1
|
Seo KJ, Hill M, Ryu J, Chiang CH, Rachinskiy I, Qiang Y, Jang D, Trumpis M, Wang C, Viventi J, Fang H. A Soft, High-Density Neuroelectronic Array. NPJ FLEXIBLE ELECTRONICS 2023; 7:40. [PMID: 37692908 PMCID: PMC10487278 DOI: 10.1038/s41528-023-00271-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 07/23/2023] [Indexed: 09/12/2023]
Abstract
Techniques to study brain activities have evolved dramatically, yet tremendous challenges remain in acquiring high-throughput electrophysiological recordings minimally invasively. Here, we develop an integrated neuroelectronic array that is filamentary, high-density and flexible. Specifically, with a design of single-transistor multiplexing and current sensing, the total 256 neuroelectrodes achieve only a 2.3 × 0.3 mm2 area, unprecedentedly on a flexible substrate. A novel single-transistor multiplexing acquisition circuit further reduces noise from the electrodes, decreased the footprint of each pixel, and potentially increased the device lifetime. The filamentary neuroelectronic array also integrates with a rollable contact pad design, allowing the device to be injected through a syringe, enabling potential minimally invasive array delivery. Successful acute auditory experiments in rats validate the ability of the array to record neural signals with high tone decoding accuracy. Together, these results establish soft, high-density neuroelectronic arrays as promising devices for neuroscience research and clinical applications.
Collapse
Affiliation(s)
- Kyung Jin Seo
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755 USA
| | - Mackenna Hill
- Department of Biomedical Engineering, Duke University, Durham, NC 27708 USA
| | - Jaehyeon Ryu
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755 USA
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115 USA
| | - Chia-Han Chiang
- Department of Biomedical Engineering, Duke University, Durham, NC 27708 USA
| | - Iakov Rachinskiy
- Department of Biomedical Engineering, Duke University, Durham, NC 27708 USA
| | - Yi Qiang
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755 USA
| | - Dongyeol Jang
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755 USA
| | - Michael Trumpis
- Department of Biomedical Engineering, Duke University, Durham, NC 27708 USA
| | - Charles Wang
- Department of Biomedical Engineering, Duke University, Durham, NC 27708 USA
| | - Jonathan Viventi
- Department of Biomedical Engineering, Duke University, Durham, NC 27708 USA
| | - Hui Fang
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755 USA
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115 USA
| |
Collapse
|
2
|
Hu Z, Niu Q, Hsiao BS, Yao X, Zhang Y. Bioactive polymer-enabled conformal neural interface and its application strategies. MATERIALS HORIZONS 2023; 10:808-828. [PMID: 36597872 DOI: 10.1039/d2mh01125e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Neural interface is a powerful tool to control the varying neuron activities in the brain, where the performance can directly affect the quality of recording neural signals and the reliability of in vivo connection between the brain and external equipment. Recent advances in bioelectronic innovation have provided promising pathways to fabricate flexible electrodes by integrating electrodes on bioactive polymer substrates. These bioactive polymer-based electrodes can enable the conformal contact with irregular tissue and result in low inflammation when compared to conventional rigid inorganic electrodes. In this review, we focus on the use of silk fibroin and cellulose biopolymers as well as certain synthetic polymers to offer the desired flexibility for constructing electrode substrates for a conformal neural interface. First, the development of a neural interface is reviewed, and the signal recording methods and tissue response features of the implanted electrodes are discussed in terms of biocompatibility and flexibility of corresponding neural interfaces. Following this, the material selection, structure design and integration of conformal neural interfaces accompanied by their effective applications are described. Finally, we offer our perspectives on the evolution of desired bioactive polymer-enabled neural interfaces, regarding the biocompatibility, electrical properties and mechanical softness.
Collapse
Affiliation(s)
- Zhanao Hu
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, Shanghai Engineering Research Center of Nano-Biomaterials and Regenerative Medicine, College of Materials Science and Engineering, Donghua University, Shanghai, 201620, People's Republic of China.
| | - Qianqian Niu
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, Shanghai Engineering Research Center of Nano-Biomaterials and Regenerative Medicine, College of Materials Science and Engineering, Donghua University, Shanghai, 201620, People's Republic of China.
| | - Benjamin S Hsiao
- Department of Chemistry, Stony Brook University, Stony Brook, New York, 11794-3400, USA
| | - Xiang Yao
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, Shanghai Engineering Research Center of Nano-Biomaterials and Regenerative Medicine, College of Materials Science and Engineering, Donghua University, Shanghai, 201620, People's Republic of China.
| | - Yaopeng Zhang
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, Shanghai Engineering Research Center of Nano-Biomaterials and Regenerative Medicine, College of Materials Science and Engineering, Donghua University, Shanghai, 201620, People's Republic of China.
| |
Collapse
|
3
|
Rao AT, Chou KL, Patil PG. Localization of deep brain stimulation trajectories via automatic mapping of microelectrode recordings to MRI. J Neural Eng 2023; 20. [PMID: 36763997 DOI: 10.1088/1741-2552/acbb2b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 02/10/2023] [Indexed: 02/12/2023]
Abstract
Objective. Suboptimal electrode placement during subthalamic nucleus deep brain stimulation (STN DBS) surgery may arise from several sources, including frame-based targeting errors and intraoperative brain shift. We present a computer algorithm that can accurately localize intraoperative microelectrode recording (MER) tracks on preoperative magnetic resonance imaging (MRI) in real-time, thereby predicting deviation between the surgical plan and the MER trajectories.Approach. Random forest (RF) modeling was used to derive a statistical relationship between electrophysiological features on intraoperative MER and voxel intensity on preoperative T2-weighted MR imaging. This model was integrated into a larger algorithm that can automatically localize intraoperative MER recording tracks on preoperative MRI in real-time. To verify accuracy, targeting error of both the planned intraoperative trajectory ('planned') and the algorithm-derived trajectory ('calculated') was estimated by measuring deviation from the final DBS lead location on postoperative high-resolution computed tomography ('actual').Main results. MR imaging and MERs were obtained from 24 STN DBS implant trajectories. The cross-validated RF model could accurately distinguish between gray and white matter regions along MER trajectories (AUC 0.84). When applying this model within the localization algorithm, thecalculatedMER trajectory estimate was found to be significantly closer to theactualDBS lead when compared to theplannedtrajectory recorded during surgery (1.04 mm vs 1.52 mm deviation,p< 0.002), with improvement shown in 19/24 cases (79%). When applying the algorithm to simulated DBS trajectory plans with randomized targeting error, up to 4 mm of error could be resolved to <2 mm on average (p< 0.0001).Significance. This work presents an automated system for intraoperative localization of electrodes during STN DBS surgery. This neuroengineering solution may enhance the accuracy of electrode position estimation, particularly in cases where high-resolution intraoperative imaging is not available.
Collapse
Affiliation(s)
- Akshay T Rao
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America
| | - Kelvin L Chou
- Department of Neurology, University of Michigan, Ann Arbor, MI, United States of America
| | - Parag G Patil
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America.,Department of Neurology, University of Michigan, Ann Arbor, MI, United States of America.,Department of Neurosurgery, University of Michigan, Ann Arbor, MI, United States of America
| |
Collapse
|
4
|
Dixon AR, Vondra I. Biting Innovations of Mosquito-Based Biomaterials and Medical Devices. MATERIALS 2022; 15:ma15134587. [PMID: 35806714 PMCID: PMC9267633 DOI: 10.3390/ma15134587] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 06/15/2022] [Accepted: 06/17/2022] [Indexed: 02/01/2023]
Abstract
Mosquitoes are commonly viewed as pests and deadly predators by humans. Despite this perception, investigations of their survival-based behaviors, select anatomical features, and biological composition have led to the creation of several beneficial technologies for medical applications. In this review, we briefly explore these mosquito-based innovations by discussing how unique characteristics and behaviors of mosquitoes drive the development of select biomaterials and medical devices. Mosquito-inspired microneedles have been fabricated from a variety of materials, including biocompatible metals and polymers, to mimic of the mouthparts that some mosquitoes use to bite a host with minimal injury during blood collection. The salivary components that these mosquitoes use to reduce the clotting of blood extracted during the biting process provide a rich source of anticoagulants that could potentially be integrated into blood-contacting biomaterials or administered in therapeutics to reduce the risk of thrombosis. Mosquito movement, vision, and olfaction are other behaviors that also have the potential for inspiring the development of medically relevant technologies. For instance, viscoelastic proteins that facilitate mosquito movement are being investigated for use in tissue engineering and drug delivery applications. Even the non-wetting nanostructure of a mosquito eye has inspired the creation of a robust superhydrophobic surface coating that shows promise for biomaterial and drug delivery applications. Additionally, biosensors incorporating mosquito olfactory receptors have been built to detect disease-specific volatile organic compounds. Advanced technologies derived from mosquitoes, and insects in general, form a research area that is ripe for exploration and can uncover potential in further dissecting mosquito features for the continued development of novel medical innovations.
Collapse
Affiliation(s)
- Angela R. Dixon
- Department of Biology, College of Arts and Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
- Department of Biomedical Engineering, School of Engineering and School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA
- Correspondence:
| | - Isabelle Vondra
- Biomedical Engineering Program, Northern Illinois University, DeKalb, IL 60115, USA;
| |
Collapse
|
5
|
Rao AT, Lu CW, Askari A, Malaga KA, Chou KL, Patil PG. Clinically-derived oscillatory biomarker predicts optimal subthalamic stimulation for Parkinson's disease. J Neural Eng 2022; 19. [PMID: 35272281 DOI: 10.1088/1741-2552/ac5c8c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 03/10/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Choosing the optimal electrode trajectory, stimulation location, and stimulation amplitude in subthalamic nucleus deep brain stimulation (STN DBS) for Parkinson's disease (PD) remains a time-consuming empirical effort. In this retrospective study, we derive a data-driven electrophysiological biomarker that predicts clinical DBS location and parameters, and we consolidate this information into a quantitative score that may facilitate an objective approach to STN DBS surgery and programming. APPROACH Random-forest feature selection was applied to a dataset of 1046 microelectrode recordings sites across 20 DBS implant trajectories to identify features of oscillatory activity that predict clinically programmed volumes of tissue activation (VTA). A cross-validated classifier was used to retrospectively predict VTA regions from these features. Spatial convolution of probabilistic classifier outputs along MER trajectories produced a biomarker score that reflects the probability of localization within a clinically optimized VTA. MAIN RESULTS Biomarker scores peaked within the VTA region and were significantly correlated with percent improvement in postoperative motor symptoms (MDS-UPRDS Part III, R = 0.61, p = 0.004). Notably, the length of STN, a common criterion for trajectory selection, did not show similar correlation (R = -0.31, p = 0.18). These findings suggest that biomarker-based trajectory selection and programming may improve motor outcomes by 9 ± 3 percentage points (p = 0.047) in this dataset. SIGNIFICANCE A clinically defined electrophysiological biomarker not only predicts VTA size and location but also correlates well with motor outcomes. Use of this biomarker for trajectory selection and initial stimulation may potentially simplify STN DBS surgery and programming.
Collapse
Affiliation(s)
- Akshay T Rao
- Biomedical Engineering, University of Michigan, 1500 East Medical Center Dr., SPC 5338, Ann Arbor, Michigan, 48109-5338, UNITED STATES
| | - Charles W Lu
- Biomedical Engineering, University of Michigan, 1500 E. Medical Center Drive, Ann Arbor, Michigan, 48109-5338, UNITED STATES
| | - Asra Askari
- Biomedical Engineering, University of Michigan, 1500 E Medical Center Drive, SPC 5338, Ann Arbor, Ann Arbor, Michigan, 48109-5338, UNITED STATES
| | - Karlo A Malaga
- Biomedical Engineering, Bucknell University, 316 Academic East Building, Lewisburg, Pennsylvania, 17837, UNITED STATES
| | - Kelvin L Chou
- Neurosurgery, University of Michigan, 1500 E. Medical Center Drive, Ann Arbor, Michigan, 48109-5338, UNITED STATES
| | - Parag G Patil
- Neurosurgery, University of Michigan, 1500 E. Medical Center Drive, Ann Arbor, Michigan, 48109-5338, UNITED STATES
| |
Collapse
|
6
|
Peralta M, Jannin P, Baxter JSH. Machine learning in deep brain stimulation: A systematic review. Artif Intell Med 2021; 122:102198. [PMID: 34823832 DOI: 10.1016/j.artmed.2021.102198] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 09/23/2021] [Accepted: 10/12/2021] [Indexed: 11/16/2022]
Abstract
Deep Brain Stimulation (DBS) is an increasingly common therapy for a large range of neurological disorders, such as abnormal movement disorders. The effectiveness of DBS in terms of controlling patient symptomatology has made this procedure increasingly used over the past few decades. Concurrently, the popularity of Machine Learning (ML), a subfield of artificial intelligence, has skyrocketed and its influence has more recently extended to medical domains such as neurosurgery. Despite its growing research interest, there has yet to be a literature review specifically on the use of ML in DBS. We have followed a fully systematic methodology to obtain a corpus of 73 papers. In each paper, we identified the clinical application, the type/amount of data used, the method employed, and the validation strategy, further decomposed into 12 different sub-categories. The papers overall illustrated some existing trends in how ML is used in the context of DBS, including the breath of the problem domain and evolving techniques, as well as common frameworks and limitations. This systematic review analyzes at a broad level how ML have been recently used to address clinical problems on DBS, giving insight into how these new computational methods are helping to push the state-of-the-art of functional neurosurgery. DBS clinical workflow is complex, involves many specialists, and raises several clinical issues which have partly been addressed with artificial intelligence. However, several areas remain and those that have been recently addressed with ML are by no means considered "solved" by the community nor are they closed to new and evolving methods.
Collapse
Affiliation(s)
- Maxime Peralta
- Univ Rennes, Inserm, LTSI - UMR 1099, F-35000 Rennes, France
| | - Pierre Jannin
- Univ Rennes, Inserm, LTSI - UMR 1099, F-35000 Rennes, France
| | - John S H Baxter
- Univ Rennes, Inserm, LTSI - UMR 1099, F-35000 Rennes, France.
| |
Collapse
|
7
|
Valle ED, Welle EJ, Chestek CA, Weiland JD. Compositional and morphological properties of platinum-iridium electrodeposited on carbon fiber microelectrodes. J Neural Eng 2021; 18:10.1088/1741-2552/ac20bb. [PMID: 34428753 PMCID: PMC10756281 DOI: 10.1088/1741-2552/ac20bb] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 08/24/2021] [Indexed: 11/12/2022]
Abstract
Objective. Neural interfaces based on carbon fiber (CF) electrodes have demonstrated key positive attributes such as minimal foreign body response and mechanical strength to self-insert in brain tissue. However, carbon does not form a low impedance electrode interface with neural tissue. Electrodeposited platinum iridium (PtIr) has been used to improve electrode interface properties for metallic bioelectrodes.Approach. In this study, a PtIr electrodeposition process has been performed on CF microelectrode arrays to improve the interfacial properties of these arrays. We study the film morphology and composition as well as electrode durability and impedance.Results. A PtIr coating with a composition of 70% Pt, 30% Ir and a thickness of ∼400 nm was observed. Pt and Ir were evenly distributed within the film. Impedance was decreased by 89% @ 1 kHz. Accelerated soak testing in a heated (T= 50∘C) saline solution showed impedance increase (@ 1 kHz) of ∼12% after 36 days (89 equivalent) of soaking.
Collapse
Affiliation(s)
- Elena della Valle
- Biomedical Engineering Department, University of Michigan, Ann Arbor, MI, United States of America
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, United States of America
| | - Elissa J Welle
- Biomedical Engineering Department, University of Michigan, Ann Arbor, MI, United States of America
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, United States of America
| | - Cynthia A Chestek
- Biomedical Engineering Department, University of Michigan, Ann Arbor, MI, United States of America
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, United States of America
| | - James D Weiland
- Biomedical Engineering Department, University of Michigan, Ann Arbor, MI, United States of America
- Department of Ophthalmology and Visual Sciences, University of Michigan Kellogg Eye Center, Ann Arbor, MI, United States of America
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, United States of America
| |
Collapse
|
8
|
Connolly MJ, Cole ER, Isbaine F, de Hemptinne C, Starr PA, Willie JT, Gross RE, Miocinovic S. Multi-objective data-driven optimization for improving deep brain stimulation in Parkinson's disease. J Neural Eng 2021; 18. [PMID: 33862604 DOI: 10.1088/1741-2552/abf8ca] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 04/16/2021] [Indexed: 11/12/2022]
Abstract
Objective.Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease (PD) but its success depends on a time-consuming process of trial-and-error to identify the optimal stimulation settings for each individual patient. Data-driven optimization algorithms have been proposed to efficiently find the stimulation setting that maximizes a quantitative biomarker of symptom relief. However, these algorithms cannot efficiently take into account stimulation settings that may control symptoms but also cause side effects. Here we demonstrate how multi-objective data-driven optimization can be used to find the optimal trade-off between maximizing symptom relief and minimizing side effects.Approach.Cortical and motor evoked potential data collected from PD patients during intraoperative stimulation of the subthalamic nucleus were used to construct a framework for designing and prototyping data-driven multi-objective optimization algorithms. Using this framework, we explored how these techniques can be applied clinically, and characterized the design features critical for solving this optimization problem. Our two optimization objectives were to maximize cortical evoked potentials, a putative biomarker of therapeutic benefit, and to minimize motor potentials, a biomarker of motor side effects.Main Results.Using thisin silicodesign framework, we demonstrated how the optimal trade-off between two objectives can substantially reduce the stimulation parameter space by 61 ± 19%. The best algorithm for identifying the optimal trade-off between the two objectives was a Bayesian optimization approach with an area under the receiver operating characteristic curve of up to 0.94 ± 0.02, which was possible with the use of a surrogate model and a well-tuned acquisition function to efficiently select which stimulation settings to sample.Significance.These findings show that multi-objective optimization is a promising approach for identifying the optimal trade-off between symptom relief and side effects in DBS. Moreover, these approaches can be readily extended to newly discovered biomarkers, adapted to DBS for disorders beyond PD, and can scale with the development of more complex DBS devices.
Collapse
Affiliation(s)
- Mark J Connolly
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, United States of America
| | - Eric R Cole
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, United States of America
| | - Faical Isbaine
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA 30322, United States of America
| | - Coralie de Hemptinne
- Department of Neurology, University of Florida, Gainesville, FL 32608, United States of America
| | - Phillip A Starr
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, United States of America
| | - Jon T Willie
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Robert E Gross
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, United States of America.,Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA 30322, United States of America.,Department of Neurology, Emory University School of Medicine, 12 Executive Park Drive North East, Atlanta, GA 30322, United States of America
| | - Svjetlana Miocinovic
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, United States of America.,Department of Neurology, Emory University School of Medicine, 12 Executive Park Drive North East, Atlanta, GA 30322, United States of America
| |
Collapse
|
9
|
Scott JM, Gliske SV, Kuhlmann L, Stacey WC. Viability of Preictal High-Frequency Oscillation Rates as a Biomarker for Seizure Prediction. Front Hum Neurosci 2021; 14:612899. [PMID: 33584225 PMCID: PMC7876341 DOI: 10.3389/fnhum.2020.612899] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 12/09/2020] [Indexed: 11/13/2022] Open
Abstract
Motivation: There is an ongoing search for definitive and reliable biomarkers to forecast or predict imminent seizure onset, but to date most research has been limited to EEG with sampling rates <1,000 Hz. High-frequency oscillations (HFOs) have gained acceptance as an indicator of epileptic tissue, but few have investigated the temporal properties of HFOs or their potential role as a predictor in seizure prediction. Here we evaluate time-varying trends in preictal HFO rates as a potential biomarker of seizure prediction. Methods: HFOs were identified for all interictal and preictal periods with a validated automated detector in 27 patients who underwent intracranial EEG monitoring. We used LASSO logistic regression with several features of the HFO rate to distinguish preictal from interictal periods in each individual. We then tested these models with held-out data and evaluated their performance with the area-under-the-curve (AUC) of their receiver-operating curve (ROC). Finally, we assessed the significance of these results using non-parametric statistical tests. Results: There was variability in the ability of HFOs to discern preictal from interictal states across our cohort. We identified a subset of 10 patients in whom the presence of the preictal state could be successfully predicted better than chance. For some of these individuals, average AUC in the held-out data reached higher than 0.80, which suggests that HFO rates can significantly differentiate preictal and interictal periods for certain patients. Significance: These findings show that temporal trends in HFO rate can predict the preictal state better than random chance in some individuals. Such promising results indicate that future prediction efforts would benefit from the inclusion of high-frequency information in their predictive models and technological architecture.
Collapse
Affiliation(s)
- Jared M Scott
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States.,Biointerfaces Institute, University of Michigan, Ann Arbor, MI, United States
| | - Stephen V Gliske
- Department of Neurology, University of Michigan Hospitals, Ann Arbor, MI, United States.,Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE, United States
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - William C Stacey
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States.,Biointerfaces Institute, University of Michigan, Ann Arbor, MI, United States.,Department of Neurology, University of Michigan Hospitals, Ann Arbor, MI, United States
| |
Collapse
|
10
|
Malaga KA, Costello JT, Chou KL, Patil PG. Atlas-independent, N-of-1 tissue activation modeling to map optimal regions of subthalamic deep brain stimulation for Parkinson disease. NEUROIMAGE-CLINICAL 2020; 29:102518. [PMID: 33333464 PMCID: PMC7736726 DOI: 10.1016/j.nicl.2020.102518] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 11/25/2020] [Accepted: 11/27/2020] [Indexed: 01/13/2023]
Abstract
Neuroanatomical variations among patients are obscured in atlas-based VTA modeling. N-of-1 neuroanatomical and VTA modeling enables patient-level precision. Mean optimal stimulation is dorsomedial to the STN, near its posterior half. Individual VTAs deviate from optimal stimulation sites to varying degrees. Optimal stimulation sites for rigidity, bradykinesia, and tremor partially overlap.
Background Motor outcomes after subthalamic deep brain stimulation (STN DBS) for Parkinson disease (PD) vary considerably among patients and strongly depend on stimulation location. The objective of this retrospective study was to map the regions of optimal STN DBS for PD using an atlas-independent, fully individualized (N-of-1) tissue activation modeling approach and to assess the relationship between patient-level therapeutic volumes of tissue activation (VTAs) and motor improvement. Methods The stimulation-induced electric field for 40 PD patients treated with bilateral STN DBS was modeled using finite element analysis. Neurostimulation models were generated for each patient, incorporating their individual STN anatomy, DBS lead position and orientation, anisotropic tissue conductivity, and clinical stimulation settings. A voxel-based analysis of the VTAs was then used to map the optimal location of stimulation. The amount of stimulation in specific regions relative to the STN was measured and compared between STNs with more and less optimal stimulation, as determined by their motor improvement scores and VTA. The relationship between VTA location and motor outcome was then assessed using correlation analysis. Patient variability in terms of STN anatomy, active contact position, and VTA location were also evaluated. Results from the N-of-1 model were compared to those from a simplified VTA model. Results Tissue activation modeling mapped the optimal location of stimulation to regions medial, posterior, and dorsal to the STN centroid. These regions extended beyond the STN boundary towards the caudal zona incerta (cZI). The location of the VTA and active contact position differed significantly between STNs with more and less optimal stimulation in the dorsal-ventral and anterior-posterior directions. Therapeutic stimulation spread noticeably more in the dorsal and posterior directions, providing additional evidence for cZI as an important DBS target. There were significant linear relationships between the amount of dorsal and posterior stimulation, as measured by the VTA, and motor improvement. These relationships were more robust than those between active contact position and motor improvement. There was high variability in STN anatomy, active contact position, and VTA location among patients. Spherical VTA modeling was unable to reproduce these results and tended to overestimate the size of the VTA. Conclusion Accurate characterization of the spread of stimulation is needed to optimize STN DBS for PD. High variability in neuroanatomy, stimulation location, and motor improvement among patients highlights the need for individualized modeling techniques. The atlas-independent, N-of-1 tissue activation modeling approach presented in this study can be used to develop and evaluate stimulation strategies to improve clinical outcomes on an individual basis.
Collapse
Affiliation(s)
- Karlo A Malaga
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Joseph T Costello
- Department of Electrical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Kelvin L Chou
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA; Department of Neurosurgery, University of Michigan, Ann Arbor, MI, USA
| | - Parag G Patil
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA; Department of Neurology, University of Michigan, Ann Arbor, MI, USA; Department of Neurosurgery, University of Michigan, Ann Arbor, MI, USA.
| |
Collapse
|
11
|
Mapping of subthalamic nucleus using microelectrode recordings during deep brain stimulation. Sci Rep 2020; 10:19241. [PMID: 33159098 PMCID: PMC7648837 DOI: 10.1038/s41598-020-74196-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Accepted: 09/23/2020] [Indexed: 11/17/2022] Open
Abstract
Alongside stereotactic magnetic resonance imaging, microelectrode recording (MER) is frequently used during the deep brain stimulation (DBS) surgery for optimal target localization. The aim of this study is to optimize subthalamic nucleus (STN) mapping using MER analytical patterns. 16 patients underwent bilateral STN-DBS. MER was performed simultaneously for 5 microelectrodes in a setting of Ben’s-gun pattern in awake patients. Using spikes and background activity several different parameters and their spectral estimates in various frequency bands including low frequency (2–7 Hz), Alpha (8–12 Hz), Beta (sub-divided as Low_Beta (13–20 Hz) and High_Beta (21–30 Hz)) and Gamma (31 to 49 Hz) were computed. The optimal STN lead placement with the most optimal clinical effect/side-effect ratio accorded to the maximum spike rate in 85% of the implantation. Mean amplitude of background activity in the low beta frequency range was corresponding to right depth in 85% and right location in 94% of the implantation respectively. MER can be used for STN mapping and intraoperative decisions for the implantation of DBS electrode leads with a high accuracy. Spiking and background activity in the beta range are the most promising independent parameters for the delimitation of the proper anatomical site.
Collapse
|
12
|
Watts J, Khojandi A, Shylo O, Ramdhani RA. Machine Learning's Application in Deep Brain Stimulation for Parkinson's Disease: A Review. Brain Sci 2020; 10:E809. [PMID: 33139614 PMCID: PMC7694006 DOI: 10.3390/brainsci10110809] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 10/16/2020] [Accepted: 10/29/2020] [Indexed: 01/07/2023] Open
Abstract
Deep brain stimulation (DBS) is a surgical treatment for advanced Parkinson's disease (PD) that has undergone technological evolution that parallels an expansion in clinical phenotyping, neurophysiology, and neuroimaging of the disease state. Machine learning (ML) has been successfully used in a wide range of healthcare problems, including DBS. As computational power increases and more data become available, the application of ML in DBS is expected to grow. We review the literature of ML in DBS and discuss future opportunities for such applications. Specifically, we perform a comprehensive review of the literature from PubMed, the Institute for Scientific Information's Web of Science, Cochrane Database of Systematic Reviews, and Institute of Electrical and Electronics Engineers' (IEEE) Xplore Digital Library for ML applications in DBS. These studies are broadly placed in the following categories: (1) DBS candidate selection; (2) programming optimization; (3) surgical targeting; and (4) insights into DBS mechanisms. For each category, we provide and contextualize the current body of research and discuss potential future directions for the application of ML in DBS.
Collapse
Affiliation(s)
- Jeremy Watts
- Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA; (J.W.); (A.K.); (O.S.)
| | - Anahita Khojandi
- Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA; (J.W.); (A.K.); (O.S.)
| | - Oleg Shylo
- Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA; (J.W.); (A.K.); (O.S.)
| | - Ritesh A. Ramdhani
- Department of Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA
| |
Collapse
|
13
|
Lu CW, Chou KL, Patil PG. Correspondence of optimal stimulation and beta power varies regionally in STN DBS for Parkinson disease. Parkinsonism Relat Disord 2020; 78:124-128. [DOI: 10.1016/j.parkreldis.2020.08.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 07/27/2020] [Accepted: 08/06/2020] [Indexed: 10/23/2022]
|
14
|
Mossner JM, Chou KL, Maher AH, Persad CC, Patil PG. Localization of motor and verbal fluency effects in subthalamic DBS for Parkinson's disease. Parkinsonism Relat Disord 2020; 79:55-59. [PMID: 32866879 DOI: 10.1016/j.parkreldis.2020.08.023] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 08/14/2020] [Accepted: 08/17/2020] [Indexed: 11/16/2022]
Abstract
INTRODUCTION Subthalamic nucleus deep brain stimulation (STN DBS) improves cardinal motor symptoms of Parkinson's disease (PD) but can worsen verbal fluency (VF). An optimal site of stimulation for overall motor improvement has been previously identified using an atlas-independent, fully individualized, field-modeling approach. This study examines if cardinal motor components (bradykinesia, tremor, and rigidity) share this identified optimal improvement site and if there is co-localization with a site that worsens VF. METHODS An atlas-independent, field-modeling approach was used to identify sites of maximal STN DBS effect on overall and cardinal motor symptoms and VF in 60 patients. Anatomic coordinates were referenced to the STN midpoint. Symptom severity was assessed with the MDS-UPDRS part III and established VF scales. RESULTS Sites for improved bradykinesia and rigidity co-localized with each other and the overall part III site (0.09 mm lateral, 0.93 mm posterior, 1.75 mm dorsal). The optimal site for tremor was posterior to this site (0.10 mm lateral, 1.40 mm posterior, 1.93 mm dorsal). Semantic and phonemic VF sites were indistinguishable and co-localized medial to the motor sites (0.32 mm medial, 1.18 mm posterior, 1.74 mm dorsal). CONCLUSION This study identifies statistically distinct, maximally effective stimulation sites for tremor improvement, VF worsening, and overall and other cardinal motor improvements in STN DBS. Current electrode sizes and voltage settings stimulate all of these sites simultaneously. However, future targeted lead placement and focused directional stimulation may avoid VF worsening while maintaining motor improvements in STN DBS.
Collapse
Affiliation(s)
- James M Mossner
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI, USA
| | - Kelvin L Chou
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI, USA; Department of Neurology, University of Michigan, Ann Arbor, MI, USA
| | - Amanda H Maher
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Carol C Persad
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Parag G Patil
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI, USA; Department of Neurology, University of Michigan, Ann Arbor, MI, USA; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
| |
Collapse
|
15
|
Ozturk M, Telkes I, Jimenez-Shahed J, Viswanathan A, Tarakad A, Kumar S, Sheth SA, Ince NF. Randomized, Double-Blind Assessment of LFP Versus SUA Guidance in STN-DBS Lead Implantation: A Pilot Study. Front Neurosci 2020; 14:611. [PMID: 32655356 PMCID: PMC7325925 DOI: 10.3389/fnins.2020.00611] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 05/18/2020] [Indexed: 11/13/2022] Open
Abstract
Background: The efficacy of deep brain stimulation (DBS) therapy in Parkinson's disease (PD) patients is highly dependent on the precise localization of the target structures such as subthalamic nucleus (STN). Most commonly, microelectrode single unit activity (SUA) recordings are performed to refine the target. This process is heavily experience based and can be technically challenging. Local field potentials (LFPs), representing the activity of a population of neurons, can be obtained from the same microelectrodes used for SUA recordings and allow flexible online processing with less computational complexity due to lower sampling rate requirements. Although LFPs have been shown to contain biomarkers capable of predicting patients' symptoms and differentiating various structures, their use in the localization of the STN in the clinical practice is not prevalent. Methods: Here we present, for the first time, a randomized and double-blinded pilot study with intraoperative online LFP processing in which we compare the clinical benefit from SUA- versus LFP-based implantation. Ten PD patients referred for bilateral STN-DBS were randomly implanted using either SUA or LFP guided targeting in each hemisphere. Although both SUA and LFP were recorded for each STN, the electrophysiologist was blinded to one at a time. Three months postoperatively, the patients were evaluated by a neurologist blinded to the intraoperative recordings to assess the performance of each modality. While SUA-based decisions relied on the visual and auditory inspection of the raw traces, LFP-based decisions were given through an online signal processing and machine learning pipeline. Results: We found a dramatic agreement between LFP- and SUA-based localization (16/20 STNs) providing adequate clinical improvement (51.8% decrease in 3-month contralateral motor assessment scores), with LFP-guided implantation resulting in greater average improvement in the discordant cases (74.9%, n = 3 STNs). The selected tracks were characterized by higher activity in beta (11-32 Hz) and high-frequency (200-400 Hz) bands (p < 0.01) of LFPs and stronger non-linear coupling between these bands (p < 0.05). Conclusion: Our pilot study shows equal or better clinical benefit with LFP-based targeting. Given the robustness of the electrode interface and lower computational cost, more centers can utilize LFP as a strategic feedback modality intraoperatively, in conjunction to the SUA-guided targeting.
Collapse
Affiliation(s)
- Musa Ozturk
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
| | - Ilknur Telkes
- Department of Neuroscience and Experimental Therapeutics, Albany Medical College, Albany, NY, United States
| | - Joohi Jimenez-Shahed
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Ashwin Viswanathan
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Arjun Tarakad
- Department of Neurology, Baylor College of Medicine, Houston, TX, United States
| | - Suneel Kumar
- Department of Neurology, Baylor College of Medicine, Houston, TX, United States
| | - Sameer A. Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Nuri F. Ince
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
| |
Collapse
|