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Lucas A, Revell A, Davis KA. Artificial intelligence in epilepsy - applications and pathways to the clinic. Nat Rev Neurol 2024; 20:319-336. [PMID: 38720105 DOI: 10.1038/s41582-024-00965-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/16/2024] [Indexed: 06/06/2024]
Abstract
Artificial intelligence (AI) is rapidly transforming health care, and its applications in epilepsy have increased exponentially over the past decade. Integration of AI into epilepsy management promises to revolutionize the diagnosis and treatment of this complex disorder. However, translation of AI into neurology clinical practice has not yet been successful, emphasizing the need to consider progress to date and assess challenges and limitations of AI. In this Review, we provide an overview of AI applications that have been developed in epilepsy using a variety of data modalities: neuroimaging, electroencephalography, electronic health records, medical devices and multimodal data integration. For each, we consider potential applications, including seizure detection and prediction, seizure lateralization, localization of the seizure-onset zone and assessment for surgical or neurostimulation interventions, and review the performance of AI tools developed to date. We also discuss methodological considerations and challenges that must be addressed to successfully integrate AI into clinical practice. Our goal is to provide an overview of the current state of the field and provide guidance for leveraging AI in future to improve management of epilepsy.
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Affiliation(s)
- Alfredo Lucas
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Andrew Revell
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
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Rahimzadeh H, Kamkar H, Ghafarian P, Hoseini-Tabatabaei N, Mohammadi-Mobarakeh N, Mehvari-Habibabadi J, Hashemi-Fesharaki SS, Nazem-Zadeh MR. Exploring ASL perfusion MRI as a substitutive modality for 18F-FDG PET in determining the laterality of mesial temporal lobe epilepsy. Neurol Sci 2024; 45:2223-2243. [PMID: 37994963 DOI: 10.1007/s10072-023-07188-8] [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: 06/11/2023] [Accepted: 11/03/2023] [Indexed: 11/24/2023]
Abstract
OBJECTIVE The aim of this investigation was to determine whether a correlation could be discerned between perfusion acquired through ASL MRI and metabolic data acquired via 18F-fluorodeoxyglucose (18F-FDG) PET in mesial temporal lobe epilepsy (mTLE). METHODS ASL MRI and 18F-FDG PET data were gathered from 22 mTLE patients. Relative cerebral blood flow (rCBF) asymmetry index (AIs) were measured using ASL MRI, and standardized uptake value ratio (SUVr) maps were obtained from 18F-FDG PET, focusing on bilateral vascular territories and key bitemporal lobe structures (amygdala, hippocampus, and parahippocampus). Intra-group comparisons were carried out to detect hypoperfusion and hypometabolism between the left and right brain hemispheres for both rCBF and SUVr in right and left mTLE. Correlations between the two AIs computed for each modality were examined. RESULTS Significant correlations were observed between rCBF and SUVr AIs in the middle temporal gyrus, superior temporal gyrus, and hippocampus. Significant correlations were also found in vascular territories of the distal posterior, intermediate anterior, intermediate middle, proximal anterior, and proximal middle cerebral arteries. Intra-group comparisons unveiled significant differences in rCBF and SUVr between the left and right brain hemispheres for right mTLE, while hypoperfusion and hypometabolism were infrequently observed in any intracranial region for left mTLE. CONCLUSION The study's findings suggest promising concordance between hypometabolism estimated by 18F-FDG PET and hypoperfusion determined by ASL perfusion MRI. This raises the possibility that, with prospective technical enhancements, ASL perfusion MRI could be considered an alternative modality to 18F-FDG PET in the future.
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Affiliation(s)
- Hossein Rahimzadeh
- Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences, Tehran, Iran
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hadi Kamkar
- Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences, Tehran, Iran
- Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Pardis Ghafarian
- Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Neda Mohammadi-Mobarakeh
- Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences, Tehran, Iran
- Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Seyed-Sohrab Hashemi-Fesharaki
- Pars Advanced and Minimally Invasive Medical Manners Research Center, Pars Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammad-Reza Nazem-Zadeh
- Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences, Tehran, Iran.
- Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Iran.
- Department of Neuroscience, Monash University, Melbourne, Australia.
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Yao L, Cheng N, Chen AQ, Wang X, Gao M, Kong QX, Kong Y. Advances in Neuroimaging and Multiple Post-Processing Techniques for Epileptogenic Zone Detection of Drug-Resistant Epilepsy. J Magn Reson Imaging 2023. [PMID: 38014782 DOI: 10.1002/jmri.29157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 11/29/2023] Open
Abstract
Among the approximately 20 million patients with drug-resistant epilepsy (DRE) worldwide, the vast majority can benefit from surgery to minimize seizure reduction and neurological impairment. Precise preoperative localization of epileptogenic zone (EZ) and complete resection of the lesions can influence the postoperative prognosis. However, precise localization of EZ is difficult, and the structural and functional alterations in the brain caused by DRE vary by etiology. Neuroimaging has emerged as an approach to identify the seizure-inducing structural and functional changes in the brain, and magnetic resonance imaging (MRI) and positron emission tomography (PET) have become routine noninvasive imaging tools for preoperative evaluation of DRE in many epilepsy treatment centers. Multimodal neuroimaging offers unique advantages in detecting EZ, especially in improving the detection rate of patients with negative MRI or PET findings. This approach can characterize the brain imaging characteristics of patients with DRE caused by different etiologies, serving as a bridge between clinical and pathological findings and providing a basis for individualized clinical treatment plans. In addition to the integration of multimodal imaging modalities and the development of special scanning sequences and image post-processing techniques for early and precise localization of EZ, the application of deep machine learning for extracting image features and deep learning-based artificial intelligence have gradually improved diagnostic efficiency and accuracy. These improvements can provide clinical assistance for precisely outlining the scope of EZ and indicating the relationship between EZ and functional brain areas, thereby enabling standardized and precise surgery and ensuring good prognosis. However, most existing studies have limitations imposed by factors such as their small sample sizes or hypothesis-based study designs. Therefore, we believe that the application of neuroimaging and post-processing techniques in DRE requires further development and that more efficient and accurate imaging techniques are urgently needed in clinical practice. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Lei Yao
- Clinical Medical College, Jining Medical University, Jining, China
| | - Nan Cheng
- Medical Imaging Department, Affiliated Hospital of Jining Medical University, Jining, China
| | - An-Qiang Chen
- Medical Imaging Department, Affiliated Hospital of Jining Medical University, Jining, China
| | - Xun Wang
- Medical Imaging Department, Affiliated Hospital of Jining Medical University, Jining, China
| | - Ming Gao
- Medical Imaging Department, Affiliated Hospital of Jining Medical University, Jining, China
| | - Qing-Xia Kong
- Department of Neurology, Affiliated Hospital of Jining Medical University, Jining, China
| | - Yu Kong
- Medical Imaging Department, Affiliated Hospital of Jining Medical University, Jining, China
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Rahimzadeh H, Kamkar H, Hoseini-Tabatabaei N, Mobarakeh NM, Habibabadi JM, Hashemi-Fesharaki SS, Nazem-Zadeh MR. Alteration of intracranial blood perfusion in temporal lobe epilepsy, an arterial spin labeling study. Heliyon 2023; 9:e14854. [PMID: 37089370 PMCID: PMC10119575 DOI: 10.1016/j.heliyon.2023.e14854] [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: 09/25/2022] [Revised: 03/19/2023] [Accepted: 03/20/2023] [Indexed: 04/25/2023] Open
Abstract
Background A critical necessity before surgical resection in mesial temporal lobe epilepsy (mTLE) is lateralizing the seizure focus in the temporal lobe. This study aimed to investigate the differences in perfusion pattern changes in right and left mTLE. Methods 42 mTLE patients (22 left and 20 right mTLE) and 14 controls were surveyed with pulsed arterial spin labeling at 3.0 T. The mean cerebral blood flow (CBF) and asymmetry index (AI) were calculated in the bilateral temporal lobe, amygdala, hippocampus, parahippocampus, and nine bilateral vascular territories ROIs. The alterations in whole-brain CBF were identified using statistical parametric mapping (SPM). Results CBF decreased in ipsilateral sides in both epilepsy subcohorts, with right mTLE showing a significant difference in most ROIs while left mTLE exhibiting no significant change. CBF comparison of left mTLE and controls showed a significant drop in ROI analysis in left middle temporal and left intermediate posterior cerebral artery and in AI analysis in parahippocampus, distal anterior cerebral artery, distal middle cerebral artery, and intermediate anterior cerebral artery. CBF hypoperfusion was seen in ROI analysis in the left intermediate anterior cerebral artery, left middle temporal, right middle temporal, left superior temporal in the right mTLE compared to controls. Left mTLE CBF differed significantly from right mTLE CBF in right distal middle cerebral artery ROI and AI of proximal middle cerebral artery. Conclusion Our result revealed that mTLE affects extratemporal regions and both mTLE subcohorts with different perfusion patterns, which may enhance the performance of preoperative MRI assessment in lateralization procedures.
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Affiliation(s)
- Hossein Rahimzadeh
- Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences, Tehran, Iran
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hadi Kamkar
- Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences, Tehran, Iran
- Bioinformatics and Biophysics Department, Tarbiat Modares University, Tehran, Iran
| | | | - Neda Mohammadi Mobarakeh
- Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences, Tehran, Iran
- Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Iran
| | | | | | - Mohammad-Reza Nazem-Zadeh
- Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences, Tehran, Iran
- Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Iran
- Department of Neuroscience, Monash University, Melbourne, Australia
- Corresponding author.Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences, Tehran, Iran.
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Sarbisheh I, Tapak L, Fallahi A, Fardmal J, Sadeghifar M, Nazemzadeh M, Mehvari Habibabadi J. Cortical thickness analysis in temporal lobe epilepsy using fully Bayesian spectral method in magnetic resonance imaging. BMC Med Imaging 2022; 22:222. [PMID: 36544100 PMCID: PMC9768883 DOI: 10.1186/s12880-022-00949-5] [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: 05/19/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Temporal lobe epilepsy (TLE) is the most common type of epilepsy associated with changes in the cerebral cortex throughout the brain. Magnetic resonance imaging (MRI) is widely used for detecting such anomalies; nevertheless, it produces spatially correlated data that cannot be considered by the usual statistical models. This study aimed to compare cortical thicknesses between patients with TLE and healthy controls by considering the spatial dependencies across different regions of the cerebral cortex in MRI. METHODS In this study, T1-weighted MRI was performed on 20 healthy controls and 33 TLE patients. Nineteen patients had a left TLE and 14 had a right TLE. Cortical thickness was measured for all individuals in 68 regions of the cerebral cortex based on images. Fully Bayesian spectral method was utilized to compare the cortical thickness of different brain regions between groups. Neural networks model was used to classify the patients using the identified regions. RESULTS For the left TLE patients, cortical thinning was observed in bilateral caudal anterior cingulate, lateral orbitofrontal (ipsilateral), the bilateral rostral anterior cingulate, frontal pole and temporal pole (ipsilateral), caudal middle frontal and rostral middle frontal (contralateral side). For the right TLE patients, cortical thinning was only observed in the entorhinal area (ipsilateral). The AUCs of the neural networks for classification of left and right TLE patients versus healthy controls were 0.939 and 1.000, respectively. CONCLUSION Alteration of cortical gray matter thickness was evidenced as common effect of epileptogenicity, as manifested by the patients in this study using the fully Bayesian spectral method by taking into account the complex structure of the data.
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Affiliation(s)
- Iman Sarbisheh
- grid.411950.80000 0004 0611 9280Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Leili Tapak
- grid.411950.80000 0004 0611 9280Department of Biostatistics, School of Public Health and Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Alireza Fallahi
- grid.411705.60000 0001 0166 0922Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Instruments Institute (AMTII), Tehran University of Medical Sciences, Tehran, Iran ,grid.459564.f0000 0004 0482 9174Biomedical Engineering Department, Hamedan University of Technology, Hamedan, Iran
| | - Javad Fardmal
- grid.411950.80000 0004 0611 9280Department of Biostatistics, School of Public Health and Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Majid Sadeghifar
- grid.411807.b0000 0000 9828 9578Department of Statistics, Faculty of Science, Bu-Ali Sina University, Hamadan, Iran
| | - MohammadReza Nazemzadeh
- grid.411705.60000 0001 0166 0922Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Instruments Institute (AMTII), Tehran University of Medical Sciences, Tehran, Iran ,grid.411705.60000 0001 0166 0922Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Iran
| | - Jafar Mehvari Habibabadi
- grid.411036.10000 0001 1498 685XDepartment of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
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Lee DA, Lee HJ, Park BS, Lee YJ, Park KM. Can we predict anti-seizure medication response in focal epilepsy using machine learning? Clin Neurol Neurosurg 2021; 211:107037. [PMID: 34800813 DOI: 10.1016/j.clineuro.2021.107037] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 11/08/2021] [Accepted: 11/13/2021] [Indexed: 10/19/2022]
Abstract
OBJECTIVE The aim of this study was to evaluate the feasibility of machine learning approach based on clinical factors and diffusion tensor imaging (DTI) to predict anti-seizure medication (ASM) response in focal epilepsy. We hypothesized that ASM response in focal epilepsy can be predicted using a machine learning approach. METHODS In this retrospective study conducted at a tertiary hospital, we enrolled 160 patients with newly diagnosed focal epilepsy. All of them underwent DTI from January 2017 to July 2019, with a follow-up at least 12 months after the diagnosis of epilepsy based on regular evaluation of ASMs. We analyzed the patients' clinical characteristics, and the conventional DTI measurements and extracted the structural connectomic profiles from the DTI. We employed the support vector machine (SVM) algorithm, and a k-fold cross-validation was executed. RESULTS The highest accuracy of classification was ensured based on the clinical factors. A SVM classifier based on the clinical factors revealed an accuracy of 87.5% and an area under curve (AUC) of 0.882. Another SVM classifier based on the conventional DTI measures demonstrated an accuracy of 62.5% and an AUC of 0.611. In addition, an SVM classifier based on the structural connectomic profiles revealed an accuracy of 68.7% and an AUC of 0.667. The AUC of the ROC curve generated from the clinical factors was significantly higher than the ROC curves based on the conventional DTI measures or structural connectomic profiles. CONCLUSION Machine learning approach is useful in predicting the ASM response in focal epilepsy. The clinical factor is more important than the conventional DTI measures and structural connectomic profiles in predicting the ASM response in focal epilepsy.
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Affiliation(s)
- Dong Ah Lee
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Ho-Joon Lee
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Bong Soo Park
- Department of Internal medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Yoo Jin Lee
- Department of Internal medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Kang Min Park
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
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Elisevich K, Davoodi-Bojd E, Heredia JG, Soltanian-Zadeh H. Prospective Quantitative Neuroimaging Analysis of Putative Temporal Lobe Epilepsy. Front Neurol 2021; 12:747580. [PMID: 34803885 PMCID: PMC8602195 DOI: 10.3389/fneur.2021.747580] [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: 07/26/2021] [Accepted: 09/20/2021] [Indexed: 11/22/2022] Open
Abstract
Purpose: A prospective study of individual and combined quantitative imaging applications for lateralizing epileptogenicity was performed in a cohort of consecutive patients with a putative diagnosis of mesial temporal lobe epilepsy (mTLE). Methods: Quantitative metrics were applied to MRI and nuclear medicine imaging studies as part of a comprehensive presurgical investigation. The neuroimaging analytics were conducted remotely to remove bias. All quantitative lateralizing tools were trained using a separate dataset. Outcomes were determined after 2 years. Of those treated, some underwent resection, and others were implanted with a responsive neurostimulation (RNS) device. Results: Forty-eight consecutive cases underwent evaluation using nine attributes of individual or combinations of neuroimaging modalities: 1) hippocampal volume, 2) FLAIR signal, 3) PET profile, 4) multistructural analysis (MSA), 5) multimodal model analysis (MMM), 6) DTI uncertainty analysis, 7) DTI connectivity, and 9) fMRI connectivity. Of the 24 patients undergoing resection, MSA, MMM, and PET proved most effective in predicting an Engel class 1 outcome (>80% accuracy). Both hippocampal volume and FLAIR signal analysis showed 76% and 69% concordance with an Engel class 1 outcome, respectively. Conclusion: Quantitative multimodal neuroimaging in the context of a putative mTLE aids in declaring laterality. The degree to which there is disagreement among the various quantitative neuroimaging metrics will judge whether epileptogenicity can be confined sufficiently to a particular temporal lobe to warrant further study and choice of therapy. Prediction models will improve with continued exploration of combined optimal neuroimaging metrics.
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Affiliation(s)
- Kost Elisevich
- Department of Clinical Neurosciences, Spectrum Health, Grand Rapids, MI, United States
- Department of Surgery, College of Human Medicine, Michigan State University, Grand Rapids, MI, United States
| | - Esmaeil Davoodi-Bojd
- Radiology and Research Administration, Henry Ford Health System, Detroit, MI, United States
| | - John G. Heredia
- Imaging Physics, Department of Radiology, Spectrum Health, Grand Rapids, MI, United States
| | - Hamid Soltanian-Zadeh
- Radiology and Research Administration, Henry Ford Health System, Detroit, MI, United States
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
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Comparison of multimodal findings on epileptogenic side in temporal lobe epilepsy using self-organizing maps. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2021; 35:249-266. [PMID: 34347200 DOI: 10.1007/s10334-021-00948-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 06/20/2021] [Accepted: 07/19/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVE To develop a decision-making tool to evaluate and compare the performance of neuroimaging markers with clinical findings and the significance of attributes for presurgical lateralization of mesial temporal lobe epilepsy (mTLE). METHODS Thirty-five unilateral mTLE patients who qualified as candidates for surgical resection were studied. Seizure semiology, ictal EEG, ictal epileptogenic zone, interictal-irritative zone, and MRI findings were used as clinical markers. Hippocampal T1 volumetry and FLAIR intensity, DTI estimated; mean diffusivity (MD) in the hippocampus and fractional anisotropy (FA) in posteroinferior cingulum and crus of fornix, and the output of logistic regression method on volumetrics of the hippocampus, amygdala, and thalamus were adopted as neuroimaging markers. The self-organizing map (SOM) method was applied to markers to provide predictive methods for mTLE lateralization. RESULTS The SOM clustered all clinical attributes correctly with 100% accuracy and sensitivity for both the left and right mTLE. Among the clinical markers, seizure semiology and interictal-irrelative zone are the most sensitive attribute for the left-mTLE group lateralization. The accuracy achieved by applying the SOM method to the neuroimaging attributes was 94%, while the sensitivity was achieved 90% for left and 100% for right mTLE. SOM evidence indicated that the hippocampal volume is the most sensitive attribute for the prediction of the laterality in left-mTLE groups. CONCLUSION The proposed SOM method showed that neuroimaging markers may not replace with clinical findings. Nevertheless, multimodal neuroimaging can play an effective role in preoperative lateralization to reduce the costs and risks of surgical resection.
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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.
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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
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Princich JP, Donnelly-Kehoe PA, Deleglise A, Vallejo-Azar MN, Pascariello GO, Seoane P, Veron Do Santos JG, Collavini S, Nasimbera AH, Kochen S. Diagnostic Performance of MRI Volumetry in Epilepsy Patients With Hippocampal Sclerosis Supported Through a Random Forest Automatic Classification Algorithm. Front Neurol 2021; 12:613967. [PMID: 33692740 PMCID: PMC7937810 DOI: 10.3389/fneur.2021.613967] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 01/18/2021] [Indexed: 01/07/2023] Open
Abstract
Introduction: Several methods offer free volumetry services for MR data that adequately quantify volume differences in the hippocampus and its subregions. These methods are frequently used to assist in clinical diagnosis of suspected hippocampal sclerosis in temporal lobe epilepsy. A strong association between severity of histopathological anomalies and hippocampal volumes was reported using MR volumetry with a higher diagnostic yield than visual examination alone. Interpretation of volumetry results is challenging due to inherent methodological differences and to the reported variability of hippocampal volume. Furthermore, normal morphometric differences are recognized in diverse populations that may need consideration. To address this concern, we highlighted procedural discrepancies including atlas definition and computation of total intracranial volume that may impact volumetry results. We aimed to quantify diagnostic performance and to propose reference values for hippocampal volume from two well-established techniques: FreeSurfer v.06 and volBrain-HIPS. Methods: Volumetry measures were calculated using clinical T1 MRI from a local population of 61 healthy controls and 57 epilepsy patients with confirmed unilateral hippocampal sclerosis. We further validated the results by a state-of-the-art machine learning classification algorithm (Random Forest) computing accuracy and feature relevance to distinguish between patients and controls. This validation process was performed using the FreeSurfer dataset alone, considering morphometric values not only from the hippocampus but also from additional non-hippocampal brain regions that could be potentially relevant for group classification. Mean reference values and 95% confidence intervals were calculated for left and right hippocampi along with hippocampal asymmetry degree to test diagnostic accuracy. Results: Both methods showed excellent classification performance (AUC:> 0.914) with noticeable differences in absolute (cm3) and normalized volumes. Hippocampal asymmetry was the most accurate discriminator from all estimates (AUC:1~0.97). Similar results were achieved in the validation test with an automatic classifier (AUC:>0.960), disclosing hippocampal structures as the most relevant features for group differentiation among other brain regions. Conclusion: We calculated reference volumetry values from two commonly used methods to accurately identify patients with temporal epilepsy and hippocampal sclerosis. Validation with an automatic classifier confirmed the principal role of the hippocampus and its subregions for diagnosis.
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Affiliation(s)
- Juan Pablo Princich
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina.,Hospital de Pediatría J.P Garrahan, Departamento de Neuroimágenes, Buenos Aires, Argentina
| | - Patricio Andres Donnelly-Kehoe
- Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas (CIFASIS) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Grupo de Procesamiento de Señales Multimedia - División Neuroimágenes, Universidad Nacional de Rosario, Rosario, Argentina
| | - Alvaro Deleglise
- Instituto de Fisiología y Biofísica B. Houssay (IFIBIO), Consejo Nacional de Investigaciones Científicas y Técnicas, Departamento de Fisiología y Biofísica, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Mariana Nahir Vallejo-Azar
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina
| | - Guido Orlando Pascariello
- Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas (CIFASIS) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Grupo de Procesamiento de Señales Multimedia - División Neuroimágenes, Universidad Nacional de Rosario, Rosario, Argentina
| | - Pablo Seoane
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina.,Hospital J.M Ramos Mejía, Centro de Epilepsia, Buenos Aires, Argentina
| | - Jose Gabriel Veron Do Santos
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina
| | - Santiago Collavini
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina.,Instituto de investigación en Electrónica, Control y Procesamiento de Señales (LEICI), Universidad Nacional de La Plata-Consejo Nacional de Investigaciones Científicas y Técnicas, La Plata, Argentina.,Instituto de Ingeniería y Agronomía, Universidad Nacional Arturo Jauretche, Florencio Varela, Argentina
| | - Alejandro Hugo Nasimbera
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina.,Hospital J.M Ramos Mejía, Centro de Epilepsia, Buenos Aires, Argentina
| | - Silvia Kochen
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina
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Jber M, Habibabadi JM, Sharifpour R, Marzbani H, Hassanpour M, Seyfi M, Mobarakeh NM, Keihani A, Hashemi-Fesharaki SS, Ay M, Nazem-Zadeh MR. Temporal and extratemporal atrophic manifestation of temporal lobe epilepsy using voxel-based morphometry and corticometry: clinical application in lateralization of epileptogenic zone. Neurol Sci 2021; 42:3305-3325. [PMID: 33389247 DOI: 10.1007/s10072-020-05003-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Accepted: 12/14/2020] [Indexed: 12/21/2022]
Abstract
BACKGROUND Advances in MRI acquisition and data processing have become important for revealing brain structural changes. Previous studies have reported widespread structural brain abnormalities and cortical thinning in patients with temporal lobe epilepsy (TLE), as the most common form of focal epilepsy. METHODS In this research, healthy control cases (n = 20) and patients with left TLE (n = 19) and right TLE (n = 14) were recruited, all underwent 3.0 T MRI with magnetization-prepared rapid gradient echo sequence to acquire T1-weighted images. Morphometric alterations in gray matter were identified using voxel-based morphometry (VBM). Volumetric alterations in subcortical structures and cortical thinning were also determined. RESULTS Patients with left TLE demonstrated more prevailing and widespread changes in subcortical volumes and cortical thickness than right TLE, mainly in the left hemisphere, compared to the healthy group. Both VBM analysis and subcortical volumetry detected significant hippocampal atrophy in ipsilateral compared to contralateral side in TLE group. In addition to hippocampus, subcortical volumetry found the thalamus and pallidum bilaterally vulnerable to the TLE. Furthermore, the TLE patients underwent cortical thinning beyond the temporal lobe, affecting gray matter cortices in frontal, parietal, and occipital lobes in the majority of patients, more prevalently for left TLE cases. Exploiting volume changes in individual patients in the hippocampus alone led to 63.6% sensitivity and 100% specificity for lateralization of TLE. CONCLUSION Alteration of gray matter volumes in subcortical regions and neocortical temporal structures and also cortical gray matter thickness were evidenced as common effects of epileptogenicity, as manifested by the majority of cases in this study.
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Affiliation(s)
- Majdi Jber
- Medical School, International Campus, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Roya Sharifpour
- Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences, Tehran, Iran
| | - Hengameh Marzbani
- Department of Biomedical Engineering, Amirkabir University of Technology (AUT), Tehran, Iran
| | - Masoud Hassanpour
- Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences, Tehran, Iran
| | - Milad Seyfi
- Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences, Tehran, Iran
| | - Neda Mohammadi Mobarakeh
- Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences, Tehran, Iran
| | - Ahmedreza Keihani
- Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Mohammadreza Ay
- Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad-Reza Nazem-Zadeh
- Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Iran.
- Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences, Tehran, Iran.
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Pattern analysis of glucose metabolic brain data for lateralization of MRI-negative temporal lobe epilepsy. Epilepsy Res 2020; 167:106474. [PMID: 32992074 DOI: 10.1016/j.eplepsyres.2020.106474] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 08/25/2020] [Accepted: 09/17/2020] [Indexed: 11/23/2022]
Abstract
In this paper, we assessed the reliability of glucose metabolic brain data for identifying lateralization of magnetic resonance imaging (MRI)-negative temporal lobe epilepsy (TLE) patients. We designed and developed an efficacious and automatic metabolic-wise lateralization framework. The proposed lateralization framework comprises three main systematic levels. In the first stage of our investigation, we pre-processed interictal fluorodeoxyglucose positron emission tomography images to extract glucose metabolic brain data. In the second stage, we used a voxel selection method involving a feature-ranking strategy to select the most discriminative metabolic voxels. Finally, we used a support vector machine followed by a 10-fold cross-validation strategy to assess the proposed lateralization framework in 27 patients with right MRI-negative TLE and 29 patients with left MRI-negative TLE. The proposed lateralization framework achieved an excellent accuracy of 96.43 % concordance with experienced PET interpreter. Thus, we show that pattern analysis of glucose metabolic brain data can accurately lateralize MRI-negative TLE patients in the clinical setting.
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Mahmoudi F, Elisevich K, Bagher-Ebadian H, Nazem-Zadeh MR, Davoodi-Bojd E, Schwalb JM, Kaur M, Soltanian-Zadeh H. Correction: Data mining MR image features of select structures for lateralization of mesial temporal lobe epilepsy. PLoS One 2018; 13:e0209866. [PMID: 30571727 PMCID: PMC6301553 DOI: 10.1371/journal.pone.0209866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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