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Zhu Q, Li S, Meng X, Xu Q, Zhang Z, Shao W, Zhang D. Spatio-Temporal Graph Hubness Propagation Model for Dynamic Brain Network Classification. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:2381-2394. [PMID: 38319754 DOI: 10.1109/tmi.2024.3363014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
Dynamic brain network has the advantage over static brain network in characterizing the variation pattern of functional brain connectivity, and it has attracted increasing attention in brain disease diagnosis. However, most of the existing dynamic brain networks analysis methods rely on extracting features from independent brain networks divided by sliding windows, making them hard to reveal the high-order dynamic evolution laws of functional brain networks. Additionally, they cannot effectively extract the spatio-temporal topology features in dynamic brain networks. In this paper, we propose to use optimal transport (OT) theory to capture the topology evolution of the dynamic brain networks, and develop a multi-channel spatio-temporal graph convolutional network that collaboratively extracts the temporal and spatial features from the evolution networks. Specifically, we first adaptively evaluate the graph hubness of brain regions in the brain network of each time window, which comprehensively models information transmission among multiple brain regions. Second, the hubness propagation information across adjacent time windows is captured by optimal transport, describing high-order topology evolution of dynamic brain networks. Moreover, we develop a spatio-temporal graph convolutional network with attention mechanism to collaboratively extract the intrinsic temporal and spatial topology information from the above networks. Finally, the multi-layer perceptron is adopted for classifying the dynamic brain network. The extensive experiment on the collected epilepsy dataset and the public ADNI dataset show that our proposed method not only outperforms several state-of-the-art methods in brain disease diagnosis, but also reveals the key dynamic alterations of brain connectivities between patients and healthy controls.
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Huang Y, Wang N, Li W, Feng T, Zhang H, Fan X, Chen S, Wang Y, Shan Y, Wei P, Zhao G. Aberrant individual structure covariance network in patients with mesial temporal lobe epilepsy. Front Neurosci 2024; 18:1381385. [PMID: 38784092 PMCID: PMC11112066 DOI: 10.3389/fnins.2024.1381385] [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: 02/03/2024] [Accepted: 04/08/2024] [Indexed: 05/25/2024] Open
Abstract
Objective Mesial temporal lobe epilepsy (mTLE) is a complex neurological disorder that has been recognized as a widespread global network disorder. The group-level structural covariance network (SCN) could reveal the structural connectivity disruption of the mTLE but could not reflect the heterogeneity at the individual level. Methods This study adopted a recently proposed individual structural covariance network (IDSCN) method to clarify the alternated structural covariance connection mode in mTLE and to associate IDSCN features with the clinical manifestations and regional brain atrophy. Results We found significant IDSCN abnormalities in the ipsilesional hippocampus, ipsilesional precentral gyrus, bilateral caudate, and putamen in mTLE patients than in healthy controls. Moreover, the IDSCNs of these areas were positively correlated with the gray matter atrophy rate. Finally, we identified several connectivities with weak associations with disease duration, frequency, and surgery outcome. Significance Our research highlights the role of hippo-thalamic-basal-cortical circuits in the pathophysiologic process of disrupted whole-brain morphological covariance networks in mTLE, and builds a bridge between brain-wide covariance network changes and regional brain atrophy.
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Affiliation(s)
- Yuda Huang
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
| | - Ningrui Wang
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
- Beijing Municipal Geriatric Medical Research Center, Beijing, China
| | - Wei Li
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
| | - Tao Feng
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
| | - Huaqiang Zhang
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
| | - Xiaotong Fan
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
| | - Sichang Chen
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
| | - Yihe Wang
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
| | - Yongzhi Shan
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
| | - Penghu Wei
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
- School of Public Health and Management, Ningxia Medical University, Yinchuan, China
- Clinical Research Center for Epilepsy Capital Medical University, Beijing, China
| | - Guoguang Zhao
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
- School of Public Health and Management, Ningxia Medical University, Yinchuan, China
- Clinical Research Center for Epilepsy Capital Medical University, Beijing, China
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Iaccarino L, Llibre-Guerra JJ, McDade E, Edwards L, Gordon B, Benzinger T, Hassenstab J, Kramer JH, Li Y, Miller BL, Miller Z, Morris JC, Mundada N, Perrin RJ, Rosen HJ, Soleimani-Meigooni D, Strom A, Tsoy E, Wang G, Xiong C, Allegri R, Chrem P, Vazquez S, Berman SB, Chhatwal J, Masters CL, Farlow MR, Jucker M, Levin J, Salloway S, Fox NC, Day GS, Gorno-Tempini ML, Boxer AL, La Joie R, Bateman R, Rabinovici GD. Molecular neuroimaging in dominantly inherited versus sporadic early-onset Alzheimer's disease. Brain Commun 2024; 6:fcae159. [PMID: 38784820 PMCID: PMC11114609 DOI: 10.1093/braincomms/fcae159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 03/14/2024] [Accepted: 05/01/2024] [Indexed: 05/25/2024] Open
Abstract
Approximately 5% of Alzheimer's disease patients develop symptoms before age 65 (early-onset Alzheimer's disease), with either sporadic (sporadic early-onset Alzheimer's disease) or dominantly inherited (dominantly inherited Alzheimer's disease) presentations. Both sporadic early-onset Alzheimer's disease and dominantly inherited Alzheimer's disease are characterized by brain amyloid-β accumulation, tau tangles, hypometabolism and neurodegeneration, but differences in topography and magnitude of these pathological changes are not fully elucidated. In this study, we directly compared patterns of amyloid-β plaque deposition and glucose hypometabolism in sporadic early-onset Alzheimer's disease and dominantly inherited Alzheimer's disease individuals. Our analysis included 134 symptomatic sporadic early-onset Alzheimer's disease amyloid-Positron Emission Tomography (PET)-positive cases from the University of California, San Francisco, Alzheimer's Disease Research Center (mean ± SD age 59.7 ± 5.6 years), 89 symptomatic dominantly inherited Alzheimer's disease cases (age 45.8 ± 9.3 years) and 102 cognitively unimpaired non-mutation carriers from the Dominantly Inherited Alzheimer Network study (age 44.9 ± 9.2). Each group underwent clinical and cognitive examinations, 11C-labelled Pittsburgh Compound B-PET and structural MRI. 18F-Fluorodeoxyglucose-PET was also available for most participants. Positron Emission Tomography scans from both studies were uniformly processed to obtain a standardized uptake value ratio (PIB50-70 cerebellar grey reference and FDG30-60 pons reference) images. Statistical analyses included pairwise global and voxelwise group comparisons and group-independent component analyses. Analyses were performed also adjusting for covariates including age, sex, Mini-Mental State Examination, apolipoprotein ε4 status and average composite cortical of standardized uptake value ratio. Compared with dominantly inherited Alzheimer's disease, sporadic early-onset Alzheimer's disease participants were older at age of onset (mean ± SD, 54.8 ± 8.2 versus 41.9 ± 8.2, Cohen's d = 1.91), with more years of education (16.4 ± 2.8 versus 13.5 ± 3.2, d = 1) and more likely to be apolipoprotein ε4 carriers (54.6% ε4 versus 28.1%, Cramer's V = 0.26), but similar Mini-Mental State Examination (20.6 ± 6.1 versus 21.2 ± 7.4, d = 0.08). Sporadic early-onset Alzheimer's disease had higher global cortical Pittsburgh Compound B-PET binding (mean ± SD standardized uptake value ratio, 1.92 ± 0.29 versus 1.58 ± 0.44, d = 0.96) and greater global cortical 18F-fluorodeoxyglucose-PET hypometabolism (mean ± SD standardized uptake value ratio, 1.32 ± 0.1 versus 1.39 ± 0.19, d = 0.48) compared with dominantly inherited Alzheimer's disease. Fully adjusted comparisons demonstrated relatively higher Pittsburgh Compound B-PET standardized uptake value ratio in the medial occipital, thalami, basal ganglia and medial/dorsal frontal regions in dominantly inherited Alzheimer's disease versus sporadic early-onset Alzheimer's disease. Sporadic early-onset Alzheimer's disease showed relatively greater 18F-fluorodeoxyglucose-PET hypometabolism in Alzheimer's disease signature temporoparietal regions and caudate nuclei, whereas dominantly inherited Alzheimer's disease showed relatively greater hypometabolism in frontal white matter and pericentral regions. Independent component analyses largely replicated these findings by highlighting common and unique Pittsburgh Compound B-PET and 18F-fluorodeoxyglucose-PET binding patterns. In summary, our findings suggest both common and distinct patterns of amyloid and glucose hypometabolism in sporadic and dominantly inherited early-onset Alzheimer's disease.
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Affiliation(s)
- Leonardo Iaccarino
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Jorge J Llibre-Guerra
- The Dominantly Inherited Alzheimer Network (DIAN), St Louis, MO 63108, USA
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Eric McDade
- The Dominantly Inherited Alzheimer Network (DIAN), St Louis, MO 63108, USA
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Lauren Edwards
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Brian Gordon
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Tammie Benzinger
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Jason Hassenstab
- The Dominantly Inherited Alzheimer Network (DIAN), St Louis, MO 63108, USA
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Joel H Kramer
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Yan Li
- Department of Biostatistics, Washington University in St Louis, St Louis, MO 63110, USA
| | - Bruce L Miller
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Zachary Miller
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - John C Morris
- The Dominantly Inherited Alzheimer Network (DIAN), St Louis, MO 63108, USA
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Nidhi Mundada
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Richard J Perrin
- Department of Pathology and Immunology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Howard J Rosen
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - David Soleimani-Meigooni
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Amelia Strom
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Elena Tsoy
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Guoqiao Wang
- Department of Biostatistics, Washington University in St Louis, St Louis, MO 63110, USA
| | - Chengjie Xiong
- Department of Biostatistics, Washington University in St Louis, St Louis, MO 63110, USA
| | - Ricardo Allegri
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires 1428, Argentina
| | - Patricio Chrem
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires 1428, Argentina
| | - Silvia Vazquez
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires 1428, Argentina
| | - Sarah B Berman
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Jasmeer Chhatwal
- Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Colin L Masters
- Department of Neuroscience, Florey Institute, The University of Melbourne, Melbourne 3052, Australia
| | - Martin R Farlow
- Neuroscience Center, Indiana University School of Medicine at Indianapolis, Indiana, IN 46202, USA
| | - Mathias Jucker
- DZNE-German Center for Neurodegenerative Diseases, Tübingen 72076, Germany
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-University, Munich 80539, Germany
- German Center for Neurodegenerative Diseases, Munich 81377, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich 81377, Germany
| | - Stephen Salloway
- Memory & Aging Program, Butler Hospital, Brown University in Providence, RI 02906, USA
| | - Nick C Fox
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London Institute of Neurology, London WC1N 3BG, UK
| | - Gregory S Day
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL 33224, USA
| | - Maria Luisa Gorno-Tempini
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Adam L Boxer
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Randall Bateman
- The Dominantly Inherited Alzheimer Network (DIAN), St Louis, MO 63108, USA
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Gil D Rabinovici
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143, USA
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Ballerini A, Arienzo D, Stasenko A, Schadler A, Vaudano AE, Meletti S, Kaestner E, McDonald CR. Spatial patterns of gray and white matter compromise relate to age of seizure onset in temporal lobe epilepsy. Neuroimage Clin 2023; 39:103473. [PMID: 37531834 PMCID: PMC10415805 DOI: 10.1016/j.nicl.2023.103473] [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: 04/29/2023] [Revised: 06/29/2023] [Accepted: 07/06/2023] [Indexed: 08/04/2023]
Abstract
OBJECTIVE Temporal Lobe Epilepsy (TLE) is frequently a neurodevelopmental disorder, involving subcortical volume loss, cortical atrophy, and white matter (WM) disruption. However, few studies have addressed how these pathological changes in TLE relate to one another. In this study, we investigate spatial patterns of gray and white matter degeneration in TLE and evaluate the hypothesis that the relationship among these patterns varies as a function of the age at which seizures begin. METHODS Eighty-two patients with TLE and 59 healthy controls were enrolled. T1-weighted images were used to obtain hippocampal volumes and cortical thickness estimates. Diffusion-weighted imaging was used to obtain fractional anisotropy (FA) and mean diffusivity (MD) of the superficial WM (SWM) and deep WM tracts. Analysis of covariance was used to examine patterns of WM and gray matter alterations in TLE relative to controls, controlling for age and sex. Sliding window correlations were then performed to examine the relationships between SWM degeneration, cortical thinning, and hippocampal atrophy across ages of seizure onset. RESULTS Cortical thinning in TLE followed a widespread, bilateral pattern that was pronounced in posterior centroparietal regions, whereas SWM and deep WM loss occurred mostly in ipsilateral, temporolimbic regions compared to controls. Window correlations revealed a relationship between hippocampal volume loss and whole brain SWM disruption in patients who developed epilepsy during childhood. On the other hand, in patients with adult-onset TLE, co-occurring cortical and SWM alterations were observed in the medial temporal lobe ipsilateral to the seizure focus. SIGNIFICANCE Our results suggest that although cortical, hippocampal and WM alterations appear spatially discordant at the group level, the relationship among these features depends on the age at which seizures begin. Whereas neurodevelopmental aspects of TLE may result in co-occurring WM and hippocampal degeneration near the epileptogenic zone, the onset of seizures in adulthood may set off a cascade of SWM microstructural loss and cortical atrophy of a neurodegenerative nature.
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Affiliation(s)
- Alice Ballerini
- Department of Biomedical, Metabolic and Neural Science, University of Modena and Reggio Emilia, Modena, Italy; Department of Psychiatry, University of California, San Diego, USA
| | - Donatello Arienzo
- Department of Psychiatry, University of California, San Diego, USA; Center for Multimodal Imaging and Genetics, University of California, San Diego, USA
| | - Alena Stasenko
- Department of Psychiatry, University of California, San Diego, USA; Center for Multimodal Imaging and Genetics, University of California, San Diego, USA
| | - Adam Schadler
- Department of Psychiatry, University of California, San Diego, USA; Center for Multimodal Imaging and Genetics, University of California, San Diego, USA
| | - Anna Elisabetta Vaudano
- Department of Biomedical, Metabolic and Neural Science, University of Modena and Reggio Emilia, Modena, Italy; Neurology Unit, OCB Hospital, AOU Modena, Italy
| | - Stefano Meletti
- Department of Biomedical, Metabolic and Neural Science, University of Modena and Reggio Emilia, Modena, Italy; Neurology Unit, OCB Hospital, AOU Modena, Italy
| | - Erik Kaestner
- Department of Psychiatry, University of California, San Diego, USA; Center for Multimodal Imaging and Genetics, University of California, San Diego, USA
| | - Carrie R McDonald
- Department of Psychiatry, University of California, San Diego, USA; Center for Multimodal Imaging and Genetics, University of California, San Diego, USA; Department of Radiation Medicine & Applied Sciences, University of California, San Diego, USA.
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Arnold TC, Kini LG, Bernabei JM, Revell AY, Das SR, Stein JM, Lucas TH, Englot DJ, Morgan VL, Litt B, Davis KA. Remote effects of temporal lobe epilepsy surgery: Long-term morphological changes after surgical resection. Epilepsia Open 2023; 8:559-570. [PMID: 36944585 PMCID: PMC10235552 DOI: 10.1002/epi4.12733] [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: 07/26/2022] [Accepted: 03/16/2023] [Indexed: 03/23/2023] Open
Abstract
OBJECTIVE Epilepsy surgery is an effective treatment for drug-resistant patients. However, how different surgical approaches affect long-term brain structure remains poorly characterized. Here, we present a semiautomated method for quantifying structural changes after epilepsy surgery and compare the remote structural effects of two approaches, anterior temporal lobectomy (ATL), and selective amygdalohippocampectomy (SAH). METHODS We studied 36 temporal lobe epilepsy patients who underwent resective surgery (ATL = 22, SAH = 14). All patients received same-scanner MR imaging preoperatively and postoperatively (mean 2 years). To analyze postoperative structural changes, we segmented the resection zone and modified the Advanced Normalization Tools (ANTs) longitudinal cortical pipeline to account for resections. We compared global and regional annualized cortical thinning between surgical treatments. RESULTS Across procedures, there was significant cortical thinning in the ipsilateral insula, fusiform, pericalcarine, and several temporal lobe regions outside the resection zone as well as the contralateral hippocampus. Additionally, increased postoperative cortical thickness was seen in the supramarginal gyrus. Patients treated with ATL exhibited greater annualized cortical thinning compared with SAH cases (ATL: -0.08 ± 0.11 mm per year, SAH: -0.01 ± 0.02 mm per year, t = 2.99, P = 0.006). There were focal postoperative differences between the two treatment groups in the ipsilateral insula (P = 0.039, corrected). Annualized cortical thinning rates correlated with preoperative cortical thickness (r = 0.60, P < 0.001) and had weaker associations with age at surgery (r = -0.33, P = 0.051) and disease duration (r = -0.42, P = 0.058). SIGNIFICANCE Our evidence suggests that selective procedures are associated with less cortical thinning and that earlier surgical intervention may reduce long-term impacts on brain structure.
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Affiliation(s)
- T. Campbell Arnold
- Department of Bioengineering, School of Engineering & Applied ScienceUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Neuroengineering and TherapeuticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Lohith G. Kini
- Department of Bioengineering, School of Engineering & Applied ScienceUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Neuroengineering and TherapeuticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - John M. Bernabei
- Department of Bioengineering, School of Engineering & Applied ScienceUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Neuroengineering and TherapeuticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Andrew Y. Revell
- Center for Neuroengineering and TherapeuticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Neuroscience, School of Engineering & Applied ScienceUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sandhitsu R. Das
- Department of Neurology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Joel M. Stein
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Timothy H. Lucas
- Center for Neuroengineering and TherapeuticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Neurosurgery, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Dario J. Englot
- Department of Neurological SurgeryVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Radiology and Radiological SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA
- Institute of Imaging ScienceVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Victoria L. Morgan
- Department of Neurological SurgeryVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Radiology and Radiological SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA
- Institute of Imaging ScienceVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Brian Litt
- Department of Bioengineering, School of Engineering & Applied ScienceUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Neuroengineering and TherapeuticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Neurology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Kathryn A. Davis
- Center for Neuroengineering and TherapeuticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Neurology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Liu Y, Bao S, Englot DJ, Morgan VL, Taylor WD, Wei Y, Oguz I, Landman BA, Lyu I. Hierarchical particle optimization for cortical shape correspondence in temporal lobe resection. Comput Biol Med 2023; 152:106414. [PMID: 36525831 PMCID: PMC9832438 DOI: 10.1016/j.compbiomed.2022.106414] [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: 06/13/2022] [Revised: 11/18/2022] [Accepted: 12/03/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Anterior temporal lobe resection is an effective treatment for temporal lobe epilepsy. The post-surgical structural changes could influence the follow-up treatment. Capturing post-surgical changes necessitates a well-established cortical shape correspondence between pre- and post-surgical surfaces. Yet, most cortical surface registration methods are designed for normal neuroanatomy. Surgical changes can introduce wide ranging artifacts in correspondence, for which conventional surface registration methods may not work as intended. METHODS In this paper, we propose a novel particle method for one-to-one dense shape correspondence between pre- and post-surgical surfaces with temporal lobe resection. The proposed method can handle partial structural abnormality involving non-rigid changes. Unlike existing particle methods using implicit particle adjacency, we consider explicit particle adjacency to establish a smooth correspondence. Moreover, we propose hierarchical optimization of particles rather than full optimization of all particles at once to avoid trappings of locally optimal particle update. RESULTS We evaluate the proposed method on 25 pairs of T1-MRI with pre- and post-simulated resection on the anterior temporal lobe and 25 pairs of patients with actual resection. We show improved accuracy over several cortical regions in terms of ROI boundary Hausdorff distance with 4.29 mm and Dice similarity coefficients with average value 0.841, compared to existing surface registration methods on simulated data. In 25 patients with actual resection of the anterior temporal lobe, our method shows an improved shape correspondence in qualitative and quantitative evaluation on parcellation-off ratio with average value 0.061 and cortical thickness changes. We also show better smoothness of the correspondence without self-intersection, compared with point-wise matching methods which show various degrees of self-intersection. CONCLUSION The proposed method establishes a promising one-to-one dense shape correspondence for temporal lobe resection. The resulting correspondence is smooth without self-intersection. The proposed hierarchical optimization strategy could accelerate optimization and improve the optimization accuracy. According to the results on the paired surfaces with temporal lobe resection, the proposed method outperforms the compared methods and is more reliable to capture cortical thickness changes.
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Affiliation(s)
- Yue Liu
- College of Information Science and Engineering, Northeastern University, Shenyang, China; Department of Electrical Engineering and Computer Science, Vanderbilt University, TN, USA
| | - Shunxing Bao
- Department of Electrical Engineering and Computer Science, Vanderbilt University, TN, USA
| | - Dario J Englot
- Department of Neurological Surgery, Vanderbilt University Medical Center, TN, USA
| | - Victoria L Morgan
- Department of Radiology & Radiological Science, Vanderbilt University Medical Center, TN, USA
| | - Warren D Taylor
- Department of Psychiatry & Behavioral Science, Vanderbilt University Medical Center, TN, USA
| | - Ying Wei
- College of Information Science and Engineering, Northeastern University, Shenyang, China; Information Technology R&D Innovation Center of Peking University, Shaoxing, China; Changsha Hisense Intelligent System Research Institute Co., Ltd, China
| | - Ipek Oguz
- Department of Electrical Engineering and Computer Science, Vanderbilt University, TN, USA
| | - Bennett A Landman
- Department of Electrical Engineering and Computer Science, Vanderbilt University, TN, USA
| | - Ilwoo Lyu
- Department of Computer Science and Engineering, UNIST, Ulsan, South Korea.
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Akıncı T, Gündüz A, Özkara Ç, Kızıltan ME. The Thalamic and Intracortical Inhibitory Function of Somatosensory System Is Unchanged in Mesial Temporal Lobe Epilepsy With Hippocampal Sclerosis. J Clin Neurophysiol 2023; 40:45-52. [PMID: 33675312 DOI: 10.1097/wnp.0000000000000839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
PURPOSE In mesial temporal lobe epilepsy with hippocampal sclerosis, there is parietal atrophy and cognitive involvement in related domains. In this context, we hypothesized that inhibitory input into somatosensory cortex and thalamus may be increased in these patients, which could improve after epilepsy surgery. Thus, we analyzed the inhibitory function of somatosensory system by studying surround inhibition (SI) and recovery function of somatosensory evoked potentials in patients with mesial temporal lobe epilepsy with hippocampal sclerosis. METHODS Nine patients with unoperated mesial temporal lobe epilepsy with hippocampal sclerosis, 10 patients who underwent epilepsy surgery, and 12 healthy subjects were included. For SI of somatosensory evoked potentials, we recorded somatosensory evoked potentials after stimulating median or ulnar nerve at wrist separately and after median and ulnar nerves simultaneously and calculated SI% in all participants. For recovery function of somatosensory evoked potentials, paired stimulation of median nerve at 40- and 100-millisecond intervals was performed. We compared the findings among groups. As a secondary analysis, we determined the outliers in the patient group and analyzed the relation to the clinical findings. RESULTS The mean SI% or recovery function was similar among three groups. However, there were five patients with SI loss on normal side in the patient group, which was related to the antiseizure drugs. CONCLUSIONS In contrast to our hypothesis, both intracortical (SI) and thalamic/striatal (recovery function) inhibitory modulation of the somatosensory cortex was not altered in mesial temporal lobe epilepsy with hippocampal sclerosis and did not differ in surgical and nonsurgical groups.
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Affiliation(s)
- Tuba Akıncı
- Department of Neurology, Cerrahpaşa Medical Faculty, Istanbul University-Cerrahpaşa (I.U.C), Istanbul, Turkey
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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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9
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Kim D, Lee J, Moon J, Moon T. Interpretable deep learning-based hippocampal sclerosis classification. Epilepsia Open 2022; 7:747-757. [PMID: 36177546 PMCID: PMC9712484 DOI: 10.1002/epi4.12655] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 09/26/2022] [Indexed: 12/30/2022] Open
Abstract
OBJECTIVE To evaluate the performance of a deep learning model for hippocampal sclerosis classification on the clinical dataset and suggest plausible visual interpretation for the model prediction. METHODS T2-weighted oblique coronal images of the brain MRI epilepsy protocol performed on patients were used. The training set included 320 participants with 160 no, 100 left and 60 right hippocampal sclerosis, and cross-validation was implemented. The test set consisted of 302 participants with 252 no, 25 left and 25 right hippocampal sclerosis. As the test set was imbalanced, we took an average of the accuracy achieved within each group to measure a balanced accuracy for multiclass and binary classifications. The dataset was composed to include not only healthy participants but also participants with abnormalities besides hippocampal sclerosis in the control group. We visualized the reasons for the model prediction using the layer-wise relevance propagation method. RESULTS When evaluated on the validation of the training set, we achieved multiclass and binary classification accuracy of 87.5% and 88.8% from the voting ensemble of six models. Evaluated on the test sets, we achieved multiclass and binary classification accuracy of 91.5% and 89.76%. The distinctly sparse visual interpretations were provided for each individual participant and group to suggest the contribution of each input voxel to the prediction on the MRI. SIGNIFICANCE The current interpretable deep learning-based model is promising for adapting effectively to clinical settings by utilizing commonly used data, such as MRI, with realistic abnormalities faced by neurologists to support the diagnosis of hippocampal sclerosis with plausible visual interpretation.
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Affiliation(s)
- Dohyun Kim
- Department of Artificial IntelligenceSungkyunkwan UniversitySuwonSouth Korea
| | - Jungtae Lee
- Application Engineering Team, Memory BusinessSamsung Electronics Co., Ltd.SuwonSouth Korea
| | - Jangsup Moon
- Department of NeurologySeoul National University HospitalSeoulSouth Korea,Department of Genomic MedicineSeoul National University HospitalSeoulSouth Korea
| | - Taesup Moon
- Department of Electrical and Computer EngineeringSeoul National UniversitySeoulSouth Korea,ASRI/INMC/IPAI/AIISSeoul National UniversitySeoulSouth Korea
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10
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Brain structural connectivity sub typing in unilateral temporal lobe epilepsy. Brain Imaging Behav 2022; 16:2220-2228. [PMID: 35674920 DOI: 10.1007/s11682-022-00691-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/21/2022] [Indexed: 11/02/2022]
Abstract
To categorize and clinically characterize subtypes of brain structural connectivity patterns in unilateral temporal lobe epilepsy (TLE). Voxel based morphometry (VBM) and surfaced based morphometry (SBM) analysis were used to detect brain structural alterations associated with TLE from MRI data. Principal component analysis (PCA) was performed to identify subtypes of brain structural connectivity patterns. Correlation analysis was used to explore associations between PC scores and clinical characteristics. A total of 59 patients with TLE and 100 healthy adults were included in this study. Widespread cortical atrophy was shown in both left and right TLE (P < 0.05, FWE corrected). Six principal components (PCs) that explained more than 70% of the variance were extracted for left and right TLE, reflecting patterns of brain structural connectivity. PCs representing perisylvian connectivity were positively correlated with verbal IQ (left TLE: r = 0.696, P < 0.001; right TLE: r = 0.484, P = 0.012) and total IQ (left TLE r = 0.608, P < 0.001) and negatively correlated with disease duration (r = -0.448, P = 0.009). In left TLE, the PC in the ipsilateral mesial temporal region was negatively correlated with age at onset (r = -0.382, P = 0.028). In right TLE, the PC representing the default mode network was negatively correlated with number of antiepileptic drugs (r = -0.407, P = 0.039). This study categorized subtypes of unilateral TLE based on brain structural connectivity patterns. Findings may provide insight into seizure pathways, the pathophysiology of epilepsy, including comorbidities such as cognitive impairment, and help predict treatment outcomes.
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11
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Khalife MR, Scott RC, Hernan AE. Mechanisms for Cognitive Impairment in Epilepsy: Moving Beyond Seizures. Front Neurol 2022; 13:878991. [PMID: 35645970 PMCID: PMC9135108 DOI: 10.3389/fneur.2022.878991] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
There has been a major emphasis on defining the role of seizures in the causation of cognitive impairments like memory deficits in epilepsy. Here we focus on an alternative hypothesis behind these deficits, emphasizing the mechanisms of information processing underlying healthy cognition characterized as rate, temporal and population coding. We discuss the role of the underlying etiology of epilepsy in altering neural networks thereby leading to both the propensity for seizures and the associated cognitive impairments. In addition, we address potential treatments that can recover the network function in the context of a diseased brain, thereby improving both seizure and cognitive outcomes simultaneously. This review shows the importance of moving beyond seizures and approaching the deficits from a system-level perspective with the guidance of network neuroscience.
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Affiliation(s)
- Mohamed R. Khalife
- Division of Neuroscience, Nemours Children's Health, Wilmington, DE, United States
- Psychological and Brain Sciences, University of Delaware, Newark, DE, United States
| | - Rod C. Scott
- Division of Neuroscience, Nemours Children's Health, Wilmington, DE, United States
- Psychological and Brain Sciences, University of Delaware, Newark, DE, United States
- Institute of Child Health, Neurosciences Unit University College London, London, United Kingdom
| | - Amanda E. Hernan
- Division of Neuroscience, Nemours Children's Health, Wilmington, DE, United States
- Psychological and Brain Sciences, University of Delaware, Newark, DE, United States
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12
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Li W, Jiang Y, Qin Y, Li X, Lei D, Zhang H, Luo C, Gong Q, Zhou D, An D. Cortical remodeling before and after successful temporal lobe epilepsy surgery. Acta Neurol Scand 2022; 146:144-151. [PMID: 35506500 DOI: 10.1111/ane.13631] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/11/2022] [Accepted: 04/24/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVES To explore dynamic alterations of cortical thickness before and after successful anterior temporal lobectomy (ATL) in patients with unilateral mesial temporal lobe epilepsy (mTLE). MATERIALS AND METHODS High-resolution T1-weighted MRI was obtained in 28 mTLE patients who achieved seizure freedom for at least 24 months after ATL and 29 healthy controls. Patients were scanned at five timepoints, including before surgery, 3, 6, 12 and 24 months after surgery. Preoperative cortical thickness of mTLE patients were compared with healthy controls. Dynamic alterations of cortical thickness before and after surgery were compared among five scans using linear mixed models. RESULTS Patients with mTLE showed cortical thinning pre-surgically in ipsilateral entorhinal cortex, parahippocampal gyrus, inferior parietal cortex, lateral occipital cortex; contralateral pericalcarine cortex (PCC); and bilateral caudal middle frontal gyrus (cMFG), paracentral lobule, precentral gyrus (PCG), superior parietal cortex. Cortical thickening was observed in contralateral rostral anterior cingulate cortex (rACC). Patients showed postsurgical cortical thinning in ipsilateral temporal lobe, fusiform gyrus, caudal anterior cingulate cortex, lingual gyrus, and insula. Ipsilateral cMFG, PCC, and contralateral PCG showed significant cortical thickening after surgery. In addition, contralateral rACC showed cortical thickening at 3 months follow-up, however, with obvious cortical thinning at 24 months follow-up. CONCLUSIONS Mesial temporal lobe epilepsy patients showed widespread cortical thinning before and after anterior temporal lobectomy. Progressive cortical thinning mainly existed in neighboring regions of resection. Postoperative cortical thickening may indicate cortical remodeling after successful surgery.
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Affiliation(s)
- Wei Li
- Department of Neurology, West China Hospital Sichuan University Chengdu China
| | - Yuchao Jiang
- MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of life Science and technology, The Clinical Hospital of Chengdu Brain Science Institute University of Electronic Science and Technology of China Chengdu China
| | - Yingjie Qin
- Department of Neurology, West China Hospital Sichuan University Chengdu China
| | - Xiuli Li
- Department of Radiology, Huaxi MR Research Center, West China Hospital Sichuan University Chengdu China
| | - Du Lei
- Department of Radiology, Huaxi MR Research Center, West China Hospital Sichuan University Chengdu China
| | - Heng Zhang
- Department of Neurosurgery, West China Hospital Sichuan University Chengdu China
| | - Cheng Luo
- MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of life Science and technology, The Clinical Hospital of Chengdu Brain Science Institute University of Electronic Science and Technology of China Chengdu China
| | - Qiyong Gong
- Department of Radiology, Huaxi MR Research Center, West China Hospital Sichuan University Chengdu China
| | - Dong Zhou
- Department of Neurology, West China Hospital Sichuan University Chengdu China
| | - Dongmei An
- Department of Neurology, West China Hospital Sichuan University Chengdu China
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13
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Fernandez AM, Gutekunst CA, Grogan DP, Pedersen NP, Gross RE. Loss of efferent projections of the hippocampal formation in the mouse intrahippocampal kainic acid model. Epilepsy Res 2022; 180:106863. [DOI: 10.1016/j.eplepsyres.2022.106863] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 12/15/2021] [Accepted: 01/17/2022] [Indexed: 11/16/2022]
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14
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Zhao Y, Zhang C, Yang H, Liu C, Yu T, Lu J, Chen N, Li K. Recovery of cortical atrophy in patients with temporal lobe epilepsy after successful anterior temporal lobectomy. Epilepsy Behav 2021; 123:108272. [PMID: 34500432 DOI: 10.1016/j.yebeh.2021.108272] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 08/09/2021] [Accepted: 08/14/2021] [Indexed: 11/30/2022]
Abstract
The aims of this study were to investigate whether the cortical atrophy caused by temporal lobe epilepsy (TLE) was reversible after successful anterior temporal lobectomy (ATL) and to further observe whether possible changes are related to age at surgery and cognitive changes. Twelve patients with unilateral mesial TLE who received ATL and remained seizure free in one year follow-up were included. They underwent two MRI scans few days before and oneyear after surgery. Thirty age- and sex-matched healthy participants were recruited as controls. Group comparisons were used to test the differences in cortical thickness (CTh) between the pre-/postsurgical patients and controls. Longitudinal test was used to directly show postsurgical changes of the patients. Besides, the correlations between regional cortical volume (CVo) changes and age at surgery or cognitive changes were also tested. Compared with controls, the patients with TLE showed dispersed cortical thinning especially in the bilateral frontal lobes before surgery and no significant cortical thinning except for cortices near the resected areas after surgery. The longitudinal analysis showed CTh increment in the ipsilateral precentral and postcentral gyrus, cuneus and widespread in the contralateral cortex. In the volumetric analysis, the CVo changes in the contralateral hemisphere were negatively correlated with age at surgery and positively correlated with MoCA score changes. This study suggests that the cortical atrophy caused by TLE could recover after successful ATL. The recovery ability is greater in younger subjects and is positively related to cognitive recovery. These findings could serve as new clues that patients with TLE can benefit from timely and successful ATL.
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Affiliation(s)
- Yongxiang Zhao
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China; Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing 100053, PR China
| | - Chao Zhang
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China; Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing 100053, PR China; Department of Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province 221006, PR China
| | - Hongyu Yang
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China; Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing 100053, PR China; Department of Radiology, Luhe Hospital, Capital Medical University, Beijing 101100, PR China
| | - Chang Liu
- Department of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China
| | - Tao Yu
- Department of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China; Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing 100053, PR China
| | - Nan Chen
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China; Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing 100053, PR China.
| | - Kuncheng Li
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China; Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing 100053, PR China.
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15
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Chen X, Wang Y, Kopetzky SJ, Butz-Ostendorf M, Kaiser M. Connectivity within regions characterizes epilepsy duration and treatment outcome. Hum Brain Mapp 2021; 42:3777-3791. [PMID: 33973688 PMCID: PMC8288103 DOI: 10.1002/hbm.25464] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 04/13/2021] [Accepted: 04/26/2021] [Indexed: 11/11/2022] Open
Abstract
Finding clear connectome biomarkers for temporal lobe epilepsy (TLE) patients, in particular at early disease stages, remains a challenge. Currently, the whole-brain structural connectomes are analyzed based on coarse parcellations (up to 1,000 nodes). However, such global parcellation-based connectomes may be unsuitable for detecting more localized changes in patients. Here, we use a high-resolution network (~50,000-nodes overall) to identify changes at the local level (within brain regions) and test its relation with duration and surgical outcome. Patients with TLE (n = 33) and age-, sex-matched healthy subjects (n = 36) underwent high-resolution (~50,000 nodes) structural network construction based on deterministic tracking of diffusion tensor imaging. Nodes were allocated to 68 cortical regions according to the Desikan-Killany atlas. The connectivity within regions was then used to predict surgical outcome. MRI processing, network reconstruction, and visualization of network changes were integrated into the NICARA (https://nicara.eu). Lower clustering coefficient and higher edge density were found for local connectivity within regions in patients, but were absent for the global network between regions (68 cortical regions). Local connectivity changes, in terms of the number of changed regions and the magnitude of changes, increased with disease duration. Local connectivity yielded a better surgical outcome prediction (Mean value: 95.39% accuracy, 92.76% sensitivity, and 100% specificity) than global connectivity. Connectivity within regions, compared to structural connectivity between brain regions, can be a more efficient biomarker for epilepsy assessment and surgery outcome prediction of medically intractable TLE.
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Affiliation(s)
- Xue Chen
- College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China.,School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Yanjiang Wang
- College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China
| | - Sebastian J Kopetzky
- Biomax Informatics AG, Brain Science, Planegg, Germany.,TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | | | - Marcus Kaiser
- School of Computing, Newcastle University, Newcastle upon Tyne, UK.,NIHR Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham, Nottingham, UK.,Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK.,School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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16
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Li W, Jiang Y, Qin Y, Zhou B, Lei D, Luo C, Zhang H, Gong Q, Zhou D, An D. Dynamic gray matter and intrinsic activity changes after epilepsy surgery. Acta Neurol Scand 2021; 143:261-270. [PMID: 33058145 DOI: 10.1111/ane.13361] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 09/20/2020] [Accepted: 10/05/2020] [Indexed: 02/05/2023]
Abstract
OBJECTIVES To explore the dynamic changes of gray matter volume and intrinsic brain activity following anterior temporal lobectomy (ATL) in patients with unilateral mesial temporal lobe epilepsy (mTLE) who achieved seizure-free for 2 years. MATERIALS AND METHODS High-resolution T1-weighted MRI and resting-state functional MRI data were obtained in ten mTLE patients at five serial timepoints: before surgery, 3, 6, 12, and 24 months after surgery. The gray matter volume (GMV) and amplitude of low-frequency fluctuations (ALFF) were compared among the five scans to depict the dynamic changes after ATL. RESULTS After successful ATL, GMV decreased in several ipsilateral brain regions: ipsilateral insula, thalamus, and putamen showed gradual gray matter atrophy from 3 to 24 months, while ipsilateral superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, middle occipital gyrus, inferior occipital gyrus, caudate nucleus, lingual gyrus, and fusiform gyrus showed significant GMV decrease at 3 months follow-up, without further changes. Ipsilateral insula showed gradual ALFF decrease from 3 to 24 months after surgery. Ipsilateral superior temporal gyrus showed ALFF decrease at 3 months follow-up, without further changes. Ipsilateral thalamus and cerebellar vermis showed obvious ALFF increase after surgery. CONCLUSIONS Surgical resection may lead to a short-term reduction of gray matter volume and intrinsic brain activity in neighboring regions, while the progressive gray matter atrophy may be due to possible intrinsic mechanism of mTLE. Dynamic ALFF changes provide evidence that disrupted focal spontaneous activities were reorganized after successful surgery.
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Affiliation(s)
- Wei Li
- Department of Neurology West China Hospital Sichuan University Chengdu China
| | - Yuchao Jiang
- The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation Center for Information in Medicine School of life Science and technology University of Electronic Science and Technology of China Chengdu China
| | - Yingjie Qin
- Department of Neurology West China Hospital Sichuan University Chengdu China
| | - Baiwan Zhou
- Department of Radiology Huaxi MR Research Center West China Hospital Sichuan University Chengdu China
| | - Du Lei
- Department of Radiology Huaxi MR Research Center West China Hospital Sichuan University Chengdu China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation Center for Information in Medicine School of life Science and technology University of Electronic Science and Technology of China Chengdu China
- Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu China
| | - Heng Zhang
- Department of Neurosurgery West China Hospital Sichuan University Chengdu China
| | - Qiyong Gong
- Department of Radiology Huaxi MR Research Center West China Hospital Sichuan University Chengdu China
| | - Dong Zhou
- Department of Neurology West China Hospital Sichuan University Chengdu China
| | - Dongmei An
- Department of Neurology West China Hospital Sichuan University Chengdu China
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17
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Kaestner E, Reyes A, Chen A, Rao J, Macari AC, Choi JY, Qiu D, Hewitt K, Wang ZI, Drane DL, Hermann B, Busch RM, Punia V, McDonald CR. Atrophy and cognitive profiles in older adults with temporal lobe epilepsy are similar to mild cognitive impairment. Brain 2021; 144:236-250. [PMID: 33279986 PMCID: PMC7880670 DOI: 10.1093/brain/awaa397] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 09/02/2020] [Accepted: 09/21/2020] [Indexed: 11/14/2022] Open
Abstract
Epilepsy incidence and prevalence peaks in older adults yet systematic studies of brain ageing and cognition in older adults with epilepsy remain limited. Here, we characterize patterns of cortical atrophy and cognitive impairment in 73 older adults with temporal lobe epilepsy (>55 years) and compare these patterns to those observed in 70 healthy controls and 79 patients with amnestic mild cognitive impairment, the prodromal stage of Alzheimer's disease. Patients with temporal lobe epilepsy were recruited from four tertiary epilepsy surgical centres; amnestic mild cognitive impairment and control subjects were obtained from the Alzheimer's Disease Neuroimaging Initiative database. Whole brain and region of interest analyses were conducted between patient groups and controls, as well as between temporal lobe epilepsy patients with early-onset (age of onset <50 years) and late-onset (>50 years) seizures. Older adults with temporal lobe epilepsy demonstrated a similar pattern and magnitude of medial temporal lobe atrophy to amnestic mild cognitive impairment. Region of interest analyses revealed pronounced medial temporal lobe thinning in both patient groups in bilateral entorhinal, temporal pole, and fusiform regions (all P < 0.05). Patients with temporal lobe epilepsy demonstrated thinner left entorhinal cortex compared to amnestic mild cognitive impairment (P = 0.02). Patients with late-onset temporal lobe epilepsy had a more consistent pattern of cortical thinning than patients with early-onset epilepsy, demonstrating decreased cortical thickness extending into the bilateral fusiform (both P < 0.01). Both temporal lobe epilepsy and amnestic mild cognitive impairment groups showed significant memory and language impairment relative to healthy control subjects. However, despite similar performances in language and memory encoding, patients with amnestic mild cognitive impairment demonstrated poorer delayed memory performances relative to both early and late-onset temporal lobe epilepsy. Medial temporal lobe atrophy and cognitive impairment overlap between older adults with temporal lobe epilepsy and amnestic mild cognitive impairment highlights the risks of growing old with epilepsy. Concerns regarding accelerated ageing and Alzheimer's disease co-morbidity in older adults with temporal lobe epilepsy suggests an urgent need for translational research aimed at identifying common mechanisms and/or targeting symptoms shared across a broad neurological disease spectrum.
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Affiliation(s)
- Erik Kaestner
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - Anny Reyes
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
- San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
| | - Austin Chen
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - Jun Rao
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - Anna Christina Macari
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - Joon Yul Choi
- Epilepsy Center and Department of Neurology, Cleveland Clinic, Cleveland, OH, USA
| | - Deqiang Qiu
- Department of Radiology, Emory University School of Medicine, Atlanta, GA, USA
| | - Kelsey Hewitt
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Zhong Irene Wang
- Epilepsy Center and Department of Neurology, Cleveland Clinic, Cleveland, OH, USA
| | - Daniel L Drane
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, University of Washington, Seattle, WA, USA
| | - Bruce Hermann
- Matthews Neuropsychology Section, University of Wisconsin, Madison, WI, USA
| | - Robyn M Busch
- Epilepsy Center and Department of Neurology, Cleveland Clinic, Cleveland, OH, USA
| | - Vineet Punia
- Epilepsy Center and Department of Neurology, Cleveland Clinic, Cleveland, OH, USA
| | - Carrie R McDonald
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
- San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
- Department of Psychiatry, University of California, San Diego, CA, USA
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18
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Tanoue Y, Uda T, Hoshi H, Shigihara Y, Kawashima T, Nakajo K, Tsuyuguchi N, Goto T. Specific Oscillatory Power Changes and Their Efficacy for Determining Laterality in Mesial Temporal Lobe Epilepsy: A Magnetoencephalographic Study. Front Neurol 2021; 12:617291. [PMID: 33633670 PMCID: PMC7900569 DOI: 10.3389/fneur.2021.617291] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 01/21/2021] [Indexed: 01/22/2023] Open
Abstract
Appropriate determination of the epileptic focus and its laterality are important for the diagnosis of mesial temporal lobe epilepsy (MTLE). The aims of this study are to establish a specific oscillatory distribution and laterality of the oscillatory power in unilateral MTLE with frequency analysis of magnetoencephalography (MEG), and to confirm their potential to carry significant information for determining lateralization of the epileptic focus. Thirty-five patients with MTLE [left (LtMTLE), 16; right (RtMTLE), 19] and 102 healthy control volunteers (CTR) were enrolled. Cortical oscillatory powers were compared among the groups by contrasting the source images using a one-way ANOVA model for each frequency band. Further, to compare the lateralization of regional oscillatory powers between LtMTLEs and RtMTLEs, the laterality index (LI) was calculated for four brain regions (frontal, temporal, parietal, and occipital) in each frequency band, which were compared between patient groups (LtMTLE, RtMTLE, and CTR), and used for machine learning prediction of the groups with support vector machine (SVM) with linear kernel function. Significant oscillatory power differences between MTLE and CTR were found in certain areas. In the theta to high-frequency oscillation bands, there were marked increases in the parietal lobe, especially on the left side, in LtMTLE. In the theta, alpha, and high-gamma bands, there were marked increases in the parietal lobe, especially on the right side in RtMTLE. Compared with CTR, LIs were significantly higher in 24/28 regions in LtMTLE, but lower in 4/28 regions and higher in 10/28 regions in RtMTLE. LI at the temporal lobe in the theta band was significantly higher in LtMTLE and significantly lower in RtMTLE. Comparing LtMTLE and RtMTLE, there were significant LI differences in most regions and frequencies (21/28 regions). In all frequency bands, there were significant differences between LtMTLE and RtMTLE in the temporal and parietal lobes. The leave-one-out cross-validation of the linear-SVM showed the classification accuracy to be over 91%, where the model had high specificity over 96% and mild sensitivity ~68–75%. Using MEG frequency analysis, the characteristics of the oscillatory power distribution in the MTLE were demonstrated. Compared with CTR, LIs shifted to the side of the epileptic focus in the temporal lobe in the theta band. The machine learning approach also confirmed that LIs have significant predictive ability in the lateralization of the epileptic focus. These results provide useful additional information for determining the laterality of the focus.
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Affiliation(s)
- Yuta Tanoue
- Department of Neurosurgery, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Takehiro Uda
- Department of Neurosurgery, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Hideyuki Hoshi
- Precision Medicine Centre, Hokuto Hospital, Obihiro City, Japan
| | - Yoshihito Shigihara
- Precision Medicine Centre, Hokuto Hospital, Obihiro City, Japan.,Precision Medicine Centre, Kumagaya General Hospital, Kumagaya, Japan
| | - Toshiyuki Kawashima
- Department of Neurosurgery, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Kosuke Nakajo
- Department of Neurosurgery, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Naohiro Tsuyuguchi
- Department of Neurosurgery, Graduate School of Medicine, Osaka City University, Osaka, Japan.,Department of Neurosurgery, Faculty of Medicine, Kindai University, Osaka, Japan
| | - Takeo Goto
- Department of Neurosurgery, Graduate School of Medicine, Osaka City University, Osaka, Japan
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19
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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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20
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Neuroimaging-based brain-age prediction in diverse forms of epilepsy: a signature of psychosis and beyond. Mol Psychiatry 2021; 26:825-834. [PMID: 31160692 PMCID: PMC7910210 DOI: 10.1038/s41380-019-0446-9] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 04/17/2019] [Accepted: 05/03/2019] [Indexed: 12/17/2022]
Abstract
Epilepsy is a diverse brain disorder, and the pathophysiology of its various forms and comorbidities is largely unknown. A recent machine learning method enables us to estimate an individual's "brain-age" from MRI; this brain-age prediction is expected as a novel individual biomarker of neuropsychiatric disorders. The aims of this study were to estimate the brain-age for various categories of epilepsy and to evaluate clinical discrimination by brain-age for (1) the effect of psychosis on temporal lobe epilepsy (TLE), (2) psychogenic nonepileptic seizures (PNESs) from MRI-negative epilepsies, and (3) progressive myoclonic epilepsy (PME) from juvenile myoclonic epilepsy (JME). In total, 1196 T1-weighted MRI scans from healthy controls (HCs) were used to build a brain-age prediction model with support vector regression. Using the model, we calculated the brain-predicted age difference (brain-PAD: predicted age-chronological age) of the HCs and 318 patients with epilepsy. We compared the brain-PAD values based on the research questions. As a result, all categories of patients except for extra-temporal lobe focal epilepsy showed a significant increase in brain-PAD. TLE with hippocampal sclerosis presented a significantly higher brain-PAD than several other categories. The mean brain-PAD in TLE with inter-ictal psychosis was 10.9 years, which was significantly higher than TLE without psychosis (5.3 years). PNES showed a comparable mean brain-PAD (10.6 years) to that of epilepsy patients. PME had a higher brain-PAD than JME (22.0 vs. 9.3 years). In conclusion, neuroimaging-based brain-age prediction can provide novel insight into or clinical usefulness for the diverse symptoms of epilepsy.
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21
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Roggenhofer E, Muller S, Santarnecchi E, Melie-Garcia L, Wiest R, Kherif F, Draganski B. Remodeling of brain morphology in temporal lobe epilepsy. Brain Behav 2020; 10:e01825. [PMID: 32945137 PMCID: PMC7667340 DOI: 10.1002/brb3.1825] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 08/10/2020] [Accepted: 08/14/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Mesial temporal lobe epilepsy (TLE) is one of the most widespread neurological network disorders. Computational anatomy MRI studies demonstrate a robust pattern of cortical volume loss. Most statistical analyses provide information about localization of significant focal differences in a segregationist way. Multivariate Bayesian modeling provides a framework allowing inferences about inter-regional dependencies. We adopt this approach to answer following questions: Which structures within a pattern of dynamic epilepsy-associated brain anatomy reorganization best predict TLE pathology. Do these structures differ between TLE subtypes? METHODS We acquire clinical and MRI data from TLE patients with and without hippocampus sclerosis (n = 128) additional to healthy volunteers (n = 120). MRI data were analyzed in the computational anatomy framework of SPM12 using classical mass-univariate analysis followed by multivariate Bayesian modeling. RESULTS After obtaining TLE-associated brain anatomy pattern, we estimate predictive power for disease and TLE subtypes using Bayesian model selection and comparison. We show that ipsilateral para-/hippocampal regions contribute most to disease-related differences between TLE and healthy controls independent of TLE laterality and subtype. Prefrontal cortical changes are more discriminative for left-sided TLE, whereas thalamus and temporal pole for right-sided TLE. The presence of hippocampus sclerosis was linked to stronger involvement of thalamus and temporal lobe regions; frontoparietal involvement was predominant in absence of sclerosis. CONCLUSIONS Our topology inferences on brain anatomy demonstrate a differential contribution of structures within limbic and extralimbic circuits linked to main effects of TLE and hippocampal sclerosis. We interpret our results as evidence for TLE-related spatial modulation of anatomical networks.
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Affiliation(s)
- Elisabeth Roggenhofer
- Neurology Department, Department of Clinical Neuroscience, HUG, University Hospitals and Faculty of Medicine Geneva, Geneva, Switzerland.,Department of Clinical Neurosciences, LREN, CHUV, University of Lausanne, Lausanne, Switzerland
| | - Sandrine Muller
- Department of Clinical Neurosciences, LREN, CHUV, University of Lausanne, Lausanne, Switzerland
| | - Emiliano Santarnecchi
- Berenson-Allen Center for Non-Invasive Brain Stimulation, Cognitive Neurology Department, Beth Israel Medical Center, Harvard Medical School, Boston, MA, USA.,Siena Brain Investigation and Neuromodulation Lab, Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Lester Melie-Garcia
- Department of Clinical Neurosciences, LREN, CHUV, University of Lausanne, Lausanne, Switzerland.,Applied Signal Processing Group, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, University Hospital Inselspital, University of Bern, Bern, Switzerland
| | - Ferath Kherif
- Department of Clinical Neurosciences, LREN, CHUV, University of Lausanne, Lausanne, Switzerland
| | - Bogdan Draganski
- Department of Clinical Neurosciences, LREN, CHUV, University of Lausanne, Lausanne, Switzerland.,Department of Neurology, Max-Planck-Institute for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, Germany
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22
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Binder JR, Tong JQ, Pillay SB, Conant LL, Humphries CJ, Raghavan M, Mueller WM, Busch RM, Allen L, Gross WL, Anderson CT, Carlson CE, Lowe MJ, Langfitt JT, Tivarus ME, Drane DL, Loring DW, Jacobs M, Morgan VL, Allendorfer JB, Szaflarski JP, Bonilha L, Bookheimer S, Grabowski T, Vannest J, Swanson SJ. Temporal lobe regions essential for preserved picture naming after left temporal epilepsy surgery. Epilepsia 2020; 61:1939-1948. [PMID: 32780878 DOI: 10.1111/epi.16643] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 07/10/2020] [Accepted: 07/20/2020] [Indexed: 11/30/2022]
Abstract
OBJECTIVE To define left temporal lobe regions where surgical resection produces a persistent postoperative decline in naming visual objects. METHODS Pre- and postoperative brain magnetic resonance imaging data and picture naming (Boston Naming Test) scores were obtained prospectively from 59 people with drug-resistant left temporal lobe epilepsy. All patients had left hemisphere language dominance at baseline and underwent surgical resection or ablation in the left temporal lobe. Postoperative naming assessment occurred approximately 7 months after surgery. Surgical lesions were mapped to a standard template, and the relationship between presence or absence of a lesion and the degree of naming decline was tested at each template voxel while controlling for effects of overall lesion size. RESULTS Patients declined by an average of 15% in their naming score, with wide variation across individuals. Decline was significantly related to damage in a cluster of voxels in the ventral temporal lobe, located mainly in the fusiform gyrus approximately 4-6 cm posterior to the temporal tip. Extent of damage to this region explained roughly 50% of the variance in outcome. Picture naming decline was not related to hippocampal or temporal pole damage. SIGNIFICANCE The results provide the first statistical map relating lesion location in left temporal lobe epilepsy surgery to picture naming decline, and they support previous observations of transient naming deficits from electrical stimulation in the basal temporal cortex. The critical lesion is relatively posterior and could be avoided in many patients undergoing left temporal lobe surgery for intractable epilepsy.
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Affiliation(s)
- Jeffrey R Binder
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Jia-Qing Tong
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Sara B Pillay
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Lisa L Conant
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Colin J Humphries
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Manoj Raghavan
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Wade M Mueller
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Robyn M Busch
- Department of Neurology, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Linda Allen
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - William L Gross
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | | | - Chad E Carlson
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Mark J Lowe
- Department of Radiology, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - John T Langfitt
- Department of Neurology, University of Rochester, Rochester, New York, USA
| | - Madalina E Tivarus
- Department of Imaging Sciences, University of Rochester, Rochester, New York, USA
| | - Daniel L Drane
- Department of Neurology and Pediatrics, Emory University, Atlanta, Georgia, USA
| | - David W Loring
- Department of Neurology and Pediatrics, Emory University, Atlanta, Georgia, USA
| | - Monica Jacobs
- Department of Psychology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Victoria L Morgan
- Department of Radiology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jane B Allendorfer
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Jerzy P Szaflarski
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Leonardo Bonilha
- Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Susan Bookheimer
- Department of Neurology, University of California, Los Angeles, California, USA
| | - Thomas Grabowski
- Department of Neurology, University of Washington, Seattle, Washington, USA
| | - Jennifer Vannest
- Department of Neurology, University of Cincinnati, Cincinnati, Ohio, USA
| | - Sara J Swanson
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
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23
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Hatton SN, Huynh KH, Bonilha L, Abela E, Alhusaini S, Altmann A, Alvim MKM, Balachandra AR, Bartolini E, Bender B, Bernasconi N, Bernasconi A, Bernhardt B, Bargallo N, Caldairou B, Caligiuri ME, Carr SJA, Cavalleri GL, Cendes F, Concha L, Davoodi-bojd E, Desmond PM, Devinsky O, Doherty CP, Domin M, Duncan JS, Focke NK, Foley SF, Gambardella A, Gleichgerrcht E, Guerrini R, Hamandi K, Ishikawa A, Keller SS, Kochunov PV, Kotikalapudi R, Kreilkamp BAK, Kwan P, Labate A, Langner S, Lenge M, Liu M, Lui E, Martin P, Mascalchi M, Moreira JCV, Morita-Sherman ME, O’Brien TJ, Pardoe HR, Pariente JC, Ribeiro LF, Richardson MP, Rocha CS, Rodríguez-Cruces R, Rosenow F, Severino M, Sinclair B, Soltanian-Zadeh H, Striano P, Taylor PN, Thomas RH, Tortora D, Velakoulis D, Vezzani A, Vivash L, von Podewils F, Vos SB, Weber B, Winston GP, Yasuda CL, Zhu AH, Thompson PM, Whelan CD, Jahanshad N, Sisodiya SM, McDonald CR. White matter abnormalities across different epilepsy syndromes in adults: an ENIGMA-Epilepsy study. Brain 2020; 143:2454-2473. [PMID: 32814957 PMCID: PMC7567169 DOI: 10.1093/brain/awaa200] [Citation(s) in RCA: 101] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 04/07/2020] [Accepted: 04/30/2020] [Indexed: 12/22/2022] Open
Abstract
The epilepsies are commonly accompanied by widespread abnormalities in cerebral white matter. ENIGMA-Epilepsy is a large quantitative brain imaging consortium, aggregating data to investigate patterns of neuroimaging abnormalities in common epilepsy syndromes, including temporal lobe epilepsy, extratemporal epilepsy, and genetic generalized epilepsy. Our goal was to rank the most robust white matter microstructural differences across and within syndromes in a multicentre sample of adult epilepsy patients. Diffusion-weighted MRI data were analysed from 1069 healthy controls and 1249 patients: temporal lobe epilepsy with hippocampal sclerosis (n = 599), temporal lobe epilepsy with normal MRI (n = 275), genetic generalized epilepsy (n = 182) and non-lesional extratemporal epilepsy (n = 193). A harmonized protocol using tract-based spatial statistics was used to derive skeletonized maps of fractional anisotropy and mean diffusivity for each participant, and fibre tracts were segmented using a diffusion MRI atlas. Data were harmonized to correct for scanner-specific variations in diffusion measures using a batch-effect correction tool (ComBat). Analyses of covariance, adjusting for age and sex, examined differences between each epilepsy syndrome and controls for each white matter tract (Bonferroni corrected at P < 0.001). Across 'all epilepsies' lower fractional anisotropy was observed in most fibre tracts with small to medium effect sizes, especially in the corpus callosum, cingulum and external capsule. There were also less robust increases in mean diffusivity. Syndrome-specific fractional anisotropy and mean diffusivity differences were most pronounced in patients with hippocampal sclerosis in the ipsilateral parahippocampal cingulum and external capsule, with smaller effects across most other tracts. Individuals with temporal lobe epilepsy and normal MRI showed a similar pattern of greater ipsilateral than contralateral abnormalities, but less marked than those in patients with hippocampal sclerosis. Patients with generalized and extratemporal epilepsies had pronounced reductions in fractional anisotropy in the corpus callosum, corona radiata and external capsule, and increased mean diffusivity of the anterior corona radiata. Earlier age of seizure onset and longer disease duration were associated with a greater extent of diffusion abnormalities in patients with hippocampal sclerosis. We demonstrate microstructural abnormalities across major association, commissural, and projection fibres in a large multicentre study of epilepsy. Overall, patients with epilepsy showed white matter abnormalities in the corpus callosum, cingulum and external capsule, with differing severity across epilepsy syndromes. These data further define the spectrum of white matter abnormalities in common epilepsy syndromes, yielding more detailed insights into pathological substrates that may explain cognitive and psychiatric co-morbidities and be used to guide biomarker studies of treatment outcomes and/or genetic research.
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Affiliation(s)
- Sean N Hatton
- Department of Neurosciences, Center for Multimodal Imaging and Genetics,
University of California San Diego, La Jolla 92093 CA, USA
| | - Khoa H Huynh
- Center for Multimodal Imaging and Genetics, University of California San
Diego, La Jolla 92093 CA, USA
| | - Leonardo Bonilha
- Department of Neurology, Medical University of South Carolina,
Charleston 29425 SC, USA
| | - Eugenio Abela
- Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry,
Psychology and Neuroscience, Kings College London, London SE5 9NU UK
| | - Saud Alhusaini
- Neurology Department, Yale School of Medicine, New Haven 6510 CT,
USA
- Molecular and Cellular Therapeutics, The Royal College of Surgeons in
Ireland, Dublin, Ireland
| | - Andre Altmann
- Centre of Medical Image Computing, Department of Medical Physics and Biomedical
Engineering, University College London, London WC1V 6LJ, UK
| | - Marina K M Alvim
- Department of Neurology, University of Campinas - UNICAMP, Campinas 13083-888
São Paulo, Brazil
| | - Akshara R Balachandra
- Center for Multimodal Imaging and Genetics, UCSD School of
Medicine, La Jolla 92037 CA, USA
- Boston University School of Medicine, Boston 2118 MA, USA
| | - Emanuele Bartolini
- Pediatric Neurology, Neurogenetics and Neurobiology Unit and Laboratories,
Children’s Hospital A. Meyer-University of Florence, Florence, Italy
- USL Centro Toscana, Neurology Unit, Nuovo Ospedale Santo Stefano,
Prato, Italy
| | - Benjamin Bender
- Department of Diagnostic and Interventional Neuroradiology, University Hospital
Tübingen, Tübingen 72076, Germany
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute, McGill
University, Montreal H3A 2B4 QC, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute, McGill
University, Montreal H3A 2B4 QC, Canada
| | - Boris Bernhardt
- Montreal Neurological Institute, McGill University, Montreal
H3A2B4 QC, Canada
| | - Núria Bargallo
- Magnetic Resonance Image Core Facility, Institut d’Investigacions Biomèdiques
August Pi i Sunyer (IDIBAPS), Barcelona 8036 Barcelona, Spain
| | - Benoit Caldairou
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute, McGill
University, Montreal H3A 2B4 QC, Canada
| | - Maria E Caligiuri
- Neuroscience Research Center, University Magna Graecia, viale Europa,
Germaneto, 88100, Catanzaro, Italy
| | - Sarah J A Carr
- Neuroscience, Institute of Psychiatry, Psychology and
Neuroscience, De Crespigny Park, London SE5 8AF, UK
| | - Gianpiero L Cavalleri
- Royal College of Surgeons in Ireland, School of Pharmacy and Biomolecular
Sciences, Dublin D02 YN77 Ireland
- FutureNeuro Research Centre, Science Foundation Ireland, Dublin
D02 YN77, Ireland
| | - Fernando Cendes
- Department of Neurology, University of Campinas - UNICAMP, Campinas 13083-888
São Paulo, Brazil
| | - Luis Concha
- Institute of Neurobiology, Universidad Nacional Autonoma de
Mexico, Queretaro 76230, Mexico
| | - Esmaeil Davoodi-bojd
- Radiology and Research Administration, Henry Ford Hospital, 1
Detroit 48202 MI, USA
| | - Patricia M Desmond
- Department of Radiology, Royal Melbourne Hospital, University of
Melbourne, Melbourne 3050 Victoria, Australia
| | | | - Colin P Doherty
- Division of Neurology, Trinity College Dublin, TBSI, Pearce
Street, Dublin D02 R590, Ireland
- FutureNeuro SFI Centre for Neurological Disease, RCSI, St Stephen’s
Green, Dublin D02 H903, Ireland
| | - Martin Domin
- Functional Imaging Unit, University Medicine Greifswald,
Greifswald 17475 M/V, Germany
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of
Neurology, Queen Square, London WC1N 3BG, UK
- MRI Unit, Chalfont Centre for Epilepsy, Chalfont-St-Peter,
Buckinghamshire SL9 0RJ, UK
| | - Niels K Focke
- Clinical Neurophysiology, University Medicine Göttingen, 37099
Göttingen, Germany
- Department of Epileptology, University of Tübingen, 72076
Tübingen, Germany
| | | | - Antonio Gambardella
- Royal College of Surgeons in Ireland, School of Pharmacy and Biomolecular
Sciences, Dublin D02 YN77 Ireland
- Institute of Neurology, University Magna Graecia, 88100,
Catanzaro, Italy
| | | | - Renzo Guerrini
- Pediatric Neurology, Neurogenetics and Neurobiology Unit and Laboratories,
Children’s Hospital A. Meyer-University of Florence, Florence, Italy
| | - Khalid Hamandi
- The Wales Epilepsy Unit, Cardiff and Vale University Health
Board, Cardiff CF144XW, UK
- Brain Research Imaging Centre, Cardiff University, Cardiff CF24
4HQ, UK
| | - Akari Ishikawa
- Department of Neurology, University of Campinas - UNICAMP, Campinas 13083-888
São Paulo, Brazil
| | - Simon S Keller
- Institute of Translational Medicine, University of Liverpool,
Liverpool L69 3BX, UK
- Walton Centre NHS Foundation Trust, Liverpool L9 7LJ, UK
| | - Peter V Kochunov
- Maryland Psychiatric Research Center, 55 Wade Ave, Baltimore
21228, MD, USA
| | - Raviteja Kotikalapudi
- Department of Neurology and Epileptology, University Hospital
Tübingen, Tübingen 72076 BW, Germany
- Department of Diagnostic and Interventional Neuroradiology, University Hospital
Tübingen, Tübingen 72076 BW, Germany
| | - Barbara A K Kreilkamp
- Institute of Translational Medicine, University of Liverpool,
Liverpool L69 3BX, UK
- Walton Centre NHS Foundation Trust, Liverpool L9 7LJ, UK
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash
University, Melbourne 3004 Victoria, Australia
- Department of Medicine, University of Melbourne, Royal Melbourne
Hospital, Parkville 3050 Victoria, Australia
| | - Angelo Labate
- Neuroscience Research Center, University Magna Graecia, viale Europa,
Germaneto, 88100, Catanzaro, Italy
- Institute of Neurology, University Magna Graecia, 88100,
Catanzaro, Italy
| | - Soenke Langner
- Institute for Diagnostic Radiology and Neuroradiology, Ernst Moritz Arndt
University Greifswald Faculty of Medicine, Greifswald 17475, Germany
- Institute for Diagnostic and Interventional Radiology, Pediatric and
Neuroradiology, Rostock University Medical Centre, Rostock 18057, Germany
| | - Matteo Lenge
- Pediatric Neurology, Neurogenetics and Neurobiology Unit and Laboratories,
Children’s Hospital A. Meyer-University of Florence, Florence, Italy
- Functional and Epilepsy Neurosurgery Unit, Children’s Hospital A.
Meyer-University of Florence, Florence 50139, Italy
| | - Min Liu
- Department of Neurology, Montreal Neurological Institute,
Montreal H3A 2B4 QC, Canada
| | - Elaine Lui
- Department of Radiology, Royal Melbourne Hospital, University of
Melbourne, Melbourne 3050 Victoria, Australia
- Department of Medicine and Radiology, University of Melbourne,
3Parkville 3050 Victoria, Australia
| | - Pascal Martin
- Department of Epileptology, University of Tübingen, 72076
Tübingen, Germany
| | - Mario Mascalchi
- Meyer Children Hospital University of Florence, Florence 50130
Tuscany, Italy
| | - José C V Moreira
- Department of Neurology, University of Campinas - UNICAMP, Campinas 13083-888
São Paulo, Brazil
| | - Marcia E Morita-Sherman
- Department of Neurology, University of Campinas - UNICAMP, Campinas 13083-888
São Paulo, Brazil
- Cleveland Clinic, Cleveland 44195 OH, USA
| | - Terence J O’Brien
- Department of Neuroscience, Central Clinical School, Monash
University, Melbourne 3004 Victoria, Australia
- Department of Medicine, University of Melbourne, Royal Melbourne
Hospital, Parkville 3050 Victoria, Australia
- Department of Neurology, Alfred Health, Melbourne 3004 Victoria,
Australia
| | - Heath R Pardoe
- Department of Neurology, New York University School of Medicine,
New York City 10016 NY, USA
| | - José C Pariente
- Magnetic Resonance Image Core Facility, Institut d’Investigacions Biomèdiques
August Pi i Sunyer (IDIBAPS), Barcelona 8036 Barcelona, Spain
| | - Letícia F Ribeiro
- Department of Neurology, University of Campinas - UNICAMP, Campinas 13083-888
São Paulo, Brazil
| | - Mark P Richardson
- Division of Neuroscience, King’s College London, Institute of
Psychiatry, London SE5 8AB, UK
| | - Cristiane S Rocha
- Department of Neurology, University of Campinas - UNICAMP, Campinas 13083-888
São Paulo, Brazil
| | - Raúl Rodríguez-Cruces
- Montreal Neurological Institute, McGill University, Montreal
H3A2B4 QC, Canada
- Institute of Neurobiology, Universidad Nacional Autonoma de
Mexico, Queretaro 76230, Mexico
| | - Felix Rosenow
- Epilepsy Center Frankfurt Rhine-Main, University Hospital Frankfurt,
Germany, Frankfurt 60528 Hesse, Germany
- Center for Personalized Translational Epilepsy Research (CePTER),
Goethe-University Frankfurt, Frankfurt a. M. 60528, Germany
| | - Mariasavina Severino
- Neuroradiology Unit, IRCCS Istituto Giannina Gaslini, Genoa 16147
Liguria, Italy
| | - Benjamin Sinclair
- Department of Medicine, University of Melbourne, Royal Melbourne
Hospital, Parkville 3050 Victoria, Australia
- Department of Neurology, Alfred Health, Melbourne 3004 Victoria,
Australia
| | - Hamid Soltanian-Zadeh
- Radiology and Research Administration, Henry Ford Health System,
Detroit 48202-2692 MI, USA
- School of Electrical and Computer Engineering, University of
Tehran, Tehran 14399-57131, Iran
| | - Pasquale Striano
- IRCCS Istituto Giannina Gaslini, Genoa 16147 Liguria, Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal
and Child Health, University of Genova, Genova, Italy
| | - Peter N Taylor
- School of Computing, Newcastle University, Urban Sciences Building, Science
Square, Newcastle upon Tyne NE4 5TG, UK
| | - Rhys H Thomas
- Translational and Clinical Research Institute, Newcastle
University, Newcastle upon Tyne NE2 4HH, UK
- Royal Victoria Infirmary, Newcastle upon Tyne NE1 4LP, UK
| | - Domenico Tortora
- Radiology and Research Administration, Henry Ford Health System,
Detroit 48202-2692 MI, USA
| | - Dennis Velakoulis
- Royal Melbourne Hospital, Melbourne 3050 Victoria, Australia
- University of Melbourne, Parkville, Melbourne 3050 Victoria,
Australia
| | - Annamaria Vezzani
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano
20156 Italy
| | - Lucy Vivash
- Department of Neuroscience, Central Clinical School, Monash
University, Melbourne 3004 Victoria, Australia
- Department of Medicine, University of Melbourne, Royal Melbourne
Hospital, Parkville 3050 Victoria, Australia
| | - Felix von Podewils
- Epilepsy Center, University Medicine Greifswald, Greifswald 17489
Mecklenburg-Vorpommern, Germany
| | - Sjoerd B Vos
- Centre for Medical Image Computing, University College London,
London, WC1V 6LJ, UK
- Epilepsy Society, MRI Unit, Chalfont St Peter, Buckinghamshire,
SL9 0RJ, UK
| | - Bernd Weber
- Institute of Experimental Epileptology and Cognition Research, University of
Bonn, Venusberg Campus 1, Bonn 53127 NRW, Germany
| | - Gavin P Winston
- Epilepsy Society, MRI Unit, Chalfont St Peter, Buckinghamshire,
SL9 0RJ, UK
- Department of Medicine, Division of Neurology, Queen's
University, Kingston K7L 3N6 ON, Canada
- MRI Unit, Chalfont Centre for Epilepsy, Chalfont-St-Peter,
Buckinghamshire, SL9 0RJ UK
| | - Clarissa L Yasuda
- Department of Neurology, University of Campinas - UNICAMP, Campinas 13083-888
São Paulo, Brazil
| | - Alyssa H Zhu
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and
Informatics, USC Keck School of Medicine, Los Angeles 90232 CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and
Informatics, USC Keck School of Medicine, Los Angeles 90232 CA, USA
| | - Christopher D Whelan
- Molecular and Cellular Therapeutics, The Royal College of Surgeons in
Ireland, Dublin, Ireland
- Research and Early Development (RED), Biogen Inc., Cambridge, MA
02139, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and
Informatics, USC Keck School of Medicine, Los Angeles 90232 CA, USA
| | - Sanjay M Sisodiya
- MRI Unit, Chalfont Centre for Epilepsy, Chalfont-St-Peter,
Buckinghamshire, SL9 0RJ UK
- Chalfont Centre for Epilepsy, Chalfont-St-Peter, SL9 0RJ Bucks,
UK
| | - Carrie R McDonald
- Department of Psychiatry, Center for Multimodal Imaging and Genetics,
University of California San Diego, La Jolla 92093 CA, USA
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24
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Sisodiya SM, Whelan CD, Hatton SN, Huynh K, Altmann A, Ryten M, Vezzani A, Caligiuri ME, Labate A, Gambardella A, Ives‐Deliperi V, Meletti S, Munsell BC, Bonilha L, Tondelli M, Rebsamen M, Rummel C, Vaudano AE, Wiest R, Balachandra AR, Bargalló N, Bartolini E, Bernasconi A, Bernasconi N, Bernhardt B, Caldairou B, Carr SJ, Cavalleri GL, Cendes F, Concha L, Desmond PM, Domin M, Duncan JS, Focke NK, Guerrini R, Hamandi K, Jackson GD, Jahanshad N, Kälviäinen R, Keller SS, Kochunov P, Kowalczyk MA, Kreilkamp BA, Kwan P, Lariviere S, Lenge M, Lopez SM, Martin P, Mascalchi M, Moreira JC, Morita‐Sherman ME, Pardoe HR, Pariente JC, Raviteja K, Rocha CS, Rodríguez‐Cruces R, Seeck M, Semmelroch MK, Sinclair B, Soltanian‐Zadeh H, Stein DJ, Striano P, Taylor PN, Thomas RH, Thomopoulos SI, Velakoulis D, Vivash L, Weber B, Yasuda CL, Zhang J, Thompson PM, McDonald CR. The ENIGMA-Epilepsy working group: Mapping disease from large data sets. Hum Brain Mapp 2020; 43:113-128. [PMID: 32468614 PMCID: PMC8675408 DOI: 10.1002/hbm.25037] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 05/01/2020] [Accepted: 05/03/2020] [Indexed: 02/06/2023] Open
Abstract
Epilepsy is a common and serious neurological disorder, with many different constituent conditions characterized by their electro clinical, imaging, and genetic features. MRI has been fundamental in advancing our understanding of brain processes in the epilepsies. Smaller-scale studies have identified many interesting imaging phenomena, with implications both for understanding pathophysiology and improving clinical care. Through the infrastructure and concepts now well-established by the ENIGMA Consortium, ENIGMA-Epilepsy was established to strengthen epilepsy neuroscience by greatly increasing sample sizes, leveraging ideas and methods established in other ENIGMA projects, and generating a body of collaborating scientists and clinicians to drive forward robust research. Here we review published, current, and future projects, that include structural MRI, diffusion tensor imaging (DTI), and resting state functional MRI (rsfMRI), and that employ advanced methods including structural covariance, and event-based modeling analysis. We explore age of onset- and duration-related features, as well as phenomena-specific work focusing on particular epilepsy syndromes or phenotypes, multimodal analyses focused on understanding the biology of disease progression, and deep learning approaches. We encourage groups who may be interested in participating to make contact to further grow and develop ENIGMA-Epilepsy.
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Affiliation(s)
- Sanjay M. Sisodiya
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of NeurologyLondonUK
- Chalfont Centre for EpilepsyBucksUK
| | - Christopher D. Whelan
- Department of Molecular and Cellular TherapeuticsThe Royal College of Surgeons in IrelandDublinIreland
| | - Sean N. Hatton
- Center for Multimodal Imaging and GeneticsUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Khoa Huynh
- Center for Multimodal Imaging and GeneticsUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Andre Altmann
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
| | - Mina Ryten
- UCL Queen Square Institute of NeurologyLondonUK
| | - Annamaria Vezzani
- Department of NeuroscienceIstituto di Ricerche Farmacologiche Mario Negri IRCCSMilanItaly
| | - Maria Eugenia Caligiuri
- Neuroscience Research Center, Department of Medical and Surgical SciencesUniversity “Magna Græcia" of CatanzaroCatanzaroItaly
| | - Angelo Labate
- Neuroscience Research Center, Department of Medical and Surgical SciencesUniversity “Magna Græcia" of CatanzaroCatanzaroItaly
- Institute of NeurologyUniversity “Magna Græcia" of CatanzaroCatanzaroItaly
| | - Antonio Gambardella
- Neuroscience Research Center, Department of Medical and Surgical SciencesUniversity “Magna Græcia" of CatanzaroCatanzaroItaly
- Institute of NeurologyUniversity “Magna Græcia" of CatanzaroCatanzaroItaly
| | | | - Stefano Meletti
- Department of Biomedical, Metabolic, and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
- Neurology UnitOCB Hospital, AOU ModenaModenaItaly
| | - Brent C. Munsell
- Department of PsychiatryUniversity of North CarolinaChapel HillNorth CarolinaUSA
- Department of Computer ScienceUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - Leonardo Bonilha
- Department of NeurologyMedical University of South CarolinaCharlestonSouth CarolinaUSA
| | | | - Michael Rebsamen
- Support Center for Advanced NeuroimagingUniversity Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of BernBernSwitzerland
| | - Christian Rummel
- Support Center for Advanced NeuroimagingUniversity Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of BernBernSwitzerland
| | - Anna Elisabetta Vaudano
- Department of Biomedical, Metabolic, and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
- Neurology UnitOCB Hospital, AOU ModenaModenaItaly
| | - Roland Wiest
- Support Center for Advanced NeuroimagingUniversity Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of BernBernSwitzerland
| | - Akshara R. Balachandra
- Center for Multimodal Imaging and GeneticsUniversity of California San DiegoLa JollaCaliforniaUSA
- Boston University School of MedicineBostonMassachusettsUSA
| | - Núria Bargalló
- Magnetic Resonance Image Core FacilityInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de BarcelonaBarcelonaSpain
- Radiology Department of Center of Image DiagnosisHospital Clinic de BarcelonaBarcelonaSpain
| | - Emanuele Bartolini
- Neurology UnitUSL Centro Toscana, Nuovo Ospedale Santo StefanoPratoItaly
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy LaboratoryMontreal Neurological Institute, McGill UniversityMontrealQuébecCanada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy LaboratoryMontreal Neurological Institute, McGill UniversityMontrealQuébecCanada
| | - Boris Bernhardt
- McConnell Brain Imaging CenterMontreal Neurological Institute, McGill UniversityMontrealQuébecCanada
| | - Benoit Caldairou
- Neuroimaging of Epilepsy LaboratoryMontreal Neurological Institute, McGill UniversityMontrealQuébecCanada
| | - Sarah J.A. Carr
- NeuroscienceInstitute of Psychiatry, Psychology and NeuroscienceLondonUK
| | - Gianpiero L. Cavalleri
- School of Pharmacy and Biomolecular SciencesThe Royal College of Surgeons in IrelandDublinIreland
- FutureNeuro SFI Research CentreDublinIreland
| | - Fernando Cendes
- Department of Neurology and Neuroimaging LaboratoryUniversity of Campinas – UNICAMPCampinasSão PauloBrazil
| | - Luis Concha
- Instituto de NeurobiologíaUniversidad Nacional Autónoma de MéxicoQuerétaroMexico
| | - Patricia M. Desmond
- Department of RadiologyRoyal Melbourne Hospital, University of MelbourneMelbourneVictoriaAustralia
| | - Martin Domin
- Functional Imaging Unit, Department of Diagnostic Radiology and NeuroradiologyUniversity Medicine GreifswaldGreifswaldGermany
| | - John S. Duncan
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of NeurologyLondonUK
- Chalfont Centre for EpilepsyBucksUK
| | - Niels K. Focke
- University Medicine GöttingenClinical NeurophysiologyGöttingenGermany
| | - Renzo Guerrini
- Pediatric Neurology, Neurogenetics and Neurobiology Unit and LaboratoriesChildren's Hospital A. Meyer‐University of FlorenceFlorenceItaly
| | - Khalid Hamandi
- The Wales Epilepsy Unit, Department of NeurologyUniversity Hospital of WalesCardiffUK
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
| | - Graeme D. Jackson
- Department of NeurologyAustin HealthHeidelbergVictoriaAustralia
- Florey Department of Neuroscience and Mental HealthUniversity of MelbourneVictoriaAustralia
| | - Neda Jahanshad
- Imaging Genetics CenterMark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Reetta Kälviäinen
- Kuopio University HospitalMember of EpiCARE ERNKuopioFinland
- Institute of Clinical MedicineNeurology, University of Eastern FinlandKuopioFinland
| | - Simon S. Keller
- Institute of Systems, Molecular and Integrative BiologyUniversity of LiverpoolLiverpoolUK
- The Walton CentreNHS Foundation TrustLiverpoolUK
| | - Peter Kochunov
- Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Magdalena A. Kowalczyk
- Florey Department of Neuroscience and Mental HealthUniversity of MelbourneVictoriaAustralia
| | - Barbara A.K. Kreilkamp
- Institute of Systems, Molecular and Integrative BiologyUniversity of LiverpoolLiverpoolUK
- The Walton CentreNHS Foundation TrustLiverpoolUK
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical SchoolMonash UniversityMelbourneVictoriaAustralia
| | - Sara Lariviere
- McConnell Brain Imaging CenterMontreal Neurological Institute, McGill UniversityMontrealQuébecCanada
| | - Matteo Lenge
- Pediatric Neurology, Neurogenetics and Neurobiology Unit and LaboratoriesChildren's Hospital A. Meyer‐University of FlorenceFlorenceItaly
- Functional and Epilepsy Neurosurgery Unit, Neurosurgery DepartmentChildren's Hospital A. Meyer‐University of FlorenceFlorenceItaly
| | - Seymour M. Lopez
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
| | - Pascal Martin
- Department of Neurology and EpileptologyHertie Institute for Clinical Brain Research, University Hospital TübingenTübingenGermany
| | - Mario Mascalchi
- 'Mario Serio' Department of Clinical and Experimental Medical SciencesUniversity of FlorenceFlorenceItaly
| | - José C.V. Moreira
- Department of Neurology and Neuroimaging LaboratoryUniversity of Campinas – UNICAMPCampinasSão PauloBrazil
| | - Marcia E. Morita‐Sherman
- Department of Neurology and Neuroimaging LaboratoryUniversity of Campinas – UNICAMPCampinasSão PauloBrazil
- Cleveland Clinic Neurological InstituteClevelandOhioUSA
| | - Heath R. Pardoe
- Department of NeurologyNew York University School of MedicineNew YorkNew YorkUSA
| | - Jose C. Pariente
- Magnetic Resonance Image Core FacilityInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de BarcelonaBarcelonaSpain
| | - Kotikalapudi Raviteja
- Department of Neurology and EpileptologyHertie Institute for Clinical Brain Research, University Hospital TübingenTübingenGermany
- Department of Diagnostic and Interventional NeuroradiologyUniversity Hospitals TübingenTübingenGermany
- Department of Clinical NeurophysiologyUniversity Hospital GöttingenGoettingenGermany
| | - Cristiane S. Rocha
- Department of Neurology and Neuroimaging LaboratoryUniversity of Campinas – UNICAMPCampinasSão PauloBrazil
| | - Raúl Rodríguez‐Cruces
- Instituto de NeurobiologíaUniversidad Nacional Autónoma de MéxicoQuerétaroMexico
- Montreal Neurological Institute and HospitalMcGill UniversityMontrealQuébecCanada
| | | | - Mira K.H.G. Semmelroch
- Florey Department of Neuroscience and Mental HealthUniversity of MelbourneVictoriaAustralia
- The Florey Institute of Neuroscience and Mental HealthAustin CampusHeidelbergVictoriaAustralia
| | - Benjamin Sinclair
- Department of NeuroscienceMonash UniversityMelbourneVictoriaAustralia
- Alfred HealthMelbourneVictoriaAustralia
| | - Hamid Soltanian‐Zadeh
- Radiology and Research AdministrationHenry Ford Health SystemDetroitMichiganUSA
- School of Electrical and Computer EngineeringCollege of Engineering, University of TehranTehranIran
| | - Dan J. Stein
- South African Medical Research Council Unit on Risk & Resilience in Mental Disorders, Dept of Psychiatry & Neuroscience InstituteUniversity of Cape Townon Risk & Resilience in Mental DisordersCape TownSouth Africa
| | - Pasquale Striano
- Pediatric Neurology and Muscular Diseases UnitIRCCS Istituto 'G. Gaslini'GenovaItaly
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child HealthUniversity of GenovaItaly
| | - Peter N. Taylor
- School of ComputingNewcastle UniversityNewcastle upon TyneUK
| | - Rhys H. Thomas
- Institute of Translational and Clinical ResearchNewcastle UniversityNewcastle upon TyneUK
| | - Sophia I. Thomopoulos
- Imaging Genetics CenterMark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Dennis Velakoulis
- Department of Medicine, Royal Melbourne HospitalUniversity of MelbourneParkvilleVictoriaUK
- Department of NeuropsychiatryRoyal Melbourne HospitalParkvilleVictoriaAustralia
| | - Lucy Vivash
- Department of NeuroscienceMonash UniversityMelbourneVictoriaAustralia
- Department of NeurologyRoyal Melbourne HospitalMelbourneVictoriaAustralia
| | - Bernd Weber
- Institute of Experimental Epileptology and Cognition ResearchUniversity of BonnBonnGermany
| | - Clarissa Lin Yasuda
- Department of Neurology and Neuroimaging LaboratoryUniversity of Campinas – UNICAMPCampinasSão PauloBrazil
| | - Junsong Zhang
- Cognitive Science DepartmentSchool of Informatics, Xiamen UniversityXiamenChina
| | - Paul M. Thompson
- Imaging Genetics CenterMark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Carrie R. McDonald
- Department of PsychiatryUniversity of California San DiegoLa JollaCaliforniaUSA
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Weng Y, Larivière S, Caciagli L, Vos de Wael R, Rodríguez-Cruces R, Royer J, Xu Q, Bernasconi N, Bernasconi A, Thomas Yeo BT, Lu G, Zhang Z, Bernhardt BC. Macroscale and microcircuit dissociation of focal and generalized human epilepsies. Commun Biol 2020; 3:244. [PMID: 32424317 PMCID: PMC7234993 DOI: 10.1038/s42003-020-0958-5] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 04/15/2020] [Indexed: 01/01/2023] Open
Abstract
Thalamo-cortical pathology plays key roles in both generalized and focal epilepsies, but there is little work directly comparing these syndromes at the level of whole-brain mechanisms. Using multimodal imaging, connectomics, and computational simulations, we examined thalamo-cortical and cortico-cortical signatures and underlying microcircuits in 96 genetic generalized (GE) and 107 temporal lobe epilepsy (TLE) patients, along with 65 healthy controls. Structural and functional network profiling highlighted extensive atrophy, microstructural disruptions and decreased thalamo-cortical connectivity in TLE, while GE showed only subtle structural anomalies paralleled by enhanced thalamo-cortical connectivity. Connectome-informed biophysical simulations indicated modest increases in subcortical drive contributing to cortical dynamics in GE, while TLE presented with reduced subcortical drive and imbalanced excitation-inhibition within limbic and somatomotor microcircuits. Multiple sensitivity analyses supported robustness. Our multiscale analyses differentiate human focal and generalized epilepsy at the systems-level, showing paradoxically more severe microcircuit and macroscale imbalances in the former.
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Affiliation(s)
- Yifei Weng
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, H3A2B4, Canada
| | - Sara Larivière
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, H3A2B4, Canada
| | - Lorenzo Caciagli
- University College London Queen Square Institute of Neurology, London, United Kingdom
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Reinder Vos de Wael
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, H3A2B4, Canada
| | - Raúl Rodríguez-Cruces
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, H3A2B4, Canada
| | - Jessica Royer
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, H3A2B4, Canada
| | - Qiang Xu
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Neda Bernasconi
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, H3A2B4, Canada
| | - Andrea Bernasconi
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, H3A2B4, Canada
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, Centre for Sleep and Cognition, Clinical Imaging Research Centre and N.1 Institute for Health, National University of Singapore, Singapore, Singapore
| | - Guangming Lu
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, H3A2B4, Canada.
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26
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Boutzoukas EM, Crutcher J, Somoza E, Sepeta LN, You X, Gaillard WD, Wallace GL, Berl MM. Cortical thickness in childhood left focal epilepsy: Thinning beyond the seizure focus. Epilepsy Behav 2020; 102:106825. [PMID: 31816479 PMCID: PMC6962541 DOI: 10.1016/j.yebeh.2019.106825] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 11/20/2019] [Accepted: 11/24/2019] [Indexed: 11/18/2022]
Abstract
OBJECTIVE Structural brain differences are found in adults and children with epilepsy, yet pediatric samples have been heterogeneous regarding seizure type, magnetic resonance imaging (MRI) findings, and hemisphere of seizure focus. This study examines whether cortical thickness and surface area differ between children with left-hemisphere focal epilepsy (LHE) and age-matched typically developing (TD) peers. We examined whether age differentially moderated cortical thickness between groups and if cortical thickness was associated with duration of epilepsy, seizure frequency, or neuropsychological functioning. METHODS Thirty-five children with LHE and 35 TD children completed neuropsychological testing and 3T MR imaging. Neuropsychological measures included general intelligence and executive functioning. All MRIs were normal. Surface-based morphometric processing and analyses were conducted using FreeSurfer 6.0. Regression analyses compared age by cortical thickness differences between groups. Correlational analyses examined associations between cortical thickness in these areas with neuropsychological functioning or epilepsy characteristics. RESULTS Left-hemisphere focal epilepsy displayed decreased cortical thickness bilaterally compared to TD controls across 6 brain regions but no differences in surface area. Moderation analyses revealed quadratic relationships between age and cortical thickness for left frontoparietal-cingulate and right superior frontal regions. Higher performance intelligence quotient (IQ) (PIQ) and verbal IQ (VIQ) and fewer parent reported executive function problems were associated with greater cortical thickness in TD children. SIGNIFICANCE Children with LHE displayed thinner cortex extending beyond the hemisphere of seizure focus. The nonlinear pattern of cortical thickness across age occurring in TD children is not evident in the same manner in children with LHE. These differences in cortical thickness patterns were greatest in children 8-12 years old. Greater cortical thickness was associated with higher IQ and fewer executive control problems in daily activities in TD children. Thus, differences in cortical thickness in the absence of differences in surface area, suggest cortical thickness may be a sensitive proxy of subtle neuroanatomical changes that are related to neuropsychological functioning.
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Affiliation(s)
- Emanuel M Boutzoukas
- Comprehensive Pediatric Epilepsy Program, Children's National Medical Center, Washington, DC, USA
| | - Jason Crutcher
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
| | - Eduardo Somoza
- Comprehensive Pediatric Epilepsy Program, Children's National Medical Center, Washington, DC, USA
| | - Leigh N Sepeta
- Comprehensive Pediatric Epilepsy Program, Children's National Medical Center, Washington, DC, USA; Department of Psychiatry and Behavioral Sciences, The George Washington University, Washington, DC, USA
| | - Xiaozhen You
- Comprehensive Pediatric Epilepsy Program, Children's National Medical Center, Washington, DC, USA; Department of Pediatrics and Neurology, The George Washington University, Washington, DC, USA
| | - William D Gaillard
- Comprehensive Pediatric Epilepsy Program, Children's National Medical Center, Washington, DC, USA; Department of Pediatrics and Neurology, The George Washington University, Washington, DC, USA
| | - Gregory L Wallace
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA; Department of Speech, Language, and Hearing Sciences, The George Washington University, Washington, DC, USA
| | - Madison M Berl
- Comprehensive Pediatric Epilepsy Program, Children's National Medical Center, Washington, DC, USA; Department of Psychiatry and Behavioral Sciences, The George Washington University, Washington, DC, USA.
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27
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Rahatli FK, Sezer T, Has AC, Agildere AM. Evaluation of cortical thickness and brain volume on 3 Tesla magnetic resonance imaging in children with frontal lobe epilepsy. Neurol Sci 2019; 41:825-833. [DOI: 10.1007/s10072-019-04135-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 10/31/2019] [Indexed: 11/30/2022]
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28
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Yu Y, Chu L, Liu C, Huang M, Wang H. Alterations of white matter network in patients with left and right non-lesional temporal lobe epilepsy. Eur Radiol 2019; 29:6750-6761. [PMID: 31286187 DOI: 10.1007/s00330-019-06295-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 05/07/2019] [Accepted: 05/29/2019] [Indexed: 11/27/2022]
Abstract
OBJECTIVES The goal of this study was to investigate alterations of white matter (WM) network in patients with left non-lesional temporal lobe epilepsy (nl-TLE) and right nl-TLE to assess the relationship between the white matter network properties and clinical parameters. METHODS T1 magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) were acquired for 45 participants, including 30 nl-TLE patients (13 left, 17 right) and 15 healthy controls. Diffusion tensor tractography was computed to model the WM structural network. The topologic properties of the WM network were obtained by graph theoretical analysis, and the between-group differences in global and nodal properties of the WM network were examined by network-based statistical analysis (NBS). The relationship between WM network properties and clinical parameters was assessed by Pearson's correlation analysis. RESULTS NBS results indicated that patients with left and right nl-TLE experienced distinct changes of WM nodal and global network properties compared with HCs. Positive correlation coefficients were found in several regions. The structural disruptions of networks in the two nl-TLE groups were observed to be different in distribution and severity. CONCLUSIONS This study provides evidence for changes of the WM network topological properties and structural connectivity in nl-TLE patients, which provide useful insights for the understanding of disease mechanisms of TLE and improving treatment outcomes for nl-TLE. KEY POINTS • This study aims to investigate alterations of white matter (WM) network in patients with non-lesional temporal lobe epilepsy (nl-TLE). • Network-based statistical analysis results indicated that patients with left and right nl-TLE experienced distinct changes of WM nodal and global network properties compared with healthy controls. • This study provides useful insights for the understanding of disease mechanisms of TLE and improving treatment outcomes for nl-TLE.
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Affiliation(s)
- Yunli Yu
- Department of Neurology, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, Jiangsu, China.,Department of Neurology, The Affiliated Hospital of Guizhou Medical University, No.28 Guiyi Street, Guiyang, 550004, Guizhou, China
| | - Lan Chu
- Department of Neurology, The Affiliated Hospital of Guizhou Medical University, No.28 Guiyi Street, Guiyang, 550004, Guizhou, China. .,Department of Neurology, Institute of Neuroscience, Soochow University, Suzhou, 215123, Jiangsu, China.
| | - Chunfeng Liu
- Department of Neurology, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, Jiangsu, China.,Department of Neurology, Institute of Neuroscience, Soochow University, Suzhou, 215123, Jiangsu, China
| | - Mingming Huang
- Department of Image, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Houfen Wang
- Department of Neurology, The Affiliated Hospital of Guizhou Medical University, No.28 Guiyi Street, Guiyang, 550004, Guizhou, China
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Jackson DC, Jones JE, Hsu DA, Stafstrom CE, Lin JJ, Almane D, Koehn MA, Seidenberg M, Hermann BP. Language function in childhood idiopathic epilepsy syndromes. BRAIN AND LANGUAGE 2019; 193:4-9. [PMID: 29610055 DOI: 10.1016/j.bandl.2017.12.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Accepted: 12/14/2017] [Indexed: 06/08/2023]
Abstract
PURPOSE To examine the impact of diverse syndromes of focal and generalized epilepsy on language function in children with new and recent onset epilepsy. Of special interest was the degree of shared language abnormality across epilepsy syndromes and the unique effects associated with specific epilepsy syndromes. METHODS Participants were 136 youth with new or recent-onset (diagnosis within past 12 months) epilepsy and 107 healthy first-degree cousin controls. The participants with epilepsy included 20 with Temporal Lobe Epilepsy (TLE; M age = 12.99 years, SD = 3.11), 41 with Benign Epilepsy with Centrotemporal Spikes (BECTS; M age = 10.32, SD = 1.67), 42 with Juvenile Myoclonic Epilepsy (JME; M age = 14.85, SD = 2.75) and 33 with absence epilepsy (M age = 10.55, SD = 2.76). All children were administered a comprehensive test battery which included multiple measures of language and language-dependent abilities (i.e., verbal intelligence, vocabulary, verbal reasoning, object naming, reception word recognition, word reading, spelling, lexical and semantic fluency, verbal list learning and delayed verbal memory). Test scores were adjusted for age and gender and analyzed via MANCOVA. RESULTS Language abnormalities were found in all epilepsy patient groups. The most broadly affected children were those with TLE and absence epilepsy, whose performance differed significantly from controls on 8 of 11 and 9 of 11 tests respectively. Although children with JME and BECTS were less affected, significant differences from controls were found on 4 of 11 tests each. While each group had a unique profile of language deficits, commonalities were apparent across both idiopathic generalized and localization-related diagnostic categories. DISCUSSION The localization related and generalized idiopathic childhood epilepsies examined here were associated with impact on diverse language abilities early in the course of the disorder.
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Affiliation(s)
- D C Jackson
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - J E Jones
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - D A Hsu
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - C E Stafstrom
- Department of Neurology, Johns Hopkins University, Baltimore, MD, United States
| | - J J Lin
- Department of Clinical Neurology, University of California - Irvine, Irvine, CA, United States
| | - D Almane
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - M A Koehn
- Epilepsy Center, Marshfield Clinic, Marshfield, WI, United States
| | - M Seidenberg
- Department of Psychology, Rosalind Franklin School of Medicine and Science, North Chicago, IL, United States
| | - B P Hermann
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States.
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30
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Hwang G, Nair VA, Mathis J, Cook CJ, Mohanty R, Zhao G, Tellapragada N, Ustine C, Nwoke OO, Rivera-Bonet C, Rozman M, Allen L, Forseth C, Almane DN, Kraegel P, Nencka A, Felton E, Struck AF, Birn R, Maganti R, Conant LL, Humphries CJ, Hermann B, Raghavan M, DeYoe EA, Binder JR, Meyerand E, Prabhakaran V. Using Low-Frequency Oscillations to Detect Temporal Lobe Epilepsy with Machine Learning. Brain Connect 2019; 9:184-193. [PMID: 30803273 PMCID: PMC6484357 DOI: 10.1089/brain.2018.0601] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The National Institutes of Health-sponsored Epilepsy Connectome Project aims to characterize connectivity changes in temporal lobe epilepsy (TLE) patients. The magnetic resonance imaging protocol follows that used in the Human Connectome Project, and includes 20 min of resting-state functional magnetic resonance imaging acquired at 3T using 8-band multiband imaging. Glasser parcellation atlas was combined with the FreeSurfer subcortical regions to generate resting-state functional connectivity (RSFC), amplitude of low-frequency fluctuations (ALFFs), and fractional ALFF measures. Seven different frequency ranges such as Slow-5 (0.01-0.027 Hz) and Slow-4 (0.027-0.073 Hz) were selected to compute these measures. The goal was to train machine learning classification models to discriminate TLE patients from healthy controls, and to determine which combination of the resting state measure and frequency range produced the best classification model. The samples included age- and gender-matched groups of 60 TLE patients and 59 healthy controls. Three traditional machine learning models were trained: support vector machine, linear discriminant analysis, and naive Bayes classifier. The highest classification accuracy was obtained using RSFC measures in the Slow-4 + 5 band (0.01-0.073 Hz) as features. Leave-one-out cross-validation accuracies were ∼83%, with receiver operating characteristic area-under-the-curve reaching close to 90%. Increased connectivity from right area posterior 9-46v in TLE patients contributed to the high accuracies. With increased sample sizes in the near future, better machine learning models will be trained not only to aid the diagnosis of TLE, but also as a tool to understand this brain disorder.
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Affiliation(s)
- Gyujoon Hwang
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Veena A. Nair
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Jed Mathis
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Cole J. Cook
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Rosaleena Mohanty
- Department of Electrical Engineering, University of Wisconsin-Madison, Madison, Wisconsin
| | - Gengyan Zhao
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | | | - Candida Ustine
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | | | | | - Megan Rozman
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Linda Allen
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Courtney Forseth
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Dace N. Almane
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Peter Kraegel
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Andrew Nencka
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Elizabeth Felton
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Aaron F. Struck
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Rasmus Birn
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin
| | - Rama Maganti
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Lisa L. Conant
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Colin J. Humphries
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Bruce Hermann
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Manoj Raghavan
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Edgar A. DeYoe
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Jeffrey R. Binder
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Elizabeth Meyerand
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin
| | - Vivek Prabhakaran
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
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31
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Lam J, DuBois JM, Rowley J, González-Otárula KA, Soucy JP, Massarweh G, Hall JA, Guiot MC, Rosa-Neto P, Kobayashi E. In vivo metabotropic glutamate receptor type 5 abnormalities localize the epileptogenic zone in mesial temporal lobe epilepsy. Ann Neurol 2019; 85:218-228. [PMID: 30597619 DOI: 10.1002/ana.25404] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Revised: 12/22/2018] [Accepted: 12/24/2018] [Indexed: 01/31/2023]
Abstract
OBJECTIVE Surgical specimens from patients with mesial temporal lobe epilepsy (MTLE) show abnormalities in tissue concentrations of metabotropic glutamate receptor type 5 (mGluR5). To clarify whether these abnormalities are specific to the epileptogenic zone (EZ), we characterized in vivo whole-brain mGluR5 availability in MTLE patients using positron emission tomography (PET) and [11 C]ABP688, a radioligand that binds specifically to the mGluR5 allosteric site. METHODS Thirty-one unilateral MTLE patients and 30 healthy controls underwent [11 C]ABP688 PET. We compared partial volume corrected [11 C]ABP688 nondisplaceable binding potentials (BPND ) between groups using region-of-interest and whole-brain voxelwise analyses. [18 F]Fluorodeoxyglucose (FDG) PET was acquired in 15 patients, for whom we calculated asymmetry indices of [11 C]ABP688 BPND and [18 F]FDG uptake to compare lateralization and localization differences. RESULTS [11 C]ABP688 BPND was focally reduced in the epileptogenic hippocampal head and amygdala (p < 0.001). Patients with hippocampal atrophy showed more extensive abnormalities, including the ipsilateral temporal neocortex (p = 0.006). [11 C]ABP688 BPND showed interhemispheric differences of higher magnitude and discriminated the epileptogenic structures more accurately when compared to [18 F]FDG uptake, which showed more widespread hypometabolism. Among 23 of 25 operated patients with >1 year of follow-up, 13 were seizure-free (Engel Ia) and showed significantly lower [11 C]ABP688 BPND in the ipsilateral entorhinal cortex. INTERPRETATION [11 C]ABP688 PET provides a focal biomarker for the EZ in MTLE with higher spatial accuracy compared to [18 F]FDG PET. Focally reduced mGluR5 availability in the EZ might reflect receptor internalization or conformational changes in response to excessive extracellular glutamate, supporting a potential role for mGluR5 as therapeutic target in human MTLE. Ann Neurol 2019; 1-11 ANN NEUROL 2019;85:218-228.
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Affiliation(s)
- Jack Lam
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Jonathan M DuBois
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Jared Rowley
- Translational Neuroimaging Laboratory, McGill University, Montreal, Quebec, Canada
| | - Karina A González-Otárula
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Jean-Paul Soucy
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,PET Unit, McConnell Brain Imaging Centre, McGill University, Montreal, Quebec, Canada
| | - Gassan Massarweh
- PET Unit, McConnell Brain Imaging Centre, McGill University, Montreal, Quebec, Canada
| | - Jeffery A Hall
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Marie-Christine Guiot
- Department of Pathology, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Pedro Rosa-Neto
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,Translational Neuroimaging Laboratory, McGill University, Montreal, Quebec, Canada.,PET Unit, McConnell Brain Imaging Centre, McGill University, Montreal, Quebec, Canada
| | - Eliane Kobayashi
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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Ofer I, LeRose C, Mast H, LeVan P, Metternich B, Egger K, Urbach H, Schulze-Bonhage A, Wagner K. Association between seizure freedom and default mode network reorganization in patients with unilateral temporal lobe epilepsy. Epilepsy Behav 2019; 90:238-246. [PMID: 30538081 DOI: 10.1016/j.yebeh.2018.10.025] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Revised: 10/19/2018] [Accepted: 10/21/2018] [Indexed: 01/10/2023]
Abstract
RATIONALE The spontaneous synchronized activity and intrinsic organization of the Default Mode Network (DMN) has been found to be altered because of epileptic activity of temporal lobe origin. Thus, the aim of the present study was to compare DMN's topological properties in patients with seizure-free (SF) and not seizure-free (NSF) temporal lobe epilepsy (TLE). METHODS Functional connectivity within the DMN was determined from an 8-minute resting state functional magnetic resonance imaging (fMRI) in 27 patients with TLE (12 SF, 15 NSF) and 15 healthy controls (HC). The DMN regions of interest were extracted according to the automated anatomical labeling (AAL) atlas. Network properties were assessed using standard graph-theoretical measures. RESULTS Analyses revealed, irrespectively of focus lateralization, borderline significance for longer paths (p = 0.049) and in trend reduced local efficiency within the DMN of SF when compared with that of NSF (p = 0.075). The SF and NSF patients did not differ in global network topology from HC (p > 0.05). At the nodal network level, the degree of central hubs was significantly reduced in SF when compared with that in NSF (0.002 ≤ p ≤ 0.080) and HC (0.001 ≤ p ≤ 0.066) while simultaneously, right anterior superior temporal gyrus revealed significantly higher degree in SF than in NSF (p = 0.005) and HC (p = 0.016). CONCLUSION Seizure freedom seems to be associated with hub redistributions that may underlie longer paths and (in trend) reduced local efficiency of the network. An associated slower system response might reduce the probability of a rapid spread of epileptic discharges over the whole network and may help to prevent hypersynchronous neuronal activity in brain networks that may result in epileptic seizures.
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Affiliation(s)
- Isabell Ofer
- Epilepsy Center, Medical Center - Faculty of Medicine, University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; Freiburg Brain Imaging Center, Medical Center - Faculty of Medicine, University of Freiburg, Germany.
| | | | - Hansjoerg Mast
- Faculty of Medicine, University of Freiburg, Germany; Department of Neuroradiology, Medical Center - Faculty of Medicine, University of Freiburg, Germany; Freiburg Brain Imaging Center, Medical Center - Faculty of Medicine, University of Freiburg, Germany
| | - Pierre LeVan
- Faculty of Medicine, University of Freiburg, Germany; Department of Radiology, Medical Physics, Medical Center - University of Freiburg, Germany
| | - Birgitta Metternich
- Epilepsy Center, Medical Center - Faculty of Medicine, University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; Freiburg Brain Imaging Center, Medical Center - Faculty of Medicine, University of Freiburg, Germany
| | - Karl Egger
- Faculty of Medicine, University of Freiburg, Germany; Department of Neuroradiology, Medical Center - Faculty of Medicine, University of Freiburg, Germany; Freiburg Brain Imaging Center, Medical Center - Faculty of Medicine, University of Freiburg, Germany
| | - Horst Urbach
- Faculty of Medicine, University of Freiburg, Germany; Department of Neuroradiology, Medical Center - Faculty of Medicine, University of Freiburg, Germany; Freiburg Brain Imaging Center, Medical Center - Faculty of Medicine, University of Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center - Faculty of Medicine, University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; Freiburg Brain Imaging Center, Medical Center - Faculty of Medicine, University of Freiburg, Germany
| | - Kathrin Wagner
- Epilepsy Center, Medical Center - Faculty of Medicine, University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; Freiburg Brain Imaging Center, Medical Center - Faculty of Medicine, University of Freiburg, Germany
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33
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Matsubara T, Ogata K, Hironaga N, Uehara T, Mitsudo T, Shigeto H, Maekawa T, Tobimatsu S. Monaural 40-Hz auditory steady-state magnetic responses can be useful for identifying epileptic focus in mesial temporal lobe epilepsy. Clin Neurophysiol 2018; 130:341-351. [PMID: 30669010 DOI: 10.1016/j.clinph.2018.11.026] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 10/19/2018] [Accepted: 11/28/2018] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Patients with mesial temporal lobe epilepsy (mTLE) often exhibit central auditory processing (CAP) dysfunction. Monaural 40-Hz auditory steady-state magnetic responses (ASSRs) were recorded to explore the pathophysiology of mTLE. METHODS Eighteen left mTLE patients, 11 right mTLE patients and 16 healthy controls (HCs) were examined. Monaural clicks were presented at a rate of 40 Hz. Phase-locking factor (PLF) and power values were analyzed within bilateral Heschl's gyri. RESULTS Monaural 40-Hz ASSR demonstrated temporal frequency dynamics in both PLF and power data. Symmetrical hemispheric contralaterality was revealed in HCs. However, predominant contralaterality was absent in mTLE patients. Specifically, right mTLE patients exhibited a lack of contralaterality in response to left ear but not right ear stimulation, and vice versa in left mTLE patients. CONCLUSION This is the first study to use monaural 40-Hz ASSR with unilateral mTLE patients to clarify the relationship between CAP and epileptic focus. CAP dysfunction was characterized by a lack of contralaterality corresponding to epileptic focus. SIGNIFICANCE Monaural 40-Hz ASSR can provide useful information for localizing epileptic focus in mTLE patients.
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Affiliation(s)
- Teppei Matsubara
- Department of Clinical Neurophysiology, Neurological Institute, Faculty of Medicine, Graduate School of Medical Sciences, Kyushu University, Japan.
| | - Katsuya Ogata
- Department of Clinical Neurophysiology, Neurological Institute, Faculty of Medicine, Graduate School of Medical Sciences, Kyushu University, Japan
| | - Naruhito Hironaga
- Department of Clinical Neurophysiology, Neurological Institute, Faculty of Medicine, Graduate School of Medical Sciences, Kyushu University, Japan
| | - Taira Uehara
- Department of Clinical Neurophysiology, Neurological Institute, Faculty of Medicine, Graduate School of Medical Sciences, Kyushu University, Japan
| | - Takako Mitsudo
- Department of Clinical Neurophysiology, Neurological Institute, Faculty of Medicine, Graduate School of Medical Sciences, Kyushu University, Japan
| | - Hiroshi Shigeto
- Epilepsy and Sleep Center, Fukuoka Sanno Hospital, Fukuoka, Japan
| | | | - Shozo Tobimatsu
- Department of Clinical Neurophysiology, Neurological Institute, Faculty of Medicine, Graduate School of Medical Sciences, Kyushu University, Japan
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Alhusaini S, Kowalczyk MA, Yasuda CL, Semmelroch MK, Katsurayama M, Zabin M, Zanão T, Nogueira MH, Alvim MK, Ferraz VR, Tsai MH, Fitzsimons M, Lopes-Cendes I, Doherty CP, Cavalleri GL, Cendes F, Jackson GD, Delanty N. Normal cerebral cortical thickness in first-degree relatives of temporal lobe epilepsy patients. Neurology 2018; 92:e351-e358. [DOI: 10.1212/wnl.0000000000006834] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Accepted: 09/20/2018] [Indexed: 11/15/2022] Open
Abstract
ObjectiveTo examine cerebral cortex thickness in asymptomatic first-degree relatives of patients with mesial temporal lobe epilepsy (MTLE).MethodsWe investigated 127 asymptomatic first-degree relatives of patients with MTLE due to hippocampal sclerosis (HS) (mean age ± SD = 39.4 ± 13 years) and 203 healthy control individuals (mean age ± SD = 36.0 ± 11 years). Participants underwent a comprehensive clinical evaluation and structural brain MRI at 3 study sites. Images were processed simultaneously at each site using a surface-based morphometry method to quantify global brain measures, hippocampal volumes, and cerebral cortical thickness. Differences in brain measures between relatives of patients and controls were examined using generalized models, while controlling for relevant covariates, including age and sex.ResultsNone of the asymptomatic first-degree relatives of MTLE + HS patients showed evidence of HS on qualitative image assessments. Compared to the healthy controls, the asymptomatic relatives of patients displayed no significant differences in intracranial volume, average hemispheric surface area, or hippocampal volume. Similarly, no significant cerebral cortical thinning was identified in the relatives of patients. This was consistent across the 3 cohorts.ConclusionLack of cortical thickness changes in the asymptomatic relatives of patients indicates that the previously characterized MTLE + HS-related cortical thinning is not heritable, and is likely driven by disease-related factors. This finding therefore argues for early and aggressive intervention in patients with medically intractable epilepsy.
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Kuhn T, Gullett JM, Boutzoukas AE, Bohsali A, Mareci TH, FitzGerald DB, Carney PR, Bauer RM. Temporal lobe epilepsy affects spatial organization of entorhinal cortex connectivity. Epilepsy Behav 2018; 88:87-95. [PMID: 30243111 PMCID: PMC6294293 DOI: 10.1016/j.yebeh.2018.06.038] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 06/18/2018] [Accepted: 06/20/2018] [Indexed: 12/15/2022]
Abstract
Evidence for structural connectivity patterns within the medial temporal lobe derives primarily from postmortem histological studies. In humans and nonhuman primates, the parahippocampal gyrus (PHg) is subdivided into parahippocampal (PHc) and perirhinal (PRc) cortices, which receive input from distinct cortical networks. Likewise, their efferent projections to the entorhinal cortex (ERc) are distinct. The PHc projects primarily to the medial ERc (M-ERc). The PRc projects primarily to the lateral portion of the ERc (L-ERc). Both M-ERc and L-ERc, via the perforant pathway, project to the dentate gyrus and hippocampal (HC) subfields. Until recently, these neural circuits could not be visualized in vivo. Diffusion tensor imaging algorithms have been developed to segment gray matter structures based on probabilistic connectivity patterns. However, these algorithms have not yet been applied to investigate connectivity in the temporal lobe or changes in connectivity architecture related to disease processes. In this study, this segmentation procedure was used to classify ERc gray matter based on PRc, ERc, and HC connectivity patterns in 7 patients with temporal lobe epilepsy (TLE) without hippocampal sclerosis (mean age, 14.86 ± 3.34 years) and 7 healthy controls (mean age, 23.86 ± 2.97 years). Within samples paired t-tests allowed for comparison of ERc connectivity between epileptogenic and contralateral hemispheres. In healthy controls, there were no significant within-group differences in surface area, volume, or cluster number of ERc connectivity-defined regions (CDR). Likewise, in line with histology results, ERc CDR in the control group were well-organized, uniform, and segregated via PRc/PHc afferent and HC efferent connections. Conversely, in TLE, there were significantly more PRc and HC CDR clusters in the epileptogenic than the contralateral hemisphere. The surface area of the PRc CDR was greater, and that of the HC CDRs was smaller, in the epileptogenic hemisphere as well. Further, there was no clear delineation between M-ERc and L-ERc connectivity with PRc, PHc or HC in TLE. These results suggest a breakdown of the spatial organization of PHg-ERc-HC connectivity in TLE. Whether this breakdown is the cause or result of epileptic activity remains an exciting research question.
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Affiliation(s)
- Taylor Kuhn
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, United States of America; Department of Physical Therapy, University of Florida, Gainesville, FL, United States of America.
| | - Joseph M Gullett
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, United States of America; Department of VA Brain Rehabilitation Research Center, Malcolm Randall VA Center Gainesville, FL, United States of America
| | - Angelique E Boutzoukas
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, United States of America
| | - Anastasia Bohsali
- Department of Neurology, University of Florida, Gainesville, FL, United States of America
| | - Thomas H Mareci
- Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, FL, United States of America
| | - David B FitzGerald
- Department of VA Brain Rehabilitation Research Center, Malcolm Randall VA Center Gainesville, FL, United States of America
| | - Paul R Carney
- Department of Pediatrics, University of Florida, Gainesville, FL, United States of America; Department of Neurology, University of Florida, Gainesville, FL, United States of America; Department of Neuroscience, University of Florida, Gainesville, FL, United States of America; J. Crayton Pruitt Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States of America; B.J. and Eve Wilder Epilepsy Center Excellence, University of Florida, Gainesville, FL, United States of America
| | - Russell M Bauer
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, United States of America; Department of VA Brain Rehabilitation Research Center, Malcolm Randall VA Center Gainesville, FL, United States of America
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36
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Oyegbile TO, VanMeter JW, Motamedi G, Zecavati N, Santos C, Lee Earn Chun C, Gaillard WD, Hermann B. Executive dysfunction is associated with an altered executive control network in pediatric temporal lobe epilepsy. Epilepsy Behav 2018; 86:145-152. [PMID: 30001910 PMCID: PMC7395827 DOI: 10.1016/j.yebeh.2018.04.022] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2018] [Revised: 04/12/2018] [Accepted: 04/29/2018] [Indexed: 11/28/2022]
Abstract
OBJECTIVES Children with temporal lobe epilepsy (TLE) exhibit executive dysfunction on traditional neuropsychological tests. However, there is limited evidence of neural network alterations associated with this clinical executive dysfunction. The objective of this study was to characterize working memory deficits in children with TLE via activation of the executive control network on functional magnetic resonance imaging (fMRI) and determine the relationships to fMRI behavioral findings and traditional neuropsychological tests. EXPERIMENTAL DESIGN Functional magnetic resonance imaging was conducted on 17 children with TLE and 18 healthy control participants (age 8-16 years) while they performed the N-back task in order to assess activation of the executive control network. N-back accuracy, N-back reaction time, and traditional neuropsychological tests (Delis-Kaplan Executive Function System [D-KEFS] color-word interference and card-sort test) were also assessed. PRINCIPAL OBSERVATIONS Children with TLE exhibited executive dysfunction on D-KEFS testing, reduced N-back accuracy, and increased N-back reaction time compared with healthy controls; D-KEFS and N-back behavioral findings were significantly correlated. Children with TLE also exhibited significant reduction in activation of the frontal lobe within the executive control network compared to healthy controls. These alterations were significantly correlated with N-back behavioral findings and D-KEFS testing. CONCLUSIONS Children with TLE exhibit executive dysfunction, which correlates with executive control network alterations. This lends validity to the theory that the executive control network contributes to working memory function. The findings also indicate that children with TLE have network alterations in nontemporal brain regions.
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Affiliation(s)
| | | | | | | | - Cesar Santos
- Georgetown University Medical Center, Washington, D.C
| | | | - William D. Gaillard
- Georgetown University Medical Center, Washington, D.C.,Children’s National Medical Center, Washington, DC
| | - Bruce Hermann
- University of Wisconsin School of Medicine and Public Health, Madison, WI
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Gleichgerrcht E, Munsell B, Bhatia S, Vandergrift WA, Rorden C, McDonald C, Edwards J, Kuzniecky R, Bonilha L. Deep learning applied to whole-brain connectome to determine seizure control after epilepsy surgery. Epilepsia 2018; 59:1643-1654. [PMID: 30098002 DOI: 10.1111/epi.14528] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 07/14/2018] [Accepted: 07/15/2018] [Indexed: 01/02/2023]
Abstract
OBJECTIVE We evaluated whether deep learning applied to whole-brain presurgical structural connectomes could be used to predict postoperative seizure outcome more accurately than inference from clinical variables in patients with mesial temporal lobe epilepsy (TLE). METHODS Fifty patients with unilateral TLE were classified either as having persistent disabling seizures (SZ) or becoming seizure-free (SZF) at least 1 year after epilepsy surgery. Their presurgical structural connectomes were reconstructed from whole-brain diffusion tensor imaging. A deep network was trained based on connectome data to classify seizure outcome using 5-fold cross-validation. RESULTS Classification accuracy of our trained neural network showed positive predictive value (PPV; seizure freedom) of 88 ± 7% and mean negative predictive value (NPV; seizure refractoriness) of 79 ± 8%. Conversely, a classification model based on clinical variables alone yielded <50% accuracy. The specific features that contributed to high accuracy classification of the neural network were located not only in the ipsilateral temporal and extratemporal regions, but also in the contralateral hemisphere. SIGNIFICANCE Deep learning demonstrated to be a powerful statistical approach capable of isolating abnormal individualized patterns from complex datasets to provide a highly accurate prediction of seizure outcomes after surgery. Features involved in this predictive model were both ipsilateral and contralateral to the clinical foci and spanned across limbic and extralimbic networks.
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Affiliation(s)
- Ezequiel Gleichgerrcht
- Department of Neurology, Medical University of South Carolina, Charleston, South Carolina
| | - Brent Munsell
- Department of Computer Science, College of Charleston, Charleston, South Carolina
| | - Sonal Bhatia
- Department of Neurology, Medical University of South Carolina, Charleston, South Carolina
| | - William A Vandergrift
- Department of Neurosurgery, Medical University of South Carolina, Charleston, South Carolina
| | - Chris Rorden
- Department of Psychology, University of South Carolina, Columbia, South Carolina
| | - Carrie McDonald
- Department of Psychology, University of California, San Diego, San Diego, California
| | - Jonathan Edwards
- Department of Neurology, Medical University of South Carolina, Charleston, South Carolina
| | - Ruben Kuzniecky
- Department of Neurology, Hofstra Northwell School of Medicine, Great Neck, New York
| | - Leonardo Bonilha
- Department of Neurology, Medical University of South Carolina, Charleston, South Carolina
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Raj A, Powell F. Models of Network Spread and Network Degeneration in Brain Disorders. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 3:788-797. [PMID: 30170711 DOI: 10.1016/j.bpsc.2018.07.012] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 07/11/2018] [Accepted: 07/11/2018] [Indexed: 01/01/2023]
Abstract
Network analysis can provide insight into key organizational principles of brain structure and help identify structural changes associated with brain disease. Though static differences between diseased and healthy networks are well characterized, the study of network dynamics, or how brain networks change over time, is increasingly central to understanding ongoing brain changes throughout disease. Accordingly, we present a short review of network models of spread, network dynamics, and network degeneration. Borrowing from recent suggestions, we divide this review into two processes by which brain networks can change: dynamics on networks, which are functional and pathological consequences taking place atop a static structural brain network; and dynamics of networks, which constitutes a changing structural brain network. We focus on diffusion magnetic resonance imaging-based structural or anatomic connectivity graphs. We address psychiatric disorders like schizophrenia; developmental disorders like epilepsy; stroke; and Alzheimer's disease and other neurodegenerative diseases.
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Affiliation(s)
- Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California.
| | - Fon Powell
- Department of Radiology, Weill Cornell Medicine, New York, New York
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Jin B, Krishnan B, Adler S, Wagstyl K, Hu W, Jones S, Najm I, Alexopoulos A, Zhang K, Zhang J, Ding M, Wang S, Wang ZI. Automated detection of focal cortical dysplasia type II with surface-based magnetic resonance imaging postprocessing and machine learning. Epilepsia 2018; 59:982-992. [PMID: 29637549 DOI: 10.1111/epi.14064] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/27/2018] [Indexed: 01/13/2023]
Abstract
OBJECTIVE Focal cortical dysplasia (FCD) is a major pathology in patients undergoing surgical resection to treat pharmacoresistant epilepsy. Magnetic resonance imaging (MRI) postprocessing methods may provide essential help for detection of FCD. In this study, we utilized surface-based MRI morphometry and machine learning for automated lesion detection in a mixed cohort of patients with FCD type II from 3 different epilepsy centers. METHODS Sixty-one patients with pharmacoresistant epilepsy and histologically proven FCD type II were included in the study. The patients had been evaluated at 3 different epilepsy centers using 3 different MRI scanners. T1-volumetric sequence was used for postprocessing. A normal database was constructed with 120 healthy controls. We also included 35 healthy test controls and 15 disease test controls with histologically confirmed hippocampal sclerosis to assess specificity. Features were calculated and incorporated into a nonlinear neural network classifier, which was trained to identify lesional cluster. We optimized the threshold of the output probability map from the classifier by performing receiver operating characteristic (ROC) analyses. Success of detection was defined by overlap between the final cluster and the manual labeling. Performance was evaluated using k-fold cross-validation. RESULTS The threshold of 0.9 showed optimal sensitivity of 73.7% and specificity of 90.0%. The area under the curve for the ROC analysis was 0.75, which suggests a discriminative classifier. Sensitivity and specificity were not significantly different for patients from different centers, suggesting robustness of performance. Correct detection rate was significantly lower in patients with initially normal MRI than patients with unequivocally positive MRI. Subgroup analysis showed the size of the training group and normal control database impacted classifier performance. SIGNIFICANCE Automated surface-based MRI morphometry equipped with machine learning showed robust performance across cohorts from different centers and scanners. The proposed method may be a valuable tool to improve FCD detection in presurgical evaluation for patients with pharmacoresistant epilepsy.
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Affiliation(s)
- Bo Jin
- Department of Neurology, School of Medicine, Epilepsy Center, Second Affiliated Hospital, Zhejiang University, Hangzhou, China.,Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
| | - Balu Krishnan
- Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
| | - Sophie Adler
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK.,Great Ormond Street Hospital for Children, London, UK
| | - Konrad Wagstyl
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK.,Brain Mapping Unit, Institute of Psychiatry, University of Cambridge, Cambridge, UK
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Stephen Jones
- Imaging Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Imad Najm
- Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
| | | | - Kai Zhang
- Department of Neurosurgery, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Meiping Ding
- Department of Neurology, School of Medicine, Epilepsy Center, Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Shuang Wang
- Department of Neurology, School of Medicine, Epilepsy Center, Second Affiliated Hospital, Zhejiang University, Hangzhou, China
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Beheshti I, Sone D, Farokhian F, Maikusa N, Matsuda H. Gray Matter and White Matter Abnormalities in Temporal Lobe Epilepsy Patients with and without Hippocampal Sclerosis. Front Neurol 2018; 9:107. [PMID: 29593628 PMCID: PMC5859011 DOI: 10.3389/fneur.2018.00107] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 02/13/2018] [Indexed: 01/21/2023] Open
Abstract
The presentation and distribution of gray matter (GM) and white matter (WM) abnormalities in temporal lobe epilepsy (TLE) have been widely studied. Here, we investigated the GM and WM abnormalities in TLE patients with and without hippocampal sclerosis (HS) in five groups of participants: healthy controls (HCs) (n = 28), right TLE patients with HS (n = 26), right TLE patients without HS (n = 30), left TLE patients with HS (n = 25), and left TLE patients without HS (n = 27). We performed a flexible factorial statistical test in a whole-brain voxel-based morphometry analysis to identify significant GM and WM abnormalities and analysis of variance of hippocampal and amygdala regions among the five groups using the FreeSurfer procedure. Furthermore, we conducted multiple regression analysis to assess regional GM and WM changes with disease duration. We observed significant ipsilateral mesiotemporal GM and WM volume reductions in TLE patients with HS compared with HCs. We also observed a slight GM amygdala swelling in right TLE patients without HS. The regression analysis revealed significant negative GM and WM changes with disease duration specifically in left TLE patients with HS. The observed GM and WM abnormalities may contribute to our understanding of the root of epilepsy mechanisms.
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Affiliation(s)
- Iman Beheshti
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Daichi Sone
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Farnaz Farokhian
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Japan.,College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Norihide Maikusa
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Hiroshi Matsuda
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Japan
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Decreased neurite density within frontostriatal networks is associated with executive dysfunction in temporal lobe epilepsy. Epilepsy Behav 2018; 78:187-193. [PMID: 29126704 PMCID: PMC5756677 DOI: 10.1016/j.yebeh.2017.09.012] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Revised: 09/11/2017] [Accepted: 09/16/2017] [Indexed: 11/21/2022]
Abstract
OBJECTIVE Executive dysfunction is observed in a sizable number of patients with refractory temporal lobe epilepsy (TLE). The frontostriatal network has been proposed to play a significant role in executive functioning, however, because of the complex architecture of these tracts, it is difficult to generate measures of fiber tract microstructure using standard diffusion tensor imaging. To examine the association between frontostriatal network compromise and executive dysfunction in TLE, we applied an advanced, multishell diffusion model, restriction spectrum imaging (RSI), that isolates measures of intraaxonal diffusion and may provide better estimates of fiber tract compromise in TLE. METHODS Restriction spectrum imaging scans were obtained from 32 patients with TLE [16 right TLE (RTLE); 16 left TLE (LTLE)] and 24 healthy controls (HC). An RSI-derived measure of intraaxonal anisotropic diffusion (neurite density; ND) was calculated for the inferior frontostriatal tract (IFS) and superior frontostriatal tract (SFS) and compared between patients with TLE and HC. Spearman correlations were performed to evaluate the relationships between ND of each tract and verbal (i.e., D-KEFS Category Switching Accuracy and Color-Word Interference Inhibition/Switching) and visuomotor (Trail Making Test) set-shifting performances in patients with TLE. RESULTS Patients with TLE demonstrated reductions in ND of the left and right IFS, but not SFS, compared with HC. Reduction in ND of left and right IFS was associated with poorer performance on verbal set-shifting in TLE. Increases in extracellular diffusion (isotropic hindered; IH) were not associated with executive dysfunction in the patient group. SIGNIFICANCE Restriction spectrum imaging-derived ND revealed microstructural changes within the IFS in patients with TLE, which was associated with poorer executive functioning. This suggests that axonal/myelin loss to fiber networks connecting the striatum to the inferior frontal cortex is likely contributing to executive dysfunction in TLE.
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42
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Tan YL, Kim H, Lee S, Tihan T, Ver Hoef L, Mueller SG, Barkovich AJ, Xu D, Knowlton R. Quantitative surface analysis of combined MRI and PET enhances detection of focal cortical dysplasias. Neuroimage 2017; 166:10-18. [PMID: 29097316 DOI: 10.1016/j.neuroimage.2017.10.065] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Accepted: 10/29/2017] [Indexed: 01/18/2023] Open
Abstract
OBJECTIVE Focal cortical dysplasias (FCDs) often cause pharmacoresistant epilepsy, and surgical resection can lead to seizure-freedom. Magnetic resonance imaging (MRI) and positron emission tomography (PET) play complementary roles in FCD identification/localization; nevertheless, many FCDs are small or subtle, and difficult to find on routine radiological inspection. We aimed to automatically detect subtle or visually-unidentifiable FCDs by building a classifier based on an optimized cortical surface sampling of combined MRI and PET features. METHODS Cortical surfaces of 28 patients with histopathologically-proven FCDs were extracted. Morphology and intensity-based features characterizing FCD lesions were calculated vertex-wise on each cortical surface, and fed to a 2-step (Support Vector Machine and patch-based) classifier. Classifier performance was assessed compared to manual lesion labels. RESULTS Our classifier using combined feature selections from MRI and PET outperformed both quantitative MRI and multimodal visual analysis in FCD detection (93% vs 82% vs 68%). No false positives were identified in the controls, whereas 3.4% of the vertices outside FCD lesions were also classified to be lesional ("extralesional clusters"). Patients with type I or IIa FCDs displayed a higher prevalence of extralesional clusters at an intermediate distance to the FCD lesions compared to type IIb FCDs (p < 0.05). The former had a correspondingly lower chance of positive surgical outcome (71% vs 91%). CONCLUSIONS Machine learning with multimodal feature sampling can improve FCD detection. The spread of extralesional clusters characterize different FCD subtypes, and may represent structurally or functionally abnormal tissue on a microscopic scale, with implications for surgical outcomes.
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Affiliation(s)
- Yee-Leng Tan
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA; Department of Neurology, National Neuroscience Institute, Singapore.
| | - Hosung Kim
- Laboratory of Neuro Imaging, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
| | - Seunghyun Lee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
| | - Tarik Tihan
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
| | - Lawrence Ver Hoef
- Department of Neurology, University of Alabama, Birmingham, United Kingdom.
| | - Susanne G Mueller
- Department of Radiology, Seoul National University Hospital, Republic of Korea.
| | | | - Duan Xu
- Department of Radiology, Seoul National University Hospital, Republic of Korea.
| | - Robert Knowlton
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
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Muhlhofer W, Tan Y, Mueller SG, Knowlton R. MRI
‐negative temporal lobe epilepsy—What do we know? Epilepsia 2017; 58:727-742. [DOI: 10.1111/epi.13699] [Citation(s) in RCA: 91] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/11/2017] [Indexed: 01/01/2023]
Affiliation(s)
- Wolfgang Muhlhofer
- University of California San Francisco (UCSF) San Francisco California U.S.A
- University of Alabama Birmingham (UAB) Birmingham Alabama U.S.A
| | - Yee‐Leng Tan
- University of California San Francisco (UCSF) San Francisco California U.S.A
- National Neuroscience Institute Singapore Singapore
| | - Susanne G. Mueller
- University of California San Francisco (UCSF) San Francisco California U.S.A
- Center for Imaging of Neurodegenerative Diseases (CIND) San Francisco California U.S.A
- Department of Radiology UCSF San Francisco CaliforniaU.S.A
| | - Robert Knowlton
- University of California San Francisco (UCSF) San Francisco California U.S.A
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Kreilkamp BA, Weber B, Richardson MP, Keller SS. Automated tractography in patients with temporal lobe epilepsy using TRActs Constrained by UnderLying Anatomy (TRACULA). Neuroimage Clin 2017; 14:67-76. [PMID: 28138428 PMCID: PMC5257189 DOI: 10.1016/j.nicl.2017.01.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Revised: 12/06/2016] [Accepted: 01/04/2017] [Indexed: 12/19/2022]
Abstract
PURPOSE A detailed understanding of white matter tract alterations in patients with temporal lobe epilepsy (TLE) is important as it may provide useful information for likely side of seizure onset, cognitive impairment and postoperative prognosis. However, most diffusion-tensor imaging (DTI) studies have relied on manual reconstruction of tract bundles, despite the recent development of automated techniques. In the present study, we used an automated white matter tractography analysis approach to quantify temporal lobe white matter tract alterations in TLE and determine the relationships between tract alterations, the extent of hippocampal atrophy and the chronicity and severity of the disorder. METHODS We acquired preoperative T1-weighted and DTI data in 64 patients with well-characterized TLE, with imaging and histopathological evidence of hippocampal sclerosis. Identical acquisitions were collected for 44 age- and sex-matched healthy controls. We employed automatic probabilistic tractography DTI analysis using TRActs Constrained by UnderLying Anatomy (TRACULA) available in context of Freesurfer software for the reconstruction of major temporal lobe tract bundles. We determined the factors influencing probabilistic tract reconstruction and investigated alterations of DTI scalar metrics along white matter tracts with respect to hippocampal volume, which was automatically estimated using Freesurfer's morphometric pipelines. We also explored the relationships between white matter tract alterations and duration of epilepsy, age of onset of epilepsy and seizure burden (defined as a function of seizure frequency and duration of epilepsy). RESULTS Whole-tract diffusion characteristics of patients with TLE differed according to side of epilepsy and were significantly different between patients and controls. Waypoint comparisons along each tract revealed that patients had significantly altered tissue characteristics of the ipsilateral inferior-longitudinal, uncinate fasciculus, superior longitudinal fasciculus and cingulum relative to controls. Changes were more widespread (ipsilaterally and contralaterally) in patients with left TLE while patients with right TLE showed changes that remained spatially confined in ipsilateral tract regions. We found no relationship between DTI alterations and volume of the epileptogenic hippocampus. DTI alterations of anterior ipsilateral uncinate and inferior-longitudinal fasciculus correlated with duration of epilepsy (over and above effects of age) and age at onset of epilepsy. Seizure burden correlated with tissue characteristics of the uncinate fasciculus. CONCLUSION This study shows that TRACULA permits the detection of alterations of DTI tract scalar metrics in patients with TLE. It also provides the opportunity to explore relationships with structural volume measurements and clinical variables along white matter tracts. Our data suggests that the anterior temporal lobe portions of the uncinate and inferior-longitudinal fasciculus may be particularly vulnerable to pathological alterations in patients with TLE. These alterations are unrelated to the extent of hippocampal atrophy (and therefore potentially mediated by independent mechanisms) but influenced by chronicity and severity of the disorder.
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Affiliation(s)
- Barbara A.K. Kreilkamp
- Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, UK
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Bernd Weber
- Department of Epileptology, University of Bonn, Germany
- Department of NeuroCognition/Imaging, Life&Brain Research Center, Bonn, Germany
| | - Mark P. Richardson
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Simon S. Keller
- Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, UK
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, UK
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
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45
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Alhusaini S, Whelan CD, Sisodiya SM, Thompson PM. Quantitative magnetic resonance imaging traits as endophenotypes for genetic mapping in epilepsy. NEUROIMAGE-CLINICAL 2016; 12:526-534. [PMID: 27672556 PMCID: PMC5030372 DOI: 10.1016/j.nicl.2016.09.005] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2016] [Revised: 07/21/2016] [Accepted: 09/05/2016] [Indexed: 12/18/2022]
Abstract
Over the last decade, the field of imaging genomics has combined high-throughput genotype data with quantitative magnetic resonance imaging (QMRI) measures to identify genes associated with brain structure, cognition, and several brain-related disorders. Despite its successful application in different psychiatric and neurological disorders, the field has yet to be advanced in epilepsy. In this article we examine the relevance of imaging genomics for future genetic studies in epilepsy from three perspectives. First, we discuss prior genome-wide genetic mapping efforts in epilepsy, considering the possibility that some studies may have been constrained by inherent theoretical and methodological limitations of the genome-wide association study (GWAS) method. Second, we offer a brief overview of the imaging genomics paradigm, from its original inception, to its role in the discovery of important risk genes in a number of brain-related disorders, and its successful application in large-scale multinational research networks. Third, we provide a comprehensive review of past studies that have explored the eligibility of brain QMRI traits as endophenotypes for epilepsy. While the breadth of studies exploring QMRI-derived endophenotypes in epilepsy remains narrow, robust syndrome-specific neuroanatomical QMRI traits have the potential to serve as accessible and relevant intermediate phenotypes for future genetic mapping efforts in epilepsy. QMRI traits have the potential to serve as robust intermediate phenotypes for brain-related disorders. Hippocampal volume is the most promising neuroimaging endophenotype for MTLE + HS. Imaging genomics holds great promise in advancing epilepsy genetic research. Studies are encouraged to explore the validity of QMRI traits as endophenotypes for epilepsy.
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Affiliation(s)
- Saud Alhusaini
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Christopher D Whelan
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Sanjay M Sisodiya
- Department of Clinical and Experimental Epilepsy, University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, London, UK
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
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Abdelnour F, Raj A, Devinsky O, Thesen T. Network Analysis on Predicting Mean Diffusivity Change at Group Level in Temporal Lobe Epilepsy. Brain Connect 2016; 6:607-620. [PMID: 27405726 DOI: 10.1089/brain.2015.0381] [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] [Indexed: 11/12/2022] Open
Abstract
The two most common types of temporal lobe epilepsy are medial temporal sclerosis (TLE-MTS) epilepsy and MRI-normal temporal lobe epilepsy (TLE-no). TLE-MTS is specified by its stereotyped focus and spread pattern of neuronal damage, with pronounced neuronal loss in the hippocampus. TLE-no exhibits normal-appearing hippocampus and more widespread neuronal loss. In both cases, neuronal loss spread appears to be constrained by the white matter connections. Both varieties of epilepsy reveal pathological abnormalities in increased mean diffusivity (MD). We model MD distribution as a simple consequence of the propagation of neuronal damage. By applying this model on the structural brain connectivity network of healthy subjects, we can predict at group level the MD gray matter change in the epilepsy cohorts relative to a control group. Diffusion tensor imaging images were acquired from 10 patients with TLE-MTS, 11 patients with TLE-no, and 35 healthy subjects. Statistical validation at the group level suggests high correlation with measured neuronal loss (R = 0.56 for the TLE-MTS group and R = 0.364 for the TLE-no group). The results of this exploratory work pave the way for potential future clinical application of the proposed model on individual patients, including predicting neuronal loss spread, identification of seizure onset zones, and helping in surgical planning.
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Affiliation(s)
- Farras Abdelnour
- 1 Department of Radiology, Weill Cornell Medical College , New York, New York
| | - Ashish Raj
- 1 Department of Radiology, Weill Cornell Medical College , New York, New York
| | - Orrin Devinsky
- 2 Department of Neurology, New York University , New York, New York
| | - Thomas Thesen
- 2 Department of Neurology, New York University , New York, New York
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Takaya S, Liu H, Greve DN, Tanaka N, Leveroni C, Cole AJ, Stufflebeam SM. Altered anterior-posterior connectivity through the arcuate fasciculus in temporal lobe epilepsy. Hum Brain Mapp 2016; 37:4425-4438. [PMID: 27452151 DOI: 10.1002/hbm.23319] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2016] [Revised: 07/04/2016] [Accepted: 07/07/2016] [Indexed: 11/09/2022] Open
Abstract
How the interactions between cortices through a specific white matter pathway change during cognitive processing in patients with epilepsy remains unclear. Here, we used surface-based structural connectivity analysis to examine the change in structural connectivity with Broca's area/the right Broca's homologue in the lateral temporal and inferior parietal cortices through the arcuate fasciculus (AF) in 17 patients with left temporal lobe epilepsy (TLE) compared with 17 healthy controls. Then, we investigated its functional relevance to the changes in task-related responses and task-modulated functional connectivity with Broca's area/the right Broca's homologue during a semantic classification task of a single word. The structural connectivity through the AF pathway and task-modulated functional connectivity with Broca's area decreased in the left midtemporal cortex. Furthermore, task-related response decreased in the left mid temporal cortex that overlapped with the region showing a decrease in the structural connectivity. In contrast, the region showing an increase in the structural connectivity through the AF overlapped with the regions showing an increase in task-modulated functional connectivity in the left inferior parietal cortex. These structural and functional changes in the overlapping regions were correlated. The results suggest that the change in the structural connectivity through the left frontal-temporal AF pathway underlies the altered functional networks between the frontal and temporal cortices during the language-related processing in patients with left TLE. The left frontal-parietal AF pathway might be employed to connect anterior and posterior brain regions during language processing and compensate for the compromised left frontal-temporal AF pathway. Hum Brain Mapp 37:4425-4438, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Shigetoshi Takaya
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Hesheng Liu
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Douglas N Greve
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Naoaki Tanaka
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Catherine Leveroni
- Harvard Medical School, Boston, Massachusetts.,Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts
| | - Andrew J Cole
- Harvard Medical School, Boston, Massachusetts.,Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Steven M Stufflebeam
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Harvard Medical School, Boston, Massachusetts
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Tondelli M, Vaudano AE, Ruggieri A, Meletti S. Cortical and subcortical brain alterations in Juvenile Absence Epilepsy. NEUROIMAGE-CLINICAL 2016; 12:306-11. [PMID: 27551668 PMCID: PMC4983643 DOI: 10.1016/j.nicl.2016.07.007] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2016] [Revised: 07/12/2016] [Accepted: 07/14/2016] [Indexed: 12/28/2022]
Abstract
Despite the common assumption that genetic generalized epilepsies are characterized by a macroscopically normal brain on magnetic resonance imaging, subtle structural brain alterations have been detected by advanced neuroimaging techniques in Childhood Absence Epilepsy syndrome. We applied quantitative structural MRI analysis to a group of adolescents and adults with Juvenile Absence Epilepsy (JAE) in order to investigate micro-structural brain changes using different brain measures. We examined grey matter volumes, cortical thickness, surface areas, and subcortical volumes in 24 patients with JAE compared to 24 healthy controls; whole-brain voxel-based morphometry (VBM) and Freesurfer analyses were used. When compared to healthy controls, patients revealed both grey matter volume and surface area reduction in bilateral frontal regions, anterior cingulate, and right mesial-temporal lobe. Correlation analysis with disease duration showed that longer disease was correlated with reduced surface area in right pre- and post-central gyrus. A possible effect of valproate treatment on brain structures was excluded. Our results indicate that subtle structural brain changes are detectable in JAE and are mainly located in anterior nodes of regions known to be crucial for awareness, attention and memory.
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Affiliation(s)
- Manuela Tondelli
- Department of Biomedical, Metabolic, and Neural Science, University of Modena and Reggio Emilia, Modena, Italy; NOCSAE Hospital, AUSL Modena, Italy
| | - Anna Elisabetta Vaudano
- Department of Biomedical, Metabolic, and Neural Science, University of Modena and Reggio Emilia, Modena, Italy; NOCSAE Hospital, AUSL Modena, Italy
| | - Andrea Ruggieri
- Department of Biomedical, Metabolic, and Neural Science, University of Modena and Reggio Emilia, Modena, Italy
| | - Stefano Meletti
- Department of Biomedical, Metabolic, and Neural Science, University of Modena and Reggio Emilia, Modena, Italy; NOCSAE Hospital, AUSL Modena, Italy
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Reyes A, Thesen T, Wang X, Hahn D, Yoo D, Kuzniecky R, Devinsky O, Blackmon K. Resting-state functional MRI distinguishes temporal lobe epilepsy subtypes. Epilepsia 2016; 57:1475-84. [PMID: 27374869 DOI: 10.1111/epi.13456] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/07/2016] [Indexed: 12/29/2022]
Abstract
OBJECTIVE We assessed whether presurgical resting state functional magnetic resonance imaging (fMRI) provides information for distinguishing temporal lobe epilepsy (TLE) with mesial temporal sclerosis (TLE-MTS) from TLE without MTS (TLE-noMTS). METHODS Thirty-four patients with TLE and 34 sex-/age-matched controls consented to a research imaging protocol. MTS status was confirmed by histologic evaluation of surgical tissue (TLE-MTS = 16; TLE-noMTS = 18). The fractional amplitude of low-frequency fluctuations (fALFFs) in the blood oxygen level-dependent (BOLD) resting-state fMRI signal, a marker of local metabolic demand at rest, was averaged at five regions of interest (ROIs; hippocampus, amygdala, frontal, occipital, and temporal lobe), along with corresponding volume and cortical thickness estimates. ROIs were labeled ipsilateral or contralateral according to seizure lateralization and compared across TLE-MTS, TLE-noMTS, and healthy controls (HCs). MTS status was regressed on ipsilateral hippocampal volume and fALFF to test for independent contributions. RESULTS The TLE-MTS group had reduced fALFF in the ipsilateral amygdala and hippocampus; whereas, the TLE-noMTS group had marginally reduced fALFF in the ipsilateral amygdala but not hippocampus. These results were consistently obtained with and without application of global signal regression (GSR). Ipsilateral hippocampal volume contributed to 37% of the variance in MTS status (p < 0.001) and fALFF contributed an additional 10% (p = 0.021). Two MTS cases were accurately classified with fALFF but not volume, and three were accurately classified with volume but not fALFF. At the lobar level, fALFF (with GSR) was reduced in the ipsilateral temporal and bilateral frontal lobes of patients with TLE-MTS and bilateral frontal lobes of patients with TLE-noMTS in the context of normal cortical thickness. SIGNIFICANCE This study indicates that resting-state fMRI provides complementary functional information for MTS classification. Findings validate fALFF as a measure of regional brain integrity in TLE and highlight the value of using multi-modal imaging to provide independent diagnostic information in presurgical epilepsy evaluations.
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Affiliation(s)
- Anny Reyes
- NYU Comprehensive Epilepsy Center, Department of Neurology, New York University School of Medicine, New York, New York, U.S.A.,Department of Psychology, New York University, New York, New York, U.S.A
| | - Thomas Thesen
- NYU Comprehensive Epilepsy Center, Department of Neurology, New York University School of Medicine, New York, New York, U.S.A.,Department of Radiology, New York University School of Medicine, New York, New York, U.S.A
| | - Xiuyuan Wang
- NYU Comprehensive Epilepsy Center, Department of Neurology, New York University School of Medicine, New York, New York, U.S.A
| | - Daniel Hahn
- NYU Comprehensive Epilepsy Center, Department of Neurology, New York University School of Medicine, New York, New York, U.S.A
| | - Daeil Yoo
- NYU Comprehensive Epilepsy Center, Department of Neurology, New York University School of Medicine, New York, New York, U.S.A
| | - Ruben Kuzniecky
- NYU Comprehensive Epilepsy Center, Department of Neurology, New York University School of Medicine, New York, New York, U.S.A
| | - Orrin Devinsky
- NYU Comprehensive Epilepsy Center, Department of Neurology, New York University School of Medicine, New York, New York, U.S.A
| | - Karen Blackmon
- NYU Comprehensive Epilepsy Center, Department of Neurology, New York University School of Medicine, New York, New York, U.S.A
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50
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Conrad BN, Rogers BP, Abou-Khalil B, Morgan VL. Increased MRI volumetric correlation contralateral to seizure focus in temporal lobe epilepsy. Epilepsy Res 2016; 126:53-61. [PMID: 27429056 DOI: 10.1016/j.eplepsyres.2016.07.001] [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: 03/02/2016] [Revised: 06/17/2016] [Accepted: 07/01/2016] [Indexed: 10/21/2022]
Abstract
Quantification of volumetric correlation may be sensitive to disease alterations undetected by standard voxel based morphometry (VBM) such as subtle, synchronous alterations in regional volumes, and may provide complementary evidence of the structural impact of temporal lobe epilepsy (TLE) on the brain. The purpose of this study was to quantify differences of regional volumetric correlation in right (RTLE) and left (LTLE) TLE patients compared to healthy controls. A T1 weighted 3T MRI was acquired (1mm(3)) in 44 drug resistant unilateral TLE patients (n=26 RTLE, n=18 LTLE) and 44 individually age and gender matched healthy controls. Images were processed using a standard VBM framework and volumetric correlation was calculated across subjects in 90 regions and compared between patients and controls. Results were summarized across hemispheres and region groups. There was increased correlation involving the contralateral homologues of the seizure foci/network in the limbic, subcortical and temporal regions in both RTLE and LTLE. Outside these regions, results implied widespread correlated alterations across several contralateral lobes in LTLE, with more focal changes in RTLE. These findings complement previous volumetric studies in TLE describing more ipsilateral atrophy, by revealing subtle coordinated volumetric changes to identify a more widespread effect of TLE across the brain.
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Affiliation(s)
- Benjamin N Conrad
- Vanderbilt University Institute of Imaging Science, Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, USA
| | - Baxter P Rogers
- Vanderbilt University Institute of Imaging Science, Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, USA
| | | | - Victoria L Morgan
- Vanderbilt University Institute of Imaging Science, Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, USA.
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