanistic interpretability, these models have far-reaching applications in model-based seizure forecasting and stimulation design for closed-loop neurostimulation.

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Analyzing Local Field Potentials in the healthy basal ganglia under Deep Brain Stimulation

Cited by 2 publications

References 11 publications

Point process modeling reveals anatomical non-uniform distribution across the Subthalamic Nucleus in Parkinson's disease

Point process modeling reveals anatomical non-uniform distribution across the Subthalamic Nucleus in Parkinson's disease

Predictive Modeling of Evoked Intracranial EEG Response to Medial Temporal Lobe Stimulation in Patients with Epilepsy

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