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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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Wang C, Zhang J, Song S, Li Z, Yin S, Duan W, Wei Z, Qi M, Sun W, Zhang L, Chen L, Gao X, Mao Y, Wang H, Chen L, Li C. Imaging epileptic foci in mouse models via a low-density lipoprotein receptor-related protein-1 targeting strategy. EBioMedicine 2020; 63:103156. [PMID: 33348091 PMCID: PMC7753923 DOI: 10.1016/j.ebiom.2020.103156] [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: 07/29/2020] [Revised: 10/24/2020] [Accepted: 11/18/2020] [Indexed: 11/27/2022] Open
Abstract
Background In the setting of drug-resistant epilepsy (DRE), the success of surgery depends on the ability to accurately locate the epileptic foci to be resected or disconnected. However, the epileptic foci in a considerable percentage of the DRE patients cannot be adequately localised. This warrants the need for a reliable imaging strategy to identify the “concealed” epileptic regions. Methods Brain specimens from DRE patients and kainate-induced epileptic mouse models were immuno-stained to evaluate the integrity of the blood-brain barrier (BBB). The expression of low-density lipoprotein receptor-related protein-1 (LRP1) in the epileptic region of DRE patients and kainate models was studied by immunofluorescence. A micellar-based LRP1-targeted paramagnetic probe (Gd3+-LP) was developed and its ability to define the epileptic foci was investigated by magnetic resonance imaging (MRI). Findings The integrity of the BBB in the epileptic region of DRE patients and kainate mouse models were demonstrated. LRP1 expression levels in the epileptic foci of DRE patients and kainate models were 1.70–2.38 and 2.32–3.97 folds higher than in the control brain tissues, respectively. In vivo MRI demonstrated that Gd3+-LP offered 1.68 times higher (P < 0.05) T1-weighted intensity enhancement in the ipsilateral hippocampus of chronic kainite models than the control probe without LRP1 specificity. Interpretation The expression of LRP1 is up-regulated in vascular endothelium, activated glia in both DRE patients and kainate models. LRP1-targeted imaging strategy may provide an alternative strategy to define the “concealed” epileptic foci by overcoming the intact BBB. Funding This work was supported by the National Natural Science Foundation, Shanghai Science and Technology Committee, Shanghai Municipal Science and Technology, Shanghai Municipal Health and Family Planning Commission and the National Postdoctoral Program for Innovative Talents.
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Affiliation(s)
- Cong Wang
- Key Laboratory of Smart Drug Delivery, Ministry of Education, School of Pharmacy, Fudan University, Shanghai, China; National Pharmaceutical Engineering Research Center, China State Institute of Pharmaceutical Industry, Shanghai, China
| | - Jianping Zhang
- Institute of Modern Physics, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Molecular Imaging Probes, Shanghai, China; Department of Nuclear Medicine, Shanghai Cancer Center, Fudan University, Shanghai, China; Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Sciences, Fudan University, Shanghai, China
| | - Shaoli Song
- Department of Nuclear Medicine, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zhi Li
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Shujie Yin
- Key Laboratory of Smart Drug Delivery, Ministry of Education, School of Pharmacy, Fudan University, Shanghai, China
| | - Wenjia Duan
- Key Laboratory of Smart Drug Delivery, Ministry of Education, School of Pharmacy, Fudan University, Shanghai, China
| | - Zixuan Wei
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Ming Qi
- Department of Nuclear Medicine, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Wanbing Sun
- Department of Neurology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Lu Zhang
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Luo Chen
- Key Laboratory of Smart Drug Delivery, Ministry of Education, School of Pharmacy, Fudan University, Shanghai, China
| | - Xihui Gao
- School of Basic Medical Sciences, Fudan University, Shanghai, China
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Hao Wang
- National Pharmaceutical Engineering Research Center, China State Institute of Pharmaceutical Industry, Shanghai, China.
| | - Liang Chen
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
| | - Cong Li
- Key Laboratory of Smart Drug Delivery, Ministry of Education, School of Pharmacy, Fudan University, Shanghai, China.
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An electric-field-responsive paramagnetic contrast agent enhances the visualization of epileptic foci in mouse models of drug-resistant epilepsy. Nat Biomed Eng 2020; 5:278-289. [PMID: 32989285 DOI: 10.1038/s41551-020-00618-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Accepted: 08/27/2020] [Indexed: 02/03/2023]
Abstract
For patients with drug-resistant focal epilepsy, excision of the epileptogenic zone is the most effective treatment approach. However, the surgery is less effective in the 15-30% of patients whose lesions are not distinct when visualized by magnetic resonance imaging (MRI). Here, we show that an intravenously administered MRI contrast agent consisting of a paramagnetic polymer coating encapsulating a superparamagnetic cluster of ultrasmall superparamagnetic iron oxide crosses the blood-brain barrier and improves lesion visualization with high sensitivity and target-to-background ratio. In kainic-acid-induced mouse models of drug-resistant focal epilepsy, electric-field changes in the brain associated with seizures trigger breakdown of the contrast agent, restoring the T1-weighted magnetic resonance signal, which otherwise remains quenched due to the distance-dependent magnetic resonance tuning effect between the cluster and the coating. The electric-field-responsive contrast agent may increase the probability of detecting seizure foci in patients and facilitate the study of brain diseases associated with epilepsy.
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Talo M, Yildirim O, Baloglu UB, Aydin G, Acharya UR. Convolutional neural networks for multi-class brain disease detection using MRI images. Comput Med Imaging Graph 2019; 78:101673. [PMID: 31635910 DOI: 10.1016/j.compmedimag.2019.101673] [Citation(s) in RCA: 92] [Impact Index Per Article: 18.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 10/03/2019] [Accepted: 10/04/2019] [Indexed: 01/02/2023]
Abstract
The brain disorders may cause loss of some critical functions such as thinking, speech, and movement. So, the early detection of brain diseases may help to get the timely best treatment. One of the conventional methods used to diagnose these disorders is the magnetic resonance imaging (MRI) technique. Manual diagnosis of brain abnormalities is time-consuming and difficult to perceive the minute changes in the MRI images, especially in the early stages of abnormalities. Proper selection of the features and classifiers to obtain the highest performance is a challenging task. Hence, deep learning models have been widely used for medical image analysis over the past few years. In this study, we have employed the AlexNet, Vgg-16, ResNet-18, ResNet-34, and ResNet-50 pre-trained models to automatically classify MR images in to normal, cerebrovascular, neoplastic, degenerative, and inflammatory diseases classes. We have also compared their classification performance with pre-trained models, which are the state-of-art architectures. We have obtained the best classification accuracy of 95.23% ± 0.6 with the ResNet-50 model among the five pre-trained models. Our model is ready to be tested with huge MRI images of brain abnormalities. The outcome of the model will also help the clinicians to validate their findings after manual reading of the MRI images.
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Affiliation(s)
- Muhammed Talo
- Department of Computer Engineering, Munzur University, Tunceli, Turkey
| | - Ozal Yildirim
- Department of Computer Engineering, Munzur University, Tunceli, Turkey.
| | | | - Galip Aydin
- Department of Computer Engineering, Fırat University, Elazığ, Turkey
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore School of Social Sciences, Singapore; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan
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5
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Martinez-Murcia FJ, Górriz JM, Ramírez J, Ortiz A. Convolutional Neural Networks for Neuroimaging in Parkinson's Disease: Is Preprocessing Needed? Int J Neural Syst 2018; 28:1850035. [PMID: 30215285 DOI: 10.1142/s0129065718500351] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Spatial and intensity normalizations are nowadays a prerequisite for neuroimaging analysis. Influenced by voxel-wise and other univariate comparisons, where these corrections are key, they are commonly applied to any type of analysis and imaging modalities. Nuclear imaging modalities such as PET-FDG or FP-CIT SPECT, a common modality used in Parkinson's disease diagnosis, are especially dependent on intensity normalization. However, these steps are computationally expensive and furthermore, they may introduce deformations in the images, altering the information contained in them. Convolutional neural networks (CNNs), for their part, introduce position invariance to pattern recognition, and have been proven to classify objects regardless of their orientation, size, angle, etc. Therefore, a question arises: how well can CNNs account for spatial and intensity differences when analyzing nuclear brain imaging? Are spatial and intensity normalizations still needed? To answer this question, we have trained four different CNN models based on well-established architectures, using or not different spatial and intensity normalization preprocessings. The results show that a sufficiently complex model such as our three-dimensional version of the ALEXNET can effectively account for spatial differences, achieving a diagnosis accuracy of 94.1% with an area under the ROC curve of 0.984. The visualization of the differences via saliency maps shows that these models are correctly finding patterns that match those found in the literature, without the need of applying any complex spatial normalization procedure. However, the intensity normalization - and its type - is revealed as very influential in the results and accuracy of the trained model, and therefore must be well accounted.
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Affiliation(s)
| | - Juan M Górriz
- * Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain.,† Department of Psychiatry, University of Cambridge, Cambridge CB2 OSZ, UK
| | - Javier Ramírez
- * Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Andres Ortiz
- ‡ Department of Communications Engineering, University of Malaga, Malaga, Spain
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Chen Z, An Y, Zhao B, Yang W, Yu Q, Cai L, Ni H, Yin J. The value of resting-state functional magnetic resonance imaging for detecting epileptogenic zones in patients with focal epilepsy. PLoS One 2017; 12:e0172094. [PMID: 28199371 PMCID: PMC5310782 DOI: 10.1371/journal.pone.0172094] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Accepted: 01/31/2017] [Indexed: 02/03/2023] Open
Abstract
Objective To determine the value of resting-state functional magnetic resonance imaging (RS-fMRI) based on the local analysis methods regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), and fractional ALFF (fALFF), for detecting epileptogenic zones (EZs). Methods A total of 42 consecutive patients with focal epilepsy were enrolled. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of visually assessed RS-fMRI, MRI, magnetic resonance spectroscopy (MRS), video electroencephalography (VEEG), and positron-emission tomography computed tomography (PET-CT) in EZ localization were evaluated to assess their diagnostic abilities. ReHo, ALFF, and fALFF were also compared for their diagnostic values. Results RS-fMRI showed comparable sensitivity to PET (83.3%) and specificity to VEEG (66.7%), respectively, for EZ localization in patients with focal epilepsy. There were no significant differences between RS-fMRI and the other localization techniques in terms of sensitivity, specificity, PPV, and NPV. The sensitivities of ReHo, ALFF, and fALFF were 69.4%, 52.8%, and 38.9%, respectively, and for specificities of 66.7%, 83.3%, and 66.7%, respectively. There were no significant differences among ReHo, ALFF, and fALFF, except that ReHo was more sensitive than fALFF. Conclusions RS-fMRI may be an efficient tool for detecting EZs in focal epilepsy patients.
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Affiliation(s)
- Zhijuan Chen
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Yang An
- First Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Bofeng Zhao
- Radiology Department, Tianjin First Central Hospital, Tianjin, China
| | - Weidong Yang
- First Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Qing Yu
- Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China
| | - Li Cai
- Clinical PET-CT Center, Tianjin Medical University General Hospital, Tianjin, China
| | - Hongyan Ni
- Radiology Department, Tianjin First Central Hospital, Tianjin, China
| | - Jianzhong Yin
- Radiology Department, Tianjin First Central Hospital, Tianjin, China
- * E-mail:
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Elkins KC, Moncayo VM, Kim H, Olson LD. Utility of gray-matter segmentation of ictal-Interictal perfusion SPECT and interictal 18 F-FDG-PET in medically refractory epilepsy. Epilepsy Res 2017; 130:93-100. [DOI: 10.1016/j.eplepsyres.2017.01.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2016] [Revised: 01/01/2017] [Accepted: 01/24/2017] [Indexed: 12/20/2022]
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8
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Legaz-Aparicio AG, Verdú-Monedero R, Larrey-Ruiz J, Morales-Sánchez J, López-Mir F, Naranjo V, Bernabéu Á. Efficient Variational Approach to Multimodal Registration of Anatomical and Functional Intra-Patient Tumorous Brain Data. Int J Neural Syst 2016; 27:1750014. [DOI: 10.1142/s0129065717500149] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This paper addresses the functional localization of intra-patient images of the brain. Functional images of the brain (fMRI and PET) provide information about brain function and metabolism whereas anatomical images (MRI and CT) supply the localization of structures with high spatial resolution. The goal is to find the geometric correspondence between functional and anatomical images in order to complement and fuse the information provided by each imaging modality. The proposed approach is based on a variational formulation of the image registration problem in the frequency domain. It has been implemented as a C/C[Formula: see text] library which is invoked from a GUI. This interface is routinely used in the clinical setting by physicians for research purposes (Inscanner, Alicante, Spain), and may be used as well for diagnosis and surgical planning. The registration of anatomic and functional intra-patient images of the brain makes it possible to obtain a geometric correspondence which allows for the localization of the functional processes that occur in the brain. Through 18 clinical experiments, it has been demonstrated how the proposed approach outperforms popular state-of-the-art registration methods in terms of efficiency, information theory-based measures (such as mutual information) and actual registration error (distance in space of corresponding landmarks).
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Affiliation(s)
| | - Rafael Verdú-Monedero
- Universidad Politécnica de Cartagena, Plaza del Hospital, 1. Cartagena, 30202, Spain
| | - Jorge Larrey-Ruiz
- Universidad Politécnica de Cartagena, Plaza del Hospital, 1. Cartagena, 30202, Spain
| | - Juan Morales-Sánchez
- Universidad Politécnica de Cartagena, Plaza del Hospital, 1. Cartagena, 30202, Spain
| | - Fernando López-Mir
- Universidad Politécnica de Valencia, Camino de Vera s/n, Valencia, 46022, Spain
| | - Valery Naranjo
- Universidad Politécnica de Valencia, Camino de Vera s/n, Valencia, 46022, Spain
| | - Ángela Bernabéu
- Inscanner S.L., Unidad de Resonancia Magnética, Avenida de Dénia, 78. Alicante, 03016, Spain
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9
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Tomasevic NM, Neskovic AM, Neskovic NJ. Correlated EEG Signals Simulation Based on Artificial Neural Networks. Int J Neural Syst 2016; 27:1750008. [PMID: 27873552 DOI: 10.1142/s0129065717500083] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In recent years, simulation of the human electroencephalogram (EEG) data found its important role in medical domain and neuropsychology. In this paper, a novel approach to simulation of two cross-correlated EEG signals is proposed. The proposed method is based on the principles of artificial neural networks (ANN). Contrary to the existing EEG data simulators, the ANN-based approach was leveraged solely on the experimentally acquired EEG data. More precisely, measured EEG data were utilized to optimize the simulator which consisted of two ANN models (each model responsible for generation of one EEG sequence). In order to acquire the EEG recordings, the measurement campaign was carried out on a healthy awake adult having no cognitive, physical or mental load. For the evaluation of the proposed approach, comprehensive quantitative and qualitative statistical analysis was performed considering probability distribution, correlation properties and spectral characteristics of generated EEG processes. The obtained results clearly indicated the satisfactory agreement with the measurement data.
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Affiliation(s)
- Nikola M Tomasevic
- 1 University of Belgrade, The Mihajlo Pupin Institute, Volgina 15, 11060 Belgrade, Serbia
| | - Aleksandar M Neskovic
- 2 School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, P. O. Box 3554, 11120 Belgrade, Serbia
| | - Natasa J Neskovic
- 2 School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, P. O. Box 3554, 11120 Belgrade, Serbia
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10
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Liu Y, Ning Y, Li S, Zhou P, Rymer WZ, Zhang Y. Three-Dimensional Innervation Zone Imaging from Multi-Channel Surface EMG Recordings. Int J Neural Syst 2016; 25:1550024. [PMID: 26160432 DOI: 10.1142/s0129065715500240] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
There is an unmet need to accurately identify the locations of innervation zones (IZs) of spastic muscles, so as to guide botulinum toxin (BTX) injections for the best clinical outcome. A novel 3D IZ imaging (3DIZI) approach was developed by combining the bioelectrical source imaging and surface electromyogram (EMG) decomposition methods to image the 3D distribution of IZs in the target muscles. Surface IZ locations of motor units (MUs), identified from the bipolar map of their MU action potentials (MUAPs) were employed as a prior knowledge in the 3DIZI approach to improve its imaging accuracy. The performance of the 3DIZI approach was first optimized and evaluated via a series of designed computer simulations, and then validated with the intramuscular EMG data, together with simultaneously recorded 128-channel surface EMG data from the biceps of two subjects. Both simulation and experimental validation results demonstrate the high performance of the 3DIZI approach in accurately reconstructing the distributions of IZs and the dynamic propagation of internal muscle activities in the biceps from high-density surface EMG recordings.
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Affiliation(s)
- Yang Liu
- Department of Biomedical Engineering, University of Houston, 3605 Cullen Blvd, Houston, TX77004, USA
| | - Yong Ning
- Department of Biomedical Engineering, University of Houston, 3605 Cullen Blvd, Houston, TX77004, USA
| | - Sheng Li
- Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center at Houston, 7000 Fannin St., Houston, TX, USA.,TIRR Memorial Hermann Research Center, 1300 Moursund St., Houston, TX, USA
| | - Ping Zhou
- Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center at Houston, 7000 Fannin St., Houston, TX, USA.,TIRR Memorial Hermann Research Center, 1300 Moursund St., Houston, TX, USA
| | - William Z Rymer
- Sensory Motor Performance Program, Rehabilitation Institute of Chicago, 345 East Superior St., Chicago, IL, USA.,Department of Physical Medicine and Rehabilitation, Northwestern University, 710 North Lake Shore Drive, Chicago, IL, USA
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, 3605 Cullen Blvd, Houston, TX77004, USA
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Luo C, Zhang Y, Cao W, Huang Y, Yang F, Wang J, Tu S, Wang X, Yao D. Altered Structural and Functional Feature of Striato-Cortical Circuit in Benign Epilepsy with Centrotemporal Spikes. Int J Neural Syst 2015; 25:1550027. [PMID: 26126612 DOI: 10.1142/s0129065715500276] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Benign epilepsy with centrotemporal spikes (BECT) is the most common form of childhood idiopathic focal epilepsy syndrome. We investigated quantitative evidence regarding brain morphology and functional connectivity features to provide insight into the neuroanatomical foundation of this disorder, using high resolution T1-weighted magnetic resonance imaging (MRI) and resting state functional MRI in 21 patients with BECT and in 20 healthy children. The functional connectivity analysis, seeded at the regions with altered gray-matter (GM) volume in voxel-based morphometry (VBM) analysis, was further performed. Then, the observed structural and functional alteration were investigated for their association with the clinical and behavior manifestations. The increased GM volume in the striatum and fronto-temporo-parietal cortex (striato-cortical circuit) was observed in BECT. The decreased connections were found among the motor network and frontostriatal loop, and between the default mode network (DMN) and language regions. Additionally, the GM of striatum was negatively correlated with age at epilepsy onset. The current observations may contribute to the understanding of the altered structural and functional feature of striato-cortical circuit in patients with BECT. The findings also implied alterations of the motor network and DMN, which were associated with the epileptic activity in patients with BECT. This further suggested that the onset of BECT might have enduring structural and functional effects on brain maturation.
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Affiliation(s)
- Cheng Luo
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging, Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yaodan Zhang
- Neurology Department, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, NanChong 637007, China
| | - Weifang Cao
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging, Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yue Huang
- Pediatric Department, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, NanChong 637007, China
| | - Fei Yang
- Neurology Department, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, NanChong 637007, China
| | - Jianjun Wang
- Pediatric Department, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, NanChong 637007, China
| | - Shipeng Tu
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging, Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xiaoming Wang
- Neurology Department, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, NanChong 637007, China
| | - Dezhong Yao
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging, Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
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Geertsema EE, Visser GH, Velis DN, Claus SP, Zijlmans M, Kalitzin SN. Automated Seizure Onset Zone Approximation Based on Nonharmonic High-Frequency Oscillations in Human Interictal Intracranial EEGs. Int J Neural Syst 2015; 25:1550015. [DOI: 10.1142/s012906571550015x] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
A novel automated algorithm is proposed to approximate the seizure onset zone (SOZ), while providing reproducible output. The SOZ, a surrogate marker for the epileptogenic zone (EZ), was approximated from intracranial electroencephalograms (iEEG) of nine people with temporal lobe epilepsy (TLE), using three methods: (1) Total ripple length (TRL): Manually segmented high-frequency oscillations, (2) Rippleness (R): Area under the curve (AUC) of the autocorrelation functions envelope, and (3) Autoregressive model residual variation (ARR, novel algorithm): Time-variation of residuals from autoregressive models of iEEG windows. TRL, R, and ARR results were compared in terms of separability, using Kolmogorov–Smirnov tests, and performance, using receiver operating characteristic (ROC) curves, to the gold standard for SOZ delineation: visual observation of ictal video-iEEGs. TRL, R, and ARR can distinguish signals from iEEG channels located within the SOZ from those outside it (p < 0.01). The ROC AUC was 0.82 for ARR, while it was 0.79 for TRL, and 0.64 for R. ARR outperforms TRL and R, and may be applied to identify channels in the SOZ automatically in interictal iEEGs of people with TLE. ARR, interpreted as evidence for nonharmonicity of high-frequency EEG components, could provide a new way to delineate the EZ, thus contributing to presurgical workup.
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Affiliation(s)
- Evelien E. Geertsema
- MIRA-Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands
| | - Gerhard H. Visser
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands
| | - Demetrios N. Velis
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands
- Department of Neurosurgery, Academic Center for Neurosurgery, VUmc, Free University Medical Center, Amsterdam, The Netherlands
| | - Steven P. Claus
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands
- Department of Clinical Neurophysiology, VUmc, Free University Medical Center, Amsterdam, The Netherlands
| | - Maeike Zijlmans
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, The Netherlands
| | - Stiliyan N. Kalitzin
- Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, 2130SW, Heemstede, The Netherlands
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
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13
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MRI characterization of temporal lobe epilepsy using rapidly measurable spatial indices with hemisphere asymmetries and gender features. Neuroradiology 2015; 57:873-86. [PMID: 26032924 DOI: 10.1007/s00234-015-1540-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2015] [Accepted: 05/04/2015] [Indexed: 10/23/2022]
Abstract
INTRODUCTION The paucity of morphometric markers for hemispheric asymmetries and gender variations in hippocampi and amygdalae in temporal lobe epilepsy (TLE) calls for better characterization of TLE by finding more useful prognostic MRI parameter(s). METHODS T1-weighted MRI (3 T) morphometry using multiple parameters of hippocampus-parahippocampus (angular and linear measures, volumetry) and amygdalae (volumetry) including their hemispheric asymmetry indices (AI) were evaluated in both genders. The cutoff values of parameters were statistically estimated from measurements of healthy subjects to characterize TLE (57 patients, 55% male) alterations. RESULTS TLE had differential categories with hippocampal atrophy, parahippocampal angle (PHA) acuteness, and several other parametric changes. Bilateral TLE categories were much more prevalent compared to unilateral TLE categories. Female patients were considerably more disposed to bilateral TLE categories than male patients. Male patients displayed diverse categories of unilateral abnormalities. Few patients (both genders) had combined bilateral appearances of hippocampal atrophy, amygdala atrophy, PHA acuteness, and increase in hippocampal angle (HA) where medial distance ratio (MDR) varied among genders. TLE had gender-specific and hemispheric dominant alterations in AI of parameters. Maximum magnitude of parametric changes in TLE includes (a) AI increase in HA of both genders, (b) HA increase (bilateral) in female patients, and (c) increase in ratio of amygdale/hippocampal volume (unilateral, right hemispheric), and AI decrease in MDR, in male patients. CONCLUSION Multiparametric MRI studies of hippocampus and amygdalae, including their hemispheric asymmetry, underscore better characterization of TLE. Rapidly measurable single-slice parameters (HA, PHA, MDR) can readily delineate TLE in a time-constrained clinical setting, which contrasts with customary three-dimensional hippocampal volumetry that requires many slice computation.
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14
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Liu K, Yu ZL, Wu W, Gu Z, Li Y. STRAPS: A Fully Data-Driven Spatio-Temporally Regularized Algorithm for M/EEG Patch Source Imaging. Int J Neural Syst 2015; 25:1550016. [DOI: 10.1142/s0129065715500161] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
For M/EEG-based distributed source imaging, it has been established that the L2-norm-based methods are effective in imaging spatially extended sources, whereas the L1-norm-based methods are more suited for estimating focal and sparse sources. However, when the spatial extents of the sources are unknown a priori, the rationale for using either type of methods is not adequately supported. Bayesian inference by exploiting the spatio-temporal information of the patch sources holds great promise as a tool for adaptive source imaging, but both computational and methodological limitations remain to be overcome. In this paper, based on state-space modeling of the M/EEG data, we propose a fully data-driven and scalable algorithm, termed STRAPS, for M/EEG patch source imaging on high-resolution cortices. Unlike the existing algorithms, the recursive penalized least squares (RPLS) procedure is employed to efficiently estimate the source activities as opposed to the computationally demanding Kalman filtering/smoothing. Furthermore, the coefficients of the multivariate autoregressive (MVAR) model characterizing the spatial-temporal dynamics of the patch sources are estimated in a principled manner via empirical Bayes. Extensive numerical experiments demonstrate STRAPS's excellent performance in the estimation of locations, spatial extents and amplitudes of the patch sources with varying spatial extents.
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Affiliation(s)
- Ke Liu
- College of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, P. R. China
| | - Zhu Liang Yu
- College of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, P. R. China
| | - Wei Wu
- College of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, P. R. China
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA
| | - Zhenghui Gu
- College of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, P. R. China
| | - Yuanqing Li
- College of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, P. R. China
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15
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Liu Y, Zhao Q, Zhang L. Uncorrelated Multiway Discriminant Analysis for Motor Imagery EEG Classification. Int J Neural Syst 2015; 25:1550013. [DOI: 10.1142/s0129065715500136] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Motor imagery-based brain–computer interfaces (BCIs) training has been proved to be an effective communication system between human brain and external devices. A practical problem in BCI-based systems is how to correctly and efficiently identify and extract subject-specific features from the blurred scalp electroencephalography (EEG) and translate those features into device commands in order to control external devices. In real BCI-based applications, we usually define frequency bands and channels configuration that related to brain activities beforehand. However, a steady configuration usually loses effects due to individual variability among different subjects in practical applications. In this study, a robust tensor-based method is proposed for a multiway discriminative subspace extraction from tensor-represented EEG data, which performs well in motor imagery EEG classification without the prior neurophysiologic knowledge like channels configuration and active frequency bands. Motor imagery EEG patterns in spatial-spectral-temporal domain are detected directly from the multidimensional EEG, which may provide insights to the underlying cortical activity patterns. Extensive experiment comparisons have been performed on a benchmark dataset from the famous BCI competition III as well as self-acquired data from healthy subjects and stroke patients. The experimental results demonstrate the superior performance of the proposed method over the contemporary methods.
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Affiliation(s)
- Ye Liu
- Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Qibin Zhao
- Laboratory for Advanced Brain Signal Processing, Brain Science Institute, RIKEN, Saitama, Japan
| | - Liqing Zhang
- Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
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16
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Chyzhyk D, Graña M, Öngür D, Shinn AK. Discrimination of schizophrenia auditory hallucinators by machine learning of resting-state functional MRI. Int J Neural Syst 2015; 25:1550007. [PMID: 25753600 DOI: 10.1142/s0129065715500070] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Auditory hallucinations (AH) are a symptom that is most often associated with schizophrenia, but patients with other neuropsychiatric conditions, and even a small percentage of healthy individuals, may also experience AH. Elucidating the neural mechanisms underlying AH in schizophrenia may offer insight into the pathophysiology associated with AH more broadly across multiple neuropsychiatric disease conditions. In this paper, we address the problem of classifying schizophrenia patients with and without a history of AH, and healthy control (HC) subjects. To this end, we performed feature extraction from resting state functional magnetic resonance imaging (rsfMRI) data and applied machine learning classifiers, testing two kinds of neuroimaging features: (a) functional connectivity (FC) measures computed by lattice auto-associative memories (LAAM), and (b) local activity (LA) measures, including regional homogeneity (ReHo) and fractional amplitude of low frequency fluctuations (fALFF). We show that it is possible to perform classification within each pair of subject groups with high accuracy. Discrimination between patients with and without lifetime AH was highest, while discrimination between schizophrenia patients and HC participants was worst, suggesting that classification according to the symptom dimension of AH may be more valid than discrimination on the basis of traditional diagnostic categories. FC measures seeded in right Heschl's gyrus (RHG) consistently showed stronger discriminative power than those seeded in left Heschl's gyrus (LHG), a finding that appears to support AH models focusing on right hemisphere abnormalities. The cortical brain localizations derived from the features with strong classification performance are consistent with proposed AH models, and include left inferior frontal gyrus (IFG), parahippocampal gyri, the cingulate cortex, as well as several temporal and prefrontal cortical brain regions. Overall, the observed findings suggest that computational intelligence approaches can provide robust tools for uncovering subtleties in complex neuroimaging data, and have the potential to advance the search for more neuroscience-based criteria for classifying mental illness in psychiatry research.
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Affiliation(s)
- Darya Chyzhyk
- Computational Intelligence Group, Universidad del Pais Vasco (UPV/EHU), San Sebastian 20018, Spain
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17
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Computer-aided diagnosis of alcoholism-related EEG signals. Epilepsy Behav 2014; 41:257-63. [PMID: 25461226 DOI: 10.1016/j.yebeh.2014.10.001] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2014] [Revised: 09/28/2014] [Accepted: 10/03/2014] [Indexed: 02/05/2023]
Abstract
Alcoholism is a severe disorder that affects the functionality of neurons in the central nervous system (CNS) and alters the behavior of the affected person. Electroencephalogram (EEG) signals can be used as a diagnostic tool in the evaluation of subjects with alcoholism. The neurophysiological interpretation of EEG signals in persons with alcoholism (PWA) is based on observation and interpretation of the frequency and power in their EEGs compared to EEG signals from persons without alcoholism. This paper presents a review of the known features of EEGs obtained from PWA and proposes that the impact of alcoholism on the brain can be determined by computer-aided analysis of EEGs through extracting the minute variations in the EEG signals that can differentiate the EEGs of PWA from those of nonaffected persons. The authors advance the idea of automated computer-aided diagnosis (CAD) of alcoholism by employing the EEG signals. This is achieved through judicious combination of signal processing techniques such as wavelet, nonlinear dynamics, and chaos theory and pattern recognition and classification techniques. A CAD system is cost-effective and efficient and can be used as a decision support system by physicians in the diagnosis and treatment of alcoholism especially those who do not specialize in alcoholism or neurophysiology. It can also be of great value to rehabilitation centers to assess PWA over time and to monitor the impact of treatment aimed at minimizing or reversing the effects of the disease on the brain. A CAD system can be used to determine the extent of alcoholism-related changes in EEG signals (low, medium, high) and the effectiveness of therapeutic plans.
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18
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Sharma P, Khan YU, Farooq O, Tripathi M, Adeli H. A Wavelet-Statistical Features Approach for Nonconvulsive Seizure Detection. Clin EEG Neurosci 2014; 45:274-284. [PMID: 24934269 DOI: 10.1177/1550059414535465] [Citation(s) in RCA: 65] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2014] [Accepted: 04/21/2014] [Indexed: 11/16/2022]
Abstract
The detection of nonconvulsive seizures (NCSz) is a challenge because of the lack of physical symptoms, which may delay the diagnosis of the disease. Many researchers have reported automatic detection of seizures. However, few investigators have concentrated on detection of NCSz. This article proposes a method for reliable detection of NCSz. The electroencephalography (EEG) signal is usually contaminated by various nonstationary noises. Signal denoising is an important preprocessing step in the analysis of such signals. In this study, a new wavelet-based denoising approach using cubical thresholding has been proposed to reduce noise from the EEG signal prior to analysis. Three statistical features were extracted from wavelet frequency bands, encompassing the frequency range of 0 to 8, 8 to 16, 16 to 32, and 0 to 32 Hz. Extracted features were used to train linear classifier to discriminate between normal and seizure EEGs. The performance of the method was tested on a database of nine patients with 24 seizures in 80 hours of EEG recording. All the seizures were successfully detected, and false positive rate was found to be 0.7 per hour.
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Affiliation(s)
- Priyanka Sharma
- Z. H. College of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
| | - Yusuf Uzzaman Khan
- Z. H. College of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
| | - Omar Farooq
- Z. H. College of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
| | | | - Hojjat Adeli
- Department of Biomedical Engineering, The Ohio State University, Columbus, OH 43210
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19
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Nazem-Zadeh MR, Elisevich KV, Schwalb JM, Bagher-Ebadian H, Mahmoudi F, Soltanian-Zadeh H. Lateralization of temporal lobe epilepsy by multimodal multinomial hippocampal response-driven models. J Neurol Sci 2014; 347:107-18. [PMID: 25300772 DOI: 10.1016/j.jns.2014.09.029] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2014] [Revised: 09/16/2014] [Accepted: 09/18/2014] [Indexed: 11/28/2022]
Abstract
PURPOSE Multiple modalities are used in determining laterality in mesial temporal lobe epilepsy (mTLE). It is unclear how much different imaging modalities should be weighted in decision-making. The purpose of this study is to develop response-driven multimodal multinomial models for lateralization of epileptogenicity in mTLE patients based upon imaging features in order to maximize the accuracy of noninvasive studies. METHODS AND MATERIALS The volumes, means and standard deviations of FLAIR intensity and means of normalized ictal-interictal SPECT intensity of the left and right hippocampi were extracted from preoperative images of a retrospective cohort of 45 mTLE patients with Engel class I surgical outcomes, as well as images of a cohort of 20 control, nonepileptic subjects. Using multinomial logistic function regression, the parameters of various univariate and multivariate models were estimated. Based on the Bayesian model averaging (BMA) theorem, response models were developed as compositions of independent univariate models. RESULTS A BMA model composed of posterior probabilities of univariate response models of hippocampal volumes, means and standard deviations of FLAIR intensity, and means of SPECT intensity with the estimated weighting coefficients of 0.28, 0.32, 0.09, and 0.31, respectively, as well as a multivariate response model incorporating all mentioned attributes, demonstrated complete reliability by achieving a probability of detection of one with no false alarms to establish proper laterality in all mTLE patients. CONCLUSION The proposed multinomial multivariate response-driven model provides a reliable lateralization of mesial temporal epileptogenicity including those patients who require phase II assessment.
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Affiliation(s)
- Mohammad-Reza Nazem-Zadeh
- Department of Research Administration, Henry Ford Health System, Detroit, MI 48202, USA; Department of Radiology, Henry Ford Health System, Detroit, MI, 48202, USA.
| | - Kost V Elisevich
- Department of Clinical Neurosciences, Spectrum Health Medical Group, Grand Rapids, MI 49503, USA.
| | - Jason M Schwalb
- Department of Neurosurgery, Henry Ford Health System, Detroit, MI 48202, USA.
| | - Hassan Bagher-Ebadian
- Department of Radiology, Henry Ford Health System, Detroit, MI, 48202, USA; Department of Neurology, Henry Ford Health System, Detroit, MI 48202, USA.
| | - Fariborz Mahmoudi
- Department of Research Administration, Henry Ford Health System, Detroit, MI 48202, USA; Department of Radiology, Henry Ford Health System, Detroit, MI, 48202, USA; Computer and IT engineering Faculty, Islamic Azad University, Qazvin Branch, Iran.
| | - Hamid Soltanian-Zadeh
- Department of Research Administration, Henry Ford Health System, Detroit, MI 48202, USA; Department of Radiology, Henry Ford Health System, Detroit, MI, 48202, USA; Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer, University of Tehran, Tehran, Iran.
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21
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Yuan Q, Zhou W, Yuan S, Li X, Wang J, Jia G. Epileptic EEG classification based on kernel sparse representation. Int J Neural Syst 2014; 24:1450015. [PMID: 24694170 DOI: 10.1142/s0129065714500154] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The automatic identification of epileptic EEG signals is significant in both relieving heavy workload of visual inspection of EEG recordings and treatment of epilepsy. This paper presents a novel method based on the theory of sparse representation to identify epileptic EEGs. At first, the raw EEG epochs are preprocessed via Gaussian low pass filtering and differential operation. Then, in the scheme of sparse representation based classification (SRC), a test EEG sample is sparsely represented on the training set by solving l1-minimization problem, and the represented residuals associated with ictal and interictal training samples are computed. The test EEG sample is categorized as the class that yields the minimum represented residual. So unlike the conventional EEG classification methods, the choice and calculation of EEG features are avoided in the proposed framework. Moreover, the kernel trick is employed to generate a kernel version of the SRC method for improving the separability between ictal and interictal classes. The satisfactory recognition accuracy of 98.63% for ictal and interictal EEG classification and for ictal and normal EEG classification has been achieved by the kernel SRC. In addition, the fast speed makes the kernel SRC suit for the real-time seizure monitoring application in the near future.
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Affiliation(s)
- Qi Yuan
- School of Information Science and Engineering, Shandong University, Jinan 250100, P. R. China , Suzhou Institute of Shandong University, Suzhou 215123, P. R. China
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Parazzini M, Fiocchi S, Liorni I, Priori A, Ravazzani P. Computational modeling of transcranial direct current stimulation in the child brain: implications for the treatment of refractory childhood focal epilepsy. Int J Neural Syst 2014; 24:1430006. [PMID: 24475898 DOI: 10.1142/s012906571430006x] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Transcranial direct current stimulation (tDCS) was recently proposed for the treatment of epilepsy. However, the electrode arrangement for this case is debated. This paper analyzes the influence of the position of the anodal electrode on the electric field in the brain. The simulation shows that moving the anode from scalp to shoulder does influence the electric field not only in the cortex, but also in deeper brain regions. The electric field decreases dramatically in the brain area without epileptiform activity.
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Affiliation(s)
- Marta Parazzini
- CNR Consiglio Nazionale delle Ricerche, Istituto di Elettronica e di Ingegneria, dell'Informazione e delle Telecomunicazioni IEIIT, Piazza Leonardo da Vinci 32, Milano 20133, Italy
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