1
|
Liu J, Chen S, Chen J, Wang B, Zhang Q, Xiao L, Zhang D, Cai X. Structural Brain Connectivity Guided Optimal Contact Selection for Deep Brain Stimulation of the Subthalamic Nucleus. World Neurosurg 2024; 188:e546-e554. [PMID: 38823445 DOI: 10.1016/j.wneu.2024.05.150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Revised: 05/23/2024] [Accepted: 05/24/2024] [Indexed: 06/03/2024]
Abstract
BACKGROUND Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective therapy in ameliorating the motor symptoms of Parkinson disease. However, postoperative optimal contact selection is crucial for achieving the best outcome of deep brain stimulation of the subthalamic nucleus surgery, but the process is currently a trial-and-error and time-consuming procedure that relies heavily on surgeons' clinical experience. METHODS In this study, we propose a structural brain connectivity guided optimal contact selection method for deep brain stimulation of the subthalamic nucleus. Firstly, we reconstruct the DBS electrode location and estimate the stimulation range using volume of tissue activated from each DBS contact. Then, we extract the structural connectivity features by concatenating fractional anisotropy and the number of streamlines features of activated regions and the whole brain regions. Finally, we use a convolutional neural network with convolutional block attention module to identify the structural connectivity features for the optimal contact selection. RESULTS We review the data of 800 contacts from 100 patients with Parkinson disease for the experiment. The proposed method achieves promising results, with the average accuracy of 97.63%, average precision of 94.50%, average recall of 94.46%, and average specificity of 98.18%, respectively. Our method can provide the suggestion for optimal contact selection. CONCLUSIONS Our proposed method can improve the efficiency and accuracy of DBS optimal contact selection, reduce the dependence on surgeons' experience, and has the potential to facilitate the development of advanced DBS technology.
Collapse
Affiliation(s)
- Jiali Liu
- Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
| | - Shouxuan Chen
- Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
| | - Jianwei Chen
- Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
| | - Bo Wang
- Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
| | - Qiusheng Zhang
- Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
| | - Linxia Xiao
- Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Center for High Performance Computing, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
| | - Doudou Zhang
- Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
| | - Xiaodong Cai
- Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
| |
Collapse
|
2
|
Patrick EE, Fleeting CR, Patel DR, Casauay JT, Patel A, Shepherd H, Wong JK. Modeling the volume of tissue activated in deep brain stimulation and its clinical influence: a review. Front Hum Neurosci 2024; 18:1333183. [PMID: 38660012 PMCID: PMC11039793 DOI: 10.3389/fnhum.2024.1333183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 03/26/2024] [Indexed: 04/26/2024] Open
Abstract
Deep brain stimulation (DBS) is a neuromodulatory therapy that has been FDA approved for the treatment of various disorders, including but not limited to, movement disorders (e.g., Parkinson's disease and essential tremor), epilepsy, and obsessive-compulsive disorder. Computational methods for estimating the volume of tissue activated (VTA), coupled with brain imaging techniques, form the basis of models that are being generated from retrospective clinical studies for predicting DBS patient outcomes. For instance, VTA models are used to generate target-and network-based probabilistic stimulation maps that play a crucial role in predicting DBS treatment outcomes. This review defines the methods for calculation of tissue activation (or modulation) including ones that use heuristic and clinically derived estimates and more computationally involved ones that rely on finite-element methods and biophysical axon models. We define model parameters and provide a comparison of commercial, open-source, and academic simulation platforms available for integrated neuroimaging and neural activation prediction. In addition, we review clinical studies that use these modeling methods as a function of disease. By describing the tissue-activation modeling methods and highlighting their application in clinical studies, we provide the neural engineering and clinical neuromodulation communities with perspectives that may influence the adoption of modeling methods for future DBS studies.
Collapse
Affiliation(s)
- Erin E. Patrick
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States
| | - Chance R. Fleeting
- College of Medicine, University of Florida, Gainesville, FL, United States
| | - Drashti R. Patel
- College of Medicine, University of Florida, Gainesville, FL, United States
| | - Jed T. Casauay
- College of Medicine, University of Florida, Gainesville, FL, United States
| | - Aashay Patel
- College of Medicine, University of Florida, Gainesville, FL, United States
| | - Hunter Shepherd
- College of Medicine, University of Florida, Gainesville, FL, United States
| | - Joshua K. Wong
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| |
Collapse
|
3
|
Fu T, Gao Y, Huang X, Zhang D, Liu L, Wang P, Yin X, Lin H, Yuan J, Ai S, Wu X. Brain connectome-based imaging markers for identifiable signature of migraine with and without aura. Quant Imaging Med Surg 2024; 14:194-207. [PMID: 38223049 PMCID: PMC10784058 DOI: 10.21037/qims-23-827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 10/07/2023] [Indexed: 01/16/2024]
Abstract
Background Cortical spreading depression (CSD) has been considered the prominent theory for migraine with aura (MwA). However, it is also argued that CSD can exist in patients in a silent state, and not manifest as aura. Thus, the MwA classification based on aura may be questionable. This study aimed to capture whole-brain connectome-based imaging markers with identifiable signatures for MwA and migraine without aura (MwoA). Methods A total of 88 migraine patients (32 MwA) and 49 healthy controls (HC) underwent a diffusion tensor imaging and resting-state functional magnetic resonance imaging scan. The whole-brain structural connectivity (SC) and functional connectivity (FC) analysis was employed to extract imaging features. The extracted features were subjected to an all-relevant feature selection process within cross-validation loops to pinpoint attributes demonstrating substantial efficacy for patient categorization. Based on the identified features, the predictive ability of the random forest classifiers constructed with the 88 migraine patients' sample was tested using an independent sample of 32 migraine patients (eight MwA). Results Compared to MwoA and HC, MwA showed two reduced SC and six FC (five increased and one reduced) features [all P<0.01, after false discovery rate (FDR) correction], involving frontal areas, temporal areas, visual areas, amygdala, and thalamus. A total of four imaging features were significantly correlated with clinical rating scales in all patients (r=-0.38 to 0.47, P<0.01, after FDR correction). The predictive ability of the random forest classifiers achieved an accuracy of 78.1% in the external sample to identify MwA. Conclusions The whole-brain connectivity features in our results may serve as connectome-based imaging markers for MwA identification. The alterations of SC and FC strength provide possible evidence in further understanding the heterogeneity and mechanism of MwA which may help for patient-specific decision-making.
Collapse
Affiliation(s)
- Tong Fu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yujia Gao
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xiaobin Huang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Di Zhang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Lindong Liu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Peng Wang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Hai Lin
- Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Jianmin Yuan
- Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Shuyue Ai
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xinying Wu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| |
Collapse
|
4
|
Andrews L, Keller SS, Osman-Farah J, Macerollo A. A structural magnetic resonance imaging review of clinical motor outcomes from deep brain stimulation in movement disorders. Brain Commun 2023; 5:fcad171. [PMID: 37304793 PMCID: PMC10257440 DOI: 10.1093/braincomms/fcad171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 04/05/2023] [Accepted: 05/30/2023] [Indexed: 06/13/2023] Open
Abstract
Patients with movement disorders treated by deep brain stimulation do not always achieve successful therapeutic alleviation of motor symptoms, even in cases where surgery is without complications. Magnetic resonance imaging (MRI) offers methods to investigate structural brain-related factors that may be predictive of clinical motor outcomes. This review aimed to identify features which have been associated with variability in clinical post-operative motor outcomes in patients with Parkinson's disease, dystonia, and essential tremor from structural MRI modalities. We performed a literature search for articles published between 1 January 2000 and 1 April 2022 and identified 5197 articles. Following screening through our inclusion criteria, we identified 60 total studies (39 = Parkinson's disease, 11 = dystonia syndromes and 10 = essential tremor). The review captured a range of structural MRI methods and analysis techniques used to identify factors related to clinical post-operative motor outcomes from deep brain stimulation. Morphometric markers, including volume and cortical thickness were commonly identified in studies focused on patients with Parkinson's disease and dystonia syndromes. Reduced metrics in basal ganglia, sensorimotor and frontal regions showed frequent associations with reduced motor outcomes. Increased structural connectivity to subcortical nuclei, sensorimotor and frontal regions was also associated with greater motor outcomes. In patients with tremor, increased structural connectivity to the cerebellum and cortical motor regions showed high prevalence across studies for greater clinical motor outcomes. In addition, we highlight conceptual issues for studies assessing clinical response with structural MRI and discuss future approaches towards optimizing individualized therapeutic benefits. Although quantitative MRI markers are in their infancy for clinical purposes in movement disorder treatments, structural features obtained from MRI offer the powerful potential to identify candidates who are more likely to benefit from deep brain stimulation and provide insight into the complexity of disorder pathophysiology.
Collapse
Affiliation(s)
- Luke Andrews
- The Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L3 9TA, UK
- Department of Neurology and Neurosurgery, The Walton Centre NHS Foundation Trust, Liverpool L97LJ, UK
| | - Simon S Keller
- The Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L3 9TA, UK
| | - Jibril Osman-Farah
- Department of Neurology and Neurosurgery, The Walton Centre NHS Foundation Trust, Liverpool L97LJ, UK
| | - Antonella Macerollo
- The Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L3 9TA, UK
- Department of Neurology and Neurosurgery, The Walton Centre NHS Foundation Trust, Liverpool L97LJ, UK
| |
Collapse
|
5
|
Gadot R, Vanegas Arroyave N, Dang H, Anand A, Najera RA, Taneff LY, Bellows S, Tarakad A, Jankovic J, Horn A, Shofty B, Viswanathan A, Sheth SA. Association of clinical outcomes and connectivity in awake versus asleep deep brain stimulation for Parkinson disease. J Neurosurg 2022; 138:1016-1027. [PMID: 35932263 DOI: 10.3171/2022.6.jns212904] [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: 12/21/2021] [Accepted: 06/09/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Deep brain stimulation (DBS) for Parkinson disease (PD) is traditionally performed with awake intraoperative testing and/or microelectrode recording. Recently, however, the procedure has been increasingly performed under general anesthesia with image-based verification. The authors sought to compare structural and functional networks engaged by awake and asleep PD-DBS of the subthalamic nucleus (STN) and correlate them with clinical outcomes. METHODS Levodopa equivalent daily dose (LEDD), pre- and postoperative motor scores on the Movement Disorders Society-Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III), and total electrical energy delivered (TEED) at 6 months were retroactively assessed in patients with PD who received implants of bilateral DBS leads. In subset analysis, implanted electrodes were reconstructed using the Lead-DBS toolbox. Volumes of tissue activated (VTAs) were used as seed points in group volumetric and connectivity analysis. RESULTS The clinical courses of 122 patients (52 asleep, 70 awake) were reviewed. Operating room and procedure times were significantly shorter in asleep cases. LEDD reduction, MDS-UPDRS III score improvement, and TEED at the 6-month follow-up did not differ between groups. In subset analysis (n = 40), proximity of active contact, VTA overlap, and desired network fiber counts with motor STN correlated with lower DBS energy requirement and improved motor scores. Discriminative structural fiber tracts involving supplementary motor area, thalamus, and brainstem were associated with optimal clinical improvement. Areas of highest structural and functional connectivity with VTAs did not significantly differ between the two groups. CONCLUSIONS Compared to awake STN DBS, asleep procedures can achieve similarly optimal targeting-based on clinical outcomes, electrode placement, and connectivity estimates-in more efficient procedures and shorter operating room times.
Collapse
Affiliation(s)
- Ron Gadot
- 1Department of Neurosurgery, Baylor College of Medicine
| | - Nora Vanegas Arroyave
- 2Parkinson's Disease Center and Movement Disorders Clinic, Department of Neurology, Baylor College of Medicine, Houston, Texas; and
| | - Huy Dang
- 1Department of Neurosurgery, Baylor College of Medicine
| | - Adrish Anand
- 1Department of Neurosurgery, Baylor College of Medicine
| | | | - Lisa Yutong Taneff
- 2Parkinson's Disease Center and Movement Disorders Clinic, Department of Neurology, Baylor College of Medicine, Houston, Texas; and
| | - Steven Bellows
- 2Parkinson's Disease Center and Movement Disorders Clinic, Department of Neurology, Baylor College of Medicine, Houston, Texas; and
| | - Arjun Tarakad
- 2Parkinson's Disease Center and Movement Disorders Clinic, Department of Neurology, Baylor College of Medicine, Houston, Texas; and
| | - Joseph Jankovic
- 2Parkinson's Disease Center and Movement Disorders Clinic, Department of Neurology, Baylor College of Medicine, Houston, Texas; and
| | - Andreas Horn
- 3Department of Neurology, Movement Disorder and Neuromodulation Unit, Charité-Universitätsmedizin, Berlin, Germany
| | - Ben Shofty
- 1Department of Neurosurgery, Baylor College of Medicine
| | | | | |
Collapse
|
6
|
Gonzalez-Escamilla G, Koirala N, Bange M, Glaser M, Pintea B, Dresel C, Deuschl G, Muthuraman M, Groppa S. Deciphering the Network Effects of Deep Brain Stimulation in Parkinson's Disease. Neurol Ther 2022; 11:265-282. [PMID: 35000133 PMCID: PMC8857357 DOI: 10.1007/s40120-021-00318-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 12/21/2021] [Indexed: 10/31/2022] Open
Abstract
INTRODUCTION Deep brain stimulation of the subthalamic nucleus (STN-DBS) is an established therapy for Parkinson's disease (PD). However, a more detailed characterization of the targeted network and its grey matter (GM) terminals that drive the clinical outcome is needed. In this direction, the use of MRI after DBS surgery is now possible due to recent advances in hardware, opening a window for the clarification of the association between the affected tissue, including white matter fiber pathways and modulated GM regions, and the DBS-related clinical outcome. Therefore, we present a computational framework for reconstruction of targeted networks on postoperative MRI. METHODS We used a combination of preoperative whole-brain T1-weighted (T1w) and diffusion-weighted MRI data for morphometric integrity assessment and postoperative T1w MRI for electrode reconstruction and network reconstruction in 15 idiopathic PD patients. Within this framework, we made use of DBS lead artifact intensity profiles on postoperative MRI to determine DBS locations used as seeds for probabilistic tractography to cortical and subcortical targets within the motor circuitry. Lastly, we evaluated the relationship between brain microstructural characteristics of DBS-targeted brain network terminals and postoperative clinical outcomes. RESULTS The proposed framework showed robust performance for identifying the DBS electrode positions. Connectivity profiles between the primary motor cortex (M1), supplementary motor area (SMA), and DBS locations were strongly associated with the stimulation intensity needed for the optimal clinical outcome. Local diffusion properties of the modulated pathways were related to DBS outcomes. STN-DBS motor symptom improvement was highly associated with cortical thickness in the middle frontal and superior frontal cortices, but not with subcortical volumetry. CONCLUSION These data suggest that STN-DBS outcomes largely rely on the modulatory interference from cortical areas, particularly M1 and SMA, to DBS locations.
Collapse
Affiliation(s)
- Gabriel Gonzalez-Escamilla
- Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131, Mainz, Germany.
| | - Nabin Koirala
- Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131, Mainz, Germany
| | - Manuel Bange
- Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131, Mainz, Germany
| | - Martin Glaser
- Department of Neurosurgery, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131, Mainz, Germany
| | - Bogdan Pintea
- Department of Neurosurgery, University Hospital Bergmannsheil, Bürkle de la Camp-Platz 1, 44789, Bochum, Germany
| | - Christian Dresel
- Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131, Mainz, Germany
| | - Günther Deuschl
- Department of Neurology, Schleswig-Holstein University Hospital UKSH, Arnold-Heller-Straße 3, 24105, Kiel, Germany
| | - Muthuraman Muthuraman
- Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131, Mainz, Germany
| | - Sergiu Groppa
- Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131, Mainz, Germany.
| |
Collapse
|
7
|
Lin H, Liu Z, Yan W, Zhang D, Liu J, Xu B, Li W, Zhang Q, Cai X. Brain connectivity markers in advanced Parkinson's disease for predicting mild cognitive impairment. Eur Radiol 2021; 31:9324-9334. [PMID: 34109485 DOI: 10.1007/s00330-021-08086-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 04/29/2021] [Accepted: 05/20/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVES Mild cognitive impairment (MCI) is a well-defined non-motor manifestation and a harbinger of dementia in Parkinson's disease. This study is to investigate brain connectivity markers of MCI using diffusion tensor imaging and resting-state functional MRI, and help MCI diagnosis in PD patients. METHODS We evaluated 131 advanced PD patients (disease duration > 5 years; 59 patients with MCI) and 48 healthy control subjects who underwent a diffusion-weighted and resting-state functional MRI scanning. The patients were randomly assigned to training (n = 100) and testing (n = 31) groups. According to the Brainnetome Atlas, ROI-based structural and functional connectivity analysis was employed to extract connectivity features. To identify features with significant discriminative power for patient classification, all features were put into an all-relevant feature selection procedure within cross-validation loops. RESULTS Nine features were identified to be significantly relevant to patient classification. They showed significant differences between PD patients with and without MCI and positively correlated with the MoCA score. Five of them did not differ between general MCI subjects and healthy controls from the ADNI database, which suggested that they could uniquely play a part in the MCI diagnosis of PD. On basis of these relevant features, the random forest model constructed from the training group achieved an accuracy of 83.9% in the testing group, to discriminate patients with and without MCI. CONCLUSIONS The results of our study provide preliminary evidence that structural and functional connectivity abnormalities may contribute to cognitive impairment and allow to predict the outcome of MCI diagnosis in PD. KEY POINTS • Nine MCI markers were identified using an all-relevant feature selection procedure. • Five of nine markers differed between MCI and NC in PD, but not in general persons. • A random forest model achieved an accuracy of 83.9% for MCI diagnosis in PD.
Collapse
Affiliation(s)
- Hai Lin
- Department of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, 3002# Sungang West Road, Futian District, Shenzhen, 518035, China
- Shenzhen Key Laboratory of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
- Shenzhen University School of Medicine, Shenzhen, China
| | - Zesi Liu
- Department of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, 3002# Sungang West Road, Futian District, Shenzhen, 518035, China
- Shantou University Medical College, Shantou, Guangdong, China
| | - Wei Yan
- Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Doudou Zhang
- Department of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, 3002# Sungang West Road, Futian District, Shenzhen, 518035, China
- Shenzhen University School of Medicine, Shenzhen, China
| | - Jiali Liu
- Department of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, 3002# Sungang West Road, Futian District, Shenzhen, 518035, China
- Shenzhen University School of Medicine, Shenzhen, China
| | - Bin Xu
- Department of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, 3002# Sungang West Road, Futian District, Shenzhen, 518035, China
- Shenzhen University School of Medicine, Shenzhen, China
| | - Weiping Li
- Department of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, 3002# Sungang West Road, Futian District, Shenzhen, 518035, China
- Shenzhen Key Laboratory of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
- Shenzhen University School of Medicine, Shenzhen, China
| | - Qiusheng Zhang
- Department of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, 3002# Sungang West Road, Futian District, Shenzhen, 518035, China.
- Shenzhen University School of Medicine, Shenzhen, China.
| | - Xiaodong Cai
- Department of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, 3002# Sungang West Road, Futian District, Shenzhen, 518035, China.
- Shenzhen University School of Medicine, Shenzhen, China.
| |
Collapse
|
8
|
Huang LC, Chen LG, Wu PA, Pang CY, Lin SZ, Tsai ST, Chen SY. Effect of deep brain stimulation on brain network and white matter integrity in Parkinson's disease. CNS Neurosci Ther 2021; 28:92-104. [PMID: 34643338 PMCID: PMC8673709 DOI: 10.1111/cns.13741] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 09/22/2021] [Accepted: 09/23/2021] [Indexed: 11/27/2022] Open
Abstract
Aims The effects of subthalamic nucleus (STN)‐deep brain stimulation (DBS) on brain topological metrics, functional connectivity (FC), and white matter integrity were studied in levodopa‐treated Parkinson’s disease (PD) patients before and after DBS. Methods Clinical assessment, resting‐state functional MRI (rs‐fMRI), and diffusion tensor imaging (DTI) were performed pre‐ and post‐DBS in 15 PD patients, using a within‐subject design. The rs‐fMRI identified brain network topological metric and FC changes using graph‐theory‐ and seed‐based methods. White matter integrity was determined by DTI and tract‐based spatial statistics. Results Unified Parkinson's Disease Rating Scale III (UPDRS‐ III) scores were significantly improved by 35.3% (p < 0.01) after DBS in PD patients, compared with pre‐DBS patients without medication. Post‐DBS PD patients showed a significant decrease in the graph‐theory‐based degree and cost in the middle temporal gyrus and temporo‐occipital part‐Right. Changes in FC were seen in four brain regions, and a decrease in white matter integrity was seen in the left anterior corona radiata. The topological metrics changes were correlated with Beck Depression Inventory II (BDI‐II) and the FC changes with UPDRS‐III scores. Conclusion STN‐DBS modulated graph‐theoretical metrics, FC, and white matter integrity. Brain connectivity changes observed with multi‐modal imaging were also associated with postoperative clinical improvement. These findings suggest that the effects of STN‐DBS are caused by brain network alterations.
Collapse
Affiliation(s)
- Li-Chuan Huang
- Department of Medical Imaging, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan.,Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology, Hualien, Taiwan
| | - Li-Guo Chen
- Department of Medical Imaging, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
| | - Ping-An Wu
- Department of Medical Imaging, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
| | - Cheng-Yoong Pang
- Department of Medical Research, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan.,Cardiovascular and Metabolomics Research Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
| | - Shinn-Zong Lin
- Department of Neurosurgery, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan.,School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Sheng-Tzung Tsai
- Department of Neurosurgery, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan.,School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Shin-Yuan Chen
- Department of Neurosurgery, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan.,School of Medicine, Tzu Chi University, Hualien, Taiwan
| |
Collapse
|
9
|
Collavini S, Fernández-Corazza M, Oddo S, Princich JP, Kochen S, Muravchik CH. Improvements on spatial coverage and focality of deep brain stimulation in pre-surgical epilepsy mapping. J Neural Eng 2021; 18. [PMID: 33578398 DOI: 10.1088/1741-2552/abe5b9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 02/12/2021] [Indexed: 12/20/2022]
Abstract
Objective.Electrical stimulation mapping (ESM) of the brain using stereo-electroencephalography (SEEG) intracranial electrodes, also known as depth-ESM (DESM), is being used as part of the pre-surgical planning for brain surgery in drug-resistant epilepsy patients. Typically, DESM consists in applying the electrical stimulation using adjacent contacts of the SEEG electrodes and in recording the EEG responses to those stimuli, giving valuable information of critical brain regions to better delimit the region to resect. However, the spatial extension or coverage of the stimulated area is not well defined even though the precise electrode locations can be determined from computed tomography images.Approach.We first conduct electrical simulations of DESM for different shapes of commercial SEEG electrodes showing the stimulation extensions for different intensities of injected current. We then evaluate the performance of DESM in terms of spatial coverage and focality on two realistic head models of real patients undergoing pre-surgical evaluation. We propose a novel strategy for DESM that consist in applying the current using contacts of different SEEG electrodes (x-DESM), increasing the versatility of DESM without implanting more electrodes. We also present a clinical case where x-DESM replicated the full semiology of an epilepsy seizure using a very low-intensity current injection, when typical adjacent DESM only reproduced partial symptoms with much larger intensities. Finally, we show one example of DESM optimal stimulation to achieve maximum intensity, maximum focality or intermediate solution at a pre-defined target, and one example of temporal interference in DESM capable of increasing focality in brain regions not immediately touching the electrode contacts.Main results.It is possible to define novel current injection patterns using contacts of different electrodes (x-DESM) that might improve coverage and/or focality, depending on the characteristics of the candidate brain. If individual simulations are not possible, we provide the estimated radius of stimulation as a function of the injected current and SEEG electrode brand as a reference for the community.Significance.Our results show that subject-specific electrical stimulations are a valuable tool to use in the pre-surgical planning to visualize the extension of the stimulated regions. The methods we present here are also applicable to pre-surgical planning of tumor resections and deep brain stimulation treatments.
Collapse
Affiliation(s)
- Santiago Collavini
- Research Institute of Electronics, Control and Signal Processing (LEICI), National University of La Plata-CONICET, Calle 116 s/n, La Plata B1900, Argentina.,Neurosciences and Complex Systems Unit (EnyS), CONICET, Hosp. El Cruce 'N. Kirchner', National University A. Jauretche (UNAJ), Calchaqui 5401, Florencio Varela 1888 Buenos Aires, Argentina.,National Council of Scientific and Technical Research (CONICET), calle 8, 1467, La Plata, Buenos Aires B1904, Argentina.,Institute of Engineering and Agronomy, National University Arturo Jauretche, Av. Calchaquí 6200, Florencio Varela, Buenos Aires 1888, Argentina
| | - Mariano Fernández-Corazza
- Research Institute of Electronics, Control and Signal Processing (LEICI), National University of La Plata-CONICET, Calle 116 s/n, La Plata B1900, Argentina.,National Council of Scientific and Technical Research (CONICET), calle 8, 1467, La Plata, Buenos Aires B1904, Argentina
| | - Silvia Oddo
- Neurosciences and Complex Systems Unit (EnyS), CONICET, Hosp. El Cruce 'N. Kirchner', National University A. Jauretche (UNAJ), Calchaqui 5401, Florencio Varela 1888 Buenos Aires, Argentina.,National Council of Scientific and Technical Research (CONICET), calle 8, 1467, La Plata, Buenos Aires B1904, Argentina
| | - Juan Pablo Princich
- Neurosciences and Complex Systems Unit (EnyS), CONICET, Hosp. El Cruce 'N. Kirchner', National University A. Jauretche (UNAJ), Calchaqui 5401, Florencio Varela 1888 Buenos Aires, Argentina.,National Council of Scientific and Technical Research (CONICET), calle 8, 1467, La Plata, Buenos Aires B1904, Argentina
| | - Silvia Kochen
- Neurosciences and Complex Systems Unit (EnyS), CONICET, Hosp. El Cruce 'N. Kirchner', National University A. Jauretche (UNAJ), Calchaqui 5401, Florencio Varela 1888 Buenos Aires, Argentina.,National Council of Scientific and Technical Research (CONICET), calle 8, 1467, La Plata, Buenos Aires B1904, Argentina
| | - Carlos H Muravchik
- Research Institute of Electronics, Control and Signal Processing (LEICI), National University of La Plata-CONICET, Calle 116 s/n, La Plata B1900, Argentina.,Scientific Research Commission of the Province of Buenos Aires (CIC-PBA), Argentina
| |
Collapse
|
10
|
Middlebrooks EH, Domingo RA, Vivas-Buitrago T, Okromelidze L, Tsuboi T, Wong JK, Eisinger RS, Almeida L, Burns MR, Horn A, Uitti RJ, Wharen RE, Holanda VM, Grewal SS. Neuroimaging Advances in Deep Brain Stimulation: Review of Indications, Anatomy, and Brain Connectomics. AJNR Am J Neuroradiol 2020; 41:1558-1568. [PMID: 32816768 DOI: 10.3174/ajnr.a6693] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 06/03/2020] [Indexed: 12/18/2022]
Abstract
Deep brain stimulation is an established therapy for multiple brain disorders, with rapidly expanding potential indications. Neuroimaging has advanced the field of deep brain stimulation through improvements in delineation of anatomy, and, more recently, application of brain connectomics. Older lesion-derived, localizationist theories of these conditions have evolved to newer, network-based "circuitopathies," aided by the ability to directly assess these brain circuits in vivo through the use of advanced neuroimaging techniques, such as diffusion tractography and fMRI. In this review, we use a combination of ultra-high-field MR imaging and diffusion tractography to highlight relevant anatomy for the currently approved indications for deep brain stimulation in the United States: essential tremor, Parkinson disease, drug-resistant epilepsy, dystonia, and obsessive-compulsive disorder. We also review the literature regarding the use of fMRI and diffusion tractography in understanding the role of deep brain stimulation in these disorders, as well as their potential use in both surgical targeting and device programming.
Collapse
Affiliation(s)
- E H Middlebrooks
- From the Departments of Radiology (E.H.M., L.O.) .,Neurosurgery (E.H.M., R.A.D., T.V.-B., R.E.W., S.S.G.)
| | - R A Domingo
- Neurosurgery (E.H.M., R.A.D., T.V.-B., R.E.W., S.S.G.)
| | | | | | - T Tsuboi
- and Neurology (R.J.U.), Mayo Clinic, Jacksonville, Florida.,Department of Neurology (T.T., J.K.W., R.S.E., L.A., M.R.B.), Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, Florida
| | - J K Wong
- and Neurology (R.J.U.), Mayo Clinic, Jacksonville, Florida
| | - R S Eisinger
- and Neurology (R.J.U.), Mayo Clinic, Jacksonville, Florida
| | - L Almeida
- and Neurology (R.J.U.), Mayo Clinic, Jacksonville, Florida
| | - M R Burns
- and Neurology (R.J.U.), Mayo Clinic, Jacksonville, Florida
| | - A Horn
- Department of Neurology (T.T.), Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - R J Uitti
- Department for Neurology (A.H.), Charité, University Medicine Berlin, Berlin, Germany
| | - R E Wharen
- Neurosurgery (E.H.M., R.A.D., T.V.-B., R.E.W., S.S.G.)
| | - V M Holanda
- Center of Neurology and Neurosurgery Associates (V.M.H.), BP-A Beneficência Portuguesa de São Paulo, São Paulo, Brazil
| | - S S Grewal
- Neurosurgery (E.H.M., R.A.D., T.V.-B., R.E.W., S.S.G.)
| |
Collapse
|
11
|
Xiao Y, Lau JC, Hemachandra D, Gilmore G, Khan AR, Peters TM. Image Guidance in Deep Brain Stimulation Surgery to Treat Parkinson's Disease: A Comprehensive Review. IEEE Trans Biomed Eng 2020; 68:1024-1033. [PMID: 32746050 DOI: 10.1109/tbme.2020.3006765] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Deep brain stimulation (DBS) is an effective therapy as an alternative to pharmaceutical treatments for Parkinson's disease (PD). Aside from factors such as instrumentation, treatment plans, and surgical protocols, the success of the procedure depends heavily on the accurate placement of the electrode within the optimal therapeutic targets while avoiding vital structures that can cause surgical complications and adverse neurologic effects. Although specific surgical techniques for DBS can vary, interventional guidance with medical imaging has greatly contributed to the development, outcomes, and safety of the procedure. With rapid development in novel imaging techniques, computational methods, and surgical navigation software, as well as growing insights into the disease and mechanism of action of DBS, modern image guidance is expected to further enhance the capacity and efficacy of the procedure in treating PD. This article surveys the state-of-the-art techniques in image-guided DBS surgery to treat PD, and discusses their benefits and drawbacks, as well as future directions on the topic.
Collapse
|
12
|
Lin H, Na P, Zhang D, Liu J, Cai X, Li W. Brain connectivity markers for the identification of effective contacts in subthalamic nucleus deep brain stimulation. Hum Brain Mapp 2020; 41:2028-2036. [PMID: 31951307 PMCID: PMC7268081 DOI: 10.1002/hbm.24927] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 12/23/2019] [Accepted: 01/06/2020] [Indexed: 12/30/2022] Open
Abstract
The clinical benefit of deep brain stimulation (DBS) for Parkinson's disease (PD) is relevant to the tracts adjacent to the stimulation site, but it remains unclear what connectivity pattern is associated with effective DBS. The aim of this study was to identify clinically effective electrode contacts on the basis of brain connectivity markers derived from diffusion tensor tractography. We reviewed 77 PD patients who underwent bilateral subthalamic nucleus DBS surgery. The patients were assigned into the training (n = 58) and validation (n = 19) groups. According to the therapeutic window size, all contacts were classified into effective and ineffective groups. The whole‐brain connectivity of each contact's volume of tissue activated was estimated using tractography with preoperative diffusion tensor data. Extracted connectivity features were put into an all‐relevant feature selection procedure within cross‐validation loops, to identify features with significant discriminative power for contact classification. A total of 616 contacts on 154 DBS leads were discriminated, with 388 and 228 contacts being classified as effective and ineffective ones, respectively. After the feature selection, the connectivity of contacts with the thalamus, pallidum, hippocampus, primary motor area, supplementary motor area and superior frontal gyrus was identified to significantly contribute to contact classification. Based on these relevant features, the random forest model constructed from the training group achieved an accuracy of 84.9% in the validation group, to discriminate effective contacts from the ineffective. Our findings advanced the understanding of the specific brain connectivity patterns associated with clinical effective electrode contacts, which potentially guided postoperative DBS programming.
Collapse
Affiliation(s)
- Hai Lin
- Department of Neurosurgery, Shenzhen Second People's Hospital, First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China.,Shenzhen Key Laboratory of Neurosurgery, Shenzhen Second People's Hospital, First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China.,Shenzhen University School of Medicine, Shenzhen, Guangdong, China
| | - Peng Na
- Department of Neurosurgery, Shenzhen Second People's Hospital, First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China.,Shenzhen Key Laboratory of Neurosurgery, Shenzhen Second People's Hospital, First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China.,Shenzhen University School of Medicine, Shenzhen, Guangdong, China
| | - Doudou Zhang
- Department of Neurosurgery, Shenzhen Second People's Hospital, First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China.,Shenzhen University School of Medicine, Shenzhen, Guangdong, China
| | - Jiali Liu
- Department of Neurosurgery, Shenzhen Second People's Hospital, First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China.,Shenzhen University School of Medicine, Shenzhen, Guangdong, China
| | - Xiaodong Cai
- Department of Neurosurgery, Shenzhen Second People's Hospital, First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China.,Shenzhen University School of Medicine, Shenzhen, Guangdong, China
| | - Weiping Li
- Department of Neurosurgery, Shenzhen Second People's Hospital, First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China.,Shenzhen Key Laboratory of Neurosurgery, Shenzhen Second People's Hospital, First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China.,Shenzhen University School of Medicine, Shenzhen, Guangdong, China
| |
Collapse
|