Dataset Information

Selective Mapping of Deep Brain Stimulation Lead Currents Using Acoustoelectric Imaging.

ABSTRACT: We describe a new application of acoustoelectric imaging for non-invasive mapping of the location, magnitude and polarity of current generated by a clinical deep brain stimulation (DBS) device. Ultrasound at 1MHz was focused near the DBS device as short current pulses were injected across different DBS leads. A recording electrode detected the high-frequency acoustoelectric interaction signal. Linear scans of the US beam produced time-varying images of the magnitude and polarity of the induced current, enabling precise localization of the DBS leads within 0.70mm, a detection threshold of 1.75mA at 1 MPa and a sensitivity of 0.52 ± 0.07 ?V/(mA*MPa). Monopole and dipole configurations in saline were repeated through a human skullcap. Despite 13.8-dB ultrasound attenuation through bone, acoustoelectric imaging was still >10dB above background with a sensitivity of 0.56 ± 0.10 ?V/(mA*MPa). This proof-of-concept study indicates that selective mapping of lead currents through a DBS device may be possible using non-invasive acoustoelectric imaging.

SUBMITTER: Preston C

PROVIDER: S-EPMC6163075 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Selective Mapping of Deep Brain Stimulation Lead Currents Using Acoustoelectric Imaging.

Preston Chet C Kasoff Willard S WS Witte Russell S RS

Ultrasound in medicine & biology 20180814 11

We describe a new application of acoustoelectric imaging for non-invasive mapping of the location, magnitude and polarity of current generated by a clinical deep brain stimulation (DBS) device. Ultrasound at 1MHz was focused near the DBS device as short current pulses were injected across different DBS leads. A recording electrode detected the high-frequency acoustoelectric interaction signal. Linear scans of the US beam produced time-varying images of the magnitude and polarity of the induced c ...[more]

PMID: 30119863

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Project description:Deep brain stimulation (DBS) is a highly efficacious treatment option for movement disorders and a growing number of other indications are investigated in clinical trials. To ensure optimal treatment outcome, exact electrode placement is required. Moreover, to analyze the relationship between electrode location and clinical results, a precise reconstruction of electrode placement is required, posing specific challenges to the field of neuroimaging. Since 2014 the open source toolbox Lead-DBS is available, which aims at facilitating this process. The tool has since become a popular platform for DBS imaging. With support of a broad community of researchers worldwide, methods have been continuously updated and complemented by new tools for tasks such as multispectral nonlinear registration, structural/functional connectivity analyses, brain shift correction, reconstruction of microelectrode recordings and orientation detection of segmented DBS leads. The rapid development and emergence of these methods in DBS data analysis require us to revisit and revise the pipelines introduced in the original methods publication. Here we demonstrate the updated DBS and connectome pipelines of Lead-DBS using a single patient example with state-of-the-art high-field imaging as well as a retrospective cohort of patients scanned in a typical clinical setting at 1.5T. Imaging data of the 3T example patient is co-registered using five algorithms and nonlinearly warped into template space using ten approaches for comparative purposes. After reconstruction of DBS electrodes (which is possible using three methods and a specific refinement tool), the volume of tissue activated is calculated for two DBS settings using four distinct models and various parameters. Finally, four whole-brain tractography algorithms are applied to the patient's preoperative diffusion MRI data and structural as well as functional connectivity between the stimulation volume and other brain areas are estimated using a total of eight approaches and datasets. In addition, we demonstrate impact of selected preprocessing strategies on the retrospective sample of 51 PD patients. We compare the amount of variance in clinical improvement that can be explained by the computer model depending on the preprocessing method of choice. This work represents a multi-institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field.

Dataset Information

Selective Mapping of Deep Brain Stimulation Lead Currents Using Acoustoelectric Imaging.

Publications

Selective Mapping of Deep Brain Stimulation Lead Currents Using Acoustoelectric Imaging.

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