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Liu J, Binding L, Puntambekar I, Patodia S, Lim YM, Mryzyglod A, Xiao F, Pan S, Mito R, de Tisi J, Duncan JS, Baxendale S, Koepp M, Thom M. Microangiopathy in temporal lobe epilepsy with diffusion MRI alterations and cognitive decline. Acta Neuropathol 2024; 148:49. [PMID: 39377933 PMCID: PMC11461556 DOI: 10.1007/s00401-024-02809-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2024] [Revised: 09/23/2024] [Accepted: 09/28/2024] [Indexed: 10/09/2024]
Abstract
White matter microvascular alterations in temporal lobe epilepsy (TLE) may be relevant to acquired neurodegenerative processes and cognitive impairments associated with this condition. We quantified microvascular changes, myelin, axonal, glial and extracellular-matrix labelling in the gyral core and deep temporal lobe white matter regions in surgical resections from 44 TLE patients with or without hippocampal sclerosis. We compared this pathology data with in vivo pre-operative MRI diffusion measurements in co-registered regions and neuropsychological measures of cognitive impairment and decline. In resections, increased arteriolosclerosis was observed in TLE compared to non-epilepsy controls (greater sclerotic index, p < 0.001), independent of age. Microvascular changes included increased vascular densities in some regions but uniformly reduced mean vascular size (quantified with collagen-4, p < 0.05-0.0001), and increased pericyte coverage of small vessels and capillaries particularly in deep white matter (quantified with platelet-derived growth factor receptorβ and smooth muscle actin, p < 0.01) which was more marked the longer the duration of epilepsy (p < 0.05). We noted increased glial numbers (Olig2, Iba1) but reduced myelin (MAG, PLP) in TLE compared to controls, particularly prominent in deep white matter. Gene expression analysis showed a greater reduction of myelination genes in HS than non-HS cases and with age and correlation with diffusion MRI alterations. Glial densities and vascular size were increased with increased MRI diffusivity and vascular density with white matter abnormality quantified using fixel-based analysis. Increased perivascular space was associated with reduced fractional anisotropy as well as age-accelerated cognitive decline prior to surgery (p < 0.05). In summary, likely acquired microangiopathic changes in TLE, including vascular sclerosis, increased pericyte coverage and reduced small vessel size, may indicate a functional alteration in contractility of small vessels and haemodynamics that could impact on tissue perfusion. These morphological features correlate with white matter diffusion MRI alterations and might explain cognitive decline in TLE.
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Affiliation(s)
- Joan Liu
- Department of Clinical and Experimental Epilepsy, Department of Neuropathology, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- Department of Neuroscience, University of Westminster, London, UK
| | - Lawrence Binding
- Department of Clinical and Experimental Epilepsy, Department of Neuropathology, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK
| | - Isha Puntambekar
- Department of Clinical and Experimental Epilepsy, Department of Neuropathology, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
| | - Smriti Patodia
- Department of Clinical and Experimental Epilepsy, Department of Neuropathology, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
| | - Yau Mun Lim
- Department of Clinical and Experimental Epilepsy, Department of Neuropathology, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
| | - Alicja Mryzyglod
- Department of Clinical and Experimental Epilepsy, Department of Neuropathology, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
| | - Fenglai Xiao
- Department of Clinical and Experimental Epilepsy, Department of Neuropathology, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
| | - Shengning Pan
- Department of Statistical Science, University College London, Gower St., London, UK
| | - Remika Mito
- Department of Neuroscience and Mental Health, Florey Institute of Neuroscience and Mental Health, University of Melbourne, Victoria, Australia
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, Department of Neuropathology, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, Department of Neuropathology, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
| | - Sallie Baxendale
- Department of Clinical and Experimental Epilepsy, Department of Neuropathology, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
| | - Matthias Koepp
- Department of Clinical and Experimental Epilepsy, Department of Neuropathology, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
| | - Maria Thom
- Department of Clinical and Experimental Epilepsy, Department of Neuropathology, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK.
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Ellsay AC, Winston GP. Advances in MRI-based diagnosis of temporal lobe epilepsy: Correlating hippocampal subfield volumes with histopathology. J Neuroimaging 2024. [PMID: 39092876 DOI: 10.1111/jon.13225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 05/27/2024] [Accepted: 07/17/2024] [Indexed: 08/04/2024] Open
Abstract
Epilepsy, affecting 0.5%-1% of the global population, presents a significant challenge with 30% of patients resistant to medical treatment. Temporal lobe epilepsy, a common cause of medically refractory epilepsy, is often caused by hippocampal sclerosis (HS). HS can be divided further by subtype, as defined by the International League Against Epilepsy (ILAE). Type 1 HS, the most prevalent form (60%-80% of all cases), is characterized by cell loss and gliosis predominantly in the subfields cornu ammonis (CA1) and CA4. Type 2 HS features cell loss and gliosis primarily in the CA1 sector, and type 3 HS features cell loss and gliosis predominantly in the CA4 subfield. This literature review evaluates studies on hippocampal subfields, exploring whether observable atrophy patterns from in vivo and ex vivo magnetic resonance imaging (MRI) scans correlate with histopathological examinations with manual or automated segmentation techniques. Our findings suggest only ex vivo 1.5 Tesla (T) or 3T MRI with manual segmentation or in vivo 7T MRI with manual or automated segmentations can consistently correlate subfield patterns with histopathologically derived ILAE-HS subtypes. In conclusion, manual and automated segmentation methods offer advantages and limitations in diagnosing ILAE-HS subtypes, with ongoing research crucial for refining hippocampal subfield segmentation techniques and enhancing clinical applicability.
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Affiliation(s)
- Andrea C Ellsay
- Centre for Neuroscience Studies, Faculty of Health Sciences, Queen's University, Kingston, Ontario, Canada
| | - Gavin P Winston
- Centre for Neuroscience Studies, Faculty of Health Sciences, Queen's University, Kingston, Ontario, Canada
- Division of Neurology, Department of Medicine, Queen's University, Kingston, Ontario, Canada
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3
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Rebsamen M, Jin BZ, Klail T, De Beukelaer S, Barth R, Rezny-Kasprzak B, Ahmadli U, Vulliemoz S, Seeck M, Schindler K, Wiest R, Radojewski P, Rummel C. Clinical Evaluation of a Quantitative Imaging Biomarker Supporting Radiological Assessment of Hippocampal Sclerosis. Clin Neuroradiol 2023; 33:1045-1053. [PMID: 37358608 PMCID: PMC10654177 DOI: 10.1007/s00062-023-01308-9] [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: 03/01/2023] [Accepted: 05/09/2023] [Indexed: 06/27/2023]
Abstract
OBJECTIVE To evaluate the influence of quantitative reports (QReports) on the radiological assessment of hippocampal sclerosis (HS) from MRI of patients with epilepsy in a setting mimicking clinical reality. METHODS The study included 40 patients with epilepsy, among them 20 with structural abnormalities in the mesial temporal lobe (13 with HS). Six raters blinded to the diagnosis assessed the 3T MRI in two rounds, first using MRI only and later with both MRI and the QReport. Results were evaluated using inter-rater agreement (Fleiss' kappa [Formula: see text]) and comparison with a consensus of two radiological experts derived from clinical and imaging data, including 7T MRI. RESULTS For the primary outcome, diagnosis of HS, the mean accuracy of the raters improved from 77.5% with MRI only to 86.3% with the additional QReport (effect size [Formula: see text]). Inter-rater agreement increased from [Formula: see text] to [Formula: see text]. Five of the six raters reached higher accuracies, and all reported higher confidence when using the QReports. CONCLUSION In this pre-use clinical evaluation study, we demonstrated clinical feasibility and usefulness as well as the potential impact of a previously suggested imaging biomarker for radiological assessment of HS.
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Affiliation(s)
- Michael Rebsamen
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 10, 3010, Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Baudouin Zongxin Jin
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 10, 3010, Bern, Switzerland
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Tomas Klail
- University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Sophie De Beukelaer
- University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Rike Barth
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Beata Rezny-Kasprzak
- University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Uzeyir Ahmadli
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 10, 3010, Bern, Switzerland
| | - Serge Vulliemoz
- EEG and Epilepsy Unit, Department of Clinical Neurosciences, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Margitta Seeck
- EEG and Epilepsy Unit, Department of Clinical Neurosciences, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Kaspar Schindler
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 10, 3010, Bern, Switzerland
- Swiss Institute for Translational and Entrepreneurial Medicine, sitem-insel, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 10, 3010, Bern, Switzerland.
- Swiss Institute for Translational and Entrepreneurial Medicine, sitem-insel, Bern, Switzerland.
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 10, 3010, Bern, Switzerland
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Xiao F, Caciagli L, Wandschneider B, Sone D, Young AL, Vos SB, Winston GP, Zhang Y, Liu W, An D, Kanber B, Zhou D, Sander JW, Thom M, Duncan JS, Alexander DC, Galovic M, Koepp MJ. Identification of different MRI atrophy progression trajectories in epilepsy by subtype and stage inference. Brain 2023; 146:4702-4716. [PMID: 37807084 PMCID: PMC10629797 DOI: 10.1093/brain/awad284] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 06/30/2023] [Accepted: 08/02/2023] [Indexed: 10/10/2023] Open
Abstract
Artificial intelligence (AI)-based tools are widely employed, but their use for diagnosis and prognosis of neurological disorders is still evolving. Here we analyse a cross-sectional multicentre structural MRI dataset of 696 people with epilepsy and 118 control subjects. We use an innovative machine-learning algorithm, Subtype and Stage Inference, to develop a novel data-driven disease taxonomy, whereby epilepsy subtypes correspond to distinct patterns of spatiotemporal progression of brain atrophy.In a discovery cohort of 814 individuals, we identify two subtypes common to focal and idiopathic generalized epilepsies, characterized by progression of grey matter atrophy driven by the cortex or the basal ganglia. A third subtype, only detected in focal epilepsies, was characterized by hippocampal atrophy. We corroborate external validity via an independent cohort of 254 people and confirm that the basal ganglia subtype is associated with the most severe epilepsy.Our findings suggest fundamental processes underlying the progression of epilepsy-related brain atrophy. We deliver a novel MRI- and AI-guided epilepsy taxonomy, which could be used for individualized prognostics and targeted therapeutics.
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Affiliation(s)
- Fenglai Xiao
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- UCL-Epilepsy Society MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, Buckinghamshire, SL9 0RJ, UK
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China
| | - Lorenzo Caciagli
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- UCL-Epilepsy Society MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, Buckinghamshire, SL9 0RJ, UK
- Department of Neurology, Inselspital, Sleep-Wake-Epilepsy-Center, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Britta Wandschneider
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- UCL-Epilepsy Society MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, Buckinghamshire, SL9 0RJ, UK
| | - Daichi Sone
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- UCL-Epilepsy Society MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, Buckinghamshire, SL9 0RJ, UK
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, 105-8461, Japan
| | - Alexandra L Young
- Centre for Medical Image Computing, Departments of Computer Science, Medical Physics, and Biomedical Engineering, UCL, London, WC1E 6BT, UK
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Sjoerd B Vos
- Centre for Medical Image Computing, Departments of Computer Science, Medical Physics, and Biomedical Engineering, UCL, London, WC1E 6BT, UK
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
- Centre for Microscopy, Characterisation, and Analysis, University of Western Australia, Perth, WA 6009, Australia
| | - Gavin P Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- UCL-Epilepsy Society MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, Buckinghamshire, SL9 0RJ, UK
- Department of Medicine, Division of Neurology, Queen’s University, Kingston, K7L 3N6, Canada
- Centre for Neuroscience Studies, Queen’s University, Kingston, K7L 3N6, Canada
| | - Yingying Zhang
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China
| | - Wenyu Liu
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China
| | - Dongmei An
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China
| | - Baris Kanber
- Centre for Medical Image Computing, Departments of Computer Science, Medical Physics, and Biomedical Engineering, UCL, London, WC1E 6BT, UK
| | - Dong Zhou
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China
| | - Josemir W Sander
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- UCL-Epilepsy Society MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, Buckinghamshire, SL9 0RJ, UK
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China
- Stichting Epilepsie Instellingen Nederland – (SEIN), Heemstede, 2103SW, The Netherlands
| | - Maria Thom
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- UCL-Epilepsy Society MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, Buckinghamshire, SL9 0RJ, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Departments of Computer Science, Medical Physics, and Biomedical Engineering, UCL, London, WC1E 6BT, UK
| | - Marian Galovic
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich, Zurich, CH-8091, Switzerland
| | - Matthias J Koepp
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- UCL-Epilepsy Society MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, Buckinghamshire, SL9 0RJ, UK
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Leiberg K, de Tisi J, Duncan JS, Little B, Taylor PN, Vos SB, Winston GP, Mota B, Wang Y. Effects of anterior temporal lobe resection on cortical morphology. Cortex 2023; 166:233-242. [PMID: 37399617 DOI: 10.1016/j.cortex.2023.04.018] [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: 01/19/2023] [Revised: 04/11/2023] [Accepted: 04/16/2023] [Indexed: 07/05/2023]
Abstract
Neuroimaging can capture brain restructuring after anterior temporal lobe resection (ATLR), a surgical procedure to treat drug-resistant temporal lobe epilepsy (TLE). Here, we examine the effects of this surgery on brain morphology measured in recently-proposed independent variables. We studied 101 individuals with TLE (55 left, 46 right onset) who underwent ATLR. For each individual we considered one pre-surgical MRI and one follow-up MRI 2-13 months after surgery. We used a surface-based method to locally compute traditional morphological variables, and the independent measures K, I, and S, where K measures white matter tension, I captures isometric scaling, and S contains the remaining information about cortical shape. A normative model trained on data from 924 healthy controls was used to debias the data and account for healthy ageing effects occurring during scans. A SurfStat random field theory clustering approach assessed changes across the cortex caused by ATLR. Compared to preoperative data, surgery had marked effects on all morphological measures. Ipsilateral effects were located in the orbitofrontal and inferior frontal gyri, the pre- and postcentral gyri and supramarginal gyrus, and the lateral occipital gyrus and lingual cortex. Contralateral effects were in the lateral occipital gyrus, and inferior frontal gyrus and frontal pole. The restructuring following ATLR is reflected in widespread morphological changes, mainly in regions near the resection, but also remotely in regions that are structurally connected to the anterior temporal lobe. The causes could include mechanical effects, Wallerian degeneration, or compensatory plasticity. The study of independent measures revealed additional effects compared to traditional measures.
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Affiliation(s)
- Karoline Leiberg
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK.
| | - Jane de Tisi
- Department of Clinical & Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - John S Duncan
- Department of Clinical & Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK; Chalfont Centre for Epilepsy, Chalfont St Peter, UK
| | - Bethany Little
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK; Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Peter N Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK; Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom; Queen Square Institute of Neurology, University College London, Queen Square, London, UK
| | - Sjoerd B Vos
- Queen Square Institute of Neurology, University College London, Queen Square, London, UK; Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL, UK; Centre for Medical Image Computing, University College London, London, UK; Centre for Microscopy, Characterisation, And Analysis, The University of Western Australia, Nedlands, Australia
| | - Gavin P Winston
- Department of Clinical & Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK; MRI Unit, Epilepsy Society, Buckinghamshire, UK; Division of Neurology, Department of Medicine, Queen's University, Kingston, Ontario, Canada
| | - Bruno Mota
- MetaBIO Lab, Instituto de Física, Universidade Federal Do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK; Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom; Queen Square Institute of Neurology, University College London, Queen Square, London, UK.
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6
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Binding LP, Dasgupta D, Taylor PN, Thompson PJ, O'Keeffe AG, de Tisi J, McEvoy AW, Miserocchi A, Winston GP, Duncan JS, Vos SB. Contribution of White Matter Fiber Bundle Damage to Language Change After Surgery for Temporal Lobe Epilepsy. Neurology 2023; 100:e1621-e1633. [PMID: 36750386 PMCID: PMC10103113 DOI: 10.1212/wnl.0000000000206862] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 12/12/2022] [Indexed: 02/09/2023] Open
Abstract
BACKGROUND AND OBJECTIVES In medically refractory temporal lobe epilepsy (TLE), 30%-50% of patients experience substantial language decline after resection in the language-dominant hemisphere. In this study, we investigated the contribution of white matter fiber bundle damage to language change at 3 and 12 months after surgery. METHODS We studied 127 patients who underwent TLE surgery from 2010 to 2019. Neuropsychological testing included picture naming, semantic fluency, and phonemic verbal fluency, performed preoperatively and 3 and 12 months postoperatively. Outcome was assessed using reliable change index (RCI; clinically significant decline) and change across timepoints (postoperative scores minus preoperative scores). Functional MRI was used to determine language lateralization. The arcuate fasciculus (AF), inferior fronto-occipital fasciculus (IFOF), inferior longitudinal fasciculus, middle longitudinal fasciculus (MLF), and uncinate fasciculus were mapped using diffusion MRI probabilistic tractography. Resection masks, drawn comparing coregistered preoperative and postoperative T1 MRI scans, were used as exclusion regions on preoperative tractography to estimate the percentage of preoperative tracts transected in surgery. Chi-squared assessments evaluated the occurrence of RCI-determined language decline. Independent sample t tests and MM-estimator robust regressions were used to assess the impact of clinical factors and fiber transection on RCI and change outcomes, respectively. RESULTS Language-dominant and language-nondominant resections were treated separately for picture naming because postoperative outcomes were significantly different between these groups. In language-dominant hemisphere resections, greater surgical damage to the AF and IFOF was related to RCI decline at 3 months. Damage to the inferior frontal subfasciculus of the IFOF was related to change at 3 months. In language-nondominant hemisphere resections, increased MLF resection was associated with RCI decline at 3 months, and damage to the anterior subfasciculus was related to change at 3 months. Language-dominant and language-nondominant resections were treated as 1 cohort for semantic and phonemic fluency because there were no significant differences in postoperative decline between these groups. Postoperative seizure freedom was associated with an absence of significant language decline 12 months after surgery for semantic fluency. DISCUSSION We demonstrate a relationship between fiber transection and naming decline after temporal lobe resection. Individualized surgical planning to spare white matter fiber bundles could help to preserve language function after surgery.
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Affiliation(s)
- Lawrence Peter Binding
- From the Department of Computer Science (L.P.B., S.B.V.), Centre for Medical Image Computing, Department of Clinical and Experimental Epilepsy (L.B.P., D.D., P.N.T., P.J.T., J.d.T., A.W.M., A.M., G.P.W., J.S.D.), UCL Queen Square Institute of Neurology, and Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, University College London; Victor Horsley Department of Neurosurgery (D.D., A.W.M., A.M.), and Department of Neuropsychology (P.J.T.), National Hospital for Neurology and Neurosurgery, Queen Square, London; CNNP Lab (P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University; School of Mathematical Sciences (A.G.O.), University of Nottingham; Epilepsy Society MRI Unit (J.d.T., G.P.W., J.S.D.), Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine (G.P.W.), Division of Neurology, Queen's University, Kingston, Canada; and Centre for Microscopy (S.B.V), Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia.
| | - Debayan Dasgupta
- From the Department of Computer Science (L.P.B., S.B.V.), Centre for Medical Image Computing, Department of Clinical and Experimental Epilepsy (L.B.P., D.D., P.N.T., P.J.T., J.d.T., A.W.M., A.M., G.P.W., J.S.D.), UCL Queen Square Institute of Neurology, and Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, University College London; Victor Horsley Department of Neurosurgery (D.D., A.W.M., A.M.), and Department of Neuropsychology (P.J.T.), National Hospital for Neurology and Neurosurgery, Queen Square, London; CNNP Lab (P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University; School of Mathematical Sciences (A.G.O.), University of Nottingham; Epilepsy Society MRI Unit (J.d.T., G.P.W., J.S.D.), Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine (G.P.W.), Division of Neurology, Queen's University, Kingston, Canada; and Centre for Microscopy (S.B.V), Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
| | - Peter Neal Taylor
- From the Department of Computer Science (L.P.B., S.B.V.), Centre for Medical Image Computing, Department of Clinical and Experimental Epilepsy (L.B.P., D.D., P.N.T., P.J.T., J.d.T., A.W.M., A.M., G.P.W., J.S.D.), UCL Queen Square Institute of Neurology, and Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, University College London; Victor Horsley Department of Neurosurgery (D.D., A.W.M., A.M.), and Department of Neuropsychology (P.J.T.), National Hospital for Neurology and Neurosurgery, Queen Square, London; CNNP Lab (P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University; School of Mathematical Sciences (A.G.O.), University of Nottingham; Epilepsy Society MRI Unit (J.d.T., G.P.W., J.S.D.), Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine (G.P.W.), Division of Neurology, Queen's University, Kingston, Canada; and Centre for Microscopy (S.B.V), Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
| | - Pamela Jane Thompson
- From the Department of Computer Science (L.P.B., S.B.V.), Centre for Medical Image Computing, Department of Clinical and Experimental Epilepsy (L.B.P., D.D., P.N.T., P.J.T., J.d.T., A.W.M., A.M., G.P.W., J.S.D.), UCL Queen Square Institute of Neurology, and Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, University College London; Victor Horsley Department of Neurosurgery (D.D., A.W.M., A.M.), and Department of Neuropsychology (P.J.T.), National Hospital for Neurology and Neurosurgery, Queen Square, London; CNNP Lab (P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University; School of Mathematical Sciences (A.G.O.), University of Nottingham; Epilepsy Society MRI Unit (J.d.T., G.P.W., J.S.D.), Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine (G.P.W.), Division of Neurology, Queen's University, Kingston, Canada; and Centre for Microscopy (S.B.V), Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
| | - Aidan G O'Keeffe
- From the Department of Computer Science (L.P.B., S.B.V.), Centre for Medical Image Computing, Department of Clinical and Experimental Epilepsy (L.B.P., D.D., P.N.T., P.J.T., J.d.T., A.W.M., A.M., G.P.W., J.S.D.), UCL Queen Square Institute of Neurology, and Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, University College London; Victor Horsley Department of Neurosurgery (D.D., A.W.M., A.M.), and Department of Neuropsychology (P.J.T.), National Hospital for Neurology and Neurosurgery, Queen Square, London; CNNP Lab (P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University; School of Mathematical Sciences (A.G.O.), University of Nottingham; Epilepsy Society MRI Unit (J.d.T., G.P.W., J.S.D.), Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine (G.P.W.), Division of Neurology, Queen's University, Kingston, Canada; and Centre for Microscopy (S.B.V), Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
| | - Jane de Tisi
- From the Department of Computer Science (L.P.B., S.B.V.), Centre for Medical Image Computing, Department of Clinical and Experimental Epilepsy (L.B.P., D.D., P.N.T., P.J.T., J.d.T., A.W.M., A.M., G.P.W., J.S.D.), UCL Queen Square Institute of Neurology, and Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, University College London; Victor Horsley Department of Neurosurgery (D.D., A.W.M., A.M.), and Department of Neuropsychology (P.J.T.), National Hospital for Neurology and Neurosurgery, Queen Square, London; CNNP Lab (P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University; School of Mathematical Sciences (A.G.O.), University of Nottingham; Epilepsy Society MRI Unit (J.d.T., G.P.W., J.S.D.), Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine (G.P.W.), Division of Neurology, Queen's University, Kingston, Canada; and Centre for Microscopy (S.B.V), Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
| | - Andrew William McEvoy
- From the Department of Computer Science (L.P.B., S.B.V.), Centre for Medical Image Computing, Department of Clinical and Experimental Epilepsy (L.B.P., D.D., P.N.T., P.J.T., J.d.T., A.W.M., A.M., G.P.W., J.S.D.), UCL Queen Square Institute of Neurology, and Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, University College London; Victor Horsley Department of Neurosurgery (D.D., A.W.M., A.M.), and Department of Neuropsychology (P.J.T.), National Hospital for Neurology and Neurosurgery, Queen Square, London; CNNP Lab (P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University; School of Mathematical Sciences (A.G.O.), University of Nottingham; Epilepsy Society MRI Unit (J.d.T., G.P.W., J.S.D.), Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine (G.P.W.), Division of Neurology, Queen's University, Kingston, Canada; and Centre for Microscopy (S.B.V), Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
| | - Anna Miserocchi
- From the Department of Computer Science (L.P.B., S.B.V.), Centre for Medical Image Computing, Department of Clinical and Experimental Epilepsy (L.B.P., D.D., P.N.T., P.J.T., J.d.T., A.W.M., A.M., G.P.W., J.S.D.), UCL Queen Square Institute of Neurology, and Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, University College London; Victor Horsley Department of Neurosurgery (D.D., A.W.M., A.M.), and Department of Neuropsychology (P.J.T.), National Hospital for Neurology and Neurosurgery, Queen Square, London; CNNP Lab (P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University; School of Mathematical Sciences (A.G.O.), University of Nottingham; Epilepsy Society MRI Unit (J.d.T., G.P.W., J.S.D.), Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine (G.P.W.), Division of Neurology, Queen's University, Kingston, Canada; and Centre for Microscopy (S.B.V), Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
| | - Gavin P Winston
- From the Department of Computer Science (L.P.B., S.B.V.), Centre for Medical Image Computing, Department of Clinical and Experimental Epilepsy (L.B.P., D.D., P.N.T., P.J.T., J.d.T., A.W.M., A.M., G.P.W., J.S.D.), UCL Queen Square Institute of Neurology, and Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, University College London; Victor Horsley Department of Neurosurgery (D.D., A.W.M., A.M.), and Department of Neuropsychology (P.J.T.), National Hospital for Neurology and Neurosurgery, Queen Square, London; CNNP Lab (P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University; School of Mathematical Sciences (A.G.O.), University of Nottingham; Epilepsy Society MRI Unit (J.d.T., G.P.W., J.S.D.), Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine (G.P.W.), Division of Neurology, Queen's University, Kingston, Canada; and Centre for Microscopy (S.B.V), Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
| | - John S Duncan
- From the Department of Computer Science (L.P.B., S.B.V.), Centre for Medical Image Computing, Department of Clinical and Experimental Epilepsy (L.B.P., D.D., P.N.T., P.J.T., J.d.T., A.W.M., A.M., G.P.W., J.S.D.), UCL Queen Square Institute of Neurology, and Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, University College London; Victor Horsley Department of Neurosurgery (D.D., A.W.M., A.M.), and Department of Neuropsychology (P.J.T.), National Hospital for Neurology and Neurosurgery, Queen Square, London; CNNP Lab (P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University; School of Mathematical Sciences (A.G.O.), University of Nottingham; Epilepsy Society MRI Unit (J.d.T., G.P.W., J.S.D.), Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine (G.P.W.), Division of Neurology, Queen's University, Kingston, Canada; and Centre for Microscopy (S.B.V), Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
| | - Sjoerd B Vos
- From the Department of Computer Science (L.P.B., S.B.V.), Centre for Medical Image Computing, Department of Clinical and Experimental Epilepsy (L.B.P., D.D., P.N.T., P.J.T., J.d.T., A.W.M., A.M., G.P.W., J.S.D.), UCL Queen Square Institute of Neurology, and Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, University College London; Victor Horsley Department of Neurosurgery (D.D., A.W.M., A.M.), and Department of Neuropsychology (P.J.T.), National Hospital for Neurology and Neurosurgery, Queen Square, London; CNNP Lab (P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University; School of Mathematical Sciences (A.G.O.), University of Nottingham; Epilepsy Society MRI Unit (J.d.T., G.P.W., J.S.D.), Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine (G.P.W.), Division of Neurology, Queen's University, Kingston, Canada; and Centre for Microscopy (S.B.V), Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
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Tsuchiya T, Matsuo T, Fujimoto S, Nakata Y, Morino M. Quantitative evaluation of hippocampal gray-white matter boundary blurring in medial temporal lobe epilepsy with hippocampal sclerosis. Epilepsy Behav 2023; 140:109098. [PMID: 36736239 DOI: 10.1016/j.yebeh.2023.109098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 11/18/2022] [Accepted: 01/14/2023] [Indexed: 02/04/2023]
Abstract
INTRODUCTION The magnetic resonance imaging (MRI) findings of hippocampal sclerosis (HS) include decreased volume, increased signal intensity, and hippocampal gray-white matter boundary blurring (HGWBB). Given that the layered structure is obscure in HS, there have been no reports on the quantitative evaluation of HGWBB and its relationship with the clinical outcome. Thus, this study aims to correlate the extent of HGWBB to its clinical manifestation of HS. METHODS Fifty-four patients with temporal lobe epilepsy who underwent hippocampal resection were enrolled. To evaluate HGWBB quantitatively, we defined an index by calculating the standard deviation of the intrahippocampal signal on short tau inversion recovery. In addition, we created a prognostic scoring system using four criteria, including hippocampal signal intensity, size of hippocampal cross-sectional area, presence of temporal lobe lesions, and the HGWBB index. RESULTS The HGWBB index was significantly lower on the affected side than on the unaffected side (p < 0.001). This trend was more prominent in the poor prognosis group than that in the good prognosis group. The prognostic scoring system revealed that when three or more criteria were positive, the prognostic accuracy reached 87.5% sensitivity and 71.7% specificity. CONCLUSION The HGWBB index is useful for the diagnosis of temporal lobe epilepsy with HS and for predicting seizure outcomes when used with another index of hippocampal volume loss and increased signal intensity.
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Affiliation(s)
- Takahiro Tsuchiya
- Department of Neurosurgery, Tokyo Metropolitan Neurological Hospital, Fuchu, Tokyo, Japan
| | - Takeshi Matsuo
- Department of Neurosurgery, Tokyo Metropolitan Neurological Hospital, Fuchu, Tokyo, Japan.
| | - So Fujimoto
- Department of Neurosurgery, Tokyo Metropolitan Neurological Hospital, Fuchu, Tokyo, Japan
| | - Yasuhiro Nakata
- Department of Neuroradiology, Tokyo Metropolitan Neurological Hospital, Fuchu, Tokyo, Japan
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Adel SAA, Treit S, Abd Wahab W, Little G, Schmitt L, Wilman AH, Beaulieu C, Gross DW. Longitudinal hippocampal diffusion-weighted imaging and T2 relaxometry demonstrate regional abnormalities which are stable and predict subfield pathology in temporal lobe epilepsy. Epilepsia Open 2023; 8:100-112. [PMID: 36461649 PMCID: PMC9977756 DOI: 10.1002/epi4.12679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 11/15/2022] [Indexed: 12/04/2022] Open
Abstract
OBJECTIVE High-resolution (1 mm isotropic) diffusion tensor imaging (DTI) of the hippocampus in temporal lobe epilepsy (TLE) patients has shown patterns of hippocampal subfield diffusion abnormalities, which were consistent with hippocampal sclerosis (HS) subtype on surgical histology. The objectives of this longitudinal imaging study were to determine the stability of focal hippocampus diffusion changes over time in TLE patients, compare diffusion and quantitative T2 abnormalities of the sclerotic hippocampus, and correlate presurgical mean diffusivity (MD) and T2 maps with postsurgical histology. METHODS Nineteen TLE patients and 19 controls underwent two high-resolution (1 mm isotropic) DTI and 1.1 × 1.1 × 1 mm3 T2 relaxometry scans (in a subset of 16 TLE patients and 9 controls) of the hippocampus at 3T, with a 2.6 ± 0.8 year inter-scan interval. Within-participant hippocampal volume, MD and T2 were compared between the scans. Contralateral hippocampal changes 2.3 ± 1.0 years after surgery and ipsilateral preoperative MD maps versus postoperative subfield histopathology were evaluated in eight patients who underwent surgical resection of the hippocampus. RESULTS Reduced volume and elevated MD and T2 of sclerotic hippocampi remained unchanged between longitudinal scans. Focal regions of elevated MD and T2 in bilateral hippocampi of HS TLE were detected consistently at both scans. Regions of high MD and T2 correlated and remained consistent over time. Volume, MD, and T2 remained unchanged in postoperative contralateral hippocampus. Regional elevations of MD identified subfield neuron loss on postsurgical histology with 88% sensitivity and 88% specificity. Focal T2 elevations identified subfield neuron loss with 75% sensitivity and 88% specificity. SIGNIFICANCE Diffusion and T2 abnormalities in ipsilateral and contralateral hippocampi remained unchanged between the scans suggesting permanent microstructural alterations. MD and T2 demonstrated good sensitivity and specificity to detect hippocampal subfield neuron loss on postsurgical histology, supporting the potential that high-resolution hippocampal DTI and T2 could be used to diagnose HS subtype before surgery.
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Affiliation(s)
- Seyed Amir Ali Adel
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Sarah Treit
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Wasan Abd Wahab
- Division of Neurology, University of Alberta, Edmonton, Alberta, Canada
| | - Graham Little
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada.,Department of Computer Science, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Laura Schmitt
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
| | - Alan H Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Christian Beaulieu
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Donald W Gross
- Division of Neurology, University of Alberta, Edmonton, Alberta, Canada
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9
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Dasgupta D, Finn R, Chari A, Giampiccolo D, de Tisi J, O'Keeffe AG, Miserocchi A, McEvoy AW, Vos SB, Duncan JS. Hippocampal resection in temporal lobe epilepsy: Do we need to resect the tail? Epilepsy Res 2023; 190:107086. [PMID: 36709527 PMCID: PMC10626579 DOI: 10.1016/j.eplepsyres.2023.107086] [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: 08/22/2022] [Revised: 12/24/2022] [Accepted: 01/09/2023] [Indexed: 01/13/2023]
Abstract
INTRODUCTION Anteromesial temporal lobe resection is the most common surgical technique used to treat drug-resistant mesial temporal lobe epilepsy, particularly when secondary to hippocampal sclerosis. Structural and functional imaging data suggest the importance of sparing the posterior hippocampus for minimising language and memory deficits. Recent work has challenged the view that maximal posterior hippocampal resection improves seizure outcome. This study was designed to assess whether resection of posterior hippocampal atrophy was associated with improved seizure outcome. METHODS Retrospective analysis of a prospective database of all anteromesial temporal lobe resections performed in individuals with hippocampal sclerosis at our epilepsy surgery centre, 2013-2021. Pre- and post-operative MRI were reviewed by 2 neurosurgical fellows to assess whether the atrophic segment, displayed by automated hippocampal morphometry, was resected, and ILAE seizure outcomes were collected at 1 year and last clinical follow-up. Data analysis used univariate and binary logistic regression. RESULTS Sixty consecutive eligible patients were identified of whom 70% were seizure free (ILAE Class 1 & 2) at one year. There was no statistically significant difference in seizure freedom outcomes in patients who had complete resection of atrophic posterior hippocampus or not (Fisher's Exact test statistic 0.69, not significant at p < .05) both at one year, and at last clinical follow-up. In the multivariate analysis only a history of status epilepticus (OR=0.2, 95%CI:0.042-0.955, p = .04) at one year, and pre-operative psychiatric disorder (OR=0.145, 95%CI:0.036-0.588, p = .007) at last clinical follow-up, were associated with a reduced chance of seizure freedom. SIGNIFICANCE Our data suggest that seizure freedom is not associated with whether or not posterior hippocampal atrophy is resected. This challenges the traditional surgical dogma of maximal posterior hippocampal resection in anteromesial temporal lobe resections and is a step further optimising this surgical procedure to maximise seizure freedom and minimise associated language and memory deficits.
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Affiliation(s)
- Debayan Dasgupta
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK; Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK.
| | - Roisin Finn
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK.
| | - Aswin Chari
- Department of Neurosurgery, Great Ormond Street Hospital, London, UK; Developmental Neuroscience, Great Ormond Street Institute of Child Health, University College London, London, UK.
| | - Davide Giampiccolo
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK; Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK; Institute of Neurosciences, Cleveland Clinic London, London, UK.
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Aidan G O'Keeffe
- School of Mathematical Sciences, University of Nottingham, Nottingham, UK. aidan.o'
| | - Anna Miserocchi
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK.
| | - Andrew W McEvoy
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK.
| | - Sjoerd B Vos
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK; Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK; Centre for Microscopy, Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia.
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK.
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Passaro EA. Neuroimaging in Adults and Children With Epilepsy. Continuum (Minneap Minn) 2023; 29:104-155. [PMID: 36795875 DOI: 10.1212/con.0000000000001242] [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: 02/18/2023]
Abstract
OBJECTIVE This article discusses the fundamental importance of optimal epilepsy imaging using the International League Against Epilepsy-endorsed Harmonized Neuroimaging of Epilepsy Structural Sequences (HARNESS) protocol and the use of multimodality imaging in the evaluation of patients with drug-resistant epilepsy. It outlines a methodical approach to evaluating these images, particularly in the context of clinical information. LATEST DEVELOPMENTS Epilepsy imaging is rapidly evolving, and a high-resolution epilepsy protocol MRI is essential in evaluating newly diagnosed, chronic, and drug-resistant epilepsy. The article reviews the spectrum of relevant MRI findings in epilepsy and their clinical significance. Integrating multimodality imaging is a powerful tool in the presurgical evaluation of epilepsy, particularly in "MRI-negative" cases. For example, correlation of clinical phenomenology, video-EEG with positron emission tomography (PET), ictal subtraction single-photon emission computerized tomography (SPECT), magnetoencephalography (MEG), functional MRI, and advanced neuroimaging such as MRI texture analysis and voxel-based morphometry enhances the identification of subtle cortical lesions such as focal cortical dysplasias to optimize epilepsy localization and selection of optimal surgical candidates. ESSENTIAL POINTS The neurologist has a unique role in understanding the clinical history and seizure phenomenology, which are the cornerstones of neuroanatomic localization. When integrated with advanced neuroimaging, the clinical context has a profound impact on identifying subtle MRI lesions or finding the "epileptogenic" lesion when multiple lesions are present. Patients with an identified lesion on MRI have a 2.5-fold improved chance of achieving seizure freedom with epilepsy surgery compared with those without a lesion. This clinical-radiographic integration is essential to accurate classification, localization, determination of long-term prognosis for seizure control, and identification of candidates for epilepsy surgery to reduce seizure burden or attain seizure freedom.
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Li J, Bai YC, Wu LH, Zhang P, Wei XC, Ma CH, Yan MN, Wang YT, Chen B. Synthetic relaxometry combined with MUSE DWI and 3D-pCASL improves detection of hippocampal sclerosis. Eur J Radiol 2022; 157:110571. [DOI: 10.1016/j.ejrad.2022.110571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 10/14/2022] [Accepted: 10/23/2022] [Indexed: 11/03/2022]
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Iqbal S, Leon-Rojas JE, Galovic M, Vos SB, Hammers A, de Tisi J, Koepp MJ, Duncan JS. Volumetric analysis of the piriform cortex in temporal lobe epilepsy. Epilepsy Res 2022; 185:106971. [PMID: 35810570 PMCID: PMC10510027 DOI: 10.1016/j.eplepsyres.2022.106971] [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: 08/31/2021] [Revised: 05/13/2022] [Accepted: 06/22/2022] [Indexed: 11/03/2022]
Abstract
The piriform cortex, at the confluence of the temporal and frontal lobes, generates seizures in response to chemical convulsants and electrical stimulation. Resection of more than 50% of the piriform cortex in anterior temporal lobe resection for refractory temporal lobe epilepsy (TLE) was associated with a 16-fold higher chance of seizure freedom. The objectives of the current study were to implement a robust protocol to measure piriform cortex volumes and to quantify the correlation of these volumes with clinical characteristics of TLE. Sixty individuals with unilateral TLE (33 left) and 20 healthy controls had volumetric analysis of left and right piriform cortex and hippocampi. A protocol for segmenting and measuring the volumes of the piriform cortices was implemented, with good inter-rater and test-retest reliability. The right piriform cortex volume was consistently larger than the left piriform cortex in both healthy controls and patients with TLE. In controls, the mean volume of the right piriform cortex was 17.7% larger than the left, and the right piriform cortex extended a mean of 6 mm (Range: -4 to 12) more anteriorly than the left. This asymmetry was also seen in left and right TLE. In TLE patients overall, the piriform cortices were not significantly smaller than in controls. Hippocampal sclerosis was associated with decreased ipsilateral and contralateral piriform cortex volumes. The piriform cortex volumes, both ipsilateral and contralateral to the epileptic temporal lobe, were smaller with a longer duration of epilepsy. There was no significant association between piriform cortex volumes and the frequency of focal seizures with impaired awareness or the number of anti-seizure medications taken. Implementation of robust segmentation will enable consistent neurosurgical resection in anterior temporal lobe surgery for refractory TLE..
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Affiliation(s)
- Sabahat Iqbal
- UK National Institute for Health Research University College London Hospitals Biomedical Research Centre, and Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom; Epilepsy Society MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, Buckinghamshire, United Kingdom
| | - Jose E Leon-Rojas
- UK National Institute for Health Research University College London Hospitals Biomedical Research Centre, and Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom; Epilepsy Society MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, Buckinghamshire, United Kingdom; Facultad de Ciencias Médicas de la Salud y de la Vida, Escuela de Medicina, Universidad Internacional del Ecuador, Quito, Ecuador
| | - Marian Galovic
- UK National Institute for Health Research University College London Hospitals Biomedical Research Centre, and Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom; Epilepsy Society MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, Buckinghamshire, United Kingdom; Department of Neurology, Zurich University Hospital, Zurich, Switzerland
| | - Sjoerd B Vos
- UK National Institute for Health Research University College London Hospitals Biomedical Research Centre, and Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom; Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, United Kingdom; Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Alexander Hammers
- School of Biomedical Engineering and Imaging Sciences, Kings College, London, United Kingdom; Kings College London & Guys and St Thomas' PET Centre at St. Thomas' Hospital, United Kingdom
| | - Jane de Tisi
- UK National Institute for Health Research University College London Hospitals Biomedical Research Centre, and Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Matthias J Koepp
- UK National Institute for Health Research University College London Hospitals Biomedical Research Centre, and Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom; Epilepsy Society MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, Buckinghamshire, United Kingdom
| | - John S Duncan
- UK National Institute for Health Research University College London Hospitals Biomedical Research Centre, and Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom; Epilepsy Society MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, Buckinghamshire, United Kingdom.
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van Vliet EA, Immonen R, Prager O, Friedman A, Bankstahl JP, Wright DK, O'Brien TJ, Potschka H, Gröhn O, Harris NG. A companion to the preclinical common data elements and case report forms for in vivo rodent neuroimaging: A report of the TASK3-WG3 Neuroimaging Working Group of the ILAE/AES Joint Translational Task Force. Epilepsia Open 2022. [PMID: 35962745 DOI: 10.1002/epi4.12643] [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: 12/12/2021] [Accepted: 02/01/2022] [Indexed: 11/10/2022] Open
Abstract
The International League Against Epilepsy/American Epilepsy Society (ILAE/AES) Joint Translational Task Force established the TASK3 working groups to create common data elements (CDEs) for various aspects of preclinical epilepsy research studies, which could help improve the standardization of experimental designs. In this article, we discuss CDEs for neuroimaging data that are collected in rodent models of epilepsy, with a focus on adult rats and mice. We provide detailed CDE tables and case report forms (CRFs), and with this companion manuscript, we discuss the methodologies for several imaging modalities and the parameters that can be collected.
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Affiliation(s)
- Erwin A van Vliet
- Center for Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam UMC Location University of Amsterdam, Department of (Neuro)Pathology, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Riikka Immonen
- A.I. Virtanen Institute, University of Eastern Finland, Kuopio, Finland
| | - Ofer Prager
- Departments of Physiology and Cell Biology, Cognitive and Brain Sciences, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Alon Friedman
- Departments of Physiology and Cell Biology, Cognitive and Brain Sciences, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- Department of Medical Neuroscience and Brain Repair Center, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Jens P Bankstahl
- Department of Nuclear Medicine, Hannover Medical School, Hannover, Germany
| | - David K Wright
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Terence J O'Brien
- The Royal Melbourne Hospital, The University of Melbourne, The Alfred Hospital, Monash University, Melbourne, Victoria, Australia
| | - Heidrun Potschka
- Institute of Pharmacology, Toxicology, and Pharmacy, Ludwig-Maximilians-University, Munich, Germany
| | - Olli Gröhn
- A.I. Virtanen Institute, University of Eastern Finland, Kuopio, Finland
| | - Neil G Harris
- Department of Neurosurgery UCLA, UCLA Brain Injury Research Center, Los Angeles, California, USA
- Intellectual and Developmental Disabilities Research Center, UCLA, Los Angeles, California, USA
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Pemberton HG, Collij LE, Heeman F, Bollack A, Shekari M, Salvadó G, Alves IL, Garcia DV, Battle M, Buckley C, Stephens AW, Bullich S, Garibotto V, Barkhof F, Gispert JD, Farrar G. Quantification of amyloid PET for future clinical use: a state-of-the-art review. Eur J Nucl Med Mol Imaging 2022; 49:3508-3528. [PMID: 35389071 PMCID: PMC9308604 DOI: 10.1007/s00259-022-05784-y] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 03/25/2022] [Indexed: 12/15/2022]
Abstract
Amyloid-β (Aβ) pathology is one of the earliest detectable brain changes in Alzheimer's disease (AD) pathogenesis. The overall load and spatial distribution of brain Aβ can be determined in vivo using positron emission tomography (PET), for which three fluorine-18 labelled radiotracers have been approved for clinical use. In clinical practice, trained readers will categorise scans as either Aβ positive or negative, based on visual inspection. Diagnostic decisions are often based on these reads and patient selection for clinical trials is increasingly guided by amyloid status. However, tracer deposition in the grey matter as a function of amyloid load is an inherently continuous process, which is not sufficiently appreciated through binary cut-offs alone. State-of-the-art methods for amyloid PET quantification can generate tracer-independent measures of Aβ burden. Recent research has shown the ability of these quantitative measures to highlight pathological changes at the earliest stages of the AD continuum and generate more sensitive thresholds, as well as improving diagnostic confidence around established binary cut-offs. With the recent FDA approval of aducanumab and more candidate drugs on the horizon, early identification of amyloid burden using quantitative measures is critical for enrolling appropriate subjects to help establish the optimal window for therapeutic intervention and secondary prevention. In addition, quantitative amyloid measurements are used for treatment response monitoring in clinical trials. In clinical settings, large multi-centre studies have shown that amyloid PET results change both diagnosis and patient management and that quantification can accurately predict rates of cognitive decline. Whether these changes in management reflect an improvement in clinical outcomes is yet to be determined and further validation work is required to establish the utility of quantification for supporting treatment endpoint decisions. In this state-of-the-art review, several tools and measures available for amyloid PET quantification are summarised and discussed. Use of these methods is growing both clinically and in the research domain. Concurrently, there is a duty of care to the wider dementia community to increase visibility and understanding of these methods.
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Affiliation(s)
- Hugh G Pemberton
- GE Healthcare, Amersham, UK.
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.
- UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Fiona Heeman
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ariane Bollack
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
| | - Mahnaz Shekari
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Gemma Salvadó
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Brain Research Center, Amsterdam, The Netherlands
| | - David Vallez Garcia
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mark Battle
- GE Healthcare, Amersham, UK
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | | | | | | | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, University Hospitals of Geneva, Geneva, Switzerland
- NIMTLab, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Frederik Barkhof
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, Madrid, Spain
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15
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Leifeld J, Förster E, Reiss G, Hamad MIK. Considering the Role of Extracellular Matrix Molecules, in Particular Reelin, in Granule Cell Dispersion Related to Temporal Lobe Epilepsy. Front Cell Dev Biol 2022; 10:917575. [PMID: 35733853 PMCID: PMC9207388 DOI: 10.3389/fcell.2022.917575] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 05/23/2022] [Indexed: 11/13/2022] Open
Abstract
The extracellular matrix (ECM) of the nervous system can be considered as a dynamically adaptable compartment between neuronal cells, in particular neurons and glial cells, that participates in physiological functions of the nervous system. It is mainly composed of carbohydrates and proteins that are secreted by the different kinds of cell types found in the nervous system, in particular neurons and glial cells, but also other cell types, such as pericytes of capillaries, ependymocytes and meningeal cells. ECM molecules participate in developmental processes, synaptic plasticity, neurodegeneration and regenerative processes. As an example, the ECM of the hippocampal formation is involved in degenerative and adaptive processes related to epilepsy. The role of various components of the ECM has been explored extensively. In particular, the ECM protein reelin, well known for orchestrating the formation of neuronal layer formation in the cerebral cortex, is also considered as a player involved in the occurrence of postnatal granule cell dispersion (GCD), a morphologically peculiar feature frequently observed in hippocampal tissue from epileptic patients. Possible causes and consequences of GCD have been studied in various in vivo and in vitro models. The present review discusses different interpretations of GCD and different views on the role of ECM protein reelin in the formation of this morphological peculiarity.
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Affiliation(s)
- Jennifer Leifeld
- Department of Neuroanatomy and Molecular Brain Research, Medical Faculty, Ruhr University Bochum, Bochum, Germany
- Department of Biochemistry I—Receptor Biochemistry, Faculty of Chemistry and Biochemistry, Ruhr University Bochum, Bochum, Germany
- *Correspondence: Jennifer Leifeld, ; Eckart Förster,
| | - Eckart Förster
- Department of Neuroanatomy and Molecular Brain Research, Medical Faculty, Ruhr University Bochum, Bochum, Germany
- *Correspondence: Jennifer Leifeld, ; Eckart Förster,
| | - Gebhard Reiss
- Institute for Anatomy and Clinical Morphology, School of Medicine, Faculty of Health, Witten/ Herdecke University, Witten, Germany
| | - Mohammad I. K. Hamad
- Institute for Anatomy and Clinical Morphology, School of Medicine, Faculty of Health, Witten/ Herdecke University, Witten, Germany
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16
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Del Signore F, Vignoli M, Della Salda L, Tamburro R, Paolini A, Cerasoli I, Chincarini M, Rossi E, Ferri N, Romanucci M, Falerno I, de Pasquale F. A Magnetic Resonance-Relaxometry-Based Technique to Identify Blood Products in Brain Parenchyma: An Experimental Study on a Rabbit Model. Front Vet Sci 2022; 9:802272. [PMID: 35711807 PMCID: PMC9195168 DOI: 10.3389/fvets.2022.802272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 04/11/2022] [Indexed: 11/13/2022] Open
Abstract
Magnetic resonance relaxometry is a quantitative technique that estimates T1/T2 tissue relaxation times. This has been proven to increase MRI diagnostic accuracy of brain disorders in human medicine. However, literature in the veterinary field is scarce. In this work, a T1 and T2-based relaxometry approach has been developed. The aim is to investigate its performance in characterizing subtle brain lesions obtained with autologous blood injections in rabbits. This study was performed with a low-field scanner, typically present in veterinary clinics. The approach consisted of a semi-automatic hierarchical classification of different regions, selected from a T2 map. The classification was driven according to the relaxometry properties extracted from a set of regions selected by the radiologist to compare the suspected lesion with the healthy parenchyma. Histopathological analyses were performed to estimate the performance of the proposed classifier through receiver operating characteristic curve analyses. The classifier resulted in moderate accuracy in terms of lesion characterization.
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Affiliation(s)
- Francesca Del Signore
- Veterinary Faculty, University of Teramo, Teramo, Italy
- *Correspondence: Francesca Del Signore
| | | | | | | | | | | | | | - Emanuela Rossi
- Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise Giuseppe Caporale, Teramo, Italy
| | - Nicola Ferri
- Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise Giuseppe Caporale, Teramo, Italy
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17
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Pelliccia V, Deleo F, Gozzo F, Giovannelli G, Mai R, Cossu M, Tassi L. Early epilepsy surgery for non drug-resistant patients. Epilepsy Behav Rep 2022; 19:100542. [PMID: 35573058 PMCID: PMC9096667 DOI: 10.1016/j.ebr.2022.100542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 04/12/2022] [Accepted: 04/13/2022] [Indexed: 11/23/2022] Open
Abstract
Absence of drug-resistance is predictor of better post-surgical outcome. Post-surgical outcome in non drug-resistant patients is favourable irrespective of both the localization of surgery and the histological diagnosis. Non drug-resistant patients who underwent epilepsy surgery are more likely to successfully discontinue ASMs.
The aim of epilepsy treatment is to achieve seizure freedom. Surgery is often still considered a late option when pharmacological treatments have failed and epilepsy has become drug-resistant. We analyse the clinical features and surgical outcome in patients who underwent surgery without experiencing drug-resistance comparing with those observed in patients who became drug-resistant. Two-hundred and fifty patients with symptomatic focal epilepsy (12.1% of patients who underwent surgery at the “Claudio Munari” Epilepsy Surgery Center) were selected on the basis of initial period of seizure freedom and followed-up for at least 12 months. Patients were divided into two groups: those who underwent surgery during the initial period of seizure freedom (n = 74), and those who underwent surgery after an initial seizure-free period followed by drug-resistance (n = 176). Outcomes were significantly better in non-drug-resistant patients (p < 0.001), all of whom had Engel class Ia or Ic. In the drug-resistant group, 136 patients (77.3%) had class Ia or Ic. The median post-operative follow-up was respectively 75.0 and 84.0 months. Epilepsy surgery is a successful treatment, especially for non-drug-resistant patients with focal epilepsy with structural etiology. The timing of surgery affects the outcomes, and “early” surgery should be preferred to prevent likely drug-resistance and to improve prognosis.
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Affiliation(s)
- Veronica Pelliccia
- ”Claudio Munari” Epilepsy Surgery Center, Niguarda Hospital, Piazza Ospedale Maggiore 3, 20162 Milano, Italy
| | - Francesco Deleo
- Clinical Epileptology and Experimental Neurophysiology Unit, Fondazione IRCCS, Istituto Neurologico “C. Besta”, Via Celoria 11, 20133 Milano, Italy
| | - Francesca Gozzo
- ”Claudio Munari” Epilepsy Surgery Center, Niguarda Hospital, Piazza Ospedale Maggiore 3, 20162 Milano, Italy
| | - Ginevra Giovannelli
- ”Claudio Munari” Epilepsy Surgery Center, Niguarda Hospital, Piazza Ospedale Maggiore 3, 20162 Milano, Italy
- Neurology and Stroke Unit, Careggi Hospital, Florence, Italy
| | - Roberto Mai
- ”Claudio Munari” Epilepsy Surgery Center, Niguarda Hospital, Piazza Ospedale Maggiore 3, 20162 Milano, Italy
| | - Massimo Cossu
- ”Claudio Munari” Epilepsy Surgery Center, Niguarda Hospital, Piazza Ospedale Maggiore 3, 20162 Milano, Italy
| | - Laura Tassi
- ”Claudio Munari” Epilepsy Surgery Center, Niguarda Hospital, Piazza Ospedale Maggiore 3, 20162 Milano, Italy
- Corresponding author at: “Claudio Munari” Epilepsy Surgery Centre, Niguarda Hospital, Piazza Ospedale Maggiore 3, 20162 Milano, Italy.
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18
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Andrews AE, Perumpalath N, Puthiyakam J, Mekkattukunnel A. Hippocampal magnetic resonance imaging in focal onset seizure with impaired awareness—descriptive study from tertiary care centre in southern part of India. THE EGYPTIAN JOURNAL OF NEUROLOGY, PSYCHIATRY AND NEUROSURGERY 2021. [DOI: 10.1186/s41983-021-00347-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Temporal lobe epilepsy is the most common type of focal onset seizure. Focal onset seizure with impaired awareness, previously known as complex partial seizure (CPS), account for 18–40% of all seizure types. Hippocampal sclerosis (HS) is the most common cause of temporal lobe epilepsy, which produces focal onset seizure with impaired awareness. It may be detected in MRI visually, but bilateral abnormalities are better identified using volumetric analysis.
We aimed to compare hippocampal volume in patients with focal onset seizure with impaired awareness visually and quantitatively.
Methodology
This cross-sectional study includes clinically diagnosed cases of 56 focal onset seizure with impaired awareness undergoing MRI at a tertiary teaching hospital in the southern part of India for a duration of 18 months from February 2018 to August 2019.
Results
Out of 53 patients studied using 1.5 T MRI brain with seizure protocols, hippocampal atrophy was identified visually in 13 (24.5%) on the right side, 9 (16.98%) on the left side, and in 6 (11.32%) bilaterally. However, with volumetry, hippocampal atrophy (not taking T2 signal change) was detected in 15 (28.30%) on the right side, 10 (18.86%) on the left side, and in 7 (13.20%) bilaterally. Hippocampal volumes between ipsilateral and contralateral seizure focus were found to have no significant difference (p-0.84).
Conclusions
Though visual analysis is efficient in the diagnosis of pathology, MR volumetry may be used as an expert eye in cases of subtle volume loss.
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19
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Using quantitative MRI to study brain responses to immune challenge with interferon-α. Brain Behav Immun Health 2021; 18:100376. [PMID: 34746879 PMCID: PMC8554453 DOI: 10.1016/j.bbih.2021.100376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 10/18/2021] [Indexed: 11/20/2022] Open
Abstract
Inflammatory processes in the Central Nervous System (CNS) have been proposed to mediate the association between peripheral inflammation and the development of psychiatric disorders, but we currently lack sensitive measures of CNS inflammation for human studies in vivo. Here we used quantitative MRI (qMRI) to explore the in vivo central response to a peripheral immune challenge in healthy humans, and we assessed whether changes in quantitative relaxometry MRI parameters were associated with changes in peripheral inflammation. Quantitative relaxation times (T1 & T2) and Proton Density (PD) were measured in n = 6 healthy males (mean age = 30.5 ± 6.8 years) in two MRI assessments collected before and 24 hours after a subcutaneous injection of the proinflammatory cytokine and immune activator, interferon-alpha (IFN-α). Serum levels of immune markers and markers of blood-brain barrier integrity (S100B) were also measured before and after the injection. Region of interest and histogram-based analyses (optimized for the small sample size) showed a statistically significant increase of both T1 (t(5) = 3.78, p = 0.013) and PD (t(5) = 2.91, p = 0.033) parameters in the bilateral hippocampus after IFN-α administration. T1 peak values in bilateral hippocampus were positively correlated with serum Tumour Necrosis Factor-alpha levels at 24 h after the injection, when this cytokine peaked (Spearman's rho = 0.67, p = 0.018) and negatively correlated with S100B levels (Spearman's rho = -0.826, p = 0.001). Our data suggest that peripheral administration of IFN-α produces acute changes in brain qMRI which might indicate a brain immune response. This is supported by the association of such changes with low-grade peripheral inflammation.
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20
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Sone D, Ahmad M, Thompson PJ, Baxendale S, Vos SB, Xiao F, de Tisi J, McEvoy AW, Miserocchi A, Duncan JS, Koepp MJ, Galovic M. Optimal Surgical Extent for Memory and Seizure Outcome in Temporal Lobe Epilepsy. Ann Neurol 2021; 91:131-144. [PMID: 34741484 PMCID: PMC8916104 DOI: 10.1002/ana.26266] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 10/21/2021] [Accepted: 10/31/2021] [Indexed: 12/28/2022]
Abstract
OBJECTIVE Postoperative memory decline is an important consequence of anterior temporal lobe resection (ATLR) for temporal lobe epilepsy (TLE), and the extent of resection may be a modifiable factor. This study aimed to define optimal resection margins for cognitive outcome while maintaining a high rate of postoperative seizure freedom. METHODS This cohort study evaluated the resection extent on postoperative structural MRI using automated voxel-based methods and manual measurements in 142 consecutive patients with unilateral drug refractory TLE (74 left, 68 right TLE) who underwent standard ATLR. RESULTS Voxel-wise analyses revealed that postsurgical verbal memory decline correlated with resections of the posterior hippocampus and inferior temporal gyrus, whereas larger resections of the fusiform gyrus were associated with worsening of visual memory in left TLE. Limiting the posterior extent of left hippocampal resection to 55% reduced the odds of significant postoperative verbal memory decline by a factor of 8.1 (95% CI 1.5-44.4, p = 0.02). Seizure freedom was not related to posterior resection extent, but to the piriform cortex removal after left ATLR. In right TLE, variability of the posterior extent of resection was not associated with verbal and visual memory decline or seizures after surgery. INTERPRETATION The extent of surgical resection is an independent and modifiable risk factor for cognitive decline and seizures after left ATLR. Adapting the posterior extent of left ATLR might optimize postoperative outcome, with reduced risk of memory impairment while maintaining comparable seizure-freedom rates. The current, more lenient, approach might be appropriate for right ATLR. ANN NEUROL 2021.
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Affiliation(s)
- Daichi Sone
- Department of Clinical and Experimental Epilepsy, University College London Queen Square Institute of Neurology, London, UK.,Magnetic Resonance Imaging Unit, Epilepsy Society, Chalfont St Peter, UK.,Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan
| | - Maria Ahmad
- Department of Clinical and Experimental Epilepsy, University College London Queen Square Institute of Neurology, London, UK
| | - Pamela J Thompson
- Department of Clinical and Experimental Epilepsy, University College London Queen Square Institute of Neurology, London, UK
| | - Sallie Baxendale
- Department of Clinical and Experimental Epilepsy, University College London Queen Square Institute of Neurology, London, UK
| | - Sjoerd B Vos
- Department of Clinical and Experimental Epilepsy, University College London Queen Square Institute of Neurology, London, UK.,Magnetic Resonance Imaging Unit, Epilepsy Society, Chalfont St Peter, UK.,Centre for Medical Image Computing (CMIC), University College London, London, UK.,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Fenglai Xiao
- Department of Clinical and Experimental Epilepsy, University College London Queen Square Institute of Neurology, London, UK.,Magnetic Resonance Imaging Unit, Epilepsy Society, Chalfont St Peter, UK
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, University College London Queen Square Institute of Neurology, London, UK
| | - Andrew W McEvoy
- Department of Clinical and Experimental Epilepsy, University College London Queen Square Institute of Neurology, London, UK
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, University College London Queen Square Institute of Neurology, London, UK
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, University College London Queen Square Institute of Neurology, London, UK.,Magnetic Resonance Imaging Unit, Epilepsy Society, Chalfont St Peter, UK
| | - Matthias J Koepp
- Department of Clinical and Experimental Epilepsy, University College London Queen Square Institute of Neurology, London, UK.,Magnetic Resonance Imaging Unit, Epilepsy Society, Chalfont St Peter, UK
| | - Marian Galovic
- Department of Clinical and Experimental Epilepsy, University College London Queen Square Institute of Neurology, London, UK.,Magnetic Resonance Imaging Unit, Epilepsy Society, Chalfont St Peter, UK.,Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich, Zurich, Switzerland
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21
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Caldairou B, Foit NA, Mutti C, Fadaie F, Gill R, Lee HM, Demerath T, Urbach H, Schulze-Bonhage A, Bernasconi A, Bernasconi N. MRI-Based Machine Learning Prediction Framework to Lateralize Hippocampal Sclerosis in Patients With Temporal Lobe Epilepsy. Neurology 2021; 97:e1583-e1593. [PMID: 34475125 DOI: 10.1212/wnl.0000000000012699] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 07/30/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND AND OBJECTIVES MRI fails to reveal hippocampal pathology in 30% to 50% of temporal lobe epilepsy (TLE) surgical candidates. To address this clinical challenge, we developed an automated MRI-based classifier that lateralizes the side of covert hippocampal pathology in TLE. METHODS We trained a surface-based linear discriminant classifier that uses T1-weighted (morphology) and T2-weighted and fluid-attenuated inversion recovery (FLAIR)/T1 (intensity) features. The classifier was trained on 60 patients with TLE (mean age 35.6 years, 58% female) with histologically verified hippocampal sclerosis (HS). Images were deemed to be MRI negative in 42% of cases on the basis of neuroradiologic reading (40% based on hippocampal volumetry). The predictive model automatically labeled patients as having left or right TLE. Lateralization accuracy was compared to electroclinical data, including side of surgery. Accuracy of the classifier was further assessed in 2 independent TLE cohorts with similar demographics and electroclinical characteristics (n = 57, 58% MRI negative). RESULTS The overall lateralization accuracy was 93% (95% confidence interval 92%-94%), regardless of HS visibility. In MRI-negative TLE, the combination of T2 and FLAIR/T1 intensities provided the highest accuracy in both the training (84%, area under the curve [AUC] 0.95 ± 0.02) and validation (cohort 1 90%, AUC 0.99; cohort 2 76%, AUC 0.94) cohorts. DISCUSSION This prediction model for TLE lateralization operates on readily available conventional MRI contrasts and offers gain in accuracy over visual radiologic assessment. The combined contribution of decreased T1- and increased T2-weighted intensities makes the synthetic FLAIR/T1 contrast particularly effective in MRI-negative HS, setting the basis for broad clinical translation. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that in people with TLE and MRI-negative HS, an automated MRI-based classifier accurately determines the side of pathology.
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Affiliation(s)
- Benoit Caldairou
- From the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany
| | - Niels A Foit
- From the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany
| | - Carlotta Mutti
- From the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany
| | - Fatemeh Fadaie
- From the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany
| | - Ravnoor Gill
- From the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany
| | - Hyo Min Lee
- From the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany
| | - Theo Demerath
- From the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany
| | - Horst Urbach
- From the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany
| | - Andreas Schulze-Bonhage
- From the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany
| | - Andrea Bernasconi
- From the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany.
| | - Neda Bernasconi
- From the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany.
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22
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Sone D. Making the Invisible Visible: Advanced Neuroimaging Techniques in Focal Epilepsy. Front Neurosci 2021; 15:699176. [PMID: 34385902 PMCID: PMC8353251 DOI: 10.3389/fnins.2021.699176] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 06/28/2021] [Indexed: 12/30/2022] Open
Abstract
It has been a clinically important, long-standing challenge to accurately localize epileptogenic focus in drug-resistant focal epilepsy because more intensive intervention to the detected focus, including resection neurosurgery, can provide significant seizure reduction. In addition to neurophysiological examinations, neuroimaging plays a crucial role in the detection of focus by providing morphological and neuroanatomical information. On the other hand, epileptogenic lesions in the brain may sometimes show only subtle or even invisible abnormalities on conventional MRI sequences, and thus, efforts have been made for better visualization and improved detection of the focus lesions. Recent advance in neuroimaging has been attracting attention because of the potentials to better visualize the epileptogenic lesions as well as provide novel information about the pathophysiology of epilepsy. While the progress of newer neuroimaging techniques, including the non-Gaussian diffusion model and arterial spin labeling, could non-invasively detect decreased neurite parameters or hypoperfusion within the focus lesions, advances in analytic technology may also provide usefulness for both focus detection and understanding of epilepsy. There has been an increasing number of clinical and experimental applications of machine learning and network analysis in the field of epilepsy. This review article will shed light on recent advances in neuroimaging for focal epilepsy, including both technical progress of images and newer analytical methodologies and discuss about the potential usefulness in clinical practice.
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Affiliation(s)
- Daichi Sone
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan.,Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, United Kingdom
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23
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Princich JP, Donnelly-Kehoe PA, Deleglise A, Vallejo-Azar MN, Pascariello GO, Seoane P, Veron Do Santos JG, Collavini S, Nasimbera AH, Kochen S. Diagnostic Performance of MRI Volumetry in Epilepsy Patients With Hippocampal Sclerosis Supported Through a Random Forest Automatic Classification Algorithm. Front Neurol 2021; 12:613967. [PMID: 33692740 PMCID: PMC7937810 DOI: 10.3389/fneur.2021.613967] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 01/18/2021] [Indexed: 01/07/2023] Open
Abstract
Introduction: Several methods offer free volumetry services for MR data that adequately quantify volume differences in the hippocampus and its subregions. These methods are frequently used to assist in clinical diagnosis of suspected hippocampal sclerosis in temporal lobe epilepsy. A strong association between severity of histopathological anomalies and hippocampal volumes was reported using MR volumetry with a higher diagnostic yield than visual examination alone. Interpretation of volumetry results is challenging due to inherent methodological differences and to the reported variability of hippocampal volume. Furthermore, normal morphometric differences are recognized in diverse populations that may need consideration. To address this concern, we highlighted procedural discrepancies including atlas definition and computation of total intracranial volume that may impact volumetry results. We aimed to quantify diagnostic performance and to propose reference values for hippocampal volume from two well-established techniques: FreeSurfer v.06 and volBrain-HIPS. Methods: Volumetry measures were calculated using clinical T1 MRI from a local population of 61 healthy controls and 57 epilepsy patients with confirmed unilateral hippocampal sclerosis. We further validated the results by a state-of-the-art machine learning classification algorithm (Random Forest) computing accuracy and feature relevance to distinguish between patients and controls. This validation process was performed using the FreeSurfer dataset alone, considering morphometric values not only from the hippocampus but also from additional non-hippocampal brain regions that could be potentially relevant for group classification. Mean reference values and 95% confidence intervals were calculated for left and right hippocampi along with hippocampal asymmetry degree to test diagnostic accuracy. Results: Both methods showed excellent classification performance (AUC:> 0.914) with noticeable differences in absolute (cm3) and normalized volumes. Hippocampal asymmetry was the most accurate discriminator from all estimates (AUC:1~0.97). Similar results were achieved in the validation test with an automatic classifier (AUC:>0.960), disclosing hippocampal structures as the most relevant features for group differentiation among other brain regions. Conclusion: We calculated reference volumetry values from two commonly used methods to accurately identify patients with temporal epilepsy and hippocampal sclerosis. Validation with an automatic classifier confirmed the principal role of the hippocampus and its subregions for diagnosis.
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Affiliation(s)
- Juan Pablo Princich
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina.,Hospital de Pediatría J.P Garrahan, Departamento de Neuroimágenes, Buenos Aires, Argentina
| | - Patricio Andres Donnelly-Kehoe
- Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas (CIFASIS) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Grupo de Procesamiento de Señales Multimedia - División Neuroimágenes, Universidad Nacional de Rosario, Rosario, Argentina
| | - Alvaro Deleglise
- Instituto de Fisiología y Biofísica B. Houssay (IFIBIO), Consejo Nacional de Investigaciones Científicas y Técnicas, Departamento de Fisiología y Biofísica, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Mariana Nahir Vallejo-Azar
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina
| | - Guido Orlando Pascariello
- Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas (CIFASIS) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Grupo de Procesamiento de Señales Multimedia - División Neuroimágenes, Universidad Nacional de Rosario, Rosario, Argentina
| | - Pablo Seoane
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina.,Hospital J.M Ramos Mejía, Centro de Epilepsia, Buenos Aires, Argentina
| | - Jose Gabriel Veron Do Santos
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina
| | - Santiago Collavini
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina.,Instituto de investigación en Electrónica, Control y Procesamiento de Señales (LEICI), Universidad Nacional de La Plata-Consejo Nacional de Investigaciones Científicas y Técnicas, La Plata, Argentina.,Instituto de Ingeniería y Agronomía, Universidad Nacional Arturo Jauretche, Florencio Varela, Argentina
| | - Alejandro Hugo Nasimbera
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina.,Hospital J.M Ramos Mejía, Centro de Epilepsia, Buenos Aires, Argentina
| | - Silvia Kochen
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina
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24
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Goodkin O, Pemberton HG, Vos SB, Prados F, Das RK, Moggridge J, De Blasi B, Bartlett P, Williams E, Campion T, Haider L, Pearce K, Bargallό N, Sanchez E, Bisdas S, White M, Ourselin S, Winston GP, Duncan JS, Cardoso J, Thornton JS, Yousry TA, Barkhof F. Clinical evaluation of automated quantitative MRI reports for assessment of hippocampal sclerosis. Eur Radiol 2020; 31:34-44. [PMID: 32749588 PMCID: PMC7755617 DOI: 10.1007/s00330-020-07075-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 05/07/2020] [Accepted: 07/15/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVES Hippocampal sclerosis (HS) is a common cause of temporal lobe epilepsy. Neuroradiological practice relies on visual assessment, but quantification of HS imaging biomarkers-hippocampal volume loss and T2 elevation-could improve detection. We tested whether quantitative measures, contextualised with normative data, improve rater accuracy and confidence. METHODS Quantitative reports (QReports) were generated for 43 individuals with epilepsy (mean age ± SD 40.0 ± 14.8 years, 22 men; 15 histologically unilateral HS; 5 bilateral; 23 MR-negative). Normative data was generated from 111 healthy individuals (age 40.0 ± 12.8 years, 52 men). Nine raters with different experience (neuroradiologists, trainees, and image analysts) assessed subjects' imaging with and without QReports. Raters assigned imaging normal, right, left, or bilateral HS. Confidence was rated on a 5-point scale. RESULTS Correct designation (normal/abnormal) was high and showed further trend-level improvement with QReports, from 87.5 to 92.5% (p = 0.07, effect size d = 0.69). Largest magnitude improvement (84.5 to 93.8%) was for image analysts (d = 0.87). For bilateral HS, QReports significantly improved overall accuracy, from 74.4 to 91.1% (p = 0.042, d = 0.7). Agreement with the correct diagnosis (kappa) tended to increase from 0.74 ('fair') to 0.86 ('excellent') with the report (p = 0.06, d = 0.81). Confidence increased when correctly assessing scans with the QReport (p < 0.001, η2p = 0.945). CONCLUSIONS QReports of HS imaging biomarkers can improve rater accuracy and confidence, particularly in challenging bilateral cases. Improvements were seen across all raters, with large effect sizes, greatest for image analysts. These findings may have positive implications for clinical radiology services and justify further validation in larger groups. KEY POINTS • Quantification of imaging biomarkers for hippocampal sclerosis-volume loss and raised T2 signal-could improve clinical radiological detection in challenging cases. • Quantitative reports for individual patients, contextualised with normative reference data, improved diagnostic accuracy and confidence in a group of nine raters, in particular for bilateral HS cases. • We present a pre-use clinical validation of an automated imaging assessment tool to assist clinical radiology reporting of hippocampal sclerosis, which improves detection accuracy.
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Affiliation(s)
- Olivia Goodkin
- Centre for Medical Image Computing (CMIC), University College London, London, UK. .,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Hugh G Pemberton
- Centre for Medical Image Computing (CMIC), University College London, London, UK.,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sjoerd B Vos
- Centre for Medical Image Computing (CMIC), University College London, London, UK.,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Epilepsy Society MRI Unit, Chalfont St Peter, UK
| | - Ferran Prados
- Centre for Medical Image Computing (CMIC), University College London, London, UK.,Universitat Oberta de Catalunya, Barcelona, Spain
| | - Ravi K Das
- Clinical, Educational and Health Psychology, University College London, London, UK
| | - James Moggridge
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Bianca De Blasi
- Department of Medical Physics and Bioengineering, University College London, London, UK
| | - Philippa Bartlett
- Epilepsy Society MRI Unit, Chalfont St Peter, UK.,Department of Clinical and Experimental Epilepsy, University College London, London, UK
| | - Elaine Williams
- Wellcome Trust Centre for Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Thomas Campion
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Lukas Haider
- Department of Biomedical Imaging and Image Guided Therapy, Medical University of Vienna, Vienna, Austria.,NMR Research Unit, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Kirsten Pearce
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Nuria Bargallό
- Radiology Department, Hospital Clínic de Barcelona and Magnetic Resonance Image Core Facility, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain
| | - Esther Sanchez
- Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Sotirios Bisdas
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Mark White
- Digital Services, University College London Hospital, London, UK
| | - Sebastien Ourselin
- Department of Medical Physics and Bioengineering, University College London, London, UK.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Gavin P Winston
- Epilepsy Society MRI Unit, Chalfont St Peter, UK.,Department of Clinical and Experimental Epilepsy, University College London, London, UK.,Department of Medicine, Division of Neurology, Queen's University, Kingston, Ontario, Canada
| | - John S Duncan
- Epilepsy Society MRI Unit, Chalfont St Peter, UK.,Department of Clinical and Experimental Epilepsy, University College London, London, UK
| | - Jorge Cardoso
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - John S Thornton
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Tarek A Yousry
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Frederik Barkhof
- Centre for Medical Image Computing (CMIC), University College London, London, UK.,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK.,Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
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25
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Ahmad R, Maiworm M, Nöth U, Seiler A, Hattingen E, Steinmetz H, Rosenow F, Deichmann R, Wagner M, Gracien RM. Cortical Changes in Epilepsy Patients With Focal Cortical Dysplasia: New Insights With T 2 Mapping. J Magn Reson Imaging 2020; 52:1783-1789. [PMID: 32383241 DOI: 10.1002/jmri.27184] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 04/17/2020] [Accepted: 04/17/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND In epilepsy patients with focal cortical dysplasia (FCD) as the epileptogenic focus, global cortical signal changes are generally not visible on conventional MRI. However, epileptic seizures or antiepileptic medication might affect normal-appearing cerebral cortex and lead to subtle damage. PURPOSE To investigate cortical properties outside FCD regions with T2 -relaxometry. STUDY TYPE Prospective study. SUBJECTS Sixteen patients with epilepsy and FCD and 16 age-/sex-matched healthy controls. FIELD STRENGTH/SEQUENCE 3T, fast spin-echo T2 -mapping, fluid-attenuated inversion recovery (FLAIR), and synthetic T1 -weighted magnetization-prepared rapid acquisition of gradient-echoes (MP-RAGE) datasets derived from T1 -maps. ASSESSMENT Reconstruction of the white matter and cortical surfaces based on MP-RAGE structural images was performed to extract cortical T2 values, excluding lesion areas. Three independent raters confirmed that morphological cortical/juxtacortical changes in the conventional FLAIR datasets outside the FCD areas were definitely absent for all patients. Averaged global cortical T2 values were compared between groups. Furthermore, group comparisons of regional cortical T2 values were performed using a surface-based approach. Tests for correlations with clinical parameters were carried out. STATISTICAL TESTS General linear model analysis, permutation simulations, paired and unpaired t-tests, and Pearson correlations. RESULTS Cortical T2 values were increased outside FCD regions in patients (83.4 ± 2.1 msec, control group 81.4 ± 2.1 msec, P = 0.01). T2 increases were widespread, affecting mainly frontal, but also parietal and temporal regions of both hemispheres. Significant correlations were not observed (P ≥ 0.55) between cortical T2 values in the patient group and the number of seizures in the last 3 months or the number of anticonvulsive drugs in the medical history. DATA CONCLUSION Widespread increases in cortical T2 in FCD-associated epilepsy patients were found, suggesting that structural epilepsy in patients with FCD is not only a symptom of a focal cerebral lesion, but also leads to global cortical damage not visible on conventional MRI. EVIDENCE LEVEL 21 TECHNICAL EFFICACY STAGE: 3 J. MAGN. RESON. IMAGING 2020;52:1783-1789.
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Affiliation(s)
- Rida Ahmad
- Department of Neurology, Goethe University, Frankfurt/Main, Germany.,Department of Neuroradiology, Goethe University, Frankfurt/Main, Germany.,Brain Imaging Center, Goethe University, Frankfurt/Main, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Germany
| | - Michelle Maiworm
- Department of Neurology, Goethe University, Frankfurt/Main, Germany.,Department of Neuroradiology, Goethe University, Frankfurt/Main, Germany.,Brain Imaging Center, Goethe University, Frankfurt/Main, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Germany
| | - Ulrike Nöth
- Brain Imaging Center, Goethe University, Frankfurt/Main, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Germany
| | - Alexander Seiler
- Department of Neurology, Goethe University, Frankfurt/Main, Germany.,Brain Imaging Center, Goethe University, Frankfurt/Main, Germany
| | - Elke Hattingen
- Department of Neuroradiology, Goethe University, Frankfurt/Main, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Germany
| | - Helmuth Steinmetz
- Department of Neurology, Goethe University, Frankfurt/Main, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Germany
| | - Felix Rosenow
- Department of Neurology, Goethe University, Frankfurt/Main, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Germany.,Epilepsy Center Frankfurt Rhine-Main, Center of Neurology and Neurosurgery, Goethe University, Frankfurt/Main, Germany
| | - Ralf Deichmann
- Brain Imaging Center, Goethe University, Frankfurt/Main, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Germany
| | - Marlies Wagner
- Department of Neuroradiology, Goethe University, Frankfurt/Main, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Germany
| | - René-Maxime Gracien
- Department of Neurology, Goethe University, Frankfurt/Main, Germany.,Brain Imaging Center, Goethe University, Frankfurt/Main, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Germany
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26
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Vos SB, Winston GP, Goodkin O, Pemberton HG, Barkhof F, Prados F, Galovic M, Koepp M, Ourselin S, Cardoso MJ, Duncan JS. Hippocampal profiling: Localized magnetic resonance imaging volumetry and T2 relaxometry for hippocampal sclerosis. Epilepsia 2019; 61:297-309. [PMID: 31872873 PMCID: PMC7065164 DOI: 10.1111/epi.16416] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 12/02/2019] [Accepted: 12/02/2019] [Indexed: 12/13/2022]
Abstract
Objective Hippocampal sclerosis (HS) is the most common cause of drug‐resistant temporal lobe epilepsy, and its accurate detection is important to guide epilepsy surgery. Radiological features of HS include hippocampal volume loss and increased T2 signal, which can both be quantified to help improve detection. In this work, we extend these quantitative methods to generate cross‐sectional area and T2 profiles along the hippocampal long axis to improve the localization of hippocampal abnormalities. Methods T1‐weighted and T2 relaxometry data from 69 HS patients (32 left, 32 right, 5 bilateral) and 111 healthy controls were acquired on a 3‐T magnetic resonance imaging (MRI) scanner. Automated hippocampal segmentation and T2 relaxometry were performed and used to calculate whole‐hippocampal volumes and to estimate quantitative T2 (qT2) values. By generating a group template from the controls, and aligning this so that the hippocampal long axes were along the anterior‐posterior axis, we were able to calculate hippocampal cross‐sectional area and qT2 by a slicewise method to localize any volume loss or T2 hyperintensity. Individual patient profiles were compared with normative data generated from the healthy controls. Results Profiling of hippocampal volumetric and qT2 data could be performed automatically and reproducibly. HS patients commonly showed widespread decreases in volume and increases in T2 along the length of the affected hippocampus, and focal changes may also be identified. Patterns of atrophy and T2 increase in the left hippocampus were similar between left, right, and bilateral HS. These profiles have potential to distinguish between sclerosis affecting volume and qT2 in the whole or parts of the hippocampus, and may aid the radiological diagnosis in uncertain cases or cases with subtle or focal abnormalities where standard whole‐hippocampal measurements yield normal values. Significance Hippocampal profiling of volumetry and qT2 values can help spatially localize hippocampal MRI abnormalities and work toward improved sensitivity of subtle focal lesions.
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Affiliation(s)
- Sjoerd B Vos
- Centre for Medical Image Computing, University College London, London, UK.,Epilepsy Society MRI Unit, Chalfont St Peter, UK.,Department of Clinical and Experimental Epilepsy, University College London, London, UK.,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Gavin P Winston
- Epilepsy Society MRI Unit, Chalfont St Peter, UK.,Department of Clinical and Experimental Epilepsy, University College London, London, UK.,Division of Neurology, Department of Medicine, Queen's University, Kingston, Canada
| | - Olivia Goodkin
- Centre for Medical Image Computing, University College London, London, UK.,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Hugh G Pemberton
- Centre for Medical Image Computing, University College London, London, UK.,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Frederik Barkhof
- Centre for Medical Image Computing, University College London, London, UK.,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, National Health Service Foundation Trust, London, UK.,Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, Faculty of Brain Sciences, UCL Queen Square Institute of Neurology, University College London, London, UK.,Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands
| | - Ferran Prados
- Centre for Medical Image Computing, University College London, London, UK.,Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, Faculty of Brain Sciences, UCL Queen Square Institute of Neurology, University College London, London, UK.,eHealth Center, Open University of Catalonia, Barcelona, Spain
| | - Marian Galovic
- Epilepsy Society MRI Unit, Chalfont St Peter, UK.,Department of Clinical and Experimental Epilepsy, University College London, London, UK.,Department of Neurology, University Hospital Zurich, Zurich, Switzerland
| | - Matthias Koepp
- Epilepsy Society MRI Unit, Chalfont St Peter, UK.,Department of Clinical and Experimental Epilepsy, University College London, London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - M Jorge Cardoso
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - John S Duncan
- Epilepsy Society MRI Unit, Chalfont St Peter, UK.,Department of Clinical and Experimental Epilepsy, University College London, London, UK
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