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Tang G, Zhou H, Zeng C, Jiang Y, Li Y, Hou L, Liao K, Tan Z, Wu H, Tang Y, Cheng Y, Ling X, Guo Q, Xu H. Alterations of apparent diffusion coefficient from ultra high b-values in the bilateral thalamus and striatum in MRI-negative drug-resistant epilepsy. Epilepsia Open 2024; 9:1515-1525. [PMID: 38943548 PMCID: PMC11296122 DOI: 10.1002/epi4.12990] [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: 06/06/2023] [Revised: 04/01/2024] [Accepted: 05/26/2024] [Indexed: 07/01/2024] Open
Abstract
OBJECTIVE Subcortical nuclei such as the thalamus and striatum have been shown to be related to seizure modulation and termination, especially in drug-resistant epilepsy. Enhance diffusion-weighted imaging (eDWI) technique and tri-component model have been used in previous studies to calculate apparent diffusion coefficient from ultra high b-values (ADCuh). This study aimed to explore the alterations of ADCuh in the bilateral thalamus and striatum in MRI-negative drug-resistant epilepsy. METHODS Twenty-nine patients with MRI-negative drug-resistant epilepsy and 18 healthy controls underwent eDWI scan with 15 b-values (0-5000 s/mm2). The eDWI parameters including standard ADC (ADCst), pure water diffusion (D), and ADCuh were calculated from the 15 b-values. Regions-of-interest (ROIs) analyses were conducted in the bilateral thalamus, caudate nucleus, putamen, and globus pallidus. ADCst, D, and ADCuh values were compared between the MRI-negative drug-resistant epilepsy patients and controls using multivariate generalized linear models. Inter-rater reliability was assessed using the intra-class correlation coefficient (ICC) and Bland-Altman (BA) analysis. False discovery rate (FDR) method was applied for multiple comparisons correction. RESULTS ADCuh values in the bilateral thalamus, caudate nucleus, putamen, and globus pallidus in MRI-negative drug-resistant epilepsy were significantly higher than those in the healthy control subjects (all p < 0.05, FDR corrected). SIGNIFICANCE The alterations of the ADCuh values in the bilateral thalamus and striatum in MRI-negative drug-resistant epilepsy might reflect abnormal membrane water permeability in MRI-negative drug-resistant epilepsy. ADCuh might be a sensitive measurement for evaluating subcortical nuclei-related brain damage in epilepsy patients. PLAIN LANGUAGE SUMMARY This study aimed to explore the alterations of apparent diffusion coefficient calculated from ultra high b-values (ADCuh) in the subcortical nuclei such as the bilateral thalamus and striatum in MRI-negative drug-resistant epilepsy. The bilateral thalamus and striatum showed higher ADCuh in epilepsy patients than healthy controls. These findings may add new evidences of subcortical nuclei abnormalities related to water and ion hemostasis in epilepsy patients, which might help to elucidate the underlying epileptic neuropathophysiological mechanisms and facilitate the exploration of therapeutic targets.
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Affiliation(s)
- Guixian Tang
- Department of Nuclear Medicine, PET/CT‐MRI Center, Center of Cyclotron and PET RadiopharmaceuticalsThe First Affiliated Hospital of Jinan UniversityGuangzhouChina
| | - Hailing Zhou
- Department of RadiologyCentral People's Hospital of ZhanjiangZhanjiangChina
| | - Chunyuan Zeng
- Department of Nuclear Medicine, PET/CT‐MRI Center, Center of Cyclotron and PET RadiopharmaceuticalsThe First Affiliated Hospital of Jinan UniversityGuangzhouChina
| | - Yuanfang Jiang
- Department of Nuclear Medicine, PET/CT‐MRI Center, Center of Cyclotron and PET RadiopharmaceuticalsThe First Affiliated Hospital of Jinan UniversityGuangzhouChina
| | - Ying Li
- Department of Nuclear Medicine, PET/CT‐MRI Center, Center of Cyclotron and PET RadiopharmaceuticalsThe First Affiliated Hospital of Jinan UniversityGuangzhouChina
| | - Lu Hou
- Department of Nuclear Medicine, PET/CT‐MRI Center, Center of Cyclotron and PET RadiopharmaceuticalsThe First Affiliated Hospital of Jinan UniversityGuangzhouChina
| | - Kai Liao
- Department of Nuclear Medicine, PET/CT‐MRI Center, Center of Cyclotron and PET RadiopharmaceuticalsThe First Affiliated Hospital of Jinan UniversityGuangzhouChina
| | - Zhiqiang Tan
- Department of Nuclear Medicine, PET/CT‐MRI Center, Center of Cyclotron and PET RadiopharmaceuticalsThe First Affiliated Hospital of Jinan UniversityGuangzhouChina
| | - Huanhua Wu
- Department of Nuclear Medicine, PET/CT‐MRI Center, Center of Cyclotron and PET RadiopharmaceuticalsThe First Affiliated Hospital of Jinan UniversityGuangzhouChina
| | - Yongjin Tang
- Department of Nuclear Medicine, PET/CT‐MRI Center, Center of Cyclotron and PET RadiopharmaceuticalsThe First Affiliated Hospital of Jinan UniversityGuangzhouChina
| | - Yong Cheng
- Department of Nuclear Medicine, PET/CT‐MRI Center, Center of Cyclotron and PET RadiopharmaceuticalsThe First Affiliated Hospital of Jinan UniversityGuangzhouChina
| | - Xueying Ling
- Department of Nuclear Medicine, PET/CT‐MRI Center, Center of Cyclotron and PET RadiopharmaceuticalsThe First Affiliated Hospital of Jinan UniversityGuangzhouChina
| | - Qiang Guo
- Epilepsy Center, Guangdong 999 Brain HospitalAffiliated Brain Hospital of Jinan UniversityGuangzhouChina
| | - Hao Xu
- Department of Nuclear Medicine, PET/CT‐MRI Center, Center of Cyclotron and PET RadiopharmaceuticalsThe First Affiliated Hospital of Jinan UniversityGuangzhouChina
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McKavanagh A, Kreilkamp BAK, Chen Y, Denby C, Bracewell M, Das K, De Bezenac C, Marson AG, Taylor PN, Keller SS. Altered Structural Brain Networks in Refractory and Nonrefractory Idiopathic Generalized Epilepsy. Brain Connect 2022; 12:549-560. [PMID: 34348477 DOI: 10.1089/brain.2021.0035] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Introduction: Idiopathic generalized epilepsy (IGE) is a collection of generalized nonlesional epileptic network disorders. Around 20-40% of patients with IGE are refractory to antiseizure medication, and mechanisms underlying refractoriness are poorly understood. Here, we characterize structural brain network alterations and determine whether network alterations differ between patients with refractory and nonrefractory IGE. Methods: Thirty-three patients with IGE (10 nonrefractory and 23 refractory) and 39 age- and sex-matched healthy controls were studied. Network nodes were segmented from T1-weighted images, while connections between these nodes (edges) were reconstructed from diffusion magnetic resonance imaging (MRI). Diffusion networks of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and streamline count (Count) were studied. Differences between all patients, refractory, nonrefractory, and control groups were computed using network-based statistics. Nodal volume differences between groups were computed using Cohen's d effect size calculation. Results: Patients had significantly decreased bihemispheric FA and Count networks and increased MD and RD networks compared with controls. Alterations in network architecture, with respect to controls, differed depending on treatment outcome, including predominant FA network alterations in refractory IGE and increased nodal volume in nonrefractory IGE. Diffusion MRI networks were not influenced by nodal volume. Discussion: Although a nonlesional disorder, patients with IGE have bihemispheric structural network alterations that may differ between patients with refractory and nonrefractory IGE. Given that distinct nodal volume and FA network alterations were observed between treatment outcome groups, a multifaceted network analysis may be useful for identifying imaging biomarkers of refractory IGE.
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Affiliation(s)
- Andrea McKavanagh
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Barbara A K Kreilkamp
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
- Department of Neurology, University Medicine Göttingen, Göttingen, Germany
| | - Yachin Chen
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Christine Denby
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Martyn Bracewell
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
- School of Medical Sciences, Bangor University, Bangor, United Kingdom
- School of Psychology, Bangor University, Bangor, United Kingdom
| | - Kumar Das
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Christophe De Bezenac
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Anthony G Marson
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Peter N Taylor
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle, United Kingdom
| | - Simon S Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
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Lee DA, Lee H, Kim BJ, Park BS, Kim SE, Park KM. Identification of focal epilepsy by diffusion tensor imaging using machine learning. Acta Neurol Scand 2021; 143:637-645. [PMID: 33733467 DOI: 10.1111/ane.13407] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 02/01/2021] [Accepted: 02/07/2021] [Indexed: 12/29/2022]
Abstract
OBJECTIVE The aim of this study was to evaluate the feasibility of machine learning based on diffusion tensor imaging (DTI) measures to distinguish patients with focal epilepsy versus healthy controls and antiseizure medication (ASM) responsiveness. METHODS This was a retrospective study performed at a tertiary hospital. We enrolled 456 patients with focal epilepsy, who underwent DTI and were taking ASMs. We enrolled 100 healthy subjects as a control. We obtained the conventional DTI measures and structural connectomic profiles from the DTI. RESULTS The support vector machine (SVM) classifier based on the conventional DTI measures revealed an accuracy of 76.5% and an area under curve (AUC) of 0.604 (95% Confidence interval (CI), 0.506-0.695). Another SVM classifier combined with structural connectomic profiles demonstrated an accuracy of 82.8% and an AUC of 0.701 (95% CI, 0.606-0.784). Of the 456 patients with epilepsy, 242 patients were ASM good responders, whereas 214 patients were ASM poor responders. In the classification of the ASM responders, an SVM classifier based on the conventional DTI measures revealed an accuracy of 54.9% and an AUC of 0.551 (95% CI, 0.443-0.655). Another SVM classifier combined with structural connectomic profiles demonstrated an accuracy of 59.3% and an AUC of 0.594 (95% CI, 0.485-0.695). CONCLUSION DTI using a machine learning is useful for differentiating patients with focal epilepsy from healthy controls, but it cannot classify ASM responsiveness. Combining structural connectomic profiles results in a better classification performance than the use of conventional DTI measures alone for identifying focal epilepsy and ASM responsiveness.
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Affiliation(s)
- Dong Ah Lee
- Department of Neurology Haeundae Paik Hospital Inje University College of Medicine Busan Korea
| | - Ho‐Joon Lee
- Department of Radiology Haeundae Paik Hospital Inje University College of Medicine Busan Korea
| | - Byung Joon Kim
- Department of Neurology Haeundae Paik Hospital Inje University College of Medicine Busan Korea
| | - Bong Soo Park
- Department of Internal Medicine Haeundae Paik Hospital Inje University College of Medicine Busan Korea
| | - Sung Eun Kim
- Department of Neurology Haeundae Paik Hospital Inje University College of Medicine Busan Korea
| | - Kang Min Park
- Department of Neurology Haeundae Paik Hospital Inje University College of Medicine Busan Korea
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Lin Y, Du P, Sun H, Liang Y, Wang Z, Cui Y, Chen K, Xia Y, Yao D, Yu L, Guo D. Identifying Refractory Epilepsy Without Structural Abnormalities by Fusing the Common Spatial Patterns of Functional and Effective EEG Networks. IEEE Trans Neural Syst Rehabil Eng 2021; 29:708-717. [PMID: 33830925 DOI: 10.1109/tnsre.2021.3071785] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Drug refractory epilepsy (RE) is believed to be associated with structural lesions, but some RE patients show no significant structural abnormalities (RE-no-SA) on conventional magnetic resonance imaging scans. Since most of the medically controlled epilepsy (MCE) patients also do not exhibit structural abnormalities, a reliable assessment needs to be developed to differentiate RE-no-SA patients and MCE patients to avoid misdiagnosis and inappropriate treatment. Using resting-state scalp electroencephalogram (EEG) datasets, we extracted the spatial pattern of network (SPN) features from the functional and effective EEG networks of both RE-no-SA patients and MCE patients. Compared to the performance of traditional resting-state EEG network properties, the SPN features exhibited remarkable superiority in classifying these two groups of epilepsy patients, and accuracy values of 90.00% and 80.00% were obtained for the SPN features of the functional and effective EEG networks, respectively. By further fusing the SPN features of functional and effective networks, we demonstrated that the highest accuracy value of 96.67% could be reached, with a sensitivity of 100% and specificity of 92.86%. Overall, these findings not only indicate that the fused functional and effective SPN features are promising as reliable measurements for distinguishing RE-no-SA patients and MCE patients but also may provide a new perspective to explore the complex neurophysiology of refractory epilepsy.
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Bryant L, McKinnon ET, Taylor JA, Jensen JH, Bonilha L, de Bezenac C, Kreilkamp BAK, Adan G, Wieshmann UC, Biswas S, Marson AG, Keller SS. Fiber ball white matter modeling in focal epilepsy. Hum Brain Mapp 2021; 42:2490-2507. [PMID: 33605514 PMCID: PMC8090772 DOI: 10.1002/hbm.25382] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 02/09/2021] [Accepted: 02/10/2021] [Indexed: 12/15/2022] Open
Abstract
Multicompartment diffusion magnetic resonance imaging (MRI) approaches are increasingly being applied to estimate intra‐axonal and extra‐axonal diffusion characteristics in the human brain. Fiber ball imaging (FBI) and its extension fiber ball white matter modeling (FBWM) are such recently described multicompartment approaches. However, these particular approaches have yet to be applied in clinical cohorts. The modeling of several diffusion parameters with interpretable biological meaning may offer the development of new, noninvasive biomarkers of pharmacoresistance in epilepsy. In the present study, we used FBI and FBWM to evaluate intra‐axonal and extra‐axonal diffusion properties of white matter tracts in patients with longstanding focal epilepsy. FBI/FBWM diffusion parameters were calculated along the length of 50 white matter tract bundles and statistically compared between patients with refractory epilepsy, nonrefractory epilepsy and controls. We report that patients with chronic epilepsy had a widespread distribution of extra‐axonal diffusivity relative to controls, particularly in circumscribed regions along white matter tracts projecting to cerebral cortex from thalamic, striatal, brainstem, and peduncular regions. Patients with refractory epilepsy had significantly greater markers of extra‐axonal diffusivity compared to those with nonrefractory epilepsy. The extra‐axonal diffusivity alterations in patients with epilepsy observed in the present study could be markers of neuroinflammatory processes or a reflection of reduced axonal density, both of which have been histologically demonstrated in focal epilepsy. FBI is a clinically feasible MRI approach that provides the basis for more interpretive conclusions about the microstructural environment of the brain and may represent a unique biomarker of pharmacoresistance in epilepsy.
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Affiliation(s)
- Lorna Bryant
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK
| | - Emilie T McKinnon
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA
| | - James A Taylor
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Jens H Jensen
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA.,Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Leonardo Bonilha
- Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Christophe de Bezenac
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK
| | - Barbara A K Kreilkamp
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK.,Department of Clinical Neurophysiology, University Medicine Göttingen, Göttingen, Germany
| | - Guleed Adan
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK.,The Walton Centre NHS Foundation Trust, Liverpool, UK
| | | | | | - Anthony G Marson
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK.,The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Simon S Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK.,The Walton Centre NHS Foundation Trust, Liverpool, UK
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Poirier SE, Kwan BYM, Jurkiewicz MT, Samargandy L, Steven DA, Suller-Marti A, Lam Shin Cheung V, Khan AR, Romsa J, Prato FS, Burneo JG, Thiessen JD, Anazodo UC. 18F-FDG PET-guided diffusion tractography reveals white matter abnormalities around the epileptic focus in medically refractory epilepsy: implications for epilepsy surgical evaluation. Eur J Hybrid Imaging 2020; 4:10. [PMID: 34191151 PMCID: PMC8218143 DOI: 10.1186/s41824-020-00079-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 06/12/2020] [Indexed: 02/28/2023] Open
Abstract
BACKGROUND Hybrid PET/MRI can non-invasively improve localization and delineation of the epileptic focus (EF) prior to surgical resection in medically refractory epilepsy (MRE), especially when MRI is negative or equivocal. In this study, we developed a PET-guided diffusion tractography (PET/DTI) approach combining 18F-fluorodeoxyglucose PET (FDG-PET) and diffusion MRI to investigate white matter (WM) integrity in MRI-negative MRE patients and its potential impact on epilepsy surgical planning. METHODS FDG-PET and diffusion MRI of 14 MRI-negative or equivocal MRE patients were used to retrospectively pilot the PET/DTI approach. We used asymmetry index (AI) mapping of FDG-PET to detect the EF as brain areas showing the largest decrease in FDG uptake between hemispheres. Seed-based WM fiber tracking was performed on DTI images with a seed location in WM 3 mm from the EF. Fiber tractography was repeated in the contralateral brain region (opposite to EF), which served as a control for this study. WM fibers were quantified by calculating the fiber count, mean fractional anisotropy (FA), mean fiber length, and mean cross-section of each fiber bundle. WM integrity was assessed through fiber visualization and by normalizing ipsilateral fiber measurements to contralateral fiber measurements. The added value of PET/DTI in clinical decision-making was evaluated by a senior neurologist. RESULTS In over 60% of the patient cohort, AI mapping findings were concordant with clinical reports on seizure-onset localization and lateralization. Mean FA, fiber count, and mean fiber length were decreased in 14/14 (100%), 13/14 (93%), and 12/14 (86%) patients, respectively. PET/DTI improved diagnostic confidence in 10/14 (71%) patients and indicated that surgical candidacy be reassessed in 3/6 (50%) patients who had not undergone surgery. CONCLUSIONS We demonstrate here the utility of AI mapping in detecting the EF based on brain regions showing decreased FDG-PET activity and, when coupled with DTI, could be a powerful tool for detecting EF and assessing WM integrity in MRI-negative epilepsy. PET/DTI could be used to further enhance clinical decision-making in epilepsy surgery.
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Affiliation(s)
- Stefan E Poirier
- Lawson Imaging, Lawson Health Research Institute, 268 Grosvenor St., London, Ontario, N6A 4 V2, Canada. .,Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
| | - Benjamin Y M Kwan
- Department of Diagnostic Radiology, Queen's University, Kingston, Ontario, Canada
| | - Michael T Jurkiewicz
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Lina Samargandy
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - David A Steven
- Epilepsy Program, Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Ana Suller-Marti
- Epilepsy Program, Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | | | - Ali R Khan
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
| | - Jonathan Romsa
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Frank S Prato
- Lawson Imaging, Lawson Health Research Institute, 268 Grosvenor St., London, Ontario, N6A 4 V2, Canada.,Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Jorge G Burneo
- Epilepsy Program, Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Jonathan D Thiessen
- Lawson Imaging, Lawson Health Research Institute, 268 Grosvenor St., London, Ontario, N6A 4 V2, Canada.,Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Udunna C Anazodo
- Lawson Imaging, Lawson Health Research Institute, 268 Grosvenor St., London, Ontario, N6A 4 V2, Canada. .,Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
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Brain microstructural abnormalities correlate with KCC2 downregulation in refractory epilepsy. Neuroreport 2019; 30:409-414. [PMID: 30817684 DOI: 10.1097/wnr.0000000000001216] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Dysregulations in the expression level of Na-K-Cl cotransporter (NKCC1) and K-Cl cotransporter (KCC2) genes have been detected in the brain tissues of patients with refractory epilepsy. Given the importance of these proteins in the determination of Cl equilibrium potential (ECl), evaluation of the expression changes of these transporters might assist in optimizing the diagnostic approaches and therapeutic strategies. The present investigation evaluates the expression level chloride transporters in polymorphonuclear cells and their correlation with microstructural abnormalities. Thirty cases of drug-resistant epilepsy (confirmed with temporal lobe epilepsy diagnosis) fulfilled the considered inclusion criteria. Cases were divided into two groups, one with a detectable MRI lesion (19 participants; right side) and another with no MRI findings (11 participants). Whole-brain voxel-based analysis was performed on diffusion tensor imaging to measure fractional anisotropy and mean diffusivity; neurite orientation dispersion and density imaging was performed to map neurite density index and orientation dispersion index. Our results indicated that fractional anisotropy and mean diffusivity changed in temporal and extratemporal parts of the brain, whereas the changes in neurite density index and orientation dispersion index were exclusively obvious in the temporal lobe. Molecular studies revealed significantly lower levels of KCC2 expression in patients with epilepsy, a finding that remarkably correlated with microstructural changes as well. Our research showed that downregulation of KCC2 and microstructural abnormalities might contribute to the observed refractoriness in temporal lobe epilepsy.
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Analysis of fractional anisotropy and mean diffusivity in refractory and non-refractory idiopathic generalized epilepsies. Seizure 2018; 62:33-37. [DOI: 10.1016/j.seizure.2018.09.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 09/16/2018] [Accepted: 09/19/2018] [Indexed: 11/20/2022] Open
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