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Martin T, Jannin P, Baxter JSH. Generalisation capabilities of machine-learning algorithms for the detection of the subthalamic nucleus in micro-electrode recordings. Int J Comput Assist Radiol Surg 2024:10.1007/s11548-024-03202-2. [PMID: 38951363 DOI: 10.1007/s11548-024-03202-2] [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: 01/10/2024] [Accepted: 05/27/2024] [Indexed: 07/03/2024]
Abstract
PURPOSE Micro-electrode recordings (MERs) are a key intra-operative modality used during deep brain stimulation (DBS) electrode implantation, which allow for a trained neurophysiologist to infer the anatomy in which the electrode is placed. As DBS targets are small, such inference is necessary to confirm that the electrode is correctly positioned. Recently, machine learning techniques have been used to augment the neurophysiologist's capability. The goal of this paper is to investigate the generalisability of these methods with respect to different clinical centres and training paradigms. METHODS Five deep learning algorithms for binary classification of MER signals have been implemented. Three databases from two different clinical centres have also been collected with differing size, acquisition hardware, and annotation protocol. Each algorithm has initially been trained on the largest database, then either directly tested or fine-tuned on the smaller databases in order to estimate their generalisability. As a reference, they have also been trained from scratch on the smaller databases as well in order to estimate the effect of the differing database sizes and annotation systems. RESULTS Each network shows significantly reduced performance (on the order of a 6.5% to 16.0% reduction in balanced accuracy) when applied out-of-distribution. This reduction can be ameliorated through fine-tuning the network on the new database through transfer learning. Although, even for these small databases, it appears that retraining from scratch may still offer equivalent performance as fine-tuning with transfer learning. However, this is at the expense of significantly longer training times. CONCLUSION Generalisability is an important criterion for the success of machine learning algorithms in clinic. We have demonstrated that a variety of recent machine learning algorithms for MER classification are negatively affected by domain shift, but that this can be quickly ameliorated through simple transfer learning procedures that can be readily performed for new centres.
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Affiliation(s)
- Thibault Martin
- Laboratoire Traitement du Signal et de l'Image (LTSI, INSERM UMR 1099), Université de Rennes, Rennes, France
| | - Pierre Jannin
- Laboratoire Traitement du Signal et de l'Image (LTSI, INSERM UMR 1099), Université de Rennes, Rennes, France
| | - John S H Baxter
- Laboratoire Traitement du Signal et de l'Image (LTSI, INSERM UMR 1099), Université de Rennes, Rennes, France.
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Hosny M, Zhu M, Gao W, Elshenhab AM. STN localization using local field potentials based on wavelet packet features and stacking ensemble learning. J Neurosci Methods 2024; 407:110156. [PMID: 38703796 DOI: 10.1016/j.jneumeth.2024.110156] [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: 12/01/2023] [Revised: 02/20/2024] [Accepted: 04/27/2024] [Indexed: 05/06/2024]
Abstract
BACKGROUND DBS entails the insertion of an electrode into the patient brain, enabling Subthalamic nucleus (STN) stimulation. Accurate delineation of STN borders is a critical but time-consuming task, traditionally reliant on the neurosurgeon experience in deciphering the intricacies of microelectrode recording (MER). While clinical outcomes of MER have been satisfactory, they involve certain risks to patient safety. Recently, there has been a growing interest in exploring the potential of local field potentials (LFP) due to their correlation with the STN motor territory. METHOD A novel STN detection system, integrating LFP and wavelet packet transform (WPT) with stacking ensemble learning, is developed. Initial steps involve the inclusion of soft thresholding to increase robustness to LFP variability. Subsequently, non-linear WPT features are extracted. Finally, a unique ensemble model, comprising a dual-layer structure, is developed for STN localization. We harnessed the capabilities of support vector machine, Decision tree and k-Nearest Neighbor in conjunction with long short-term memory (LSTM) network. LSTM is pivotal for assigning adequate weights to every base model. RESULTS Results reveal that the proposed model achieved a remarkable accuracy and F1-score of 89.49% and 91.63%. COMPARISON WITH EXISTING METHODS Ensemble model demonstrated superior performance when compared to standalone base models and existing meta techniques. CONCLUSION This framework is envisioned to enhance the efficiency of DBS surgery and reduce the reliance on clinician experience for precise STN detection. This achievement is strategically significant to serve as an invaluable tool for refining the electrode trajectory, potentially replacing the current methodology based on MER.
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Affiliation(s)
- Mohamed Hosny
- Department of Electrical Engineering, Benha Faculty of Engineering, Benha University, Benha, Egypt.
| | - Minwei Zhu
- First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Wenpeng Gao
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China
| | - Ahmed M Elshenhab
- Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
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Jia Q, Jing L, Zhu Y, Han M, Jiao P, Wang Y, Xu Z, Duan Y, Wang M, Cai X. Real-Time Precise Targeting of the Subthalamic Nucleus via Transfer Learning in a Rat Model of Parkinson's Disease Based on Microelectrode Arrays. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1787-1795. [PMID: 38656860 DOI: 10.1109/tnsre.2024.3393116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
In neurodegenerative disorders, neuronal firing patterns and oscillatory activity are remarkably altered in specific brain regions, which can serve as valuable biomarkers for the identification of deep brain regions. The subthalamic nucleus (STN) has been the primary target for DBS in patients with Parkinson's disease (PD). In this study, changes in the spike firing patterns and spectral power of local field potentials (LFPs) in the pre-STN (zona incerta, ZI) and post-STN (cerebral peduncle, cp) regions were investigated in PD rats, providing crucial evidence for the functional localization of the STN. Sixteen-channel microelectrode arrays (MEAs) with sites distributed at different depths and widths were utilized to record neuronal activities. The spikes in the STN exhibited higher firing rates than those in the ZI and cp. Furthermore, the LFP power in the delta band in the STN was the greatest, followed by that in the ZI, and was greater than that in the cp. Additionally, increased LFP power was observed in the beta bands in the STN. To identify the best performing classification model, we applied various convolutional neural networks (CNNs) based on transfer learning to analyze the recorded raw data, which were processed using the Gram matrix of the spikes and the fast Fourier transform of the LFPs. The best transfer learning model achieved an accuracy of 95.16%. After fusing the spike and LFP classification results, the time precision for processing the raw data reached 500 ms. The pretrained model, utilizing raw data, demonstrated the feasibility of employing transfer learning for training models on neural activity. This approach highlights the potential for functional localization within deep brain regions.
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Chao-Chia Lu D, Boulay C, Chan ADC, Sachs AJ. A Systematic Review of Neurophysiology-Based Localization Techniques Used in Deep Brain Stimulation Surgery of the Subthalamic Nucleus. Neuromodulation 2024; 27:409-421. [PMID: 37462595 DOI: 10.1016/j.neurom.2023.02.081] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 01/13/2023] [Accepted: 02/09/2023] [Indexed: 04/05/2024]
Abstract
OBJECTIVE This systematic review is conducted to identify, compare, and analyze neurophysiological feature selection, extraction, and classification to provide a comprehensive reference on neurophysiology-based subthalamic nucleus (STN) localization. MATERIALS AND METHODS The review was carried out using the methods and guidelines of the Kitchenham systematic review and provides an in-depth analysis on methods proposed on STN localization discussed in the literature between 2000 and 2021. Three research questions were formulated, and 115 publications were identified to answer the questions. RESULTS The three research questions formulated are answered using the literature found on the respective topics. This review discussed the technologies used in past research, and the performance of the state-of-the-art techniques is also reviewed. CONCLUSION This systematic review provides a comprehensive reference on neurophysiology-based STN localization by reviewing the research questions other new researchers may also have.
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Affiliation(s)
| | | | | | - Adam J Sachs
- The Ottawa Hospital Research Institute, Ottawa, ON, Canada
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Varga I, Bakstein E, Gilmore G, May J, Novak D. Statistical segmentation model for accurate electrode positioning in Parkinson's deep brain stimulation based on clinical low-resolution image data and electrophysiology. PLoS One 2024; 19:e0298320. [PMID: 38483943 PMCID: PMC10939223 DOI: 10.1371/journal.pone.0298320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 01/22/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND Deep Brain Stimulation (DBS), applying chronic electrical stimulation of subcortical structures, is a clinical intervention applied in major neurologic disorders. In order to achieve a good clinical effect, accurate electrode placement is necessary. The primary localisation is typically based on presurgical MRI imaging, often followed by intra-operative electrophysiology recording to increase the accuracy and to compensate for brain shift, especially in cases where the surgical target is small, and there is low contrast: e.g., in Parkinson's disease (PD) and in its common target, the subthalamic nucleus (STN). METHODS We propose a novel, fully automatic method for intra-operative surgical navigation. First, the surgical target is segmented in presurgical MRI images using a statistical shape-intensity model. Next, automated alignment with intra-operatively recorded microelectrode recordings is performed using a probabilistic model of STN electrophysiology. We apply the method to a dataset of 120 PD patients with clinical T2 1.5T images, of which 48 also had available microelectrode recordings (MER). RESULTS The proposed segmentation method achieved STN segmentation accuracy around dice = 0.60 compared to manual segmentation. This is comparable to the state-of-the-art on low-resolution clinical MRI data. When combined with electrophysiology-based alignment, we achieved an accuracy of 0.85 for correctly including recording sites of STN-labelled MERs in the final STN volume. CONCLUSION The proposed method combines image-based segmentation of the subthalamic nucleus with microelectrode recordings to estimate their mutual location during the surgery in a fully automated process. Apart from its potential use in clinical targeting, the method can be used to map electrophysiological properties to specific parts of the basal ganglia structures and their vicinity.
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Affiliation(s)
- Igor Varga
- Department of Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
- Czech Centre for Phenogenomics, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic
| | - Eduard Bakstein
- Department of Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
- National Institute of Mental Health, Klecany, Czech Republic
| | - Greydon Gilmore
- Movement Disorder Centre, University Hospital, University of Western Ontario, Ontario, Canada
| | - Jaromir May
- Department of Neurosurgery, Na Homolce Hospital, Prague, Czech Republic
| | - Daniel Novak
- Department of Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
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Junaid M, Ali S, Eid F, El-Sappagh S, Abuhmed T. Explainable machine learning models based on multimodal time-series data for the early detection of Parkinson's disease. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 234:107495. [PMID: 37003039 DOI: 10.1016/j.cmpb.2023.107495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 02/23/2023] [Accepted: 03/17/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND AND OBJECTIVES Parkinson's Disease (PD) is a devastating chronic neurological condition. Machine learning (ML) techniques have been used in the early prediction of PD progression. Fusion of heterogeneous data modalities proved its capability to improve the performance of ML models. Time series data fusion supports the tracking of the disease over time. In addition, the trustworthiness of the resulting models is improved by adding model explainability features. The literature on PD has not sufficiently explored these three points. METHODS In this work, we proposed an ML pipeline for predicting the progression of PD that is both accurate and explainable. We explore the fusion of different combinations of five time series modalities from the Parkinson's Progression Markers Initiative (PPMI) real-world dataset, including patient characteristics, biosamples, medication history, motor, and non-motor function data. Each patient has six visits. The problem has been formulated in two ways: ❶ a three-class based progression prediction with 953 patients in each time series modality, and ❷ a four-class based progression prediction with 1,060 patients in each time series modality. The statistical features of these six visits were calculated from each modality and diverse feature selection methods were applied to select the most informative feature sets. The extracted features were used to train a set of well-known ML models including Support vector machines (SVM), random forests (RF), extra tree classifier (ETC), light gradient boosting machines (LGBM), and stochastic gradient descent (SGD). We examined a number of data-balancing strategies in the pipeline with different combinations of modalities. ML models have been optimized using the Bayesian optimizer. A comprehensive evaluation of various ML methods has been conducted, and the best models have been extended to provide different explainability features. RESULTS We compare the performance of ML models before and after optimization and using and without using feature selection. In the three-class experiment and with various modality fusions, the LGBM model produced the most accurate results with a 10-fold cross-validation (10-CV) accuracy of 90.73% using non-motor function modality. RF produced the best results in the four-class experiment with various modality fusions with a 10-CV accuracy of 94.57% using non-motor modality. With the fused dataset of non-motor and motor function modalities, the LGBM model outperformed the other ML models in both the 3-class and 4-class experiments (i.e., 10-CV accuracy of 94.89% and 93.73%, respectively). Using the Shapely Additive Explanations (SHAP) framework, we employed global and instance-based explanations to explain the behavior of each ML classifier. Moreover, we extended the explainability by implementing the LIME and SHAPASH local explainers. The consistency of these explainers has been explored. The resultant classifiers were accurate, explainable, and thus medically more relevant and applicable. CONCLUSIONS The select modalities and feature sets were confirmed by the literature and medical experts. The various explainers suggest that the bradykinesia (NP3BRADY) feature was the most dominant and consistent. By providing thorough insights into the influence of multiple modalities on the disease risk, the suggested approach is expected to help improve the clinical knowledge of PD progression processes.
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Affiliation(s)
- Muhammad Junaid
- Information Laboratory (InfoLab), Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, South Korea.
| | - Sajid Ali
- Information Laboratory (InfoLab), Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, South Korea.
| | - Fatma Eid
- Technology Management, Stony Brook University, New York 11794, USA.
| | - Shaker El-Sappagh
- Information Laboratory (InfoLab), College of Computing and Informatics, Sungkyunkwan University, Suwon 16419, South Korea; Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt; Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha, 13518, Egypt.
| | - Tamer Abuhmed
- Information Laboratory (InfoLab), College of Computing and Informatics, Sungkyunkwan University, Suwon 16419, South Korea.
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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.
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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
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Hosny M, Zhu M, Gao W, Fu Y. A novel deep learning model for STN localization from LFPs in Parkinson’s disease. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Merk T, Peterson V, Köhler R, Haufe S, Richardson RM, Neumann WJ. Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation. Exp Neurol 2022; 351:113993. [PMID: 35104499 PMCID: PMC10521329 DOI: 10.1016/j.expneurol.2022.113993] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 11/18/2021] [Accepted: 01/22/2022] [Indexed: 12/30/2022]
Abstract
Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.
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Affiliation(s)
- Timon Merk
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Chariteplatz 1, 10117 Berlin, Germany
| | - Victoria Peterson
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, United States
| | - Richard Köhler
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Chariteplatz 1, 10117 Berlin, Germany
| | - Stefan Haufe
- Berlin Center for Advanced Neuroimaging (BCAN), Charité - Universitätsmedizin Berlin, Chariteplatz 1, 10117 Berlin, Germany
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, United States
| | - Wolf-Julian Neumann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Chariteplatz 1, 10117 Berlin, Germany.
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Malvea A, Babaei F, Boulay C, Sachs A, Park J. Deep brain stimulation for Parkinson’s Disease: A Review and Future Outlook. Biomed Eng Lett 2022; 12:303-316. [PMID: 35892031 PMCID: PMC9308849 DOI: 10.1007/s13534-022-00226-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 12/29/2021] [Accepted: 04/03/2022] [Indexed: 11/30/2022] Open
Abstract
Parkinson's Disease (PD) is a neurodegenerative disorder that manifests as an impairment of motor and non-motor abilities due to a loss of dopamine input to deep brain structures. While there is presently no cure for PD, a variety of pharmacological and surgical therapeutic interventions have been developed to manage PD symptoms. This review explores the past, present and future outlooks of PD treatment, with particular attention paid to deep brain stimulation (DBS), the surgical procedure to deliver DBS, and its limitations. Finally, our group's efforts with respect to brain mapping for DBS targeting will be discussed.
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Affiliation(s)
- Anahita Malvea
- Faculty of Medicine, University of Ottawa, K1H 8M5 Ottawa, ON Canada
| | - Farbod Babaei
- School of Electrical Engineering and Computer Science, University of Ottawa, K1N 6N5 Ottawa, ON Canada
| | - Chadwick Boulay
- The Ottawa Hospital Research Institute, Ottawa, Ontario Canada
- The University of Ottawa Brain and Mind Research Institute, Ottawa, Ontario Canada
| | - Adam Sachs
- The Ottawa Hospital Research Institute, Ottawa, Ontario Canada
- The University of Ottawa Brain and Mind Research Institute, Ottawa, Ontario Canada
- Division of Neurosurgery, Department of Surgery, The Ottawa Hospital, Ottawa, Ontario Canada
| | - Jeongwon Park
- School of Electrical Engineering and Computer Science, University of Ottawa, K1N 6N5 Ottawa, ON Canada
- Department of Electrical and Biomedical Engineering, University of Nevada, 89557 Reno, NV USA
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11
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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.
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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
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Feghali J, Sattari SA, Wicks EE, Gami A, Rapaport S, Azad TD, Yang W, Xu R, Tamargo RJ, Huang J. External Validation of a Neural Network Model in Aneurysmal Subarachnoid Hemorrhage: A Comparison With Conventional Logistic Regression Models. Neurosurgery 2022; 90:552-561. [PMID: 35113076 DOI: 10.1227/neu.0000000000001857] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 11/10/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Interest in machine learning (ML)-based predictive modeling has led to the development of models predicting outcomes after aneurysmal subarachnoid hemorrhage (aSAH), including the Nijmegen acute subarachnoid hemorrhage calculator (Nutshell). Generalizability of such models to external data remains unclear. OBJECTIVE To externally validate the performance of the Nutshell tool while comparing it with the conventional Subarachnoid Hemorrhage International Trialists (SAHIT) models and to review the ML literature on outcome prediction after aSAH and aneurysm treatment. METHODS A prospectively maintained database of patients with aSAH presenting consecutively to our institution in the 2013 to 2018 period was used. The web-based Nutshell and SAHIT calculators were used to derive the risks of poor long-term (12-18 months) outcomes and 30-day mortality. Discrimination was evaluated using the area under the curve (AUC), and calibration was investigated using calibration plots. The literature on relevant ML models was surveyed for a synopsis. RESULTS In 269 patients with aSAH, the SAHIT models outperformed the Nutshell tool (AUC: 0.786 vs 0.689, P = .025) in predicting long-term functional outcomes. A logistic regression model of the Nutshell variables derived from our data achieved adequate discrimination (AUC = 0.759) of poor outcomes. The SAHIT models outperformed the Nutshell tool in predicting 30-day mortality (AUC: 0.810 vs 0.636, P < .001). Calibration properties were more favorable for the SAHIT models. Most published aneurysm-related ML-based outcome models lack external validation and usable testing platforms. CONCLUSION The Nutshell tool demonstrated limited performance on external validation in comparison with the SAHIT models. External validation and the dissemination of testing platforms for ML models must be emphasized.
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Affiliation(s)
- James Feghali
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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Krokidis MG, Dimitrakopoulos GN, Vrahatis AG, Tzouvelekis C, Drakoulis D, Papavassileiou F, Exarchos TP, Vlamos P. A Sensor-Based Perspective in Early-Stage Parkinson's Disease: Current State and the Need for Machine Learning Processes. SENSORS (BASEL, SWITZERLAND) 2022; 22:409. [PMID: 35062370 PMCID: PMC8777583 DOI: 10.3390/s22020409] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/02/2021] [Accepted: 01/04/2022] [Indexed: 02/04/2023]
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder associated with dysfunction of dopaminergic neurons in the brain, lack of dopamine and the formation of abnormal Lewy body protein particles. PD is an idiopathic disease of the nervous system, characterized by motor and nonmotor manifestations without a discrete onset of symptoms until a substantial loss of neurons has already occurred, enabling early diagnosis very challenging. Sensor-based platforms have gained much attention in clinical practice screening various biological signals simultaneously and allowing researchers to quickly receive a huge number of biomarkers for diagnostic and prognostic purposes. The integration of machine learning into medical systems provides the potential for optimization of data collection, disease prediction through classification of symptoms and can strongly support data-driven clinical decisions. This work attempts to examine some of the facts and current situation of sensor-based approaches in PD diagnosis and discusses ensemble techniques using sensor-based data for developing machine learning models for personalized risk prediction. Additionally, a biosensing platform combined with clinical data processing and appropriate software is proposed in order to implement a complete diagnostic system for PD monitoring.
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Affiliation(s)
- Marios G. Krokidis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
| | - Georgios N. Dimitrakopoulos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
| | - Aristidis G. Vrahatis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
| | - Christos Tzouvelekis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
| | | | | | - Themis P. Exarchos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
| | - Panayiotis Vlamos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
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14
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A novel deep recurrent convolutional neural network for subthalamic nucleus localization using local field potential signals. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.09.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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15
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Adapting the listening time for micro-electrode recordings in deep brain stimulation interventions. Int J Comput Assist Radiol Surg 2021; 16:1371-1379. [PMID: 34117594 DOI: 10.1007/s11548-021-02379-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 04/12/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE Deep brain stimulation (DBS) is a common treatment for a variety of neurological disorders which involves the precise placement of electrodes at particular subcortical locations such as the subthalamic nucleus. This placement is often guided by auditory analysis of micro-electrode recordings (MERs) which informs the clinical team as to the anatomic region in which the electrode is currently positioned. Recent automation attempts have lacked flexibility in terms of the amount of signal recorded, not allowing them to collect more signal when higher certainty is needed or less when the anatomy is unambiguous. METHODS We have addressed this problem by evaluating a simple algorithm that allows for MER signal collection to terminate once the underlying model has sufficient confidence. We have parameterized this approach and explored its performance using three underlying models composed of one neural network and two Bayesian extensions of said network. RESULTS We have shown that one particular configuration, a Bayesian model of the underlying network's certainty, outperforms the others and is relatively insensitive to parameterization. Further investigation shows that this model also allows for signals to be classified earlier without increasing the error rate. CONCLUSION We have presented a simple algorithm that records the confidence of an underlying neural network, thus allowing for MER data collection to be terminated early when sufficient confidence is reached. This has the potential to improve the efficiency of DBS electrode implantation by reducing the time required to identify anatomical structures using MERs.
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16
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Martin T, Peralta M, Gilmore G, Sauleau P, Haegelen C, Jannin P, Baxter JS. Extending convolutional neural networks for localizing the subthalamic nucleus from micro-electrode recordings in Parkinson’s disease. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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17
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Xiao L, Li C, Wang Y, Si W, Zhang D, Lin H, Cai X, Heng PA. Automatic identification of sweet spots from MERs for electrodes implantation in STN-DBS. Int J Comput Assist Radiol Surg 2021; 16:809-818. [PMID: 33907990 DOI: 10.1007/s11548-021-02377-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 04/12/2021] [Indexed: 11/28/2022]
Abstract
PURPOSE Microelectrode recordings (MERs) are a significant clinical indicator for sweet spots identification of implanted electrodes during deep brain stimulation of the subthalamic nucleus (STN) surgery. As 1D MERs signals have the unboundedness, large-range, large-amount and time-dependent characteristics, the purpose of this study is to propose an automatic and precise identification method of sweet spots from MERs, reducing the time-consuming and labor-intensive human annotations. METHODS We propose an automatic identification method of sweet spots from MERs for electrodes implantation in STN-DBS. To better imitate the surgeons' observation and obtain more intuitive contextual information, we first employ the 2D Gramian angular summation field (GASF) images generated from MERs data to perform the sweet spots determination for electrodes implantation. Then, we introduce the convolutional block attention module into convolutional neural network (CNN) to identify the 2D GASF images of sweet spots for electrodes implantation. RESULTS Experimental results illustrate that the identification result of our method is consistent with the result of doctor's decision, while our method can achieve the accuracy and precision of 96.72% and 98.97%, respectively, which outperforms state-of-the-art for intraoperative sweet spots determination. CONCLUSIONS The proposed method is the first time to automatically and accurately identify sweet spots from MERs for electrodes implantation by the combination an advanced time series-to-image encoding way with CBAM-enhanced networks model. Our method can assist neurosurgeons in automatically detecting the most likely locations of sweet spots for electrodes implantation, which can provide an important indicator for target selection while it reduces the localization error of the target during STN-DBS surgery.
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Affiliation(s)
- Linxia Xiao
- College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, 266580, China
| | - Caizi Li
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Yanjiang Wang
- College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, 266580, China
| | - Weixin Si
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Doudou Zhang
- Department of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, 518037, China.,Shenzhen University School of Medicine, Shenzhen, 518061, China
| | - Hai Lin
- Department of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, 518037, China.,Shenzhen University School of Medicine, Shenzhen, 518061, China
| | - Xiaodong Cai
- Department of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, 518037, China.,Shenzhen University School of Medicine, Shenzhen, 518061, China
| | - Pheng-Ann Heng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, 999077, China.,Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
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18
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Detection of subthalamic nucleus using novel higher-order spectra features in microelectrode recordings signals. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.04.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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19
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Hosny M, Zhu M, Gao W, Fu Y. Deep convolutional neural network for the automated detection of Subthalamic nucleus using MER signals. J Neurosci Methods 2021; 356:109145. [PMID: 33774054 DOI: 10.1016/j.jneumeth.2021.109145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 03/13/2021] [Accepted: 03/16/2021] [Indexed: 01/02/2023]
Abstract
BACKGROUND Deep brain stimulation (DBS) surgery has been extensively conducted for treating advanced Parkinson's disease (PD) patient's symptoms. DBS hinges on the localization of the subthalamic nucleus (STN) in which a permanent electrode should be accurately placed to produce electrical current. Microelectrode recording (MER) signals are routinely recorded in the procedure of DBS surgery to validate the planned trajectories. However, manual MER signals interpretation with the goal of detecting STN borders requires expertise and prone to inter-observer variability. Therefore, a computerized aided system would be beneficial to automatic detection of the dorsal and ventral borders of the STN in MER. NEW METHOD In this study, a new deep learning model based on convolutional neural system for automatic delineation of the neurophysiological borders of the STN along the electrode trajectory was developed. COMPARISON WITH EXISTING METHODS The proposed model does not involve any conventional standardization, feature extraction or selection steps. RESULTS Promising results of 98.67% accuracy, 99.03% sensitivity, 98.11% specificity, 98.79% precision and 98.91% F1-score for subject based testing were achieved using the proposed convolutional neural network (CNN) model. CONCLUSIONS This is the first study on the analysis of MER signals to detect STN using deep CNN. Traditional machine learning (ML) algorithms are often cumbersome and suffer from subjective evaluation. Though, the developed 10-layered CNN model has the capability of extracting substantial features at the convolution stage. Hence, the proposed model has the potential to deliver high performance on STN region detection which shows perspective in aiding the neurosurgeon intraoperatively.
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Affiliation(s)
- Mohamed Hosny
- Department of Electrical Engineering, Benha Faculty of Engineering, Benha University, Benha, Egypt
| | - Minwei Zhu
- First Affiliated Hospital of Harbin Medical University, 23 Youzheng Str., Nangang District, Harbin 150001, China
| | - Wenpeng Gao
- School of Life Science and Technology, Harbin Institute of Technology, 2 Yikuang Str., Nangang District, Harbin 150080, China.
| | - Yili Fu
- School of Life Science and Technology, Harbin Institute of Technology, 2 Yikuang Str., Nangang District, Harbin 150080, China
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20
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Feature Selection Based Machine Learning to Improve Prediction of Parkinson Disease. Brain Inform 2021. [DOI: 10.1007/978-3-030-86993-9_44] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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21
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Coelli S, Levi V, Del Vecchio Del Vecchio J, Mailland E, Rinaldo S, Eleopra R, Bianchi AM. An intra-operative feature-based classification of microelectrode recordings to support the subthalamic nucleus functional identification during deep brain stimulation surgery. J Neural Eng 2020; 18. [PMID: 33202390 DOI: 10.1088/1741-2552/abcb15] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 11/17/2020] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The Subthalamic Nucleus (STN) is the most selected target for the placement of the Deep Brain Stimulation (DBS) electrode to treat Parkinson's disease. Its identification is a delicate and challenging task which is based on the interpretation of the STN functional activity acquired through microelectrode recordings (MER). Aim of this work is to explore the potentiality of a set of twenty-five features to build a classification model for the discrimination of MER signals belonging to the STN. APPROACH We explored the use of different sets of spike-dependent and spike-independent features in combination with an Ensemble Trees classification (ET) algorithm on a dataset composed of thirteen patients receiving bilateral DBS. We compared results from six subsets of features and two dataset conditions (with and without standardization) using performance metrics on a leave-one-patient-out validation schema. MAIN RESULTS We obtained statistically better results (i.e., higher accuracy p-value = 0.003) on the raw dataset than on the standardized one, where the selection of seven features using a minimum redundancy maximum relevance (MRMR) algorithm provided a mean accuracy of 94.1%, comparable with the use of the full set of features. In the same conditions, the spike-dependent features provided the lowest accuracy (86.8%), while a power density-based index was shown to be a good indicator of STN activity (92.3%). SIGNIFICANCE Results suggest that a small and simple set of features can be used for an efficient classification of microelectrode recordings to implement an intraoperative support for clinical decision during deep brain stimulation surgery.
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Affiliation(s)
- Stefania Coelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Lombardia, ITALY
| | - Vincenzo Levi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Lombardia, ITALY
| | | | - Enrico Mailland
- Neurology Unit, Dipartimento di Area Medica Internistica, ASST Santi Paolo e Carlo, Milano, Lombardia, ITALY
| | - Sara Rinaldo
- Movement Disorder Unit, Department of Clinical Neurosciences, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Lombardia, ITALY
| | - Roberto Eleopra
- Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Lombardia, ITALY
| | - Anna Maria Bianchi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Lombardia, ITALY
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22
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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.
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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
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23
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Peralta M, Bui QA, Ackaouy A, Martin T, Gilmore G, Haegelen C, Sauleau P, Baxter JSH, Jannin P. SepaConvNet for Localizing the Subthalamic Nucleus Using One Second Micro-electrode Recordings. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:888-893. [PMID: 33018127 DOI: 10.1109/embc44109.2020.9175294] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Micro-electrode recording (MER) is a powerful way of localizing target structures during neurosurgical procedures such as the implantation of deep brain stimulation electrodes, which is a common treatment for Parkinson's disease and other neurological disorders. While Micro-electrode Recording (MER) provides adjunctive information to guidance assisted by pre-operative imaging, it is not unanimously used in the operating room. The lack of standard use of MER may be in part due to its long duration, which can lead to complications during the operation, or due to high degree of expertise required for their interpretation. Over the past decade, various approaches addressing automating MER analysis for target localization have been proposed, which have mainly focused on feature engineering. While the accuracies obtained are acceptable in certain configurations, one issue with handcrafted MER features is that they do not necessarily capture more subtle differences in MER that could be detected auditorily by an expert neurophysiologist. In this paper, we propose and validate a deep learning-based pipeline for subthalamic nucleus (STN) localization with micro-electrode recordings motivated by the human auditory system. Our proposed Convolutional Neural Network (CNN), referred as SepaConvNet, shows improved accuracy over two comparative networks for locating the STN from one second MER samples.
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Coelli S, Levi V, Del Vecchio Del Vecchio J, Mailland E, Rinaldo S, Eleopra R, Bianchi AM. Characterization of Microelectrode Recordings for the Subthalamic Nucleus identification in Parkinson's disease. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:3485-3488. [PMID: 33018754 DOI: 10.1109/embc44109.2020.9175299] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for Parkinson's disease, when the pharmacological approach has no more effect. DBS efficacy strongly depends on the accurate localization of the STN and the adequate positioning of the stimulation electrode during DBS stereotactic surgery. During this procedure, the analysis of microelectrode recordings (MER) is fundamental to assess the correct localization. Therefore, in this work, we explore different signal feature types for the characterization of the MER signals associated to STN from NON-STN structures. We extracted a set of spike-dependent (action potential domain) and spike-independent features in the time and frequency domain to evaluate their usefulness in distinguishing the STN from other structures. We discuss the results from a physiological and methodological point of view, showing the superiority of features having a direct electrophysiological interpretation.Clinical Relevance- The identification of a simple, clinically interpretable, and powerful set of features for the STN localization would support the clinical positioning of the DBS electrode, improving the treatment outcome.
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25
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Gonzalez-Escamilla G, Muthuraman M, Ciolac D, Coenen VA, Schnitzler A, Groppa S. Neuroimaging and electrophysiology meet invasive neurostimulation for causal interrogations and modulations of brain states. Neuroimage 2020; 220:117144. [DOI: 10.1016/j.neuroimage.2020.117144] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 06/22/2020] [Accepted: 07/02/2020] [Indexed: 12/13/2022] Open
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26
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Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10155135] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review.
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27
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Wang C, Chen X, Du L, Zhan Q, Yang T, Fang Z. Comparison of machine learning algorithms for the identification of acute exacerbations in chronic obstructive pulmonary disease. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 188:105267. [PMID: 31841787 DOI: 10.1016/j.cmpb.2019.105267] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 11/19/2019] [Accepted: 12/08/2019] [Indexed: 05/05/2023]
Abstract
OBJECTIVES Identifying acute exacerbations in chronic obstructive pulmonary disease (AECOPDs) is of utmost importance for reducing the associated mortality and financial burden. In this research, the authors aimed to develop identification models for AECOPDs and to compare the relative performance of different modeling paradigms to find the best model for this task. METHODS Data were extracted from electronic medical records (EMRs) of patients with chronic obstructive pulmonary disease who admitted to the China-Japan Friendship Hospital between February 2011 and March 2017. Five machine learning algorithms (random forest, support vector machine, logistic regression, K-nearest neighbor and naïve Bayes) were used to develop the AECOPDs identification models. Feature selection was performed to find an optimal feature subset. 10-folds cross-validation was used to find the best hyperparameters for each model. The following metrics: area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value were used to evaluate the performance of these models. RESULTS A total of 303 EMRs (AECOPDs patients:135; None AECOPDs patients: 168) were included in the study. The SVM model obtained the best performance (sensitivity: 0.80, specificity: 0.83, positive predictive value:0.81, negative predictive value:0.85 and area under the receiver operating characteristic curve: 0.90) after performing feature selection. CONCLUSIONS Our research confirms that the proposed model based on the support vector machine is a powerful tool to identify AECOPDs patients, and it is promising to provide decision support for clinicians when they are struggling to give a confirmed clinical diagnosis.
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Affiliation(s)
- Chenshuo Wang
- Institute of Electronics, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Xianxiang Chen
- Institute of Electronics, Chinese Academy of Sciences, Beijing, China; Personalized Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, China
| | - Lidong Du
- Institute of Electronics, Chinese Academy of Sciences, Beijing, China; Personalized Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, China
| | | | - Ting Yang
- China-Japan Friendship Hospital, Beijing, China.
| | - Zhen Fang
- Institute of Electronics, Chinese Academy of Sciences, Beijing, China; Personalized Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, China; University of Chinese Academy of Sciences, Beijing, China.
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28
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Warren AEL, Dalic LJ, Thevathasan W, Roten A, Bulluss KJ, Archer J. Targeting the centromedian thalamic nucleus for deep brain stimulation. J Neurol Neurosurg Psychiatry 2020; 91:339-349. [PMID: 31980515 DOI: 10.1136/jnnp-2019-322030] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 12/13/2019] [Accepted: 12/23/2019] [Indexed: 12/25/2022]
Abstract
OBJECTIVES Deep brain stimulation (DBS) of the centromedian thalamic nucleus (CM) is an emerging treatment for multiple brain diseases, including the drug-resistant epilepsy Lennox-Gastaut syndrome (LGS). We aimed to improve neurosurgical targeting of the CM by: (1) developing a structural MRI approach for CM visualisation, (2) identifying the CM's neurophysiological characteristics using microelectrode recordings (MERs) and (3) mapping connectivity from CM-DBS sites using functional MRI (fMRI). METHODS 19 patients with LGS (mean age=28 years) underwent presurgical 3T MRI using magnetisation-prepared 2 rapid acquisition gradient-echoes (MP2RAGE) and fMRI sequences; 16 patients proceeded to bilateral CM-DBS implantation and intraoperative thalamic MERs. CM visualisation was achieved by highlighting intrathalamic borders on MP2RAGE using Sobel edge detection. Mixed-effects analysis compared two MER features (spike firing rate and background noise) between ventrolateral, CM and parafasicular nuclei. Resting-state fMRI connectivity was assessed using implanted CM-DBS electrode positions as regions of interest. RESULTS The CM appeared as a hyperintense region bordering the comparatively hypointense pulvinar, mediodorsal and parafasicular nuclei. At the group level, reduced spike firing and background noise distinguished CM from the ventrolateral nucleus; however, these trends were not found in 20%-25% of individual MER trajectories. Areas of fMRI connectivity included basal ganglia, brainstem, cerebellum, sensorimotor/premotor and limbic cortex. CONCLUSIONS In the largest clinical trial of DBS undertaken in patients with LGS to date, we show that accurate targeting of the CM is achievable using 3T MP2RAGE MRI. Intraoperative MERs may provide additional localising features in some cases; however, their utility is limited by interpatient variability. Therapeutic effects of CM-DBS may be mediated via connectivity with brain networks that support diverse arousal, cognitive and sensorimotor processes.
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Affiliation(s)
- Aaron E L Warren
- Department of Medicine (Austin Health), The University of Melbourne, Melbourne, Victoria, Australia .,The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia.,Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | - Linda J Dalic
- Department of Medicine (Austin Health), The University of Melbourne, Melbourne, Victoria, Australia.,The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia.,Department of Neurology, Austin Health, Heidelberg, Victoria, Australia
| | - Wesley Thevathasan
- Department of Neurology, Austin Health, Heidelberg, Victoria, Australia.,Bionics Institute, East Melbourne, Victoria, Australia.,Department of Medicine (Royal Melbourne Hospital), The University of Melbourne, Parkville, Victoria, Australia.,Department of Neurology, Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Annie Roten
- Department of Medicine (Austin Health), The University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurology, Austin Health, Heidelberg, Victoria, Australia
| | - Kristian J Bulluss
- Bionics Institute, East Melbourne, Victoria, Australia.,Department of Neurosurgery, Austin Health, Heidelberg, Victoria, Australia.,Department of Surgery, The University of Melbourne, Parkville, Victoria, Australia
| | - John Archer
- Department of Medicine (Austin Health), The University of Melbourne, Melbourne, Victoria, Australia.,The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia.,Murdoch Children's Research Institute, Parkville, Victoria, Australia.,Department of Neurology, Austin Health, Heidelberg, Victoria, Australia
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Khosravi M, Atashzar SF, Gilmore G, Jog MS, Patel RV. Intraoperative Localization of STN During DBS Surgery Using a Data-Driven Model. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2020; 8:2500309. [PMID: 32309064 PMCID: PMC7147929 DOI: 10.1109/jtehm.2020.2969152] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2019] [Revised: 10/29/2019] [Accepted: 01/07/2020] [Indexed: 12/13/2022]
Abstract
A new approach is presented for localizing the Subthalamic Nucleus (STN) during Deep Brain Stimulation (DBS) surgery based on microelectrode recordings (MERs). DBS is an accepted treatment for individuals living with Parkinson’s Disease (PD). This surgery involves implantation of a permanent electrode inside the STN to deliver electrical current. Since the STN is a very small region inside the brain, accurate placement of an electrode is a challenging task for the surgical team. Prior to placement of the permanent electrode, microelectrode recordings of brain activity are used intraoperatively to localize the STN. The placement of the electrode and the success of the therapy depend on this location. In this paper, an objective approach is implemented to help the surgical team in localizing the STN. This is achieved by processing the MER signals and extracting features during the surgery to be used in a Machine Learning (ML) algorithm for defining the neurophysiological borders of the STN. For this purpose, a new classification approach is proposed with the goal of detecting both the dorsal and the ventral borders of the STN during the surgical procedure. Results collected from 100 PD patients in this study, show that by calculating and extracting wavelet transformation features from MER signals and using a data-driven computational deep neural network model, it is possible to detect the borders of the STN with an accuracy of 92%. The proposed method can be implemented in real-time during the surgery to model the neurophysiological nonlinearity along the path of the electrode trajectory during insertion.
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Affiliation(s)
- Mahsa Khosravi
- 1Department of Electrical and Computer EngineeringUniversity of Western OntarioLondonONN6A 3K7Canada.,2Canadian Surgical Technologies and Advanced Robotics (CSTAR)Lawson Health Research InstituteLondonONN6A 4V2Canada
| | - S Farokh Atashzar
- 3Department of Electrical and Computer EngineeringTandon School of EngineeringNew York UniversityNew YorkNY10003USA.,4Department of Mechanical and Aerospace EngineeringTandon School of EngineeringNew York UniversityNew YorkNY10003USA.,5NYU WIRELESS, Tandon School of EngineeringNew York UniversityNew YorkNY10003USA
| | - Greydon Gilmore
- 6School of Biomedical EngineeringUniversity of Western OntarioLondonONN6A 3K7Canada
| | - Mandar S Jog
- 1Department of Electrical and Computer EngineeringUniversity of Western OntarioLondonONN6A 3K7Canada.,7Department of Clinical NeurosciencesUniversity of Western OntarioLondonONN6A 3K7Canada.,8London Health Sciences CentreLondonONN6A 5W9Canada
| | - Rajni V Patel
- 1Department of Electrical and Computer EngineeringUniversity of Western OntarioLondonONN6A 3K7Canada.,2Canadian Surgical Technologies and Advanced Robotics (CSTAR)Lawson Health Research InstituteLondonONN6A 4V2Canada.,7Department of Clinical NeurosciencesUniversity of Western OntarioLondonONN6A 3K7Canada
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Karapinar Senturk Z. Early diagnosis of Parkinson's disease using machine learning algorithms. Med Hypotheses 2020; 138:109603. [PMID: 32028195 DOI: 10.1016/j.mehy.2020.109603] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 01/22/2020] [Accepted: 01/24/2020] [Indexed: 12/13/2022]
Abstract
Parkinson's disease is caused by the disruption of the brain cells that produce substance to allow brain cells to communicate with each other, called dopamine. The cells that produce dopamine in the brain are responsible for the control, adaptation and fluency of movements. When 60-80%of these cells are lost, then enough dopamine is not produced and Parkinson's motor symptoms appear. It is thought that the disease begins many years before the motor (movement related) symptoms and therefore, researchers are looking for ways to recognize the non-motor symptoms that appear early in the disease as early as possible, thereby halting the progression of the disease. In this paper, machine learning based diagnosis of Parkinson's disease is presented. The proposed diagnosis method consists of feature selection and classification processes. Feature Importance and Recursive Feature Elimination methods were considered for feature selection task. Classification and Regression Trees, Artificial Neural Networks, and Support Vector Machines were used for the classification of Parkinson's patients in the experiments. Support Vector Machines with Recursive Feature Elimination was shown to perform better than the other methods. 93.84% accuracy was achieved with the least number of voice features for Parkinson's diagnosis.
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Affiliation(s)
- Zehra Karapinar Senturk
- Duzce University, Engineering Faculty, Department of Computer Engineering, 81620 Duzce, Turkey.
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Cao L, Li J, Zhou Y, Liu Y, Zhao Y, Liu H. Online identification of functional regions in deep brain stimulation based on an unsupervised random forest with feature selection. J Neural Eng 2019; 16:066015. [DOI: 10.1088/1741-2552/ab2eb4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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Larijani B, Goodarzi P, Payab M, Tayanloo-Beik A, Sarvari M, Gholami M, Gilany K, Nasli-Esfahani E, Yarahmadi M, Ghaderi F, Arjmand B. The Design and Application of an Appropriate Parkinson's Disease Animal Model in Regenerative Medicine. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1341:89-105. [PMID: 31485993 DOI: 10.1007/5584_2019_422] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVES Aging as an inevitable and complex physiological process occurs through a progressive decrease in the potential of tissue regeneration. Given the increasing global outbreak of aging and age-related disorders, it is important to control this phenomenon. Parkinson's disease (one of the age-related neurodegenerative and progressive disorders) resulted from predominant dopaminergic neurons deficiency. Usual Parkinson's disease treatments just can lead to symptomatically relieving. Recently, cell therapy and regenerative medicine a great promise in the treatment of several types of disorders including Parkinson's disease. Herein, before starting clinical trials, preclinical studies should be performed to answer some fundamental questions about the safety and efficacy of various treatments. Additionally, developing a well-designed and approved study is required to provide an appropriate animal model with strongly reliable validation methods. Hereupon, this review will discuss about the design and application of an appropriate Parkinson's disease animal model in regenerative medicine. EVIDENCE ACQUISITION In order to conduct the present review, numbers of Parkinson's disease preclinical studies, as well as literatures related to the animal modeling, were considered. RESULTS Appropriate animal models which approved by related authorize committees should have a high similarity to humans from anatomical, physiological, behavioral, and genetic characteristics view of point. CONCLUSION It is concluded that animal studies before starting clinical trials have an important role in answering the crucial questions about the various treatments safety and efficacy. Therein, it is recommended that all of animal modeling stages be assessed by animal ethics and welfare guidelines and also evaluated by different validation tests. However, it is better to find some alternatives to replacement, refinement, and, reduction of animals. Nowadays, some novel technologies such as using imaging methods have been introduced.
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Affiliation(s)
- Bagher Larijani
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical sciences, Tehran, Iran
| | - Parisa Goodarzi
- Brain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Moloud Payab
- Obesity and Eating Habits Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Akram Tayanloo-Beik
- Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Masoumeh Sarvari
- Metabolomics and Genomics Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahdi Gholami
- Department of Toxicology & Pharmacology, Faculty of Pharmacy, Toxicology and Poisoning Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Kambiz Gilany
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium.,Integrative Oncology Department, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran
| | - Ensieh Nasli-Esfahani
- Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehrnoosh Yarahmadi
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical sciences, Tehran, Iran
| | - Firoozeh Ghaderi
- Brain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Babak Arjmand
- Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran. .,Metabolomics and Genomics Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
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