Subthalamic Nucleus Deep Brain Stimulation Modulates 2 Distinct Neurocircuits.

OBJECTIVE: Current understanding of the neuromodulatory effects of deep brain stimulation (DBS) on large-scale brain networks remains elusive, largely due to the lack of techniques that can reveal DBS-induced activity at the whole-brain level. Using a novel 3T magnetic resonance imaging (MRI)-compatible stimulator, we investigated whole-brain effects of subthalamic nucleus (STN) stimulation in patients with Parkinson disease. METHODS: Fourteen patients received STN-DBS treatment and participated in a block-design functional MRI (fMRI) experiment, wherein stimulations were delivered during "ON" blocks interleaved with "OFF" blocks. fMRI responses to low-frequency (60Hz) and high-frequency(130Hz) STN-DBS were measured 1, 3, 6, and 12 months postsurgery. To ensure reliability, multiple runs (48 minutes) of fMRI data were acquired at each postsurgical visit. Presurgical resting-state fMRI (30 minutes) data were also acquired. RESULTS: Two neurocircuits showed highly replicable, but distinct responses to STN-DBS. A circuit involving the globus pallidus internus (GPi), thalamus, and deep cerebellar nuclei was significantly activated, whereas another circuit involving the primary motor cortex (M1), putamen, and cerebellum showed DBS-induced deactivation. These 2 circuits were dissociable in terms of their DBS-induced responses and resting-state functional connectivity. The GPi circuit was frequency-dependent, selectively responding to high-frequency stimulation, whereas the M1 circuit was responsive in a time-dependent manner, showing enhanced deactivation over time. Finally, activation of the GPi circuit was associated with overall motor improvement, whereas M1 circuit deactivation was related to reduced bradykinesia. INTERPRETATION: Concurrent DBS-fMRI using 3T revealed 2 distinct circuits that responded differentially to STN-DBS and were related to divergent symptoms, a finding that may provide novel insights into the neural mechanisms underlying DBS. ANN NEUROL 2020;88:1178-1193.

Electrochemical and biological performance of hierarchical platinum-iridium electrodes structured by a femtosecond laser.

Neural electrode interfaces are essential to the stimulation safety and recording quality of various bioelectronic therapies. The recently proposed hierarchical platinum-iridium (Pt-Ir) electrodes produced by femtosecond lasers have exhibited superior electrochemical performance in vitro, but their in vivo performance is still unclear. In this study, we explored the electrochemical performance, biological response, and tissue adhesion of hierarchical Pt-Ir electrodes by implantation in adult rat brains for 1, 8, and 16 weeks. Regular smooth Pt-Ir electrodes were used as a control. The results showed that the electrochemical performance of both electrodes decreased and leveled off during implantation. However, after 16 weeks, the charge storage capacity of hierarchical electrodes stabilized at ~16.8 mC/cm2, which was 15 times that of the smooth control electrodes (1.1 mC/cm2). Moreover, the highly structured electrodes had lower impedance amplitude and cutoff frequency values. The similar histological response to smooth electrodes indicated good biocompatibility of the hierarchically structured Pt-Ir electrodes. Given their superior in vivo performance, the femtosecond laser-treated Pt-Ir electrode showed great potential for neuromodulation applications.

Measurement of Lead Localization Accuracy Based on Magnetic Resonance Imaging.

Post-implantation localization of deep brain stimulation (DBS) lead based on a magnetic resonance (MR) image is widely used. Existing localization methods use artifact center method or template registration method, which may lead to a considerable deviation of > 2 mm, and result in severe side effects or even surgical failure. Accurate measurement of lead position can instantly inform surgeons of the imprecise implantation. This study aimed to identify the influencing factors in DBS lead post-implantation localization approach, analyze their influence, and describe a localization approach that uses the individual template method to reduce the deviation. We verified that reconstructing direction should be parallel or perpendicular to lead direction, instead of the magnetic field. Besides, we used simplified relationship between magnetic field angle and deviation error to correct the localization results. The mean localization error can be reduced after correction and favors the feasibility of direct localization of DBS lead using MR images. We also discussed influence of in vivo noise on localization frequency and the possibility of using only MR images to localize the contacts.

Investigation of Artifacts and Optimization in Proton Resonance Frequency Thermometry Towards Heating Risk Monitoring of Implantable Medical Devices in Magnetic Resonance Imaging.

OBJECTIVE: Artifacts limit the application of proton resonance frequency (PRF) thermometry for on-site, individualized heating evaluations of implantable medical devices such as deep brain stimulation (DBS) for use in magnetic resonance imaging (MRI). Its properties are unclear and the research on how to choose an unaffected measurement region is insufficient. METHODS: The properties of PRF signals around the metallic DBS electrode were investigated through simulations and phantom experiments considering electromagnetic interferences from material susceptibility and the radio frequency (RF) interactions. A threshold method on phase difference Δϕ was used to define a measurement area to estimate heating at the electrode surface. Its performance was compared to that of the Bayesian magnitude method and probe measurements. RESULTS: The B0 magnetic field inhomogeneity due to the electrode susceptibility was the main influencing factor on PRF compared to the RF artifact. Δϕ around the electrode followed normal distribution but was distorted. Underestimation occurred at places with high temperature rises. The noise was increased and could be well estimated from magnitude images using a modified NEMA method. The Δϕ-threshold method based on this knowledge outperformed the Bayesian magnitude method by more than 42% in estimation error of the electrode heating. CONCLUSION: The findings favor the use of PRF with the proposed approach as a reliable method for electrode heating estimation. SIGNIFICANCE: This study clarified the influence of device artifacts and could improve the performance of PRF thermometry for individualized heating assessments of patients with implants under MRI.

Reconstructing lost BOLD signal in individual participants using deep machine learning.

Signal loss in blood oxygen level-dependent (BOLD) functional neuroimaging is common and can lead to misinterpretation of findings. Here, we reconstructed compromised fMRI signal using deep machine learning. We trained a model to learn principles governing BOLD activity in one dataset and reconstruct artificially compromised regions in an independent dataset, frame by frame. Intriguingly, BOLD time series extracted from reconstructed frames are correlated with the original time series, even though the frames do not independently carry any temporal information. Moreover, reconstructed functional connectivity maps exhibit good correspondence with the original connectivity maps, indicating that the model recovers functional relationships among brain regions. We replicated this result in two healthy datasets and in patients whose scans suffered signal loss due to intracortical electrodes. Critically, the reconstructions capture individual-specific information. Deep machine learning thus presents a unique opportunity to reconstruct compromised BOLD signal while capturing features of an individual's own functional brain organization.