1
|
Wei Z, Wang X, Liu C, Feng Y, Gan Y, Shi Y, Wang X, Liu Y, Deng Y. Microstate-based brain network dynamics distinguishing temporal lobe epilepsy patients: A machine learning approach. Neuroimage 2024; 296:120683. [PMID: 38880308 DOI: 10.1016/j.neuroimage.2024.120683] [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: 04/23/2024] [Revised: 06/02/2024] [Accepted: 06/10/2024] [Indexed: 06/18/2024] Open
Abstract
Temporal lobe epilepsy (TLE) stands as the predominant adult focal epilepsy syndrome, characterized by dysfunctional intrinsic brain dynamics. However, the precise mechanisms underlying seizures in these patients remain elusive. Our study encompassed 116 TLE patients compared with 51 healthy controls. Employing microstate analysis, we assessed brain dynamic disparities between TLE patients and healthy controls, as well as between drug-resistant epilepsy (DRE) and drug-sensitive epilepsy (DSE) patients. We constructed dynamic functional connectivity networks based on microstates and quantified their spatial and temporal variability. Utilizing these brain network features, we developed machine learning models to discriminate between TLE patients and healthy controls, and between DRE and DSE patients. Temporal dynamics in TLE patients exhibited significant acceleration compared to healthy controls, along with heightened synchronization and instability in brain networks. Moreover, DRE patients displayed notably lower spatial variability in certain parts of microstate B, E and F dynamic functional connectivity networks, while temporal variability in certain parts of microstate E and G dynamic functional connectivity networks was markedly higher in DRE patients compared to DSE patients. The machine learning model based on these spatiotemporal metrics effectively differentiated TLE patients from healthy controls and discerned DRE from DSE patients. The accelerated microstate dynamics and disrupted microstate sequences observed in TLE patients mirror highly unstable intrinsic brain dynamics, potentially underlying abnormal discharges. Additionally, the presence of highly synchronized and unstable activities in brain networks of DRE patients signifies the establishment of stable epileptogenic networks, contributing to the poor responsiveness to antiseizure medications. The model based on spatiotemporal metrics demonstrated robust predictive performance, accurately distinguishing both TLE patients from healthy controls and DRE patients from DSE patients.
Collapse
Affiliation(s)
- Zihan Wei
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China
| | - Xinpei Wang
- School of Aerospace Medicine, Fourth Military Medical University, Xi'an, China
| | - Chao Liu
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China
| | - Yan Feng
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China; Xi'an Medical University, Xi'an 710021, PR China
| | - Yajing Gan
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China
| | - Yuqing Shi
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China; Xi'an Medical University, Xi'an 710021, PR China
| | - Xiaoli Wang
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China
| | - Yonghong Liu
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China
| | - Yanchun Deng
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China.
| |
Collapse
|
2
|
Akbar MN, Ruf SF, Singh A, Faghihpirayesh R, Garner R, Bennett A, Alba C, Rocca ML, Imbiriba T, Erdoğmuş D, Duncan D. Advancing post-traumatic seizure classification and biomarker identification: Information decomposition based multimodal fusion and explainable machine learning with missing neuroimaging data. Comput Med Imaging Graph 2024; 115:102386. [PMID: 38718562 DOI: 10.1016/j.compmedimag.2024.102386] [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: 10/03/2023] [Revised: 04/16/2024] [Accepted: 04/16/2024] [Indexed: 06/03/2024]
Abstract
A late post-traumatic seizure (LPTS), a consequence of traumatic brain injury (TBI), can potentially evolve into a lifelong condition known as post-traumatic epilepsy (PTE). Presently, the mechanism that triggers epileptogenesis in TBI patients remains elusive, inspiring the epilepsy community to devise ways to predict which TBI patients will develop PTE and to identify potential biomarkers. In response to this need, our study collected comprehensive, longitudinal multimodal data from 48 TBI patients across multiple participating institutions. A supervised binary classification task was created, contrasting data from LPTS patients with those without LPTS. To accommodate missing modalities in some subjects, we took a two-pronged approach. Firstly, we extended a graphical model-based Bayesian estimator to directly classify subjects with incomplete modality. Secondly, we explored conventional imputation techniques. The imputed multimodal information was then combined, following several fusion and dimensionality reduction techniques found in the literature, and subsequently fitted to a kernel- or a tree-based classifier. For this fusion, we proposed two new algorithms: recursive elimination of correlated components (RECC) that filters information based on the correlation between the already selected features, and information decomposition and selective fusion (IDSF), which effectively recombines information from decomposed multimodal features. Our cross-validation findings showed that the proposed IDSF algorithm delivers superior performance based on the area under the curve (AUC) score. Ultimately, after rigorous statistical comparisons and interpretable machine learning examination using Shapley values of the most frequently selected features, we recommend the two following magnetic resonance imaging (MRI) abnormalities as potential biomarkers: the left anterior limb of internal capsule in diffusion MRI (dMRI), and the right middle temporal gyrus in functional MRI (fMRI).
Collapse
Affiliation(s)
- Md Navid Akbar
- Cognitive Systems Lab, Dept. of Electrical and Computer Engineering, College of Engineering, Northeastern University, 360 Huntington Ave, Boston, MA 02115, United States of America.
| | - Sebastian F Ruf
- Cognitive Systems Lab, Dept. of Electrical and Computer Engineering, College of Engineering, Northeastern University, 360 Huntington Ave, Boston, MA 02115, United States of America
| | - Ashutosh Singh
- Cognitive Systems Lab, Dept. of Electrical and Computer Engineering, College of Engineering, Northeastern University, 360 Huntington Ave, Boston, MA 02115, United States of America
| | - Razieh Faghihpirayesh
- Cognitive Systems Lab, Dept. of Electrical and Computer Engineering, College of Engineering, Northeastern University, 360 Huntington Ave, Boston, MA 02115, United States of America
| | - Rachael Garner
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Ave. 210, Los Angeles, CA 90033, United States of America
| | - Alexis Bennett
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Ave. 210, Los Angeles, CA 90033, United States of America
| | - Celina Alba
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Ave. 210, Los Angeles, CA 90033, United States of America
| | - Marianna La Rocca
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli studi di Bari "A. Moro", Bari, Italy
| | - Tales Imbiriba
- Cognitive Systems Lab, Dept. of Electrical and Computer Engineering, College of Engineering, Northeastern University, 360 Huntington Ave, Boston, MA 02115, United States of America
| | - Deniz Erdoğmuş
- Cognitive Systems Lab, Dept. of Electrical and Computer Engineering, College of Engineering, Northeastern University, 360 Huntington Ave, Boston, MA 02115, United States of America
| | - Dominique Duncan
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Ave. 210, Los Angeles, CA 90033, United States of America
| |
Collapse
|
3
|
Mercier M, Pepi C, Carfi-Pavia G, De Benedictis A, Espagnet MCR, Pirani G, Vigevano F, Marras CE, Specchio N, De Palma L. The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach. Sci Rep 2024; 14:10887. [PMID: 38740844 PMCID: PMC11091060 DOI: 10.1038/s41598-024-60622-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 04/25/2024] [Indexed: 05/16/2024] Open
Abstract
Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome. We included 123 paediatric patients who underwent epilepsy surgery at Bambino Gesù Children Hospital (January 2009-April 2020). All patients had long term video-EEG monitoring. We analysed 1-min scalp interictal EEG (wakefulness and sleep) and extracted 13 linear and non-linear EEG features (power spectral density (PSD), Hjorth, approximate entropy, permutation entropy, Lyapunov and Hurst value). We used a logistic regression (LR) as feature selection process. To quantify the correlation between EEG features and surgical outcome we used an artificial neural network (ANN) model with 18 architectures. LR revealed a significant correlation between PSD of alpha band (sleep), Mobility index (sleep) and the Hurst value (sleep and awake) with outcome. The fifty-four ANN models gave a range of accuracy (46-65%) in predicting outcome. Within the fifty-four ANN models, we found a higher accuracy (64.8% ± 7.6%) in seizure outcome prediction, using features selected by LR. The combination of PSD of alpha band, mobility and the Hurst value positively correlate with good surgical outcome.
Collapse
Affiliation(s)
- Mattia Mercier
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
- Department of Physiology, Behavioural Neuroscience PhD Program, Sapienza University, Rome, Italy
| | - Chiara Pepi
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
| | - Giusy Carfi-Pavia
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
| | - Alessandro De Benedictis
- Neurosurgery Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, 00165, Rome, Italy
| | | | - Greta Pirani
- Department of Mechanical and Aerospace Engineering - DIMA, Sapienza University of Rome, Rome, Italy
| | - Federico Vigevano
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
| | - Carlo Efisio Marras
- Neurosurgery Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, 00165, Rome, Italy
| | - Nicola Specchio
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy.
| | - Luca De Palma
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
| |
Collapse
|
4
|
Bernasconi A, Gill RS, Bernasconi N. The use of automated and AI-driven algorithms for the detection of hippocampal sclerosis and focal cortical dysplasia. Epilepsia 2024. [PMID: 38642009 DOI: 10.1111/epi.17989] [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: 01/31/2024] [Revised: 04/08/2024] [Accepted: 04/08/2024] [Indexed: 04/22/2024]
Abstract
In drug-resistant epilepsy, magnetic resonance imaging (MRI) plays a central role in detecting lesions as it offers unmatched spatial resolution and whole-brain coverage. In addition, the last decade has witnessed continued developments in MRI-based computer-aided machine-learning techniques for improved diagnosis and prognosis. In this review, we focus on automated algorithms for the detection of hippocampal sclerosis and focal cortical dysplasia, particularly in cases deemed as MRI negative, with an emphasis on studies with histologically validated data. In addition, we discuss imaging-derived prognostic markers, including response to anti-seizure medication, post-surgical seizure outcome, and cognitive reserves. We also highlight the advantages and limitations of these approaches and discuss future directions toward person-centered care.
Collapse
Affiliation(s)
- Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Ravnoor S Gill
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| |
Collapse
|
5
|
Karabacak M, Jagtiani P, Panov F, Margetis K. Predicting 30-Day Non-Seizure Outcomes Following Temporal Lobectomy with Personalized Machine Learning Models. World Neurosurg 2024; 183:e59-e70. [PMID: 38006940 DOI: 10.1016/j.wneu.2023.11.077] [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: 07/21/2023] [Revised: 11/15/2023] [Accepted: 11/16/2023] [Indexed: 11/27/2023]
Abstract
BACKGROUND Temporal lobe epilepsy is the most common reason behind drug-resistant seizures and temporal lobectomy (TL) is performed after all other efforts have been taken for a Temporal lobe epilepsy. Our study aims to develop multiple machine learning (ML) models capable of predicting postoperative outcomes following TL surgery. METHODS Data from the American College of Surgeons National Surgical Quality Improvement Program database identified patients who underwent TL surgery. We focused on 3 outcomes: prolonged length of stay (LOS), nonhome discharges, and 30-day readmissions. Six ML algorithms, TabPFN, XGBoost, LightGBM, Support Vector Machine, Random Forest, and Logistic Regression, coupled with the Optuna optimization library for hyperparameter tuning, were tested. Models with the highest area under the receiver operating characteristic (AUROC) values were included in the web application. SHapley Additive exPlanations was used to evaluate importance of predictor variables. RESULTS Our analysis included 423 patients. Of these patients, 111 (26.2%) experienced prolonged LOS, 33 (7.8%) had nonhome discharges, and 29 (6.9%) encountered 30-day readmissions. The top-performing models for each outcome were those built with the Random Forest algorithm. The Random Forest models yielded AUROCs of 0.868, 0.804, and 0.742 in predicting prolonged LOS, nonhome discharges, and 30-day readmissions, respectively. CONCLUSIONS Our study uses ML to forecast adverse postoperative outcomes following TL. We developed accessible predictive models that enhance prognosis prediction for TL surgery. Making ML models available for this purpose represents a significant advancement in shifting toward a more patient-centric, data-driven paradigm.
Collapse
Affiliation(s)
- Mert Karabacak
- Department of Neurosurgery, Mount Sinai Health System, New York, New York, USA
| | - Pemla Jagtiani
- School of Medicine, SUNY Downstate Health Sciences University, New York, New York, USA
| | - Fedor Panov
- Department of Neurosurgery, Mount Sinai Health System, New York, New York, USA
| | | |
Collapse
|
6
|
Horsley JJ, Thomas RH, Chowdhury FA, Diehl B, McEvoy AW, Miserocchi A, de Tisi J, Vos SB, Walker MC, Winston GP, Duncan JS, Wang Y, Taylor PN. Complementary structural and functional abnormalities to localise epileptogenic tissue. EBioMedicine 2023; 97:104848. [PMID: 37898096 PMCID: PMC10630610 DOI: 10.1016/j.ebiom.2023.104848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 10/11/2023] [Accepted: 10/11/2023] [Indexed: 10/30/2023] Open
Abstract
BACKGROUND When investigating suitability for epilepsy surgery, people with drug-refractory focal epilepsy may have intracranial EEG (iEEG) electrodes implanted to localise seizure onset. Diffusion-weighted magnetic resonance imaging (dMRI) may be acquired to identify key white matter tracts for surgical avoidance. Here, we investigate whether structural connectivity abnormalities, inferred from dMRI, may be used in conjunction with functional iEEG abnormalities to aid localisation of the epileptogenic zone (EZ), improving surgical outcomes in epilepsy. METHODS We retrospectively investigated data from 43 patients (42% female) with epilepsy who had surgery following iEEG. Twenty-five patients (58%) were free from disabling seizures (ILAE 1 or 2) at one year. Interictal iEEG functional, and dMRI structural connectivity abnormalities were quantified by comparison to a normative map and healthy controls. We explored whether the resection of maximal abnormalities related to improved surgical outcomes, in both modalities individually and concurrently. Additionally, we suggest how connectivity abnormalities may inform the placement of iEEG electrodes pre-surgically using a patient case study. FINDINGS Seizure freedom was 15 times more likely in patients with resection of maximal connectivity and iEEG abnormalities (p = 0.008). Both modalities separately distinguished patient surgical outcome groups and when used simultaneously, a decision tree correctly separated 36 of 43 (84%) patients. INTERPRETATION Our results suggest that both connectivity and iEEG abnormalities may localise epileptogenic tissue, and that these two modalities may provide complementary information in pre-surgical evaluations. FUNDING This research was funded by UKRI, CDT in Cloud Computing for Big Data, NIH, MRC, Wellcome Trust and Epilepsy Research UK.
Collapse
Affiliation(s)
- Jonathan J Horsley
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Rhys H Thomas
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Fahmida A Chowdhury
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Andrew W McEvoy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sjoerd B Vos
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Centre for Microscopy, Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia; Centre for Medical Image Computing, Computer Science Department, University College London, London, United Kingdom
| | - Matthew C Walker
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Gavin P Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Division of Neurology, Department of Medicine, Queen's University, Kingston, Canada
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Peter N Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.
| |
Collapse
|
7
|
Horsley JJ, Thomas RH, Chowdhury FA, Diehl B, McEvoy AW, Miserocchi A, de Tisi J, Vos SB, Walker MC, Winston GP, Duncan JS, Wang Y, Taylor PN. Complementary structural and functional abnormalities to localise epileptogenic tissue. ARXIV 2023:arXiv:2304.03192v3. [PMID: 37064531 PMCID: PMC10104180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Background When investigating suitability for epilepsy surgery, people with drug-refractory focal epilepsy may have intracranial EEG (iEEG) electrodes implanted to localise seizure onset. Diffusion-weighted magnetic resonance imaging (dMRI) may be acquired to identify key white matter tracts for surgical avoidance. Here, we investigate whether structural connectivity abnormalities, inferred from dMRI, may be used in conjunction with functional iEEG abnormalities to aid localisation of the epileptogenic zone (EZ), improving surgical outcomes in epilepsy. Methods We retrospectively investigated data from 43 patients with epilepsy who had surgery following iEEG. Twenty-five patients (58%) were free from disabling seizures (ILAE 1 or 2) at one year. Interictal iEEG functional, and dMRI structural connectivity abnormalities were quantified by comparison to a normative map and healthy controls. We explored whether the resection of maximal abnormalities related to improved surgical outcomes, in both modalities individually and concurrently. Additionally, we suggest how connectivity abnormalities may inform the placement of iEEG electrodes pre-surgically using a patient case study. Findings Seizure freedom was 15 times more likely in patients with resection of maximal connectivity and iEEG abnormalities (p=0.008). Both modalities separately distinguished patient surgical outcome groups and when used simultaneously, a decision tree correctly separated 36 of 43 (84%) patients. Interpretation Our results suggest that both connectivity and iEEG abnormalities may localise epileptogenic tissue, and that these two modalities may provide complementary information in pre-surgical evaluations. Funding This research was funded by UKRI, CDT in Cloud Computing for Big Data, NIH, MRC, Wellcome Trust and Epilepsy Research UK.
Collapse
Affiliation(s)
- Jonathan J. Horsley
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Rhys H. Thomas
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Fahmida A. Chowdhury
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Andrew W. McEvoy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sjoerd B. Vos
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Centre for Microscopy, Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
- Centre for Medical Image Computing, Computer Science Department, University College London, London, United Kingdom
| | - Matthew C. Walker
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Gavin P. Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Division of Neurology, Department of Medicine, Queen’s University, Kingston, Canada
| | - John S. Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Peter N. Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| |
Collapse
|
8
|
Eriksson MH, Ripart M, Piper RJ, Moeller F, Das KB, Eltze C, Cooray G, Booth J, Whitaker KJ, Chari A, Martin Sanfilippo P, Perez Caballero A, Menzies L, McTague A, Tisdall MM, Cross JH, Baldeweg T, Adler S, Wagstyl K. Predicting seizure outcome after epilepsy surgery: Do we need more complex models, larger samples, or better data? Epilepsia 2023; 64:2014-2026. [PMID: 37129087 PMCID: PMC10952307 DOI: 10.1111/epi.17637] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/30/2023] [Accepted: 05/01/2023] [Indexed: 05/03/2023]
Abstract
OBJECTIVE The accurate prediction of seizure freedom after epilepsy surgery remains challenging. We investigated if (1) training more complex models, (2) recruiting larger sample sizes, or (3) using data-driven selection of clinical predictors would improve our ability to predict postoperative seizure outcome using clinical features. We also conducted the first substantial external validation of a machine learning model trained to predict postoperative seizure outcome. METHODS We performed a retrospective cohort study of 797 children who had undergone resective or disconnective epilepsy surgery at a tertiary center. We extracted patient information from medical records and trained three models-a logistic regression, a multilayer perceptron, and an XGBoost model-to predict 1-year postoperative seizure outcome on our data set. We evaluated the performance of a recently published XGBoost model on the same patients. We further investigated the impact of sample size on model performance, using learning curve analysis to estimate performance at samples up to N = 2000. Finally, we examined the impact of predictor selection on model performance. RESULTS Our logistic regression achieved an accuracy of 72% (95% confidence interval [CI] = 68%-75%, area under the curve [AUC] = .72), whereas our multilayer perceptron and XGBoost both achieved accuracies of 71% (95% CIMLP = 67%-74%, AUCMLP = .70; 95% CIXGBoost own = 68%-75%, AUCXGBoost own = .70). There was no significant difference in performance between our three models (all p > .4) and they all performed better than the external XGBoost, which achieved an accuracy of 63% (95% CI = 59%-67%, AUC = .62; pLR = .005, pMLP = .01, pXGBoost own = .01) on our data. All models showed improved performance with increasing sample size, but limited improvements beyond our current sample. The best model performance was achieved with data-driven feature selection. SIGNIFICANCE We show that neither the deployment of complex machine learning models nor the assembly of thousands of patients alone is likely to generate significant improvements in our ability to predict postoperative seizure freedom. We instead propose that improved feature selection alongside collaboration, data standardization, and model sharing is required to advance the field.
Collapse
Affiliation(s)
- Maria H. Eriksson
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeuropsychologyGreat Ormond Street HospitalLondonUK
- Department of NeurologyGreat Ormond Street HospitalLondonUK
- The Alan Turing InstituteLondonUK
| | - Mathilde Ripart
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
| | - Rory J. Piper
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeurosurgeryGreat Ormond Street HospitalLondonUK
| | | | - Krishna B. Das
- Department of NeurologyGreat Ormond Street HospitalLondonUK
- Department of NeurophysiologyGreat Ormond Street HospitalLondonUK
| | - Christin Eltze
- Department of NeurophysiologyGreat Ormond Street HospitalLondonUK
| | - Gerald Cooray
- Department of NeurophysiologyGreat Ormond Street HospitalLondonUK
- Clinical NeuroscienceKarolinska InstituteSolnaSweden
| | - John Booth
- Digital Research EnvironmentGreat Ormond Street HospitalLondonUK
| | | | - Aswin Chari
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeurosurgeryGreat Ormond Street HospitalLondonUK
| | - Patricia Martin Sanfilippo
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeuropsychologyGreat Ormond Street HospitalLondonUK
| | | | - Lara Menzies
- Department of Clinical GeneticsGreat Ormond Street HospitalLondonUK
| | - Amy McTague
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeurologyGreat Ormond Street HospitalLondonUK
| | - Martin M. Tisdall
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeurosurgeryGreat Ormond Street HospitalLondonUK
| | - J. Helen Cross
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeurologyGreat Ormond Street HospitalLondonUK
- Department of NeurosurgeryGreat Ormond Street HospitalLondonUK
- Young EpilepsyLingfieldUK
| | - Torsten Baldeweg
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeuropsychologyGreat Ormond Street HospitalLondonUK
| | - Sophie Adler
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
| | - Konrad Wagstyl
- Imaging NeuroscienceUCL Queen Square Institute of NeurologyLondonUK
| |
Collapse
|
9
|
Su X, Sun Y, Liu H, Lang Q, Zhang Y, Zhang J, Wang C, Chen Y. An innovative ensemble model based on deep learning for predicting COVID-19 infection. Sci Rep 2023; 13:12322. [PMID: 37516796 PMCID: PMC10387055 DOI: 10.1038/s41598-023-39408-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 07/25/2023] [Indexed: 07/31/2023] Open
Abstract
Nowadays, global public health crises are occurring more frequently, and accurate prediction of these diseases can reduce the burden on the healthcare system. Taking COVID-19 as an example, accurate prediction of infection can assist experts in effectively allocating medical resources and diagnosing diseases. Currently, scholars worldwide use single model approaches or epidemiology models more often to predict the outbreak trend of COVID-19, resulting in poor prediction accuracy. Although a few studies have employed ensemble models, there is still room for improvement in their performance. In addition, there are only a few models that use the laboratory results of patients to predict COVID-19 infection. To address these issues, research efforts should focus on improving disease prediction performance and expanding the use of medical disease prediction models. In this paper, we propose an innovative deep learning model Whale Optimization Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM) and Artificial Neural Network (ANN) called WOCLSA which incorporates three models ANN, CNN and LSTM. The WOCLSA model utilizes the Whale Optimization Algorithm to optimize the neuron number, dropout and batch size parameters in the integrated model of ANN, CNN and LSTM, thereby finding the global optimal solution parameters. WOCLSA employs 18 patient indicators as predictors, and compares its results with three other ensemble deep learning models. All models were validated with train-test split approaches. We evaluate and compare our proposed model and other models using accuracy, F1 score, recall, AUC and precision metrics. Through many studies and tests, our results show that our prediction models can identify patients with COVID-19 infection at the AUC of 91%, 91%, and 93% respectively. Other prediction results achieve a respectable accuracy of 92.82%, 92.79%, and 91.66% respectively, f1-score of 93.41%, 92.79%, and 92.33% respectively, precision of 93.41%, 92.79%, and 92.33% respectively, recall of 93.41%, 92.79%, and 92.33% respectively. All of these exceed 91%, surpassing those of comparable models. The execution time of WOCLSA is also an advantage. Therefore, the WOCLSA ensemble model can be used to assist in verifying laboratory research results and predict and to judge various diseases in public health events.
Collapse
Affiliation(s)
- Xiaoying Su
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China
| | - Yanfeng Sun
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China
| | - Hongxi Liu
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China
| | - Qiuling Lang
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China
| | - Yichen Zhang
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China
| | - Jiquan Zhang
- School of Environment, Northeast Normal University, Changchun, 130024, China
| | - Chaoyong Wang
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China.
| | - Yanan Chen
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China
| |
Collapse
|
10
|
Tangsrivimol JA, Schonfeld E, Zhang M, Veeravagu A, Smith TR, Härtl R, Lawton MT, El-Sherbini AH, Prevedello DM, Glicksberg BS, Krittanawong C. Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future. Diagnostics (Basel) 2023; 13:2429. [PMID: 37510174 PMCID: PMC10378231 DOI: 10.3390/diagnostics13142429] [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: 05/31/2023] [Revised: 07/06/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
In recent years, there has been a significant surge in discussions surrounding artificial intelligence (AI), along with a corresponding increase in its practical applications in various facets of everyday life, including the medical industry. Notably, even in the highly specialized realm of neurosurgery, AI has been utilized for differential diagnosis, pre-operative evaluation, and improving surgical precision. Many of these applications have begun to mitigate risks of intraoperative and postoperative complications and post-operative care. This article aims to present an overview of the principal published papers on the significant themes of tumor, spine, epilepsy, and vascular issues, wherein AI has been applied to assess its potential applications within neurosurgery. The method involved identifying high-cited seminal papers using PubMed and Google Scholar, conducting a comprehensive review of various study types, and summarizing machine learning applications to enhance understanding among clinicians for future utilization. Recent studies demonstrate that machine learning (ML) holds significant potential in neuro-oncological care, spine surgery, epilepsy management, and other neurosurgical applications. ML techniques have proven effective in tumor identification, surgical outcomes prediction, seizure outcome prediction, aneurysm prediction, and more, highlighting its broad impact and potential in improving patient management and outcomes in neurosurgery. This review will encompass the current state of research, as well as predictions for the future of AI within neurosurgery.
Collapse
Affiliation(s)
- Jonathan A Tangsrivimol
- Division of Neurosurgery, Department of Surgery, Chulabhorn Hospital, Chulabhorn Royal Academy, Bangkok 10210, Thailand
- Department of Neurological Surgery, The Ohio State University Wexner Medical Center and Jame Cancer Institute, Columbus, OH 43210, USA
| | - Ethan Schonfeld
- Department Biomedical Informatics, Stanford University School of Medicine, Palo Alto, CA 94305, USA
| | - Michael Zhang
- Department of Neurosurgery, Stanford University School of Medicine, Palo Alto, CA 94305, USA
| | - Anand Veeravagu
- Stanford Neurosurgical Artificial Intelligence and Machine Learning Laboratory, Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Timothy R Smith
- Department of Neurosurgery, Computational Neuroscience Outcomes Center (CNOC), Mass General Brigham, Harvard Medical School, Boston, MA 02115, USA
| | - Roger Härtl
- Weill Cornell Medicine Brain and Spine Center, New York, NY 10022, USA
| | - Michael T Lawton
- Department of Neurosurgery, Barrow Neurological Institute (BNI), Phoenix, AZ 85013, USA
| | - Adham H El-Sherbini
- Faculty of Health Sciences, Queen's University, Kingston, ON K7L 3N6, Canada
| | - Daniel M Prevedello
- Department of Neurological Surgery, The Ohio State University Wexner Medical Center and Jame Cancer Institute, Columbus, OH 43210, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Chayakrit Krittanawong
- Cardiology Division, New York University Langone Health, New York University School of Medicine, New York, NY 10016, USA
| |
Collapse
|
11
|
Baciu M, O'Sullivan L, Torlay L, Banjac S. New insights for predicting surgery outcome in patients with temporal lobe epilepsy. A systematic review. Rev Neurol (Paris) 2023:S0035-3787(23)00884-6. [PMID: 37003897 DOI: 10.1016/j.neurol.2023.02.067] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 01/16/2023] [Accepted: 02/22/2023] [Indexed: 04/03/2023]
Abstract
Resective surgery is the treatment of choice for one-third of adult patients with focal, drug-resistant epilepsy. This procedure is associated with substantial clinical and cognitive risks. In clinical practice, there is no validated model for epilepsy surgery outcome prediction (ESOP). Meta-analyses on ESOP studies assessing prognostic factors report discrepancies in terms of study design. Our review aims to systematically investigate methodological and analytical aspects of studies predicting clinical and cognitive outcomes after temporal lobe epilepsy surgery. A systematic review of ESOP studies published between 2000 and 2022 from three databases (MEDLINE, Web of Science, and PsycINFO) was completed by following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. It yielded 4867 articles. Among them, 21 corresponded to our inclusion criteria and were therefore retained in the final review. The risk of bias was assessed using A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies (PROBAST). Data extracted from the 21 studies were analyzed using narrative synthesis and descriptive statistics. Our findings show an increase in the use of multimodal datasets and machine learning analyses in recent ESOP studies, although regression remained the most frequently used approach. We also identified a more frequent use of network notions in recent ESOP studies. Nevertheless, several methodological issues were noted, such as small sample sizes, lack of information on the follow-up period, variability in seizure outcome, and the definition of neuropsychological postoperative change. Of 21 studies, only one provided a clinical tool to anticipate the cognitive outcome after epilepsy surgery. We conclude that methodological issues should be overcome before we move towards more complete models to better predict clinical and cognitive outcomes after epilepsy surgery. Recommendations for future studies to harness the possibilities of multimodal datasets and data fusion, are provided. A stronger bridge between fundamental and clinical research may result in developing accessible clinical tools.
Collapse
Affiliation(s)
- M Baciu
- Université Grenoble Alpes, CNRS LPNC UMR 5105, 38000 Grenoble, France
| | - L O'Sullivan
- Université Grenoble Alpes, CNRS LPNC UMR 5105, 38000 Grenoble, France
| | - L Torlay
- Université Grenoble Alpes, CNRS LPNC UMR 5105, 38000 Grenoble, France
| | - S Banjac
- Université Grenoble Alpes, CNRS LPNC UMR 5105, 38000 Grenoble, France.
| |
Collapse
|
12
|
Hinds W, Modi S, Ankeeta A, Sperling MR, Pustina D, Tracy JI. Pre-surgical features of intrinsic brain networks predict single and joint epilepsy surgery outcomes. Neuroimage Clin 2023; 38:103387. [PMID: 37023491 PMCID: PMC10122017 DOI: 10.1016/j.nicl.2023.103387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 03/02/2023] [Accepted: 03/24/2023] [Indexed: 03/31/2023]
Abstract
Despite the effectiveness of surgical interventions for the treatment of intractable focal temporal lobe epilepsy (TLE), the substrates that support good outcomes are poorly understood. While algorithms have been developed for the prediction of either seizure or cognitive/psychiatric outcomes alone, no study has reported on the functional and structural architecture that supports joint outcomes. We measured key aspects of pre-surgical whole brain functional/structural network architecture and evaluated their ability to predict post-operative seizure control in combination with cognitive/psychiatric outcomes. Pre-surgically, we identified the intrinsic connectivity networks (ICNs) unique to each person through independent component analysis (ICA), and computed: (1) the spatial-temporal match between each person's ICA components and established, canonical ICNs, (2) the connectivity strength within each identified person-specific ICN, (3) the gray matter (GM) volume underlying the person-specific ICNs, and (4) the amount of variance not explained by the canonical ICNs for each person. Post-surgical seizure control and reliable change indices of change (for language [naming, phonemic fluency], verbal episodic memory, and depression) served as binary outcome responses in random forest (RF) models. The above functional and structural measures served as input predictors. Our empirically derived ICN-based measures customized to the individual showed that good joint seizure and cognitive/psychiatric outcomes depended upon higher levels of brain reserve (GM volume) in specific networks. In contrast, singular outcomes relied on systematic, idiosyncratic variance in the case of seizure control, and the weakened pre-surgical presence of functional ICNs that encompassed the ictal temporal lobe in the case of cognitive/psychiatric outcomes. Our data made clear that the ICNs differed in their propensity to provide reserve for adaptive outcomes, with some providing structural (brain), and others functional (cognitive) reserve. Our customized methodology demonstrated that when substantial unique, patient-specific ICNs are present prior to surgery there is a reliable association with poor post-surgical seizure control. These ICNs are idiosyncratic in that they did not match the canonical, normative ICNs and, therefore, could not be defined functionally, with their location likely varying by patient. This important finding suggested the level of highly individualized ICN's in the epileptic brain may signal the emergence of epileptogenic activity after surgery.
Collapse
Affiliation(s)
- Walter Hinds
- Thomas Jefferson University, Department of Neurology, and Vicky and Jack Farber Institute for Neuroscience, USA
| | - Shilpi Modi
- Thomas Jefferson University, Department of Neurology, and Vicky and Jack Farber Institute for Neuroscience, USA
| | - Ankeeta Ankeeta
- Thomas Jefferson University, Department of Neurology, and Vicky and Jack Farber Institute for Neuroscience, USA
| | - Michael R Sperling
- Thomas Jefferson University, Department of Neurology, and Vicky and Jack Farber Institute for Neuroscience, USA
| | | | - Joseph I Tracy
- Thomas Jefferson University, Department of Neurology, and Vicky and Jack Farber Institute for Neuroscience, USA.
| |
Collapse
|
13
|
Abstract
Brain surgery offers the best chance of seizure-freedom for patients with focal drug-resistant epilepsy, but only 50% achieve sustained seizure-freedom. With the explosion of data collected during routine presurgical evaluations and recent advances in computational science, we now have a tremendous potential to achieve precision epilepsy surgery: a data-driven tailoring of surgical planning. This review highlights the clinical need, the relevant computational science focusing on machine learning, and discusses some specific applications in epilepsy surgery.
Collapse
Affiliation(s)
- Lara Jehi
- Cleveland Clinic Ringgold Standard Institution, Cleveland, OH, USA
| |
Collapse
|
14
|
Can Presurgical Interhemispheric EEG Connectivity Predict Outcome in Hemispheric Surgery? A Brain Machine Learning Approach. Brain Sci 2022; 13:brainsci13010071. [PMID: 36672052 PMCID: PMC9856795 DOI: 10.3390/brainsci13010071] [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: 11/25/2022] [Revised: 12/21/2022] [Accepted: 12/27/2022] [Indexed: 12/31/2022] Open
Abstract
OBJECTIVES Hemispherotomy (HT) is a surgical option for treatment of drug-resistant seizures due to hemispheric structural lesions. Factors affecting seizure outcome have not been fully clarified. In our study, we used a brain Machine Learning (ML) approach to evaluate the possible role of Inter-hemispheric EEG Connectivity (IC) in predicting post-surgical seizure outcome. METHODS We collected 21 pediatric patients with drug-resistant epilepsy; who underwent HT in our center from 2009 to 2020; with a follow-up of at least two years. We selected 5-s windows of wakefulness and sleep pre-surgical EEG and we trained Artificial Neuronal Network (ANN) to estimate epilepsy outcome. We extracted EEG features as input data and selected the ANN with best accuracy. RESULTS Among 21 patients, 15 (71%) were seizure and drug-free at last follow-up. ANN showed 73.3% of accuracy, with 85% of seizure free and 40% of non-seizure free patients appropriately classified. CONCLUSIONS The accuracy level that we reached supports the hypothesis that pre-surgical EEG features may have the potential to predict epilepsy outcome after HT. SIGNIFICANCE The role of pre-surgical EEG data in influencing seizure outcome after HT is still debated. We proposed a computational predictive model, with an ML approach, with a high accuracy level.
Collapse
|
15
|
Sahu M, Gupta R, Ambasta RK, Kumar P. Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2022; 190:57-100. [PMID: 36008002 DOI: 10.1016/bs.pmbts.2022.03.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.
Collapse
Affiliation(s)
- Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India
| | - Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India.
| |
Collapse
|
16
|
Mirchi N, Warsi NM, Zhang F, Wong SM, Suresh H, Mithani K, Erdman L, Ibrahim GM. Decoding Intracranial EEG With Machine Learning: A Systematic Review. Front Hum Neurosci 2022; 16:913777. [PMID: 35832872 PMCID: PMC9271576 DOI: 10.3389/fnhum.2022.913777] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 05/31/2022] [Indexed: 11/13/2022] Open
Abstract
Advances in intracranial electroencephalography (iEEG) and neurophysiology have enabled the study of previously inaccessible brain regions with high fidelity temporal and spatial resolution. Studies of iEEG have revealed a rich neural code subserving healthy brain function and which fails in disease states. Machine learning (ML), a form of artificial intelligence, is a modern tool that may be able to better decode complex neural signals and enhance interpretation of these data. To date, a number of publications have applied ML to iEEG, but clinician awareness of these techniques and their relevance to neurosurgery, has been limited. The present work presents a review of existing applications of ML techniques in iEEG data, discusses the relative merits and limitations of the various approaches, and examines potential avenues for clinical translation in neurosurgery. One-hundred-seven articles examining artificial intelligence applications to iEEG were identified from 3 databases. Clinical applications of ML from these articles were categorized into 4 domains: i) seizure analysis, ii) motor tasks, iii) cognitive assessment, and iv) sleep staging. The review revealed that supervised algorithms were most commonly used across studies and often leveraged publicly available timeseries datasets. We conclude with recommendations for future work and potential clinical applications.
Collapse
Affiliation(s)
- Nykan Mirchi
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Nebras M. Warsi
- Division of Neurosurgery, Hospital for Sick Children, Department of Surgery, University of Toronto, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Frederick Zhang
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Simeon M. Wong
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Program in Neuroscience and Mental Health, Hospital for Sick Children Research Institute, Toronto, ON, Canada
| | - Hrishikesh Suresh
- Division of Neurosurgery, Hospital for Sick Children, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Karim Mithani
- Division of Neurosurgery, Hospital for Sick Children, Department of Surgery, University of Toronto, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Lauren Erdman
- Vector Institute for Artificial Intelligence, MaRS Centre, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Hospital for Sick Children, Toronto, ON, Canada
| | - George M. Ibrahim
- Division of Neurosurgery, Hospital for Sick Children, Department of Surgery, University of Toronto, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Program in Neuroscience and Mental Health, Hospital for Sick Children Research Institute, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| |
Collapse
|
17
|
Yossofzai O, Fallah A, Maniquis C, Wang S, Ragheb J, Weil AG, Brunette-Clement T, Andrade A, Ibrahim GM, Mitsakakis N, Widjaja E. Development and validation of machine learning models for prediction of seizure outcome after pediatric epilepsy surgery. Epilepsia 2022; 63:1956-1969. [PMID: 35661152 DOI: 10.1111/epi.17320] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 06/02/2022] [Accepted: 06/03/2022] [Indexed: 11/30/2022]
Abstract
OBJECTIVE There is substantial variability in reported seizure outcome following pediatric epilepsy surgery, and lack of individualized predictive tools that could evaluate the probability of seizure freedom postsurgery. The aim of this study was to develop and validate a supervised machine learning (ML) model for predicting seizure freedom after pediatric epilepsy surgery. METHODS This is a multicenter retrospective study of children who underwent epilepsy surgery at five pediatric epilepsy centers in North America. Clinical information, diagnostic investigations, and surgical characteristics were collected, and used as features to predict seizure-free outcome 1 year after surgery. The dataset was split randomly into 80% training and 20% testing data. Thirty-five combinations of five feature sets with seven ML classifiers were assessed on the training cohort using 10-fold cross-validation for model development. The performance of the optimal combination of ML classifier and feature set was evaluated in the testing cohort, and compared with logistic regression, a classical statistical approach. RESULTS Of the 801 patients included, 61.3% were seizure-free 1 year postsurgery. During model development, the best combination was XGBoost ML algorithm with five features from the univariate feature set, including number of antiseizure medications, magnetic resonance imaging lesion, age at seizure onset, video-electroencephalography concordance, and surgery type, with a mean area under the curve (AUC) of .73 (95% confidence interval [CI] = .69-.77). The combination of XGBoost and univariate feature set was then evaluated on the testing cohort and achieved an AUC of .74 (95% CI = .66-.82; sensitivity = .87, 95% CI = .81-.94; specificity = .58, 95% CI = .47-.71). The XGBoost model outperformed the logistic regression model (AUC = .72, 95% CI = .63-.80; sensitivity = .72, 95% CI = .63-.82; specificity = .66, 95% CI = .53-.77) in the testing cohort (p = .005). SIGNIFICANCE This study identified important features and validated an ML algorithm, XGBoost, for predicting the probability of seizure freedom after pediatric epilepsy surgery. Improved prognostication of epilepsy surgery is critical for presurgical counseling and will inform treatment decisions.
Collapse
Affiliation(s)
- Omar Yossofzai
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Ontario, Canada.,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Aria Fallah
- Department of Neurosurgery, University of California, Los Angeles Mattel Children's Hospital, Los Angeles, California, USA
| | - Cassia Maniquis
- Department of Neurosurgery, University of California, Los Angeles Mattel Children's Hospital, Los Angeles, California, USA
| | - Shelly Wang
- Division of Neurosurgery, Brain Institute, Nicklaus Children's Hospital, Miami, Florida, USA
| | - John Ragheb
- Division of Neurosurgery, Brain Institute, Nicklaus Children's Hospital, Miami, Florida, USA
| | - Alexander G Weil
- Department of Neurosurgery, Sainte-Justine University Hospital Center, Montreal, Quebec, Canada
| | | | - Andrea Andrade
- Department of Paediatrics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - George M Ibrahim
- Department of Neurosurgery, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Nicholas Mitsakakis
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Elysa Widjaja
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Ontario, Canada.,Division of Neurology, Hospital for Sick Children, Toronto, Ontario, Canada.,Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
| |
Collapse
|
18
|
Abstract
Artificial intelligence is already innovating in the provision of neurologic care. This review explores key artificial intelligence concepts; their application to neurologic diagnosis, prognosis, and treatment; and challenges that await their broader adoption. The development of new diagnostic biomarkers, individualization of prognostic information, and improved access to treatment are among the plethora of possibilities. These advances, however, reflect only the tip of the iceberg for the ways in which artificial intelligence may transform neurologic care in the future.
Collapse
Affiliation(s)
- James M Hillis
- Digital Clinical Research Organization, Data Science Office, Mass General Brigham, Boston, Massachusetts.,Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Bernardo C Bizzo
- Digital Clinical Research Organization, Data Science Office, Mass General Brigham, Boston, Massachusetts.,Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| |
Collapse
|
19
|
Heart Rate Variability Analysis for Seizure Detection in Neonatal Intensive Care Units. Bioengineering (Basel) 2022; 9:bioengineering9040165. [PMID: 35447725 PMCID: PMC9031489 DOI: 10.3390/bioengineering9040165] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 03/28/2022] [Accepted: 04/05/2022] [Indexed: 12/03/2022] Open
Abstract
In Neonatal Intensive Care Units (NICUs), the early detection of neonatal seizures is of utmost importance for a timely clinical intervention. Over the years, several neonatal seizure detection systems were proposed to detect neonatal seizures automatically and speed up seizure diagnosis, most based on the EEG signal analysis. Recently, research has focused on other possible seizure markers, such as electrocardiography (ECG). This work proposes an ECG-based NSD system to investigate the usefulness of heart rate variability (HRV) analysis to detect neonatal seizures in the NICUs. HRV analysis is performed considering time-domain, frequency-domain, entropy and multiscale entropy features. The performance is evaluated on a dataset of ECG signals from 51 full-term babies, 29 seizure-free. The proposed system gives results comparable to those reported in the literature: Area Under the Receiver Operating Characteristic Curve = 62%, Sensitivity = 47%, Specificity = 67%. Moreover, the system’s performance is evaluated in a real clinical environment, inevitably affected by several artefacts. To the best of our knowledge, our study proposes for the first time a multi-feature ECG-based NSD system that also offers a comparative analysis between babies suffering from seizures and seizure-free ones.
Collapse
|
20
|
Tang Y, Li W, Tao L, Li J, Long T, Li Y, Chen D, Hu S. Machine Learning-Derived Multimodal Neuroimaging of Presurgical Target Area to Predict Individual's Seizure Outcomes After Epilepsy Surgery. Front Cell Dev Biol 2022; 9:669795. [PMID: 35127691 PMCID: PMC8814443 DOI: 10.3389/fcell.2021.669795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 12/21/2021] [Indexed: 11/16/2022] Open
Abstract
Objectives: Half of the patients who have tailored resection of the suspected epileptogenic zone for drug-resistant epilepsy have recurrent postoperative seizures. Although neuroimaging has become an indispensable part of delineating the epileptogenic zone, no validated method uses neuroimaging of presurgical target area to predict an individual's post-surgery seizure outcome. We aimed to develop and validate a machine learning-powered approach incorporating multimodal neuroimaging of a presurgical target area to predict an individual's post-surgery seizure outcome in patients with drug-resistant focal epilepsy. Materials and Methods: One hundred and forty-one patients with drug-resistant focal epilepsy were classified either as having seizure-free (Engel class I) or seizure-recurrence (Engel class II through IV) at least 1 year after surgery. The presurgical magnetic resonance imaging, positron emission tomography, computed tomography, and postsurgical magnetic resonance imaging were co-registered for surgical target volume of interest (VOI) segmentation; all VOIs were decomposed into nine fixed views, then were inputted into the deep residual network (DRN) that was pretrained on Tiny-ImageNet dataset to extract and transfer deep features. A multi-kernel support vector machine (MKSVM) was used to integrate multiple views of feature sets and to predict seizure outcomes of the targeted VOIs. Leave-one-out validation was applied to develop a model for verifying the prediction. In the end, performance using this approach was assessed by calculating accuracy, sensitivity, and specificity. Receiver operating characteristic curves were generated, and the optimal area under the receiver operating characteristic curve (AUC) was calculated as a metric for classifying outcomes. Results: Application of DRN-MKSVM model based on presurgical target area neuroimaging demonstrated good performance in predicting seizure outcomes. The AUC ranged from 0.799 to 0.952. Importantly, the classification performance DRN-MKSVM model using data from multiple neuroimaging showed an accuracy of 91.5%, a sensitivity of 96.2%, a specificity of 85.5%, and AUCs of 0.95, which were significantly better than any other single-modal neuroimaging (all p ˂ 0.05). Conclusion: DRN-MKSVM, using multimodal compared with unimodal neuroimaging from the surgical target area, accurately predicted postsurgical outcomes. The preoperative individualized prediction of seizure outcomes in patients who have been judged eligible for epilepsy surgery could be conveniently facilitated. This may aid epileptologists in presurgical evaluation by providing a tool to explore various surgical options, offering complementary information to existing clinical techniques.
Collapse
Affiliation(s)
- Yongxiang Tang
- Department of Nuclear Medicine, Xiangya Hospital, Changsha, China
| | - Weikai Li
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Lue Tao
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Jian Li
- Department of Nuclear Medicine, Xiangya Hospital, Changsha, China
| | - Tingting Long
- Department of Nuclear Medicine, Xiangya Hospital, Changsha, China
| | - Yulai Li
- Department of Nuclear Medicine, Xiangya Hospital, Changsha, China
| | - Dengming Chen
- Department of Nuclear Medicine, Xiangya Hospital, Changsha, China
| | - Shuo Hu
- Department of Nuclear Medicine, Xiangya Hospital, Changsha, China
- Key Laboratory of Biological Nanotechnology of National Health Commission, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Diseases, Xiangya Hospital, Changsha, China
| |
Collapse
|
21
|
Naha A, Banerjee S, Debroy R, Basu S, Ashok G, Priyamvada P, Kumar H, Preethi A, Singh H, Anbarasu A, Ramaiah S. Network metrics, structural dynamics and density functional theory calculations identified a novel Ursodeoxycholic Acid derivative against therapeutic target Parkin for Parkinson's disease. Comput Struct Biotechnol J 2022; 20:4271-4287. [PMID: 36051887 PMCID: PMC9399899 DOI: 10.1016/j.csbj.2022.08.017] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 07/23/2022] [Accepted: 08/07/2022] [Indexed: 12/03/2022] Open
Abstract
GIN analysis revealed PARK2, LRRK2, PARK7, PINK1 and SNCA as hub-genes. Topologically favoured Parkin was considered as a therapeutic target. ADMET screening identified a novel UDCA derivative as potential lead candidate. Chemical reactivity and ligand stability were analysed through DFT simulation. Docking and MDS established novel lead as potential Parkin inhibitor.
Parkinson's disease (PD) has been designated as one of the priority neurodegenerative disorders worldwide. Although diagnostic biomarkers have been identified, early onset detection and targeted therapy are still limited. An integrated systems and structural biology approach were adopted to identify therapeutic targets for PD. From a set of 49 PD associated genes, a densely connected interactome was constructed. Based on centrality indices, degree of interaction and functional enrichments, LRRK2, PARK2, PARK7, PINK1 and SNCA were identified as the hub-genes. PARK2 (Parkin) was finalized as a potent theranostic candidate marker due to its strong association (score > 0.99) with α-synuclein (SNCA), which directly regulates PD progression. Besides, modeling and validation of Parkin structure, an extensive virtual-screening revealed small (commercially available) inhibitors against Parkin. Molecule-258 (ZINC5022267) was selected as a potent candidate based on pharmacokinetic profiles, Density Functional Theory (DFT) energy calculations (ΔE = 6.93 eV) and high binding affinity (Binding energy = -6.57 ± 0.1 kcal/mol; Inhibition constant = 15.35 µM) against Parkin. Molecular dynamics simulation of protein-inhibitor complexes further strengthened the therapeutic propositions with stable trajectories (low structural fluctuations), hydrogen bonding patterns and interactive energies (>0kJ/mol). Our study encourages experimental validations of the novel drug candidate to prevent the auto-inhibition of Parkin mediated ubiquitination in PD.
Collapse
|
22
|
Yuan J, Ran X, Liu K, Yao C, Yao Y, Wu H, Liu Q. Machine learning applications on neuroimaging for diagnosis and prognosis of epilepsy: A review. J Neurosci Methods 2021; 368:109441. [PMID: 34942271 DOI: 10.1016/j.jneumeth.2021.109441] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 10/23/2021] [Accepted: 12/11/2021] [Indexed: 02/07/2023]
Abstract
Machine learning is playing an increasingly important role in medical image analysis, spawning new advances in the clinical application of neuroimaging. There have been some reviews on machine learning and epilepsy before, and they mainly focused on electrophysiological signals such as electroencephalography (EEG) and stereo electroencephalography (SEEG), while neglecting the potential of neuroimaging in epilepsy research. Neuroimaging has its important advantages in confirming the range of the epileptic region, which is essential in presurgical evaluation and assessment after surgery. However, it is difficult for EEG to locate the accurate epilepsy lesion region in the brain. In this review, we emphasize the interaction between neuroimaging and machine learning in the context of epilepsy diagnosis and prognosis. We start with an overview of epilepsy and typical neuroimaging modalities used in epilepsy clinics, MRI, DWI, fMRI, and PET. Then, we elaborate two approaches in applying machine learning methods to neuroimaging data: (i) the conventional machine learning approach combining manual feature engineering and classifiers, (ii) the deep learning approach, such as the convolutional neural networks and autoencoders. Subsequently, the application of machine learning on epilepsy neuroimaging, such as segmentation, localization, and lateralization tasks, as well as tasks directly related to diagnosis and prognosis are looked into in detail. Finally, we discuss the current achievements, challenges, and potential future directions in this field, hoping to pave the way for computer-aided diagnosis and prognosis of epilepsy.
Collapse
Affiliation(s)
- Jie Yuan
- Shenzhen Key Laboratory of Smart Healthcare Engineering, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, PR China
| | - Xuming Ran
- Shenzhen Key Laboratory of Smart Healthcare Engineering, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, PR China
| | - Keyin Liu
- Shenzhen Key Laboratory of Smart Healthcare Engineering, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, PR China
| | - Chen Yao
- Shenzhen Second People's Hospital, Shenzhen 518035, PR China
| | - Yi Yao
- Shenzhen Children's Hospital, Shenzhen 518017, PR China
| | - Haiyan Wu
- Centre for Cognitive and Brain Sciences and Department of Psychology, University of Macau, Taipa, Macau
| | - Quanying Liu
- Shenzhen Key Laboratory of Smart Healthcare Engineering, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, PR China.
| |
Collapse
|
23
|
Denissen S, Chén OY, De Mey J, De Vos M, Van Schependom J, Sima DM, Nagels G. Towards Multimodal Machine Learning Prediction of Individual Cognitive Evolution in Multiple Sclerosis. J Pers Med 2021; 11:1349. [PMID: 34945821 PMCID: PMC8707909 DOI: 10.3390/jpm11121349] [Citation(s) in RCA: 10] [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: 11/10/2021] [Revised: 12/06/2021] [Accepted: 12/09/2021] [Indexed: 12/23/2022] Open
Abstract
Multiple sclerosis (MS) manifests heterogeneously among persons suffering from it, making its disease course highly challenging to predict. At present, prognosis mostly relies on biomarkers that are unable to predict disease course on an individual level. Machine learning is a promising technique, both in terms of its ability to combine multimodal data and through the capability of making personalized predictions. However, most investigations on machine learning for prognosis in MS were geared towards predicting physical deterioration, while cognitive deterioration, although prevalent and burdensome, remained largely overlooked. This review aims to boost the field of machine learning for cognitive prognosis in MS by means of an introduction to machine learning and its pitfalls, an overview of important elements for study design, and an overview of the current literature on cognitive prognosis in MS using machine learning. Furthermore, the review discusses new trends in the field of machine learning that might be adopted for future studies in the field.
Collapse
Affiliation(s)
- Stijn Denissen
- AIMS Laboratory, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, 1050 Brussels, Belgium; (J.D.M.); (J.V.S.); (D.M.S.); (G.N.)
- icometrix, 3012 Leuven, Belgium
| | - Oliver Y. Chén
- Faculty of Social Sciences and Law, University of Bristol, Bristol BS8 1QU, UK;
- Department of Engineering, University of Oxford, Oxford OX1 3PJ, UK
| | - Johan De Mey
- AIMS Laboratory, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, 1050 Brussels, Belgium; (J.D.M.); (J.V.S.); (D.M.S.); (G.N.)
- Department of Radiology, UZ Brussel, Vrije Universiteit Brussel, 1090 Brussels, Belgium
| | - Maarten De Vos
- Faculty of Engineering Science, KU Leuven, 3001 Leuven, Belgium;
- Faculty of Medicine, KU Leuven, 3001 Leuven, Belgium
| | - Jeroen Van Schependom
- AIMS Laboratory, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, 1050 Brussels, Belgium; (J.D.M.); (J.V.S.); (D.M.S.); (G.N.)
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, 1050 Brussels, Belgium
| | - Diana Maria Sima
- AIMS Laboratory, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, 1050 Brussels, Belgium; (J.D.M.); (J.V.S.); (D.M.S.); (G.N.)
- icometrix, 3012 Leuven, Belgium
| | - Guy Nagels
- AIMS Laboratory, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, 1050 Brussels, Belgium; (J.D.M.); (J.V.S.); (D.M.S.); (G.N.)
- icometrix, 3012 Leuven, Belgium
- St Edmund Hall, Queen’s Ln, Oxford OX1 4AR, UK
| |
Collapse
|
24
|
Machine Learning in Neuro-Oncology, Epilepsy, Alzheimer's Disease, and Schizophrenia. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:349-361. [PMID: 34862559 DOI: 10.1007/978-3-030-85292-4_39] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Applications of machine learning (ML) in translational medicine include therapeutic drug creation, diagnostic development, surgical planning, outcome prediction, and intraoperative assistance. Opportunities in the neurosciences are rich given advancement in our understanding of the brain, expanding indications for intervention, and diagnostic challenges often characterized by multiple clinical and environmental factors. We present a review of ML in neuro-oncology, epilepsy, Alzheimer's disease, and schizophrenia to highlight recent progression in these field, optimizing machine learning capabilities in their current forms. Supervised learning models appear to be the most commonly incorporated algorithm models for machine learning across the reviewed neuroscience disciplines with primary aim of diagnosis. Accuracy ranges are high from 63% to 99% across all algorithms investigated. Machine learning contributions to neurosurgery, neurology, psychiatry, and the clinical and basic science neurosciences may enhance current medical best practices while also broadening our understanding of dynamic neural networks and the brain.
Collapse
|
25
|
Maimaiti B, Meng H, Lv Y, Qiu J, Zhu Z, Xie Y, Li Y, Yu-Cheng, Zhao W, Liu J, Li M. An Overview of EEG-based Machine Learning Methods in Seizure Prediction and Opportunities for Neurologists in this Field. Neuroscience 2021; 481:197-218. [PMID: 34793938 DOI: 10.1016/j.neuroscience.2021.11.017] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 11/04/2021] [Accepted: 11/08/2021] [Indexed: 11/16/2022]
Abstract
The unpredictability of epileptic seizures is one of the most problematic aspects of the field of epilepsy. Methods or devices capable of detecting seizures minutes before they occur may help prevent injury or even death and significantly improve the quality of life. Machine learning (ML) is an emerging technology that can markedly enhance algorithm performance by interpreting data. ML has gained increasing attention from medical researchers in recent years. Its epilepsy applications range from the localization of the epileptic region, predicting the medical or surgical outcome of epilepsy, and automated electroencephalography (EEG) analysis to seizure prediction. While ML has good prospects with regard to detecting epileptic seizures via EEG signals, many clinicians are still unfamiliar with this field. This work briefly summarizes the history and recent significant progress made in this field and clarifies the essential components of the automatic seizure detection system using ML methodologies for clinicians. This review also proposes how neurologists can actively contribute to ensure improvements in seizure prediction using EEG-based ML.
Collapse
Affiliation(s)
- Buajieerguli Maimaiti
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Hongmei Meng
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China.
| | - Yudan Lv
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Jiqing Qiu
- Department of Neurological Surgery, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Zhanpeng Zhu
- Department of Neurological Surgery, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Yinyin Xie
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Yue Li
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Yu-Cheng
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Weixuan Zhao
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Jiayu Liu
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Mingyang Li
- Department of Communication Engineering, Jilin University, Changchun, Jilin, People's Republic of China.
| |
Collapse
|
26
|
Machine learning models for decision support in epilepsy management: A critical review. Epilepsy Behav 2021; 123:108273. [PMID: 34507093 DOI: 10.1016/j.yebeh.2021.108273] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/13/2021] [Accepted: 08/14/2021] [Indexed: 12/12/2022]
Abstract
PURPOSE There remain major challenges for the clinician in managing patients with epilepsy effectively. Choosing anti-seizure medications (ASMs) is subject to trial and error. About one-third of patients have drug-resistant epilepsy (DRE). Surgery may be considered for selected patients, but time from diagnosis to surgery averages 20 years. We reviewed the potential use of machine learning (ML) predictive models as clinical decision support tools to help address some of these issues. METHODS We conducted a comprehensive search of Medline and Embase of studies that investigated the application of ML in epilepsy management in terms of predicting ASM responsiveness, predicting DRE, identifying surgical candidates, and predicting epilepsy surgery outcomes. Original articles addressing these 4 areas published in English between 2000 and 2020 were included. RESULTS We identified 24 relevant articles: 6 on ASM responsiveness, 3 on DRE prediction, 2 on identifying surgical candidates, and 13 on predicting surgical outcomes. A variety of potential predictors were used including clinical, neuropsychological, imaging, electroencephalography, and health system claims data. A number of different ML algorithms and approaches were used for prediction, but only one study utilized deep learning methods. Some models show promising performance with areas under the curve above 0.9. However, most were single setting studies (18 of 24) with small sample sizes (median number of patients 55), with the exception of 3 studies that utilized large databases and 3 studies that performed external validation. There was a lack of standardization in reporting model performance. None of the models reviewed have been prospectively evaluated for their clinical benefits. CONCLUSION The utility of ML models for clinical decision support in epilepsy management remains to be determined. Future research should be directed toward conducting larger studies with external validation, standardization of reporting, and prospective evaluation of the ML model on patient outcomes.
Collapse
|
27
|
Samanta D, Beal JC, Grinspan ZM. Automated Identification of Surgical Candidates and Estimation of Postoperative Seizure Freedom in Children - A Focused Review. Semin Pediatr Neurol 2021; 39:100914. [PMID: 34620464 PMCID: PMC9082396 DOI: 10.1016/j.spen.2021.100914] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 08/09/2021] [Accepted: 08/11/2021] [Indexed: 11/15/2022]
Abstract
Surgery is an effective but underused treatment for drug-resistant epilepsy in children. Algorithms to identify surgical candidates and estimate the likelihood of postoperative clinical improvement may be valuable to improve access to epilepsy surgery. We provide a focused review of these approaches. For adults with epilepsy, tools to identify surgical candidates and predict seizure and cognitive outcomes (Ie, Cases for Epilepsy (toolsforepilepsy.com) and Epilepsy Surgery Grading Scale) have been validated and are in use. Analogous tools for children need development. A promising approach is to apply statistical learning tools to clinical datasets, such as electroencephalogram tracings, imaging studies, and the text of clinician notes. Demonstration projects suggest these techniques have the potential to be highly accurate, and await further validation and clinical application.
Collapse
Affiliation(s)
- Debopam Samanta
- Neurology Division, Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR
| | - Jules C. Beal
- Department of Pediatrics, Weill Cornell Medicine, New York, NY
| | - Zachary M. Grinspan
- Department of Pediatrics, Weill Cornell Medicine, New York, NY.,Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| |
Collapse
|
28
|
Sone D, Beheshti I. Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review. Front Neurosci 2021; 15:684825. [PMID: 34239413 PMCID: PMC8258163 DOI: 10.3389/fnins.2021.684825] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 05/31/2021] [Indexed: 12/13/2022] Open
Abstract
Epilepsy is a common neurological disorder characterized by recurrent and disabling seizures. An increasing number of clinical and experimental applications of machine learning (ML) methods for epilepsy and other neurological and psychiatric disorders are available. ML methods have the potential to provide a reliable and optimal performance for clinical diagnoses, prediction, and personalized medicine by using mathematical algorithms and computational approaches. There are now several applications of ML for epilepsy, including neuroimaging analyses. For precise and reliable clinical applications in epilepsy and neuroimaging, the diverse ML methodologies should be examined and validated. We review the clinical applications of ML models for brain imaging in epilepsy obtained from a PubMed database search in February 2021. We first present an overview of typical neuroimaging modalities and ML models used in the epilepsy studies and then focus on the existing applications of ML models for brain imaging in epilepsy based on the following clinical aspects: (i) distinguishing individuals with epilepsy from healthy controls, (ii) lateralization of the temporal lobe epilepsy focus, (iii) the identification of epileptogenic foci, (iv) the prediction of clinical outcomes, and (v) brain-age prediction. We address the practical problems and challenges described in the literature and suggest some future research directions.
Collapse
Affiliation(s)
- Daichi Sone
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan.,Department of Clinical and Experimental Epilepsy, University College London Institute of Neurology, London, United Kingdom
| | - Iman Beheshti
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada
| |
Collapse
|
29
|
Rigney G, Lennon M, Holderrieth P. The use of computational models in the management and prognosis of refractory epilepsy: A critical evaluation. Seizure 2021; 91:132-140. [PMID: 34153898 DOI: 10.1016/j.seizure.2021.06.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 05/05/2021] [Accepted: 06/06/2021] [Indexed: 11/17/2022] Open
Abstract
PURPOSE Drug resistant epilepsy (DRE) affects approximately 30 percent of individuals with epilepsy worldwide. Surgery remains the most effective treatment for individuals with DRE, but referral to surgery is low and only about 60 percent of individuals who undergo surgery experience seizure control postoperatively. The present paper evaluates the evidence for using computational models in the prediction of surgical resection sites and surgical outcomes for patients with DRE. METHODS We conducted a search in the Medline data base using the terms "refractory epilepsy", "drug-resistant epilepsy", "surgery", "computational model", and "artificial intelligence". Inclusion: original articles in English and case reports from 2000 to 2020. Reviews were excluded. RESULTS Clinical applications of computational models may lead to increased utilisation of surgical services through improving our ability to predict outcomes and by improving surgical outcomes outright. The identification and optimisation of nodes that are crucial for the genesis and propagation of epileptiform activity offers the most promising clinical applications of computational models discussed herein. CONCLUSION Advances in computational models may in the future significantly increase the application and efficacy of surgery for patients with DRE by optimising the site and amount of cortex to resect, but more research is needed before it achieves therapeutic utility.
Collapse
Affiliation(s)
- Grant Rigney
- The University of Oxford Department of Psychiatry, Warneford Hospital, Warneford Ln, Headington, Oxford OX3 7JX, United Kingdom.
| | - Matthew Lennon
- Department of Physiology, Anatomy and Genetics, Sherrington Building, University of Oxford, United Kingdom; Faculty of Medicine, University of New South Wales, NSW, Australia.
| | - Peter Holderrieth
- Department of Physiology, Anatomy and Genetics, Sherrington Building, University of Oxford, United Kingdom.
| |
Collapse
|
30
|
Alim-Marvasti A, Pérez-García F, Dahele K, Romagnoli G, Diehl B, Sparks R, Ourselin S, Clarkson MJ, Duncan JS. Machine Learning for Localizing Epileptogenic-Zone in the Temporal Lobe: Quantifying the Value of Multimodal Clinical-Semiology and Imaging Concordance. Front Digit Health 2021; 3:559103. [PMID: 34713078 PMCID: PMC8521800 DOI: 10.3389/fdgth.2021.559103] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 01/21/2021] [Indexed: 11/22/2022] Open
Abstract
Background: Epilepsy affects 50 million people worldwide and a third are refractory to medication. If a discrete cerebral focus or network can be identified, neurosurgical resection can be curative. Most excisions are in the temporal-lobe, and are more likely to result in seizure-freedom than extra-temporal resections. However, less than half of patients undergoing surgery become entirely seizure-free. Localizing the epileptogenic-zone and individualized outcome predictions are difficult, requiring detailed evaluations at specialist centers. Methods: We used bespoke natural language processing to text-mine 3,800 electronic health records, from 309 epilepsy surgery patients, evaluated over a decade, of whom 126 remained entirely seizure-free. We investigated the diagnostic performances of machine learning models using set-of-semiology (SoS) with and without hippocampal sclerosis (HS) on MRI as features, using STARD criteria. Findings: Support Vector Classifiers (SVC) and Gradient Boosted (GB) decision trees were the best performing algorithms for temporal-lobe epileptogenic zone localization (cross-validated Matthews correlation coefficient (MCC) SVC 0.73 ± 0.25, balanced accuracy 0.81 ± 0.14, AUC 0.95 ± 0.05). Models that only used seizure semiology were not always better than internal benchmarks. The combination of multimodal features, however, enhanced performance metrics including MCC and normalized mutual information (NMI) compared to either alone (p < 0.0001). This combination of semiology and HS on MRI increased both cross-validated MCC and NMI by over 25% (NMI, SVC SoS: 0.35 ± 0.28 vs. SVC SoS+HS: 0.61 ± 0.27). Interpretation: Machine learning models using only the set of seizure semiology (SoS) cannot unequivocally perform better than benchmarks in temporal epileptogenic-zone localization. However, the combination of SoS with an imaging feature (HS) enhance epileptogenic lobe localization. We quantified this added NMI value to be 25% in absolute terms. Despite good performance in localization, no model was able to predict seizure-freedom better than benchmarks. The methods used are widely applicable, and the performance enhancements by combining other clinical, imaging and neurophysiological features could be similarly quantified. Multicenter studies are required to confirm generalizability. Funding: Wellcome/EPSRC Center for Interventional and Surgical Sciences (WEISS) (203145Z/16/Z).
Collapse
Affiliation(s)
- Ali Alim-Marvasti
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), London, United Kingdom
- National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Fernando Pérez-García
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), London, United Kingdom
- School of Biomedical Engineering & Imaging Sciences (BMEIS), King's College London, London, United Kingdom
| | - Karan Dahele
- University College London Medical School, London, United Kingdom
| | - Gloria Romagnoli
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Rachel Sparks
- School of Biomedical Engineering & Imaging Sciences (BMEIS), King's College London, London, United Kingdom
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences (BMEIS), King's College London, London, United Kingdom
| | - Matthew J. Clarkson
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), London, United Kingdom
| | - John S. Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- National Hospital for Neurology and Neurosurgery, London, United Kingdom
| |
Collapse
|
31
|
Nair P, Aghoram R, Khilari M. Applications of artificial intelligence in epilepsy. INTERNATIONAL JOURNAL OF ADVANCED MEDICAL AND HEALTH RESEARCH 2021. [DOI: 10.4103/ijamr.ijamr_94_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
|
32
|
Huang J, Li Y, Xie H, Yang S, Jiang C, Sun W, Li D, Liao Y, Ba X, Xiao L. Abnormal Intrinsic Brain Activity and Neuroimaging-Based fMRI Classification in Patients With Herpes Zoster and Postherpetic Neuralgia. Front Neurol 2020; 11:532110. [PMID: 33192967 PMCID: PMC7642867 DOI: 10.3389/fneur.2020.532110] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 09/01/2020] [Indexed: 01/20/2023] Open
Abstract
Objective: Neuroimaging studies on neuropathic pain have discovered abnormalities in brain structure and function. However, the brain pattern changes from herpes zoster (HZ) to postherpetic neuralgia (PHN) remain unclear. The present study aimed to compare the brain activity between HZ and PHN patients and explore the potential neural mechanisms underlying cognitive impairment in neuropathic pain patients. Methods: Resting-state functional magnetic resonance imaging (MRI) was carried out among 28 right-handed HZ patients, 24 right-handed PHN patients, and 20 healthy controls (HC), using a 3T MRI system. The amplitude of low-frequency fluctuation (ALFF) was analyzed to detect the brain activity of the patients. Correlations between ALFF and clinical pain scales were assessed in two groups of patients. Differences in brain activity between groups were examined and used in a support vector machine (SVM) algorithm for the subjects' classification. Results: Spontaneous brain activity was reduced in both patient groups. Compared with HC, patients from both groups had decreased ALFF in the precuneus, posterior cingulate cortex, and middle temporal gyrus. Meanwhile, the neural activities of angular gyrus and middle frontal gyrus were lowered in HZ and PHN patients, respectively. Reduced ALFF in these regions was associated with clinical pain scales in PHN patients only. Using SVM algorithm, the decreased brain activity in these regions allowed for the classification of neuropathic pain patients (HZ and PHN) and HC. Moreover, HZ and PHN patients are also roughly classified by the same model. Conclusion: Our study indicated that mean ALFF values in these pain-related regions can be used as a functional MRI-based biomarker for the classification of subjects with different pain conditions. Altered brain activity might contribute to PHN-induced pain.
Collapse
Affiliation(s)
- Jiabin Huang
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Yongxin Li
- Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Huijun Xie
- Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Shaomin Yang
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Changyu Jiang
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Wuping Sun
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Disen Li
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Yuliang Liao
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Xiyuan Ba
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Lizu Xiao
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| |
Collapse
|
33
|
Alakus TB, Turkoglu I. Comparison of deep learning approaches to predict COVID-19 infection. CHAOS, SOLITONS, AND FRACTALS 2020; 140:110120. [PMID: 33519109 PMCID: PMC7833512 DOI: 10.1016/j.chaos.2020.110120] [Citation(s) in RCA: 107] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 07/10/2020] [Indexed: 05/05/2023]
Abstract
The SARS-CoV2 virus, which causes COVID-19 (coronavirus disease) has become a pandemic and has expanded all over the world. Because of increasing number of cases day by day, it takes time to interpret the laboratory findings thus the limitations in terms of both treatment and findings are emerged. Due to such limitations, the need for clinical decisions making system with predictive algorithms has arisen. Predictive algorithms could potentially ease the strain on healthcare systems by identifying the diseases. In this study, we perform clinical predictive models that estimate, using deep learning and laboratory data, which patients are likely to receive a COVID-19 disease. To evaluate the predictive performance of our models, precision, F1-score, recall, AUC, and accuracy scores calculated. Models were tested with 18 laboratory findings from 600 patients and validated with 10 fold cross-validation and train-test split approaches. The experimental results indicate that our predictive models identify patients that have COVID-19 disease at an accuracy of 86.66%, F1-score of 91.89%, precision of 86.75%, recall of 99.42%, and AUC of 62.50%. It is observed that predictive models trained on laboratory findings could be used to predict COVID-19 infection, and can be helpful for medical experts to prioritize the resources correctly. Our models (available at (https://github.com/burakalakuss/COVID-19-Clinical)) can be employed to assists medical experts in validating their initial laboratory findings, and can also be used for clinical prediction studies.
Collapse
Affiliation(s)
- Talha Burak Alakus
- Kirklareli University, Engineering Faculty, Department of Software Engineering, Kirklareli, 39000, Turkey
| | - Ibrahim Turkoglu
- Firat University, Technology Faculty, Department of Software Engineering, Elazig, 23119, Turkey
| |
Collapse
|
34
|
Andrews JP, Ammanuel S, Kleen J, Khambhati AN, Knowlton R, Chang EF. Early seizure spread and epilepsy surgery: A systematic review. Epilepsia 2020; 61:2163-2172. [PMID: 32944952 DOI: 10.1111/epi.16668] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 08/03/2020] [Accepted: 08/04/2020] [Indexed: 01/03/2023]
Abstract
OBJECTIVE A fundamental question in epilepsy surgery is how to delineate the margins of cortex that must be resected to result in seizure freedom. Whether and which areas showing seizure activity early in ictus must be removed to avoid postoperative recurrence of seizures is an area of ongoing research. Seizure spread dynamics in the initial seconds of ictus are often correlated with postoperative outcome; there is neither a consensus definition of early spread nor a concise summary of the existing literature linking seizure spread to postsurgical seizure outcomes. The present study is intended to summarize the literature that links seizure spread to postoperative seizure outcome and to provide a framework for quantitative assessment of early seizure spread. METHODS A systematic review was carried out according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A Medline search identified clinical studies reporting data on seizure spread measured by intracranial electrodes, having at least 10 subjects and reporting at least 1-year postoperative outcome in the English literature from 1990 to 2019. Studies were evaluated regarding support for a primary hypothesis: Areas of early seizure spread represent cortex with seizure-generating potential. RESULTS The search yielded 4562 studies: 15 studies met inclusion criteria and 7 studies supported the primary hypothesis. The methods and metrics used to describe seizure spread were heterogenous. The timeframe of seizure spread associated with seizure outcome ranged from 1-14 seconds, with large, well-designed, retrospective studies pointing to 3-10 seconds as most likely to provide meaningful correlates of postoperative seizure freedom. SIGNIFICANCE The complex correlation between electrophysiologic seizure spread and the potential for seizure generation needs further elucidation. Prospective cohort studies or trials are needed to evaluate epilepsy surgery targeting cortex involved in the first 3-10 seconds of ictus.
Collapse
Affiliation(s)
- John P Andrews
- Department of Neurological Surgery, School of Medicine, University of California-San Francisco, San Francisco, California, USA
| | - Simon Ammanuel
- Department of Neurological Surgery, School of Medicine, University of California-San Francisco, San Francisco, California, USA
| | - Jonathan Kleen
- Department of Neurology, School of Medicine, University of California-San Francisco, San Francisco, California, USA
| | - Ankit N Khambhati
- Department of Neurological Surgery, School of Medicine, University of California-San Francisco, San Francisco, California, USA
| | - Robert Knowlton
- Department of Neurology, School of Medicine, University of California-San Francisco, San Francisco, California, USA
| | - Edward F Chang
- Department of Neurological Surgery, School of Medicine, University of California-San Francisco, San Francisco, California, USA
| |
Collapse
|
35
|
Beniczky S, Karoly P, Nurse E, Ryvlin P, Cook M. Machine learning and wearable devices of the future. Epilepsia 2020; 62 Suppl 2:S116-S124. [PMID: 32712958 DOI: 10.1111/epi.16555] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 05/05/2020] [Accepted: 05/08/2020] [Indexed: 01/06/2023]
Abstract
Machine learning (ML) is increasingly recognized as a useful tool in healthcare applications, including epilepsy. One of the most important applications of ML in epilepsy is seizure detection and prediction, using wearable devices (WDs). However, not all currently available algorithms implemented in WDs are using ML. In this review, we summarize the state of the art of using WDs and ML in epilepsy, and we outline future development in these domains. There is published evidence for reliable detection of epileptic seizures using implanted electroencephalography (EEG) electrodes and wearable, non-EEG devices. Application of ML using the data recorded with WDs from a large number of patients could change radically the way we diagnose and manage patients with epilepsy.
Collapse
Affiliation(s)
- Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark.,Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.,Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Philippa Karoly
- The Graeme Clark Institute, The University of Melbourne, Melbourne, Vic., Australia
| | - Ewan Nurse
- The Graeme Clark Institute, The University of Melbourne, Melbourne, Vic., Australia
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, CHUV, Lausanne, Switzerland
| | - Mark Cook
- The Graeme Clark Institute, The University of Melbourne, Melbourne, Vic., Australia
| |
Collapse
|
36
|
Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:6405930. [PMID: 32300361 PMCID: PMC7142389 DOI: 10.1155/2020/6405930] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2019] [Revised: 01/07/2020] [Accepted: 01/16/2020] [Indexed: 12/11/2022]
Abstract
Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from normal controls (NCs) and for detecting abnormal brain regions in schizophrenia has several benefits and can provide a reference for the clinical diagnosis of schizophrenia. In this study, structural magnetic resonance images (sMRIs) from SZ patients and NCs were used for discriminative analysis. This study proposed an ML framework based on coarse-to-fine feature selection. The proposed framework used two-sample t-tests to extract the differences between groups first, then further eliminated the nonrelevant and redundant features with recursive feature elimination (RFE), and finally utilized the support vector machine (SVM) to learn the decision models with selected gray matter (GM) and white matter (WM) features. Previous studies have tended to report differences at the group level instead of at the individual level and cannot be widely applied. The method proposed in this study extends the diagnosis to the individual level and has a higher recognition rate than previous methods. The experimental results of this study demonstrate that the proposed framework distinguishes SZ patients from NCs, with the highest classification accuracy reaching over 85%. The identified biomarkers are also consistent with previous literature findings. As a universal method, the proposed framework can be extended to diagnose other diseases.
Collapse
|
37
|
Zhao J, Wang T, Lv Q, Zhou N. Expression of heat shock protein 70 and Annexin A1 in serum of patients with acutely severe traumatic brain injury. Exp Ther Med 2020; 19:1896-1902. [PMID: 32104246 PMCID: PMC7026958 DOI: 10.3892/etm.2019.8357] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 10/24/2019] [Indexed: 12/21/2022] Open
Abstract
Trends of early expression levels of heat shock protein 70 (Hsp70) and Annexin A1 (ANXA1) in serum of patients with acutely severe traumatic brain injury and the effects on clinical prognosis were investigated. Eighty-four patients with severe traumatic brain injury admitted to Binzhou Center Hospital from June 2014 to July 2017 were selected as the experimental group. Glasgow coma scale and acute physiology and chronic health evaluation II (APACHE II) score were obtained after admission. A further 75 healthy subjects were selected as the control group. Serum expression of Hsp70 and ANXA1 in the two groups was detected by enzyme-linked immunosorbent assay on the 1st, 2nd, 3rd and 4th day after admission. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic value of Hsp70 and ANXA1 for the death of patients with acutely severe traumatic brain injury. Compared with the control group, expression of Hsp70 in the experimental group was significantly increased on the 1st, 2nd, 3rd and 4th day after admission (P<0.05), while expression of ANXA1 was significantly decreased (P<0.05). Expression levels of serum Hsp70 in the experimental group reached the peak on the 3rd day after admission, and the difference was statistically significant compared with the 1st, 2nd and 4th day (P<0.05). Expression of ANXA1 was the lowest on the 3rd day, and the difference was statistically significant compared with the 1st, 2nd and 4th day (P<0.05). The ROC curve analysis showed that the area under the curve of serum Hsp70 and ANXA1 was, respectively, 0.721 (95% CI: 0.611-0.829) and 0.684 (95% CI: 0.569-0.799). In conclusion, Hsp70 and ANXA1 may be involved in the occurrence and progression of acutely severe traumatic brain injury. The detection of serum Hsp70 and ANXA1 has certain diagnostic value for the death of patients with acutely severe traumatic brain injury.
Collapse
Affiliation(s)
- Junjing Zhao
- Department of Neurosurgery, Binzhou Center Hospital, Binzhou, Shandong 251700, P.R. China
| | - Tao Wang
- Department of Neurosurgery, Liaocheng Third People's Hospital, Liaocheng, Shandong 252000, P.R. China
| | - Qiming Lv
- Department of Neurosurgery, Liaocheng Third People's Hospital, Liaocheng, Shandong 252000, P.R. China
| | - Nan Zhou
- Department of Health Care, Jinan Central Hospital, Jinan, Shandong 250013, P.R. China
| |
Collapse
|
38
|
Zhou B, An D, Xiao F, Niu R, Li W, Li W, Tong X, Kemp GJ, Zhou D, Gong Q, Lei D. Machine learning for detecting mesial temporal lobe epilepsy by structural and functional neuroimaging. Front Med 2020; 14:630-641. [PMID: 31912429 DOI: 10.1007/s11684-019-0718-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Accepted: 09/07/2019] [Indexed: 02/04/2023]
Abstract
Mesial temporal lobe epilepsy (mTLE), the most common type of focal epilepsy, is associated with functional and structural brain alterations. Machine learning (ML) techniques have been successfully used in discriminating mTLE from healthy controls. However, either functional or structural neuroimaging data are mostly used separately as input, and the opportunity to combine both has not been exploited yet. We conducted a multimodal ML study based on functional and structural neuroimaging measures. We enrolled 37 patients with left mTLE, 37 patients with right mTLE, and 74 healthy controls and trained a support vector ML model to distinguish them by using each measure and the combinations of the measures. For each single measure, we obtained a mean accuracy of 74% and 69% for discriminating left mTLE and right mTLE from controls, respectively, and 64% when all patients were combined. We achieved an accuracy of 78% by integrating functional data and 79% by integrating structural data for left mTLE, and the highest accuracy of 84% was obtained when all functional and structural measures were combined. These findings suggest that combining multimodal measures within a single model is a promising direction for improving the classification of individual patients with mTLE.
Collapse
Affiliation(s)
- Baiwan Zhou
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Dongmei An
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Fenglai Xiao
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, 610041, China.,Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, London, WC1E 6BT, UK
| | - Running Niu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Wenbin Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Wei Li
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Xin Tong
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Graham J Kemp
- Institute of Ageing and Chronic Disease, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, L9 7AL, UK
| | - Dong Zhou
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, 610041, China.
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, China.,Department of Psychology, School of Public Administration, Sichuan University, Chengdu, 610041, China
| | - Du Lei
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, China. .,Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK. .,Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, 45219, USA.
| |
Collapse
|
39
|
Goyal A, Ngufor C, Kerezoudis P, McCutcheon B, Storlie C, Bydon M. Can machine learning algorithms accurately predict discharge to nonhome facility and early unplanned readmissions following spinal fusion? Analysis of a national surgical registry. J Neurosurg Spine 2019; 31:568-578. [PMID: 31174185 DOI: 10.3171/2019.3.spine181367] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 03/12/2019] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Nonhome discharge and unplanned readmissions represent important cost drivers following spinal fusion. The authors sought to utilize different machine learning algorithms to predict discharge to rehabilitation and unplanned readmissions in patients receiving spinal fusion. METHODS The authors queried the 2012-2013 American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) for patients undergoing cervical or lumbar spinal fusion. Outcomes assessed included discharge to nonhome facility and unplanned readmissions within 30 days after surgery. A total of 7 machine learning algorithms were evaluated. Predictive hierarchical clustering of procedure codes was used to increase model performance. Model performance was evaluated using overall accuracy and area under the receiver operating characteristic curve (AUC), as well as sensitivity, specificity, and positive and negative predictive values. These performance metrics were computed for both the imputed and unimputed (missing values dropped) datasets. RESULTS A total of 59,145 spinal fusion cases were analyzed. The incidence rates of discharge to nonhome facility and 30-day unplanned readmission were 12.6% and 4.5%, respectively. All classification algorithms showed excellent discrimination (AUC > 0.80, range 0.85-0.87) for predicting nonhome discharge. The generalized linear model showed comparable performance to other machine learning algorithms. By comparison, all models showed poorer predictive performance for unplanned readmission, with AUC ranging between 0.63 and 0.66. Better predictive performance was noted with models using imputed data. CONCLUSIONS In an analysis of patients undergoing spinal fusion, multiple machine learning algorithms were found to reliably predict nonhome discharge with modest performance noted for unplanned readmissions. These results provide early evidence regarding the feasibility of modern machine learning classifiers in predicting these outcomes and serve as possible clinical decision support tools to facilitate shared decision making.
Collapse
Affiliation(s)
- Anshit Goyal
- 1Mayo Clinic Neuro-Informatics Laboratory
- 2Department of Neurosurgery, and
| | - Che Ngufor
- 3Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota
| | | | | | - Curtis Storlie
- 3Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota
| | - Mohamad Bydon
- 1Mayo Clinic Neuro-Informatics Laboratory
- 2Department of Neurosurgery, and
| |
Collapse
|
40
|
Abbasi B, Goldenholz DM. Machine learning applications in epilepsy. Epilepsia 2019; 60:2037-2047. [PMID: 31478577 PMCID: PMC9897263 DOI: 10.1111/epi.16333] [Citation(s) in RCA: 167] [Impact Index Per Article: 33.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 07/25/2019] [Accepted: 08/12/2019] [Indexed: 02/05/2023]
Abstract
Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recognition, language processing, and data mining, machine learning techniques have received increasing attention in medical applications, ranging from automated imaging analysis to disease forecasting. This review examines the parallel progress made in epilepsy, highlighting applications in automated seizure detection from electroencephalography (EEG), video, and kinetic data, automated imaging analysis and pre-surgical planning, prediction of medication response, and prediction of medical and surgical outcomes using a wide variety of data sources. A brief overview of commonly used machine learning approaches, as well as challenges in further application of machine learning techniques in epilepsy, is also presented. With increasing computational capabilities, availability of effective machine learning algorithms, and accumulation of larger datasets, clinicians and researchers will increasingly benefit from familiarity with these techniques and the significant progress already made in their application in epilepsy.
Collapse
Affiliation(s)
- Bardia Abbasi
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA 02215
| | | |
Collapse
|
41
|
Lopes RR, van Mourik MS, Schaft EV, Ramos LA, Baan J, Vendrik J, de Mol BAJM, Vis MM, Marquering HA. Value of machine learning in predicting TAVI outcomes. Neth Heart J 2019; 27:443-450. [PMID: 31111457 PMCID: PMC6712116 DOI: 10.1007/s12471-019-1285-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Transcatheter aortic valve implantation (TAVI) has become a commonly applied procedure for high-risk aortic valve stenosis patients. However, for some patients, this procedure does not result in the expected benefits. Previous studies indicated that it is difficult to predict the beneficial effects for specific patients. We aim to study the accuracy of various traditional machine learning (ML) algorithms in the prediction of TAVI outcomes. METHODS AND RESULTS Clinical and laboratory data from 1,478 TAVI patients from a single centre were collected. The outcome measures were improvement of dyspnoea and mortality. Three experiments were performed using (1) screening data, (2) laboratory data, and (3) the combination of both. Five well-established ML techniques were implemented, and the models were evaluated based on the area under the curve (AUC). Random forest classifier achieved the highest AUC (0.70) for predicting mortality. Logistic regression had the highest AUC (0.56) in predicting improvement of dyspnoea. CONCLUSIONS In our single-centre TAVI population, the tree-based models were slightly more accurate than others in predicting mortality. However, ML models performed poorly in predicting improvement of dyspnoea.
Collapse
Affiliation(s)
- R R Lopes
- Department of Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - M S van Mourik
- Heart Centre, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands
| | - E V Schaft
- Technical Medicine, University of Twente, Enschede, The Netherlands
| | - L A Ramos
- Department of Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam UMC, Amsterdam, The Netherlands
| | - J Baan
- Heart Centre, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands
| | - J Vendrik
- Heart Centre, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands
| | - B A J M de Mol
- Heart Centre, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands
| | - M M Vis
- Heart Centre, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands
| | - H A Marquering
- Department of Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
| |
Collapse
|
42
|
Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review. Neurosurg Rev 2019; 43:1235-1253. [PMID: 31422572 DOI: 10.1007/s10143-019-01163-8] [Citation(s) in RCA: 91] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 07/05/2019] [Accepted: 08/06/2019] [Indexed: 12/27/2022]
Abstract
Machine learning (ML) involves algorithms learning patterns in large, complex datasets to predict and classify. Algorithms include neural networks (NN), logistic regression (LR), and support vector machines (SVM). ML may generate substantial improvements in neurosurgery. This systematic review assessed the current state of neurosurgical ML applications and the performance of algorithms applied. Our systematic search strategy yielded 6866 results, 70 of which met inclusion criteria. Performance statistics analyzed included area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity. Natural language processing (NLP) was used to model topics across the corpus and to identify keywords within surgical subspecialties. ML applications were heterogeneous. The densest cluster of studies focused on preoperative evaluation, planning, and outcome prediction in spine surgery. The main algorithms applied were NN, LR, and SVM. Input and output features varied widely and were listed to facilitate future research. The accuracy (F(2,19) = 6.56, p < 0.01) and specificity (F(2,16) = 5.57, p < 0.01) of NN, LR, and SVM differed significantly. NN algorithms demonstrated significantly higher accuracy than LR. SVM demonstrated significantly higher specificity than LR. We found no significant difference between NN, LR, and SVM AUC and sensitivity. NLP topic modeling reached maximum coherence at seven topics, which were defined by modeling approach, surgery type, and pathology themes. Keywords captured research foci within surgical domains. ML technology accurately predicts outcomes and facilitates clinical decision-making in neurosurgery. NNs frequently outperformed other algorithms on supervised learning tasks. This study identified gaps in the literature and opportunities for future neurosurgical ML research.
Collapse
|
43
|
Tunthanathip T, Sae-heng S, Oearsakul T, Sakarunchai I, Kaewborisutsakul A, Taweesomboonyat C. Machine learning applications for the prediction of surgical site infection in neurological operations. Neurosurg Focus 2019; 47:E7. [DOI: 10.3171/2019.5.focus19241] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 05/21/2019] [Indexed: 12/21/2022]
Abstract
OBJECTIVESurgical site infection (SSI) following a neurosurgical operation is a complication that impacts morbidity, mortality, and economics. Currently, machine learning (ML) algorithms are used for outcome prediction in various neurosurgical aspects. The implementation of ML algorithms to learn from medical data may help in obtaining prognostic information on diseases, especially SSIs. The purpose of this study was to compare the performance of various ML models for predicting surgical infection after neurosurgical operations.METHODSA retrospective cohort study was conducted on patients who had undergone neurosurgical operations at tertiary care hospitals between 2010 and 2017. Supervised ML algorithms, which included decision tree, naive Bayes with Laplace correction, k-nearest neighbors, and artificial neural networks, were trained and tested as binary classifiers (infection or no infection). To evaluate the ML models from the testing data set, their sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), as well as their accuracy, receiver operating characteristic curve, and area under the receiver operating characteristic curve (AUC) were analyzed.RESULTSData were available for 1471 patients in the study period. The SSI rate was 4.6%, and the type of SSI was superficial, deep, and organ/space in 1.2%, 0.8%, and 2.6% of cases, respectively. Using the backward stepwise method, the authors determined that the significant predictors of SSI in the multivariable Cox regression analysis were postoperative CSF leakage/subgaleal collection (HR 4.24, p < 0.001) and postoperative fever (HR 1.67, p = 0.04). Compared with other ML algorithms, the naive Bayes had the highest performance with sensitivity at 63%, specificity at 87%, PPV at 29%, NPV at 96%, and AUC at 76%.CONCLUSIONSThe naive Bayes algorithm is highlighted as an accurate ML method for predicting SSI after neurosurgical operations because of its reasonable accuracy. Thus, it can be used to effectively predict SSI in individual neurosurgical patients. Therefore, close monitoring and allocation of treatment strategies can be informed by ML predictions in general practice.
Collapse
|
44
|
Yao L, Cai M, Chen Y, Shen C, Shi L, Guo Y. Prediction of antiepileptic drug treatment outcomes of patients with newly diagnosed epilepsy by machine learning. Epilepsy Behav 2019; 96:92-97. [PMID: 31121513 DOI: 10.1016/j.yebeh.2019.04.006] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 03/22/2019] [Accepted: 04/07/2019] [Indexed: 11/17/2022]
Abstract
OBJECTIVE The objective of this study was to build a supervised machine learning-based classifier, which can accurately predict the outcomes of antiepileptic drug (AED) treatment of patients with newly diagnosed epilepsy. METHODS We collected information from 287 patients with newly diagnosed epilepsy between 2009 and 2017 at the Second Affiliated Hospital of Zhejiang University. Patients were prospectively followed up for at least 3 years. A number of features, including demographic features, medical history, and auxiliary examinations (electroencephalogram [EEG] and magnetic resonance imaging [MRI]) are selected to distinguish patients with different remission outcomes. Seizure outcomes classified as remission and never remission. In addition, remission is further divided into early remission and late remission. Five classical machine learning algorithms, i.e., Decision Tree, Random Forest, Support Vector Machine, XGBoost, and Logistic Regression, are selected and trained by our dataset to get classification models. RESULTS Our study shows that 1) compared with the other four algorithms, the XGBoost algorithm based machine learning model achieves the best prediction performance of the AED treatment outcomes between remission and never remission patients with an F1 score of 0.947 and an area under the curve (AUC) value of 0.979; 2) The best discriminative factor for remission and never remission patients is higher number of seizures before treatment (>3); 3) XGBoost-based machine learning model also offers the best prediction between early remission and later remission patients, with an F1 score of 0.836 and an AUC value of 0.918; 4) multiple seizure type has the highest dependence to the categories of early and late remission patients. SIGNIFICANCES Our XGBoost-based machine learning classifier accurately predicts the most probable AED treatment outcome of a patient after he/she finishes all the standard examinations for the epilepsy disease. The classifier's prediction result could help disease guide counseling and eventually improve treatment strategies.
Collapse
Affiliation(s)
- Lijun Yao
- Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai 200124, PR China.
| | - Mengting Cai
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, PR China
| | - Yang Chen
- School of Computer Science, Fudan University, Shanghai 201203, PR China
| | - Chunhong Shen
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, PR China
| | - Lei Shi
- The State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100080, PR China
| | - Yi Guo
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, PR China.
| |
Collapse
|
45
|
Hwang G, Nair VA, Mathis J, Cook CJ, Mohanty R, Zhao G, Tellapragada N, Ustine C, Nwoke OO, Rivera-Bonet C, Rozman M, Allen L, Forseth C, Almane DN, Kraegel P, Nencka A, Felton E, Struck AF, Birn R, Maganti R, Conant LL, Humphries CJ, Hermann B, Raghavan M, DeYoe EA, Binder JR, Meyerand E, Prabhakaran V. Using Low-Frequency Oscillations to Detect Temporal Lobe Epilepsy with Machine Learning. Brain Connect 2019; 9:184-193. [PMID: 30803273 PMCID: PMC6484357 DOI: 10.1089/brain.2018.0601] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The National Institutes of Health-sponsored Epilepsy Connectome Project aims to characterize connectivity changes in temporal lobe epilepsy (TLE) patients. The magnetic resonance imaging protocol follows that used in the Human Connectome Project, and includes 20 min of resting-state functional magnetic resonance imaging acquired at 3T using 8-band multiband imaging. Glasser parcellation atlas was combined with the FreeSurfer subcortical regions to generate resting-state functional connectivity (RSFC), amplitude of low-frequency fluctuations (ALFFs), and fractional ALFF measures. Seven different frequency ranges such as Slow-5 (0.01-0.027 Hz) and Slow-4 (0.027-0.073 Hz) were selected to compute these measures. The goal was to train machine learning classification models to discriminate TLE patients from healthy controls, and to determine which combination of the resting state measure and frequency range produced the best classification model. The samples included age- and gender-matched groups of 60 TLE patients and 59 healthy controls. Three traditional machine learning models were trained: support vector machine, linear discriminant analysis, and naive Bayes classifier. The highest classification accuracy was obtained using RSFC measures in the Slow-4 + 5 band (0.01-0.073 Hz) as features. Leave-one-out cross-validation accuracies were ∼83%, with receiver operating characteristic area-under-the-curve reaching close to 90%. Increased connectivity from right area posterior 9-46v in TLE patients contributed to the high accuracies. With increased sample sizes in the near future, better machine learning models will be trained not only to aid the diagnosis of TLE, but also as a tool to understand this brain disorder.
Collapse
Affiliation(s)
- Gyujoon Hwang
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Veena A. Nair
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Jed Mathis
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Cole J. Cook
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Rosaleena Mohanty
- Department of Electrical Engineering, University of Wisconsin-Madison, Madison, Wisconsin
| | - Gengyan Zhao
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | | | - Candida Ustine
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | | | | | - Megan Rozman
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Linda Allen
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Courtney Forseth
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Dace N. Almane
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Peter Kraegel
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Andrew Nencka
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Elizabeth Felton
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Aaron F. Struck
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Rasmus Birn
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin
| | - Rama Maganti
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Lisa L. Conant
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Colin J. Humphries
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Bruce Hermann
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Manoj Raghavan
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Edgar A. DeYoe
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Jeffrey R. Binder
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Elizabeth Meyerand
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin
| | - Vivek Prabhakaran
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| |
Collapse
|
46
|
Wang J, Li Y, Wang Y, Huang W. Multimodal Data and Machine Learning for Detecting Specific Biomarkers in Pediatric Epilepsy Patients With Generalized Tonic-Clonic Seizures. Front Neurol 2018; 9:1038. [PMID: 30619025 PMCID: PMC6297879 DOI: 10.3389/fneur.2018.01038] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 11/19/2018] [Indexed: 01/16/2023] Open
Abstract
Previous neuroimaging studies of epilepsy with generalized tonic-clonic seizures (GTCS) focus mainly on adults. However, the neural mechanisms that underline this type of epilepsy remain unclear, especially for children. The aim of the present study was to detect the effect of epilepsy on brains of children with GTCS and to investigate whether the changes in the brain can be used to discriminate between epileptic children and healthy children at the level of the individual. To achieve this purpose, we measured gray matter (GM) volume and fractional amplitude of low-frequency fluctuation (fALFF) differences on multimodel magnetic resonance imaging in 14 children with GTCS and 30 age- and gender-matched healthy controls. The patients showed GM volume reduction and a fALFF increase in the thalamus, hippocampus, temporal and other deep nuclei. A significant decrease of fALFF was mainly found in the default mode network (DMN). In addition, epileptic duration was significantly negatively related to the GM volumes and significantly positively related to the fALFF value of right thalamus. A support vector machine (SVM) applied to the GM volume of the right thalamus correctly identified epileptic children with a statistically significant accuracy of 74.42% (P < 0.002). A SVM applied to the fALFF of the right thalamus correctly identified epileptic children with a statistically significant accuracy of 83.72% (P < 0.002). The consistent neuroimaging results indicated that the right thalamus plays an important role in reflecting the chronic damaging effect of GTCS epilepsy in children. The length of time of a child's epileptic history was correlated with greater GM volume reduction and a fALFF increase in the right thalamus. GM volumes and fALFF values in the right thalamus can identify children with GTCS from the healthy controls with high accuracy and at an individual subject level. These results are likely to be valuable in explaining the clinical problems and understanding the brain abnormalities underlying this disorder.
Collapse
Affiliation(s)
- Jianping Wang
- The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yongxin Li
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Ya Wang
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Wenhua Huang
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| |
Collapse
|
47
|
Park CH, Ohn SH. A challenge of predicting seizure frequency in temporal lobe epilepsy using neuroanatomical features. Neurosci Lett 2018; 692:115-121. [PMID: 30408498 DOI: 10.1016/j.neulet.2018.11.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 10/31/2018] [Accepted: 11/02/2018] [Indexed: 12/13/2022]
Abstract
The pathological and clinical characteristics of temporal lobe epilepsy (TLE) tend to be affected by epileptic seizures, specifically represented by seizure lateralization and frequency. Although the lateralization of the epileptogenic zone can be clarified in terms of neuroanatomical damage, there have been conflicting findings on the relationship between seizure frequency and neuroanatomical damage. In this study we sought to examine the relationship in the framework of machine learning-based predictive modeling. We acquired 60 grey matter (GM) anatomical features from structural MRI data and 46 white matter (WM) anatomical features from diffusion-weighted MRI data for 42 TLE patients and 45 healthy controls and applied the random forests method to the neuroanatomical features. We demonstrated that, whereas the neuroanatomical features were promising markers for the discrimination of the TLE patients from the healthy controls, the separation between the TLE patients with low and high seizure frequency on the basis of the neuroanatomical features was challenging. When we applied feature selection techniques for the construction of the predictive models, a greater number of features were selected as being relevant to the distinction of the TLE patients from the healthy controls than to the classification of the TLE patients according to seizure frequency. Furthermore, we adopted model-based clustering analysis and showed that seizure frequency-based subgroups were not matched well with neuroanatomy-based subgroups in the TLE patients. We propose that the challenge of predicting seizure frequency using neuroanatomical features could be attributable to considerable inter-individual variability in neuroanatomical damage among seizure frequency-based subgroups.
Collapse
Affiliation(s)
- Chang-Hyun Park
- Center for Neuroprosthetics and Brain Mind Institute, Swiss Federal Institute of Technology (EPFL), Geneva, Switzerland
| | - Suk Hoon Ohn
- Department of Physical Medicine and Rehabilitation, College of Medicine, Hallym University, Anyang, Gyeonggi, Republic of Korea.
| |
Collapse
|
48
|
Lundberg SM, Nair B, Vavilala MS, Horibe M, Eisses MJ, Adams T, Liston DE, Low DKW, Newman SF, Kim J, Lee SI. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat Biomed Eng 2018; 2:749-760. [PMID: 31001455 PMCID: PMC6467492 DOI: 10.1038/s41551-018-0304-0] [Citation(s) in RCA: 663] [Impact Index Per Article: 110.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 08/31/2018] [Indexed: 12/21/2022]
Abstract
Although anaesthesiologists strive to avoid hypoxemia during surgery, reliably predicting future intraoperative hypoxemia is not currently possible. Here, we report the development and testing of a machine-learning-based system that, in real time during general anaesthesia, predicts the risk of hypoxemia and provides explanations of the risk factors. The system, which was trained on minute-by-minute data from the electronic medical records of over fifty thousand surgeries, improved the performance of anaesthesiologists when providing interpretable hypoxemia risks and contributing factors. The explanations for the predictions are broadly consistent with the literature and with prior knowledge from anaesthesiologists. Our results suggest that if anaesthesiologists currently anticipate 15% of hypoxemia events, with this system's assistance they would anticipate 30% of them, a large portion of which may benefit from early intervention because they are associated with modifiable factors. The system can help improve the clinical understanding of hypoxemia risk during anaesthesia care by providing general insights into the exact changes in risk induced by certain patient or procedure characteristics.
Collapse
Affiliation(s)
- Scott M Lundberg
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Bala Nair
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA
- Center for Perioperative and Pain initiatives in Quality Safety Outcome, University of Washington, Seattle, WA, USA
- Harborview Injury Prevention and Research Center, University of Washington, Seattle, WA, USA
| | - Monica S Vavilala
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA
- Center for Perioperative and Pain initiatives in Quality Safety Outcome, University of Washington, Seattle, WA, USA
- Harborview Injury Prevention and Research Center, University of Washington, Seattle, WA, USA
| | - Mayumi Horibe
- Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA
| | - Michael J Eisses
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA
- Seattle Children's Hospital, Seattle, WA, USA
| | - Trevor Adams
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA
- Seattle Children's Hospital, Seattle, WA, USA
| | - David E Liston
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA
- Seattle Children's Hospital, Seattle, WA, USA
| | - Daniel King-Wai Low
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA
- Seattle Children's Hospital, Seattle, WA, USA
| | - Shu-Fang Newman
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA
- Center for Perioperative and Pain initiatives in Quality Safety Outcome, University of Washington, Seattle, WA, USA
| | - Jerry Kim
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA
- Seattle Children's Hospital, Seattle, WA, USA
| | - Su-In Lee
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
| |
Collapse
|
49
|
Nissen IA, Stam CJ, van Straaten ECW, Wottschel V, Reijneveld JC, Baayen JC, de Witt Hamer PC, Idema S, Velis DN, Hillebrand A. Localization of the Epileptogenic Zone Using Interictal MEG and Machine Learning in a Large Cohort of Drug-Resistant Epilepsy Patients. Front Neurol 2018; 9:647. [PMID: 30131762 PMCID: PMC6090046 DOI: 10.3389/fneur.2018.00647] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Accepted: 07/18/2018] [Indexed: 01/01/2023] Open
Abstract
Objective: Epilepsy surgery results in seizure freedom in the majority of drug-resistant patients. To improve surgery outcome we studied whether MEG metrics combined with machine learning can improve localization of the epileptogenic zone, thereby enhancing the chance of seizure freedom. Methods: Presurgical interictal MEG recordings of 94 patients (64 seizure-free >1y post-surgery) were analyzed to extract four metrics in source space: delta power, low-to-high-frequency power ratio, functional connectivity (phase lag index), and minimum spanning tree betweenness centrality. At the group level, we estimated the overlap of the resection area with the five highest values for each metric and determined whether this overlap differed between surgery outcomes. At the individual level, those metrics were used in machine learning classifiers (linear support vector machine (SVM) and random forest) to distinguish between resection and non-resection areas and between surgery outcome groups. Results: The highest values, for all metrics, overlapped with the resection area in more than half of the patients, but the overlap did not differ between surgery outcome groups. The classifiers distinguished the resection areas from non-resection areas with 59.94% accuracy (95% confidence interval: 59.67–60.22%) for SVM and 60.34% (59.98–60.71%) for random forest, but could not differentiate seizure-free from not seizure-free patients [43.77% accuracy (42.08–45.45%) for SVM and 49.03% (47.25–50.82%) for random forest]. Significance: All four metrics localized the resection area but did not distinguish between surgery outcome groups, demonstrating that metrics derived from interictal MEG correspond to expert consensus based on several presurgical evaluation modalities, but do not yet localize the epileptogenic zone. Metrics should be improved such that they correspond to the resection area in seizure-free patients but not in patients with persistent seizures. It is important to test such localization strategies at an individual level, for example by using machine learning or individualized models, since surgery is individually tailored.
Collapse
Affiliation(s)
- Ida A Nissen
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, Netherlands
| | - Elisabeth C W van Straaten
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, Netherlands
| | - Viktor Wottschel
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, Netherlands
| | - Jaap C Reijneveld
- Brain Tumor Center Amsterdam & Department of Neurology, VU University Medical Center, Amsterdam, Netherlands
| | - Johannes C Baayen
- Neurosurgical Center Amsterdam, VU University Medical Center, Amsterdam, Netherlands
| | - Philip C de Witt Hamer
- Brain Tumor Center Amsterdam & Department of Neurology, VU University Medical Center, Amsterdam, Netherlands.,Neurosurgical Center Amsterdam, VU University Medical Center, Amsterdam, Netherlands
| | - Sander Idema
- Neurosurgical Center Amsterdam, VU University Medical Center, Amsterdam, Netherlands
| | - Demetrios N Velis
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, Netherlands.,Neurosurgical Center Amsterdam, VU University Medical Center, Amsterdam, Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, Netherlands
| |
Collapse
|
50
|
|