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Bourdillon P, Ren L, Halgren M, Paulk AC, Salami P, Ulbert I, Fabó D, King JR, Sjoberg KM, Eskandar EN, Madsen JR, Halgren E, Cash SS. Differential cortical layer engagement during seizure initiation and spread in humans. Nat Commun 2024; 15:5153. [PMID: 38886376 PMCID: PMC11183216 DOI: 10.1038/s41467-024-48746-8] [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: 11/22/2022] [Accepted: 05/10/2024] [Indexed: 06/20/2024] Open
Abstract
Despite decades of research, we still do not understand how spontaneous human seizures start and spread - especially at the level of neuronal microcircuits. In this study, we used laminar arrays of micro-electrodes to simultaneously record the local field potentials and multi-unit neural activities across the six layers of the neocortex during focal seizures in humans. We found that, within the ictal onset zone, the discharges generated during a seizure consisted of current sinks and sources only within the infra-granular and granular layers. Outside of the seizure onset zone, ictal discharges reflected current flow in the supra-granular layers. Interestingly, these patterns of current flow evolved during the course of the seizure - especially outside the seizure onset zone where superficial sinks and sources extended into the deeper layers. Based on these observations, a framework describing cortical-cortical dynamics of seizures is proposed with implications for seizure localization, surgical targeting, and neuromodulation techniques to block the generation and propagation of seizures.
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Affiliation(s)
- Pierre Bourdillon
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
- Department of Neurosurgery, Hospital Foundation Adolphe de Rothschild, Paris, France.
- Integrative Neuroscience and Cognition Center, Paris Cité University, Paris, France.
| | - Liankun Ren
- Department of Neurology, Xuanwu Hospital, National Center for Neurological Disorders, Clinical Center for Epilepsy, Capital Medical University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Mila Halgren
- Brain and Cognitive Sciences Department and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Angelique C Paulk
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Pariya Salami
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - István Ulbert
- HUN-REN, Research Center for Natural Sciences, Institute of Cognitive Neuroscience and Psychology, Budapest, Hungary
- Faculty of Information Technology and Bionics, Péter Pázmány Catholic University, Budapest, Hungary
- Department of Neurosurgery and Neurointervention, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Dániel Fabó
- Department of Neurosurgery and Neurointervention, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Jean-Rémi King
- Laboratoire des Systèmes Perceptifs, Département d'études cognitives, École normale supérieure, PSL University, CNRS, Paris, France
| | - Kane M Sjoberg
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Harvard College, Cambridge, MA, 02138, USA
| | - Emad N Eskandar
- Department of Neurological Surgery, Albert Einstein College of Medicine - Montefiore Medical Center, Bronx, NY, USA
| | - Joseph R Madsen
- Department of Neurosurgery, Boston Children Hospital, Harvard Medical School, Boston, MA, USA
| | - Eric Halgren
- Departments of Radiology and, Neurosciences, University of California, San Diego, San Diego, CA, USA
| | - Sydney S Cash
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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Li Z, Zhao B, Hu W, Zhang C, Wang X, Liu C, Mo J, Guo Z, Yang B, Yao Y, Shao X, Zhang J, Zhang K. Practical measurements distinguishing physiological and pathological stereoelectroencephalography channels based on high-frequency oscillations in the human brain. Epilepsia Open 2024. [PMID: 38808652 DOI: 10.1002/epi4.12950] [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: 08/30/2023] [Revised: 04/07/2024] [Accepted: 04/11/2024] [Indexed: 05/30/2024] Open
Abstract
OBJECTIVE The present study aimed to identify various distinguishing features for use in the accurate classification of stereoelectroencephalography (SEEG) channels based on high-frequency oscillations (HFOs) inside and outside the epileptogenic zone (EZ). METHODS HFOs were detected in patients with focal epilepsy who underwent SEEG. Subsequently, HFOs within the seizure-onset and early spread zones were defined as pathological HFOs, whereas others were defined as physiological. Three features of HFOs were identified at the channel level, namely, morphological repetition, rhythmicity, and phase-amplitude coupling (PAC). A machine-learning (ML) classifier was then built to distinguish two HFO types at the channel level by application of the above-mentioned features, and the contributions were quantified. Further verification of the characteristics and classifier performance was performed in relation to various conscious states, imaging results, EZ location, and surgical outcomes. RESULTS Thirty-five patients were included in this study, from whom 166 104 pathological HFOs in 255 channels and 53 374 physiological HFOs in 282 channels were entered into the analysis pipeline. The results revealed that the morphological repetitions of pathological HFOs were markedly higher than those of the physiological HFOs; this was also observed for rhythmicity and PAC. The classifier exhibited high accuracy in differentiating between the two forms of HFOs, as indicated by an area under the curve (AUC) of 0.89. Both PAC and rhythmicity contributed significantly to this distinction. The subgroup analyses supported these findings. SIGNIFICANCE The suggested HFO features can accurately distinguish between pathological and physiological channels substantially improving its usefulness in clinical localization. PLAIN LANGUAGE SUMMARY In this study, we computed three quantitative features associated with HFOs in each SEEG channel and then constructed a machine learning-based classifier for the classification of pathological and physiological channels. The classifier performed well in distinguishing the two channel types under different levels of consciousness as well as in terms of imaging results, EZ location, and patient surgical outcomes.
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Affiliation(s)
- Zilin Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Baotian Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chang Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhihao Guo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Bowen Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yuan Yao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiaoqiu Shao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neurostimulation, Beijing, China
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3
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Wang Z, Guo J, van 't Klooster M, Hoogteijling S, Jacobs J, Zijlmans M. Prognostic Value of Complete Resection of the High-Frequency Oscillation Area in Intracranial EEG: A Systematic Review and Meta-Analysis. Neurology 2024; 102:e209216. [PMID: 38560817 PMCID: PMC11175645 DOI: 10.1212/wnl.0000000000209216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 01/12/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND AND OBJECTIVES High-frequency oscillations (HFOs; ripples 80-250 Hz; fast ripples [FRs] 250-500 Hz) recorded with intracranial electrodes generated excitement and debate about their potential to localize epileptogenic foci. We performed a systematic review and meta-analysis on the prognostic value of complete resection of the HFOs-area (crHFOs-area) for epilepsy surgical outcome in intracranial EEG (iEEG) accessing multiple subgroups. METHODS We searched PubMed, Embase, and Web of Science for original research from inception to October 27, 2022. We defined favorable surgical outcome (FSO) as Engel class I, International League Against Epilepsy class 1, or seizure-free status. The prognostic value of crHFOs-area for FSO was assessed by (1) the pooled FSO proportion after crHFOs-area; (2) FSO for crHFOs-area vs without crHFOs-area; and (3) the predictive performance. We defined high combined prognostic value as FSO proportion >80% + FSO crHFOs-area >without crHFOs-area + area under the curve (AUC) >0.75 and examined this for the clinical subgroups (study design, age, diagnostic type, HFOs-identification method, HFOs-rate thresholding, and iEEG state). Temporal lobe epilepsy (TLE) was compared with extra-TLE through dichotomous variable analysis. Individual patient analysis was performed for sex, affected hemisphere, MRI findings, surgery location, and pathology. RESULTS Of 1,387 studies screened, 31 studies (703 patients) met our eligibility criteria. Twenty-seven studies (602 patients) analyzed FRs and 20 studies (424 patients) ripples. Pooled FSO proportion after crHFOs-area was 81% (95% CI 76%-86%) for FRs and 82% (73%-89%) for ripples. Patients with crHFOs-area achieved more often FSO than those without crHFOs-area (FRs odds ratio [OR] 6.38, 4.03-10.09, p < 0.001; ripples 4.04, 2.32-7.04, p < 0.001). The pooled AUCs were 0.81 (0.77-0.84) for FRs and 0.76 (0.72-0.79) for ripples. Combined prognostic value was high in 10 subgroups: retrospective, children, long-term iEEG, threshold (FRs and ripples) and automated detection and interictal (FRs). FSO after complete resection of FRs-area (crFRs-area) was achieved less often in people with TLE than extra-TLE (OR 0.37, 0.15-0.89, p = 0.006). Individual patient analyses showed that crFRs-area was seen more in patients with FSO with than without MRI lesions (p = 0.02 after multiple correction). DISCUSSION Complete resection of the brain area with HFOs is associated with good postsurgical outcome. Its prognostic value holds, especially for FRs, for various subgroups. The use of HFOs for extra-TLE patients requires further evidence.
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Affiliation(s)
- Ziyi Wang
- From the Department of Neurology and Neurosurgery (Z.W., J.G., M.v.t.K., S.H., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE, the Netherlands; Department of Pediatrics (J.J.), University of Calgary, Alberta Children's Hospital, Calgary, Canada; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), Heemstede, the Netherlands
| | - Jiaojiao Guo
- From the Department of Neurology and Neurosurgery (Z.W., J.G., M.v.t.K., S.H., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE, the Netherlands; Department of Pediatrics (J.J.), University of Calgary, Alberta Children's Hospital, Calgary, Canada; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), Heemstede, the Netherlands
| | - Maryse van 't Klooster
- From the Department of Neurology and Neurosurgery (Z.W., J.G., M.v.t.K., S.H., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE, the Netherlands; Department of Pediatrics (J.J.), University of Calgary, Alberta Children's Hospital, Calgary, Canada; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), Heemstede, the Netherlands
| | - Sem Hoogteijling
- From the Department of Neurology and Neurosurgery (Z.W., J.G., M.v.t.K., S.H., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE, the Netherlands; Department of Pediatrics (J.J.), University of Calgary, Alberta Children's Hospital, Calgary, Canada; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), Heemstede, the Netherlands
| | - Julia Jacobs
- From the Department of Neurology and Neurosurgery (Z.W., J.G., M.v.t.K., S.H., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE, the Netherlands; Department of Pediatrics (J.J.), University of Calgary, Alberta Children's Hospital, Calgary, Canada; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), Heemstede, the Netherlands
| | - Maeike Zijlmans
- From the Department of Neurology and Neurosurgery (Z.W., J.G., M.v.t.K., S.H., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE, the Netherlands; Department of Pediatrics (J.J.), University of Calgary, Alberta Children's Hospital, Calgary, Canada; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), Heemstede, the Netherlands
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Cai Z, Jiang X, Bagić A, Worrell GA, Richardson M, He B. Spontaneous HFO Sequences Reveal Propagation Pathways for Precise Delineation of Epileptogenic Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.02.592202. [PMID: 38746136 PMCID: PMC11092614 DOI: 10.1101/2024.05.02.592202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Epilepsy, a neurological disorder affecting millions worldwide, poses great challenges in precisely delineating the epileptogenic zone - the brain region generating seizures - for effective treatment. High-frequency oscillations (HFOs) are emerging as promising biomarkers; however, the clinical utility is hindered by the difficulties in distinguishing pathological HFOs from non- epileptiform activities at single electrode and single patient resolution and understanding their dynamic role in epileptic networks. Here, we introduce an HFO-sequencing approach to analyze spontaneous HFOs traversing cortical regions in 40 drug-resistant epilepsy patients. This data- driven method automatically detected over 8.9 million HFOs, pinpointing pathological HFO- networks, and unveiled intricate millisecond-scale spatiotemporal dynamics, stability, and functional connectivity of HFOs in prolonged intracranial EEG recordings. These HFO sequences demonstrated a significant improvement in localization of epileptic tissue, with an 818.47% increase in concordance with seizure-onset zone (mean error: 2.92 mm), compared to conventional benchmarks. They also accurately predicted seizure outcomes for 90% AUC based on pre-surgical information using generalized linear models. Importantly, this mapping remained reliable even with short recordings (mean standard deviation: 3.23 mm for 30-minute segments). Furthermore, HFO sequences exhibited distinct yet highly repetitive spatiotemporal patterns, characterized by pronounced synchrony and predominant inward information flow from periphery towards areas involved in propagation, suggesting a crucial role for excitation-inhibition balance in HFO initiation and progression. Together, these findings shed light on the intricate organization of epileptic network and highlight the potential of HFO-sequencing as a translational tool for improved diagnosis, surgical targeting, and ultimately, better outcomes for vulnerable patients with drug-resistant epilepsy. One Sentence Summary Pathological fast brain oscillations travel like traffic along varied routes, outlining recurrently visited neural sites emerging as critical hotspots in epilepsy network.
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Sarnthein J, Neidert MC. A profile on the WISE cortical strip for intraoperative neurophysiological monitoring. Expert Rev Med Devices 2024; 21:373-379. [PMID: 38629964 DOI: 10.1080/17434440.2024.2343421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 04/11/2024] [Indexed: 05/31/2024]
Abstract
INTRODUCTION During intraoperative neurophysiological monitoring in neurosurgery, brain electrodes are placed to record electrocorticography or to inject current for direct cortical stimulation. A low impedance electrode may improve signal quality. AREAS COVERED We review here a brain electrode (WISE Cortical Strip, WCS®), where a thin polymer strip embeds platinum nanoparticles to create conductive electrode contacts. The low impedance contacts enable a high signal-to-noise ratio, allowing for better detection of small signals such as high-frequency oscillations (HFO). The softness of the WCS may hinder sliding the electrode under the dura or advancing it to deeper structures as the hippocampus but assures conformability with the cortex even in the resection cavity. We provide an extensive review on WCS including a market overview, an introduction to the device (mechanistics, cost aspects, performance standards, safety and contraindications) and an overview of the available pre- and post-approval data. EXPERT OPINION The WCS improves signal detection by lower impedance and better conformability to the cortex. The higher signal-to-noise ratio improves the detection of challenging signals. The softness of the electrode may be a disadvantage in some applications and an advantage in others.
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Affiliation(s)
- Johannes Sarnthein
- Klinik für Neurochirurgie, Universitätsspital Zürich, Universität Zürich, Zurich, Switzerland
- Klinisches Neurozentrum, Universitätsspital Zürich, Zurich, Switzerland
| | - Marian C Neidert
- Klinik für Neurochirurgie, Universitätsspital Zürich, Universität Zürich, Zurich, Switzerland
- Klinik für Neurochirurgie, Kantonsspital St. Gallen, St. Gallen, Switzerland
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Wang SH, Arnulfo G, Nobili L, Myrov V, Ferrari P, Ciuciu P, Palva S, Palva JM. Neuronal synchrony and critical bistability: Mechanistic biomarkers for localizing the epileptogenic network. Epilepsia 2024. [PMID: 38687176 DOI: 10.1111/epi.17996] [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: 11/26/2023] [Revised: 04/11/2024] [Accepted: 04/11/2024] [Indexed: 05/02/2024]
Abstract
OBJECTIVE Postsurgical seizure freedom in drug-resistant epilepsy (DRE) patients varies from 30% to 80%, implying that in many cases the current approaches fail to fully map the epileptogenic zone (EZ). We aimed to advance a novel approach to better characterize epileptogenicity and investigate whether the EZ encompasses a broader epileptogenic network (EpiNet) beyond the seizure zone (SZ) that exhibits seizure activity. METHODS We first used computational modeling to test putative complex systems-driven and systems neuroscience-driven mechanistic biomarkers for epileptogenicity. We then used these biomarkers to extract features from resting-state stereoelectroencephalograms recorded from DRE patients and trained supervised classifiers to localize the SZ against gold standard clinical localization. To further explore the prevalence of pathological features in an extended brain network outside of the clinically identified SZ, we also used unsupervised classification. RESULTS Supervised SZ classification trained on individual features achieved accuracies of .6-.7 area under the receiver operating characteristic curve (AUC). Combining all criticality and synchrony features further improved the AUC to .85. Unsupervised classification discovered an EpiNet-like cluster of brain regions, in which 51% of brain regions were outside of the SZ. Brain regions in the EpiNet-like cluster engaged in interareal hypersynchrony and locally exhibited high-amplitude bistability and excessive inhibition, which was strikingly similar to the high seizure risk regime revealed by our computational modeling. SIGNIFICANCE The finding that combining biomarkers improves SZ localization accuracy indicates that the novel mechanistic biomarkers for epileptogenicity employed here yield synergistic information. On the other hand, the discovery of SZ-like brain dynamics outside of the clinically defined SZ provides empirical evidence of an extended pathophysiological EpiNet.
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Affiliation(s)
- Sheng H Wang
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
- Le Commissariat à l'énergie atomique et aux énergies alternatives, NeuroSpin, Université Paris-Saclay, Gif-sur-Yvette, France
- Models and Inference for Neuroimaging Data, Inria, Palaiseau, France
| | - Gabriele Arnulfo
- Department of Informatics, Bioengineering, Robotics, and System Engineering, University of Genoa, Genoa, Italy
| | - Lino Nobili
- Child Neuropsychiatry Unit, Istituto di Ricovero e Cura a Carattere Scientifico Istituto Giannina Gaslini, Member of the European Reference Network EpiCARE, Genoa, Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, and Maternal and Children's Sciences, University of Genoa, Genoa, Italy
| | - Vladislav Myrov
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Paul Ferrari
- Jack H. Miller Magnetoencephalography Center, Helen DeVos Childrens Hospital, Grand Rapids, Michigan, USA
- Department of Pediatrics and Human Development, Michigan State University, East Lansing, Michigan, USA
| | - Philippe Ciuciu
- Le Commissariat à l'énergie atomique et aux énergies alternatives, NeuroSpin, Université Paris-Saclay, Gif-sur-Yvette, France
- Models and Inference for Neuroimaging Data, Inria, Palaiseau, France
| | - Satu Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
- Division of Psychology, Values, Ideologies and Social Contexts of Education, Faculty of Education and Psychology, University of Oulu, Oulu, Finland
| | - J Matias Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
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Gennari AG, Bicciato G, Lo Biundo SP, Kottke R, Stefanos-Yakoub I, Cserpan D, O'Gorman Tuura R, Ramantani G. Lesion volume and spike frequency on EEG impact perfusion values in focal cortical dysplasia: a pediatric arterial spin labeling study. Sci Rep 2024; 14:7601. [PMID: 38556543 PMCID: PMC10982306 DOI: 10.1038/s41598-024-58352-9] [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: 11/13/2023] [Accepted: 03/28/2024] [Indexed: 04/02/2024] Open
Abstract
Arterial spin labelling (ASL), an MRI sequence non-invasively imaging brain perfusion, has yielded promising results in the presurgical workup of children with focal cortical dysplasia (FCD)-related epilepsy. However, the interpretation of ASL-derived perfusion patterns remains unclear. Hence, we compared ASL qualitative and quantitative findings to their clinical, EEG, and MRI counterparts. We included children with focal structural epilepsy related to an MRI-detectable FCD who underwent single delay pseudo-continuous ASL. ASL perfusion changes were assessed qualitatively by visual inspection and quantitatively by estimating the asymmetry index (AI). We considered 18 scans from 15 children. 16 of 18 (89%) scans showed FCD-related perfusion changes: 10 were hypoperfused, whereas six were hyperperfused. Nine scans had perfusion changes larger than and seven equal to the FCD extent on anatomical images. Hyperperfusion was associated with frequent interictal spikes on EEG (p = 0.047). Perfusion changes in ASL larger than the FCD corresponded to larger lesions (p = 0.017). Higher AI values were determined by frequent interictal spikes on EEG (p = 0.004). ASL showed FCD-related perfusion changes in most cases. Further, higher spike frequency on EEG may increase ASL changes in affected children. These observations may facilitate the interpretation of ASL findings, improving treatment management, counselling, and prognostication in children with FCD-related epilepsy.
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Affiliation(s)
- Antonio Giulio Gennari
- Department of Neuropediatrics, University Children's Hospital Zurich, 75, 8032, Zurich, Switzerland
- MR-Research Centre, University Children's Hospital Zurich, Zurich, Switzerland
| | - Giulio Bicciato
- Department of Neuropediatrics, University Children's Hospital Zurich, 75, 8032, Zurich, Switzerland
- Department of Neurology, University Hospital Zurich, Zurich, Switzerland
| | - Santo Pietro Lo Biundo
- Department of Neuropediatrics, University Children's Hospital Zurich, 75, 8032, Zurich, Switzerland
| | - Raimund Kottke
- Department of Radiology, University Children's Hospital Zurich, Zurich, Switzerland
| | - Ilona Stefanos-Yakoub
- Department of Neuropediatrics, University Children's Hospital Zurich, 75, 8032, Zurich, Switzerland
| | - Dorottya Cserpan
- Department of Neuropediatrics, University Children's Hospital Zurich, 75, 8032, Zurich, Switzerland
| | - Ruth O'Gorman Tuura
- MR-Research Centre, University Children's Hospital Zurich, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
- Children's Research Centre, University Children's Hospital Zurich, Zurich, Switzerland
| | - Georgia Ramantani
- Department of Neuropediatrics, University Children's Hospital Zurich, 75, 8032, Zurich, Switzerland.
- University of Zurich, Zurich, Switzerland.
- Children's Research Centre, University Children's Hospital Zurich, Zurich, Switzerland.
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8
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Barlatey SL, Mignardot CG, Friedrichs-Maeder C, Schindler K, Wiest R, Nowacki A, Haenggi M, Z'Graggen WJ, Pollo C, Rominger A, Pyka T, Baud MO. Triggered Seizures for Ictal SPECT Imaging: A Case Series and Feasibility Study. J Nucl Med 2024; 65:470-474. [PMID: 38212073 DOI: 10.2967/jnumed.123.266515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 12/04/2023] [Accepted: 12/04/2023] [Indexed: 01/13/2024] Open
Abstract
Ictal SPECT is an informative seizure imaging technique to tailor epilepsy surgery. However, capturing the onset of unpredictable seizures is a medical and logistic challenge. Here, we sought to image planned seizures triggered by direct stimulation of epileptic networks via stereotactic electroencephalography (sEEG) electrodes. Methods: In this case series of 3 adult participants with left temporal epilepsy, we identified and stimulated sEEG contacts able to trigger patient-typical seizures. We administered 99mTc-HMPAO within 12 s of ictal onset and acquired SPECT images within 40 min without any adverse events. Results: Ictal hyperperfusion maps partially overlapped concomitant sEEG seizure activity. In both participants known for periictal aphasia, SPECT imaging revealed hyperperfusion in the speech cortex lacking sEEG coverage. Conclusion: Triggering of seizures for ictal SPECT complements discrete sEEG sampling with spatially complete images of early seizure propagation. This readily implementable method revives interest in seizure imaging to guide resective epilepsy surgery.
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Affiliation(s)
- Sabry L Barlatey
- Department of Neurosurgery, University Hospital of Bern, Bern, Switzerland
| | - Camille G Mignardot
- Sleep-Wake-Epilepsy Center and NeuroTec, Center for Experimental Neurology, Department of Neurology, University Hospital of Bern, Bern, Switzerland
| | - Cecilia Friedrichs-Maeder
- Sleep-Wake-Epilepsy Center and NeuroTec, Center for Experimental Neurology, Department of Neurology, University Hospital of Bern, Bern, Switzerland
| | - Kaspar Schindler
- Sleep-Wake-Epilepsy Center and NeuroTec, Center for Experimental Neurology, Department of Neurology, University Hospital of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital of Bern, Bern, Switzerland
| | - Andreas Nowacki
- Department of Neurosurgery, University Hospital of Bern, Bern, Switzerland
| | - Matthias Haenggi
- Department of Intensive Care Medicine, University Hospital of Bern, Bern, Switzerland; and
| | - Werner J Z'Graggen
- Department of Neurosurgery, University Hospital of Bern, Bern, Switzerland
| | - Claudio Pollo
- Department of Neurosurgery, University Hospital of Bern, Bern, Switzerland
| | - Axel Rominger
- Department of Nuclear Medicine, University Hospital of Bern, Bern, Switzerland
| | - Thomas Pyka
- Department of Nuclear Medicine, University Hospital of Bern, Bern, Switzerland
| | - Maxime O Baud
- Sleep-Wake-Epilepsy Center and NeuroTec, Center for Experimental Neurology, Department of Neurology, University Hospital of Bern, Bern, Switzerland;
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9
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Lahtinen J, Koulouri A, Rampp S, Wellmer J, Wolters C, Pursiainen S. Standardized hierarchical adaptive Lp regression for noise robust focal epilepsy source reconstructions. Clin Neurophysiol 2024; 159:24-40. [PMID: 38244372 DOI: 10.1016/j.clinph.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 11/02/2023] [Accepted: 12/02/2023] [Indexed: 01/22/2024]
Abstract
OBJECTIVE To investigate the ability of standardization to reduce source localization errors and measurement noise uncertainties for hierarchical Bayesian algorithms with L1- and L2-norms as priors in electroencephalography and magnetoencephalography of focal epilepsy. METHODS Description of the standardization methodology relying on the Hierarchical Bayesian framework, referred to as the Standardized Hierarchical Adaptive Lp-norm Regularization (SHALpR). The performance was tested using real data from two focal epilepsy patients. Simulated data that resembled the available real data was constructed for further localization and noise robustness investigation. RESULTS The proposed algorithms were compared to their non-standardized counterparts, Standardized low-resolution brain electromagnetic tomography, Standardized Shrinking LORETA-FOCUSS, and Dynamic statistical parametric maps. Based on the simulations, the standardized Hierarchical adaptive algorithm using L2-norm was noise robust for 10 dB signal-to-noise ratio (SNR), whereas the L1-norm prior worked robustly also with 5 dB SNR. The accuracy of the standardized L1-normed methodology to localize focal activity was under 1 cm for both patients. CONCLUSIONS Numerical results of the proposed methodology display improved localization and noise robustness. The proposed methodology also outperformed the compared methods when dealing with real data. SIGNIFICANCE The proposed standardized methodology, especially when employing the L1-norm, could serve as a valuable assessment tool in surgical decision-making.
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Affiliation(s)
- Joonas Lahtinen
- Faculty of Information Technology and Communication Sciences, Tampere University, Tampere 33720, Finland.
| | - Alexandra Koulouri
- Faculty of Information Technology and Communication Sciences, Tampere University, Tampere 33720, Finland.
| | - Stefan Rampp
- Department of Neurosurgery, University Hospital Halle (Saale), Halle 06097, Germany; Department of Neurosurgery, University Hospital Erlangen, Erlangen 91054, Germany; Department of Neuroradiology, University Hospital Erlangen, Erlangen 91054, Germany.
| | - Jörg Wellmer
- Ruhr-Epileptology, Department of Neurology, University Hospital Knappschaftskrankenhaus, Ruhr-University, Bochum44892, Germany.
| | - Carsten Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster 48149, Germany; Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster 48149, Germany.
| | - Sampsa Pursiainen
- Faculty of Information Technology and Communication Sciences, Tampere University, Tampere 33720, Finland.
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10
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Huang H, Zhang M, Zhao Y, Li Y, Jin W, Guo R, Liu W, Cai B, Li J, Yuan S, Huang X, Lin X, Liang ZP, Li B, Luo J. Simultaneous high-resolution whole-brain MR spectroscopy and [ 18F]FDG PET for temporal lobe epilepsy. Eur J Nucl Med Mol Imaging 2024; 51:721-733. [PMID: 37823910 DOI: 10.1007/s00259-023-06465-0] [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: 07/23/2023] [Accepted: 09/28/2023] [Indexed: 10/13/2023]
Abstract
PURPOSE Precise lateralizing the epileptogenic zone in patients with drug-resistant mesial temporal lobe epilepsy (mTLE) remains challenging, particularly when routine MRI scans are inconclusive (MRI-negative). This study aimed to investigate the synergy of fast, high-resolution, whole-brain MRSI in conjunction with simultaneous [18F]FDG PET for the lateralization of mTLE. METHODS Forty-eight drug-resistant mTLE patients (M/F 31/17, age 12-58) underwent MRSI and [18F]FDG PET on a hybrid PET/MR scanner. Lateralization of mTLE was evaluated by visual inspection and statistical classifiers of metabolic mappings against routine MRI. Additionally, this study explored how disease status influences the associations between altered N-acetyl aspartate (NAA) and FDG uptake using hierarchical moderated multiple regression. RESULTS The high-resolution whole-brain MRSI data offers metabolite maps at comparable resolution to [18F]FDG PET. Visual examinations of combined MRSI and [18F]FDG PET showed an mTLE lateralization accuracy rate of 91.7% in a 48-patient cohort, surpassing routine MRI (52.1%). Notably, out of 23 MRI-negative mTLE, combined MRSI and [18F]FDG PET helped detect 19 cases. Logistical regression models combining hippocampal NAA level and FDG uptake improved lateralization performance (AUC=0.856), while further incorporating extrahippocampal regions such as amygdala, thalamus, and superior temporal gyrus increased the AUC to 0.939. Concurrent MRSI/PET revealed a moderating influence of disease duration and hippocampal atrophy on the association between hippocampal NAA and glucose uptake, providing significant new insights into the disease's trajectory. CONCLUSION This paper reports the first metabolic imaging study using simultaneous high-resolution MRSI and [18F]FDG PET, which help visualize MRI-unidentifiable lesions and may thus advance diagnostic tools and management strategies for drug-resistant mTLE.
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Affiliation(s)
- Hui Huang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Miao Zhang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yibo Zhao
- Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana Champaign, Urbana, IL, 61801, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana Champaign, Urbana, IL, 61801, USA
| | - Yudu Li
- Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana Champaign, Urbana, IL, 61801, USA
- National Center for Supercomputing Applications, University of Illinois at Urbana Champaign, Urbana, IL, 61801, USA
| | - Wen Jin
- Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana Champaign, Urbana, IL, 61801, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana Champaign, Urbana, IL, 61801, USA
| | - Rong Guo
- Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana Champaign, Urbana, IL, 61801, USA
- Siemens Medical Solutions USA, Inc, Urbana, IL, 61801, USA
| | - Wei Liu
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Bingyang Cai
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Jiwei Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Siyu Yuan
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Xinyun Huang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xiaozhu Lin
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Zhi-Pei Liang
- Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana Champaign, Urbana, IL, 61801, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana Champaign, Urbana, IL, 61801, USA
| | - Biao Li
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Jie Luo
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
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11
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Tang Y, Xiao L, Deng C, Zhu H, Gao X, Li J, Yang Z, Liu D, Feng L, Hu S. [ 18F]FDG PET metabolic patterns in mesial temporal lobe epilepsy with different pathological types. Eur Radiol 2024; 34:887-898. [PMID: 37581655 DOI: 10.1007/s00330-023-10089-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/23/2023] [Accepted: 07/01/2023] [Indexed: 08/16/2023]
Abstract
OBJECTIVES To investigate [18F]FDG PET patterns of mesial temporal lobe epilepsy (MTLE) patients with distinct pathologic types and provide possible guidance for predicting long-term prognoses of patients undergoing epilepsy surgery. METHODS This was a retrospective review of MTLE patients who underwent anterior temporal lobectomy between 2016 and 2021. Patients were classified as having chronic inflammation and gliosis (gliosis, n = 44), hippocampal sclerosis (HS, n = 43), or focal cortical dysplasia plus HS (FCD-HS, n = 13) based on the postoperative pathological diagnosis. Metabolic patterns and the severity of metabolic abnormalities were investigated among MTLE patients and healthy controls (HCs). The standardized uptake value (SUV), SUV ratio (SUVr), and asymmetry index (AI) of regions of interest were applied to evaluate the severity of metabolic abnormalities. Imaging processing was performed with statistical parametric mapping (SPM12). RESULTS With a mean follow-up of 2.8 years, the seizure freedom (Engel class IA) rates of gliosis, HS, and FCD-HS were 54.55%, 62.79%, and 69.23%, respectively. The patients in the gliosis group presented a metabolic pattern with a larger involvement of extratemporal areas, including the ipsilateral insula. SUV, SUVr, and AI in ROIs were decreased for patients in all three MTLE groups compared with those of HCs, but the differences among all three MTLE groups were not significant. CONCLUSIONS MTLE patients with isolated gliosis had the worst prognosis and hypometabolism in the insula, but the degree of metabolic decrease did not differ from the other two groups. Hypometabolic regions should be prioritized for [18F]FDG PET presurgical evaluation rather than [18F]FDG uptake values. CLINICAL RELEVANCE STATEMENT This study proposes guidance for optimizing the operation scheme in patients with refractory MTLE and emphasizes the potential of molecular neuroimaging with PET using selected tracers to predict the postsurgical histology of patients with refractory MTLE epilepsy. KEY POINTS • MTLE patients with gliosis had poor surgical outcomes and showed a distinct pattern of decreased metabolism in the ipsilateral insula. • In the preoperative assessment of MTLE, it is recommended to prioritize the evaluation of glucose hypometabolism areas over [18F]FDG uptake values. • The degree of glucose hypometabolism in the epileptogenic focus was not associated with the surgical outcomes of MTLE.
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Affiliation(s)
- Yongxiang Tang
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan, China
| | - Ling Xiao
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan, China
| | - Chijun Deng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Haoyue Zhu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xiaomei Gao
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jian Li
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan, China
| | - Zhiquan Yang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Dingyang Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Li Feng
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
- Department of Neurology, Xiangya Hospital, Central South University (Jiangxi Branch), Nanchang, Jiangxi, China.
- National Clinical Research Center for Geriatric Diseases, Xiangya Hospital, Central South University, Changsha, Hunan, China.
| | - Shuo Hu
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan, China.
- National Clinical Research Center for Geriatric Diseases, Xiangya Hospital, Central South University, Changsha, Hunan, China.
- Key Laboratory of Biological, Nanotechnology of National Health Commission, Xiangya Hospital, Central South University, Changsha, Hunan, China.
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12
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Mora S, Turrisi R, Chiarella L, Consales A, Tassi L, Mai R, Nobili L, Barla A, Arnulfo G. NLP-based tools for localization of the epileptogenic zone in patients with drug-resistant focal epilepsy. Sci Rep 2024; 14:2349. [PMID: 38287042 PMCID: PMC10825198 DOI: 10.1038/s41598-024-51846-6] [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: 08/30/2023] [Accepted: 01/10/2024] [Indexed: 01/31/2024] Open
Abstract
Epilepsy surgery is an option for people with focal onset drug-resistant (DR) seizures but a delayed or incorrect diagnosis of epileptogenic zone (EZ) location limits its efficacy. Seizure semiological manifestations and their chronological appearance contain valuable information on the putative EZ location but their interpretation relies on extensive experience. The aim of our work is to support the localization of EZ in DR patients automatically analyzing the semiological description of seizures contained in video-EEG reports. Our sample is composed of 536 descriptions of seizures extracted from Electronic Medical Records of 122 patients. We devised numerical representations of anamnestic records and seizures descriptions, exploiting Natural Language Processing (NLP) techniques, and used them to feed Machine Learning (ML) models. We performed three binary classification tasks: localizing the EZ in the right or left hemisphere, temporal or extra-temporal, and frontal or posterior regions. Our computational pipeline reached performances above 70% in all tasks. These results show that NLP-based numerical representation combined with ML-based classification models may help in localizing the origin of the seizures relying only on seizures-related semiological text data alone. Accurate early recognition of EZ could enable a more appropriate patient management and a faster access to epilepsy surgery to potential candidates.
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Affiliation(s)
- Sara Mora
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, 16145, Genoa, Italy.
| | - Rosanna Turrisi
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, 16145, Genoa, Italy
- MaLGa Machine Learning Genoa Center, University of Genoa, 16146, Genoa, Italy
| | - Lorenzo Chiarella
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genoa, 16132, Genoa, Italy
- Child Neuropsychiatry Unit, IRCCS Istituto Giannina Gaslini, Member of the European Reference Network EpiCARE, 16147, Genoa, Italy
| | - Alessandro Consales
- Division of Neurosurgery, IRCCS Istituto Giannina Gaslini, 16147, Genoa, Italy
| | - Laura Tassi
- "Claudio Munari" Epilepsy Surgery Center, Niguarda Hospital, 20162, Milan, Italy
| | - Roberto Mai
- "Claudio Munari" Epilepsy Surgery Center, Niguarda Hospital, 20162, Milan, Italy
| | - Lino Nobili
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genoa, 16132, Genoa, Italy
- Child Neuropsychiatry Unit, IRCCS Istituto Giannina Gaslini, Member of the European Reference Network EpiCARE, 16147, Genoa, Italy
| | - Annalisa Barla
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, 16145, Genoa, Italy
- MaLGa Machine Learning Genoa Center, University of Genoa, 16146, Genoa, Italy
| | - Gabriele Arnulfo
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, 16145, Genoa, Italy
- Neuroscience Center, Helsinki Institute of Life Science (HiLife), University of Helsinki, 00014, Helsinki, Finland
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13
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Bröhl T, Rings T, Pukropski J, von Wrede R, Lehnertz K. The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 3:1338864. [PMID: 38293249 PMCID: PMC10825060 DOI: 10.3389/fnetp.2023.1338864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 12/19/2023] [Indexed: 02/01/2024]
Abstract
Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.
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Affiliation(s)
- Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Jan Pukropski
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
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14
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Doss DJ, Johnson GW, Englot DJ. Imaging and Stereotactic Electroencephalography Functional Networks to Guide Epilepsy Surgery. Neurosurg Clin N Am 2024; 35:61-72. [PMID: 38000842 PMCID: PMC10676462 DOI: 10.1016/j.nec.2023.09.001] [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] [Indexed: 11/26/2023]
Abstract
Epilepsy surgery is a potentially curative treatment of drug-resistant epilepsy that has remained underutilized both due to inadequate referrals and incomplete localization hypotheses. The complexity of patients evaluated for epilepsy surgery has increased, thus new approaches are necessary to treat these patients. The paradigm of epilepsy surgery has evolved to match this challenge, now considering the entire seizure network with the goal of disrupting it through resection, ablation, neuromodulation, or a combination. The network paradigm has the potential to aid in identification of the seizure network as well as treatment selection.
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Affiliation(s)
- Derek J Doss
- Department of Biomedical Engineering, Vanderbilt University, PMB 351631, 2301 Vanderbilt Place, Nashville, TN 37235, USA; Vanderbilt University Institute of Imaging Science (VUIIS), 1161 21st Avenue South, Medical Center North AA-1105, Nashville, TN 37232, USA; Vanderbilt Institute for Surgery and Engineering (VISE), 1161 21st Avenue South, MCN S2323, Nashville, TN 37232, USA
| | - Graham W Johnson
- Department of Biomedical Engineering, Vanderbilt University, PMB 351631, 2301 Vanderbilt Place, Nashville, TN 37235, USA; Vanderbilt University Institute of Imaging Science (VUIIS), 1161 21st Avenue South, Medical Center North AA-1105, Nashville, TN 37232, USA; Vanderbilt Institute for Surgery and Engineering (VISE), 1161 21st Avenue South, MCN S2323, Nashville, TN 37232, USA
| | - Dario J Englot
- Department of Biomedical Engineering, Vanderbilt University, PMB 351631, 2301 Vanderbilt Place, Nashville, TN 37235, USA; Vanderbilt University Institute of Imaging Science (VUIIS), 1161 21st Avenue South, Medical Center North AA-1105, Nashville, TN 37232, USA; Vanderbilt Institute for Surgery and Engineering (VISE), 1161 21st Avenue South, MCN S2323, Nashville, TN 37232, USA; Department of Neurological Surgery, Vanderbilt University Medical Center, 1161 21st Avenue South, T4224 Medical Center North, Nashville, TN 37232, USA; Department of Electrical and Computer Engineering, Vanderbilt University, PMB 351824, 2301 Vanderbilt Place, Nashville, TN 37235, USA; Department of Radiological Sciences, Vanderbilt University Medical Center, 1161 21st Avenue South, Nashville, TN 37232, USA.
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15
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Gerstl JVE, Kiseleva A, Imbach L, Sarnthein J, Fedele T. High frequency oscillations in relation to interictal spikes in predicting postsurgical seizure freedom. Sci Rep 2023; 13:21313. [PMID: 38042925 PMCID: PMC10693609 DOI: 10.1038/s41598-023-48764-4] [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: 10/09/2023] [Accepted: 11/30/2023] [Indexed: 12/04/2023] Open
Abstract
We evaluate whether interictal spikes, epileptiform HFOs and their co-occurrence (Spike + HFO) were included in the resection area with respect to seizure outcome. We also characterise the relationship between high frequency oscillations (HFOs) and propagating spikes. We analysed intracranial EEG of 20 patients that underwent resective epilepsy surgery. The co-occurrence of ripples and fast ripples was considered an HFO event; the co-occurrence of an interictal spike and HFO was considered a Spike + HFO event. HFO distribution and spike onset were compared in cases of spike propagation. Accuracy in predicting seizure outcome was 85% for HFO, 60% for Spikes, and 79% for Spike + HFO. Sensitivity was 57% for HFO, 71% for Spikes and 67% for Spikes + HFO. Specificity was 100% for HFO, 54% for Spikes and 85% for Spikes + HFO. In 2/2 patients with spike propagation, the spike onset included the HFO area. Combining interictal spikes with HFO had comparable accuracy to HFO. In patients with propagating spikes, HFO rate was maximal at the onset of spike propagation.
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Affiliation(s)
- Jakob V E Gerstl
- University College London Medical School, London, UK
- Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland
| | - Alina Kiseleva
- Institute for Cognitive Neuroscience, HSE University, Myasnitskaya Ulitsa, 20, Moscow, Russian Federation, 101000
| | - Lukas Imbach
- Swiss Epilepsy Center, Klinik Lengg, Zurich, Switzerland
| | - Johannes Sarnthein
- Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland
| | - Tommaso Fedele
- Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland.
- Institute for Cognitive Neuroscience, HSE University, Myasnitskaya Ulitsa, 20, Moscow, Russian Federation, 101000.
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16
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Withers CP, Diamond JM, Yang B, Snyder K, Abdollahi S, Sarlls J, Chapeton JI, Theodore WH, Zaghloul KA, Inati SK. Identifying sources of human interictal discharges with travelling wave and white matter propagation. Brain 2023; 146:5168-5181. [PMID: 37527460 PMCID: PMC11046055 DOI: 10.1093/brain/awad259] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 06/30/2023] [Accepted: 07/19/2023] [Indexed: 08/03/2023] Open
Abstract
Interictal epileptiform discharges have been shown to propagate from focal epileptogenic sources as travelling waves or through more rapid white matter conduction. We hypothesize that both modes of propagation are necessary to explain interictal discharge timing delays. We propose a method that, for the first time, incorporates both propagation modes to identify unique potential sources of interictal activity. We retrospectively analysed 38 focal epilepsy patients who underwent intracranial EEG recordings and diffusion-weighted imaging for epilepsy surgery evaluation. Interictal discharges were detected and localized to the most likely source based on relative delays in time of arrival across electrodes, incorporating travelling waves and white matter propagation. We assessed the influence of white matter propagation on distance of spread, timing and clinical interpretation of interictal activity. To evaluate accuracy, we compared our source localization results to earliest spiking regions to predict seizure outcomes. White matter propagation helps to explain the timing delays observed in interictal discharge sequences, underlying rapid and distant propagation. Sources identified based on differences in time of receipt of interictal discharges are often distinct from the leading electrode location. Receipt of activity propagating rapidly via white matter can occur earlier than more local activity propagating via slower cortical travelling waves. In our cohort, our source localization approach was more accurate in predicting seizure outcomes than the leading electrode location. Inclusion of white matter in addition to travelling wave propagation in our model of discharge spread did not improve overall accuracy but allowed for identification of unique and at times distant potential sources of activity, particularly in patients with persistent postoperative seizures. Since distant white matter propagation can occur more rapidly than local travelling wave propagation, combined modes of propagation within an interictal discharge sequence can decouple the commonly assumed relationship between spike timing and distance from the source. Our findings thus highlight the clinical importance of recognizing the presence of dual modes of propagation during interictal discharges, as this may be a cause of clinical mislocalization.
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Affiliation(s)
- C Price Withers
- Neurophysiology of Epilepsy Unit, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Joshua M Diamond
- Surgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Braden Yang
- Neurophysiology of Epilepsy Unit, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kathryn Snyder
- Neurophysiology of Epilepsy Unit, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Shervin Abdollahi
- Neurophysiology of Epilepsy Unit, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Joelle Sarlls
- NIH MRI Research Facility, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Julio I Chapeton
- Surgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - William H Theodore
- Clinical Epilepsy Section, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kareem A Zaghloul
- Surgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Sara K Inati
- Neurophysiology of Epilepsy Unit, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
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17
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Lainscsek C, Salami P, Carvalho VR, Mendes EMAM, Fan M, Cash SS, Sejnowski TJ. Network-motif delay differential analysis of brain activity during seizures. CHAOS (WOODBURY, N.Y.) 2023; 33:123136. [PMID: 38156987 PMCID: PMC10757649 DOI: 10.1063/5.0165904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 11/28/2023] [Indexed: 01/03/2024]
Abstract
Delay Differential Analysis (DDA) is a nonlinear method for analyzing time series based on principles from nonlinear dynamical systems. DDA is extended here to incorporate network aspects to improve the dynamical characterization of complex systems. To demonstrate its effectiveness, DDA with network capabilities was first applied to the well-known Rössler system under different parameter regimes and noise conditions. Network-motif DDA, based on cortical regions, was then applied to invasive intracranial electroencephalographic data from drug-resistant epilepsy patients undergoing presurgical monitoring. The directional network motifs between brain areas that emerge from this analysis change dramatically before, during, and after seizures. Neural systems provide a rich source of complex data, arising from varying internal states generated by network interactions.
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Affiliation(s)
| | - Pariya Salami
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA
| | | | - Eduardo M. A. M. Mendes
- Laboratório de Modelagem, Análise e Controle de Sistemas Não Lineares, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, 31270-901 Belo Horizonte, MG, Brazil
| | - Miaolin Fan
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA
| | - Sydney S. Cash
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA
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18
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Li Z, Zhao B, Hu W, Zhang C, Wang X, Zhang J, Zhang K. Machine learning-based classification of physiological and pathological high-frequency oscillations recorded by stereoelectroencephalography. Seizure 2023; 113:58-65. [PMID: 37984126 DOI: 10.1016/j.seizure.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 11/22/2023] Open
Abstract
OBJECTIVE High-frequency oscillations (HFOs) are an efficient indicator to locate the epileptogenic zone (EZ). However, physiological HFOs produced in the normal brain region may interfere with EZ localization. The present study aimed to build a machine learning-based classifier to distinguish the properties of each HFO event based on features in different domains. METHODS HFOs were detected in focal epilepsy patients from two different hospitals who underwent stereoelectroencephalography and subsequent resection surgery. Subsequently, 37 features in four different domains (time, frequency and time-frequency, entropy-based and nonlinear) were extracted for each HFO. After extraction, a fast correlation-based filter (FCBF) algorithm was applied for feature selection. The machine learning classifier was trained on the feature matrix with and without FCBF and then tested on the data set from patients in another hospital. RESULTS A dataset was compiled, consisting of 89,844 pathological HFOs and 23,613 physiological HFOs from 17 patients assigned to the training dataset. Additionally, 12,695 pathological HFOs and 5,599 physiological HFOs from 9 patients were assigned to the testing dataset. Four features (ripple band power, arithmetic mean, Petrosian fractal dimension and zero crossings) were obtained for classifier training after FCBF. The classifier showed an area under the curve (AUC) of 0.95/0.98 for FCBF/no FCBF features in the training dataset and AUC of 0.82/0.90 for FCBF/no FCBF features in the testing dataset. Our findings indicated that the classifier utilizing all features demonstrated superior performance compared to the one relying on FCBF-processed features. CONCLUSION Our classifier could reliably differentiate pathological HFOs from physiological ones, which could promote the development of HFOs in EZ localization.
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Affiliation(s)
- Zilin Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Baotian Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Beijing Key Laboratory of Neurostimulation, Beijing, China.
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Cheval M, Rodrigo S, Taussig D, Caillé F, Petrescu AM, Bottlaender M, Tournier N, Besson FL, Leroy C, Bouilleret V. [ 18F]DPA-714 PET Imaging in the Presurgical Evaluation of Patients With Drug-Resistant Focal Epilepsy. Neurology 2023; 101:e1893-e1904. [PMID: 37748889 PMCID: PMC10663012 DOI: 10.1212/wnl.0000000000207811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 07/17/2023] [Indexed: 09/27/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Translocator protein 18 kDa (TSPO) PET imaging is used to monitor glial activation. Recent studies have proposed TSPO PET as a marker of the epileptogenic zone (EZ) in drug-resistant focal epilepsy (DRFE). This study aims to assess the contributions of TSPO imaging using [18F]DPA-714 PET and [18F]FDG PET for localizing the EZ during presurgical assessment of DRFE, when phase 1 presurgical assessment does not provide enough information. METHODS We compared [18F]FDG and [18F]DPA-714 PET images of 23 patients who had undergone a phase 1 presurgical assessment, using qualitative visual analysis and quantitative analysis, at both the voxel and the regional levels. PET abnormalities (increase in binding for [18F]DPA-714 vs decrease in binding for [18F]FDG) were compared with clinical hypotheses concerning the localization of the EZ based on phase 1 presurgical assessment. The additional value of [18F]DPA-714 PET imaging to [18F]FDG for refining the localization of the EZ was assessed. To strengthen the visual analysis, [18F]DPA-714 PET imaging was also reviewed by 2 experienced clinicians blind to the EZ location. RESULTS The study included 23 patients. Visual analysis of [18F]DPA-714 PET was significantly more accurate than [18F]FDG PET to both, show anomalies (95.7% vs 56.5%, p = 0.022), and provide additional information to refine the EZ localization (65.2% vs 17.4%, p = 0.019). All 10 patients with normal [18F]FDG PET had anomalies when using [18F]DPA-714 PET. The additional value of [18F]DPA-714 PET seemed to be greater in patients with normal brain MRI or with neocortical EZ (especially if insula is involved). Regional analysis of [18F]DPA-714 and [18F]FDG PET provided similar results. However, using voxel-wise analysis, [18F]DPA-714 was more effective than [18F]FDG for unveiling clusters whose localization was more often consistent with the EZ hypothesis (87.0% vs 39.1%, p = 0.019). Nonrelevant bindings were seen in 14 of 23 patients in visual analysis and 9 patients of 23 patients in voxel-wise analysis. DISCUSSION [18F]DPA-714 PET imaging provides valuable information for presurgical assessments of patients with DRFE. TSPO PET could become an additional tool to help to the localization of the EZ, especially in patients with negative [18F]FDG PET. TRIAL REGISTRATION INFORMATION Eudract 2017-003381-27. Inclusion of the first patient: September 24, 2018. CLASSIFICATION OF EVIDENCE This study provides Class IV evidence on the utility of [18F]DPA-714 PET compared with [18F]FDG PET in identifying the epileptic zone in patients undergoing phase 1 presurgical evaluation for intractable epilepsy.
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Affiliation(s)
- Margaux Cheval
- From the Université Paris-Saclay (M.C., C.L., M.B., N.T.); BioMAPS (S.R., F.C., F.L.B.); Bicetre University Hospital (D.T., A.M.P.), Paris; and Imagerie Moléculaire In Vivo (V.B.), SHFJ, CEA, Orsay, France.
| | - Sebastian Rodrigo
- From the Université Paris-Saclay (M.C., C.L., M.B., N.T.); BioMAPS (S.R., F.C., F.L.B.); Bicetre University Hospital (D.T., A.M.P.), Paris; and Imagerie Moléculaire In Vivo (V.B.), SHFJ, CEA, Orsay, France
| | - Delphine Taussig
- From the Université Paris-Saclay (M.C., C.L., M.B., N.T.); BioMAPS (S.R., F.C., F.L.B.); Bicetre University Hospital (D.T., A.M.P.), Paris; and Imagerie Moléculaire In Vivo (V.B.), SHFJ, CEA, Orsay, France
| | - Fabien Caillé
- From the Université Paris-Saclay (M.C., C.L., M.B., N.T.); BioMAPS (S.R., F.C., F.L.B.); Bicetre University Hospital (D.T., A.M.P.), Paris; and Imagerie Moléculaire In Vivo (V.B.), SHFJ, CEA, Orsay, France
| | - Ana Maria Petrescu
- From the Université Paris-Saclay (M.C., C.L., M.B., N.T.); BioMAPS (S.R., F.C., F.L.B.); Bicetre University Hospital (D.T., A.M.P.), Paris; and Imagerie Moléculaire In Vivo (V.B.), SHFJ, CEA, Orsay, France
| | - Michel Bottlaender
- From the Université Paris-Saclay (M.C., C.L., M.B., N.T.); BioMAPS (S.R., F.C., F.L.B.); Bicetre University Hospital (D.T., A.M.P.), Paris; and Imagerie Moléculaire In Vivo (V.B.), SHFJ, CEA, Orsay, France
| | - Nicolas Tournier
- From the Université Paris-Saclay (M.C., C.L., M.B., N.T.); BioMAPS (S.R., F.C., F.L.B.); Bicetre University Hospital (D.T., A.M.P.), Paris; and Imagerie Moléculaire In Vivo (V.B.), SHFJ, CEA, Orsay, France
| | - Florent L Besson
- From the Université Paris-Saclay (M.C., C.L., M.B., N.T.); BioMAPS (S.R., F.C., F.L.B.); Bicetre University Hospital (D.T., A.M.P.), Paris; and Imagerie Moléculaire In Vivo (V.B.), SHFJ, CEA, Orsay, France
| | - Claire Leroy
- From the Université Paris-Saclay (M.C., C.L., M.B., N.T.); BioMAPS (S.R., F.C., F.L.B.); Bicetre University Hospital (D.T., A.M.P.), Paris; and Imagerie Moléculaire In Vivo (V.B.), SHFJ, CEA, Orsay, France
| | - Viviane Bouilleret
- From the Université Paris-Saclay (M.C., C.L., M.B., N.T.); BioMAPS (S.R., F.C., F.L.B.); Bicetre University Hospital (D.T., A.M.P.), Paris; and Imagerie Moléculaire In Vivo (V.B.), SHFJ, CEA, Orsay, France
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Campbell JM, Kundu B, Lee JN, Miranda M, Arain A, Taussky P, Grandhi R, Rolston JD. Evaluating the concordance of functional MRI-based language lateralization and Wada testing in epilepsy patients: A single-center analysis. Interv Neuroradiol 2023; 29:599-604. [PMID: 35979608 PMCID: PMC10549711 DOI: 10.1177/15910199221121384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/20/2022] [Accepted: 08/03/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND For patients with drug-resistant epilepsy, surgery may be effective in controlling their disease. Surgical evaluation may involve localization of the language areas using functional magnetic resonance imaging (fMRI) or Wada testing. We evaluated the accuracy of task-based fMRI versus Wada-based language lateralization in a cohort of our epilepsy patients. METHODS In a single-center, retrospective analysis, we identified patients with medically intractable epilepsy who participated in presurgical language mapping (n = 35) with fMRI and Wada testing. Demographic variables and imaging metrics were obtained. We calculated the laterality index (LI) from task-evoked fMRI activation maps across language areas during auditory and reading tasks to determine lateralization. Possible scores for LI range from -1 (strongly left-hemisphere dominant) to 1 (strongly right-hemisphere dominant). Concordance between fMRI and Wada was estimated using Cohen's Kappa coefficient. Association between the LI scores from the auditory and reading tasks was tested using Spearman's rank correlation coefficient. RESULTS The fMRI-based laterality indices were concordant with results from Wada testing in 91.4% of patients during the reading task (κ = .55) and 96.9% of patients during the auditory task (κ = .79). The mean LIs for the reading and auditory tasks were -0.52 ± 0.43 and -0.68 ± 0.42, respectively. The LI scores for the language and reading tasks were strongly correlated, r(30) = 0.57 (p = 0.001). CONCLUSION Our findings suggest that fMRI is generally an accurate, low-risk alternative to Wada testing for language lateralization. However, when fMRI indicates atypical language lateralization (e.g., bilateral dominance), patients may benefit from subsequent Wada testing or intraoperative language mapping.
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Affiliation(s)
- Justin M Campbell
- School of Medicine, University of Utah, Salt Lake City, Utah, USA
- Interdepartmental Program in Neuroscience, University of Utah, Salt Lake City, Utah, USA
| | - Bornali Kundu
- Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, USA
| | - James N Lee
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Michelle Miranda
- Department of Neurology, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, USA
| | - Amir Arain
- Department of Neurology, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, USA
| | - Philipp Taussky
- Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, USA
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Ramesh Grandhi
- Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, USA
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, USA
| | - John D Rolston
- Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, USA
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
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21
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Sinha N, Duncan JS, Diehl B, Chowdhury FA, de Tisi J, Miserocchi A, McEvoy AW, Davis KA, Vos SB, Winston GP, Wang Y, Taylor PN. Intracranial EEG Structure-Function Coupling and Seizure Outcomes After Epilepsy Surgery. Neurology 2023; 101:e1293-e1306. [PMID: 37652703 PMCID: PMC10558161 DOI: 10.1212/wnl.0000000000207661] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 06/02/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Surgery is an effective treatment for drug-resistant epilepsy, which modifies the brain's structure and networks to regulate seizure activity. Our objective was to examine the relationship between brain structure and function to determine the extent to which this relationship affects the success of the surgery in controlling seizures. We hypothesized that a stronger association between brain structure and function would lead to improved seizure control after surgery. METHODS We constructed functional and structural brain networks in patients with drug-resistant focal epilepsy by using presurgery functional data from intracranial EEG (iEEG) recordings, presurgery and postsurgery structural data from T1-weighted MRI, and presurgery diffusion-weighted MRI. We quantified the relationship (coupling) between structural and functional connectivity by using the Spearman rank correlation and analyzed this structure-function coupling at 2 spatial scales: (1) global iEEG network level and (2) individual iEEG electrode contacts using virtual surgeries. We retrospectively predicted postoperative seizure freedom by incorporating the structure-function connectivity coupling metrics and routine clinical variables into a cross-validated predictive model. RESULTS We conducted a retrospective analysis on data from 39 patients who met our inclusion criteria. Brain areas implanted with iEEG electrodes had stronger structure-function coupling in seizure-free patients compared with those with seizure recurrence (p = 0.002, d = 0.76, area under the receiver operating characteristic curve [AUC] = 0.78 [95% CI 0.62-0.93]). Virtual surgeries on brain areas that resulted in stronger structure-function coupling of the remaining network were associated with seizure-free outcomes (p = 0.007, d = 0.96, AUC = 0.73 [95% CI 0.58-0.89]). The combination of global and local structure-function coupling measures accurately predicted seizure outcomes with a cross-validated AUC of 0.81 (95% CI 0.67-0.94). These measures were complementary to other clinical variables and, when included for prediction, resulted in a cross-validated AUC of 0.91 (95% CI 0.82-1.0), accuracy of 92%, sensitivity of 93%, and specificity of 91%. DISCUSSION Our study showed that the strength of structure-function connectivity coupling may play a crucial role in determining the success of epilepsy surgery. By quantitatively incorporating structure-function coupling measures and standard-of-care clinical variables into presurgical evaluations, we may be able to better localize epileptogenic tissue and select patients for epilepsy surgery. CLASSIFICATION OF EVIDENCE This is a Class IV retrospective case series showing that structure-function mapping may help determine the outcome from surgical resection for treatment-resistant focal epilepsy.
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Affiliation(s)
- Nishant Sinha
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada.
| | - John S Duncan
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Beate Diehl
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Fahmida A Chowdhury
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Jane de Tisi
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Anna Miserocchi
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Andrew William McEvoy
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Kathryn A Davis
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Sjoerd B Vos
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Gavin P Winston
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Yujiang Wang
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Peter Neal Taylor
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
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22
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Dallmer-Zerbe I, Jiruska P, Hlinka J. Personalized dynamic network models of the human brain as a future tool for planning and optimizing epilepsy therapy. Epilepsia 2023; 64:2221-2238. [PMID: 37340565 DOI: 10.1111/epi.17690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 06/17/2023] [Accepted: 06/19/2023] [Indexed: 06/22/2023]
Abstract
Epilepsy is a common neurological disorder, with one third of patients not responding to currently available antiepileptic drugs. The proportion of pharmacoresistant epilepsies has remained unchanged for many decades. To cure epilepsy and control seizures requires a paradigm shift in the development of new approaches to epilepsy diagnosis and treatment. Contemporary medicine has benefited from the exponential growth of computational modeling, and the application of network dynamics theory to understanding and treating human brain disorders. In epilepsy, the introduction of these approaches has led to personalized epileptic network modeling that can explore the patient's seizure genesis and predict the functional impact of resection on its individual network's propensity to seize. The application of the dynamic systems approach to neurostimulation therapy of epilepsy allows designing stimulation strategies that consider the patient's seizure dynamics and long-term fluctuations in the stability of their epileptic networks. In this article, we review, in a nontechnical fashion suitable for a broad neuroscientific audience, recent progress in personalized dynamic brain network modeling that is shaping the future approach to the diagnosis and treatment of epilepsy.
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Affiliation(s)
- Isa Dallmer-Zerbe
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Premysl Jiruska
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Jaroslav Hlinka
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- National Institute of Mental Health, Klecany, Czech Republic
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23
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Zheng L, Liao P, Wu X, Cao M, Cui W, Lu L, Xu H, Zhu L, Lyu B, Wang X, Teng P, Wang J, Vogrin S, Plummer C, Luan G, Gao JH. An artificial intelligence-based pipeline for automated detection and localisation of epileptic sources from magnetoencephalography. J Neural Eng 2023; 20:046036. [PMID: 37615416 DOI: 10.1088/1741-2552/acef92] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 08/10/2023] [Indexed: 08/25/2023]
Abstract
Objective.Magnetoencephalography (MEG) is a powerful non-invasive diagnostic modality for presurgical epilepsy evaluation. However, the clinical utility of MEG mapping for localising epileptic foci is limited by its low efficiency, high labour requirements, and considerable interoperator variability. To address these obstacles, we proposed a novel artificial intelligence-based automated magnetic source imaging (AMSI) pipeline for automated detection and localisation of epileptic sources from MEG data.Approach.To expedite the analysis of clinical MEG data from patients with epilepsy and reduce human bias, we developed an autolabelling method, a deep-learning model based on convolutional neural networks and a hierarchical clustering method based on a perceptual hash algorithm, to enable the coregistration of MEG and magnetic resonance imaging, the detection and clustering of epileptic activity, and the localisation of epileptic sources in a highly automated manner. We tested the capability of the AMSI pipeline by assessing MEG data from 48 epilepsy patients.Main results.The AMSI pipeline was able to rapidly detect interictal epileptiform discharges with 93.31% ± 3.87% precision based on a 35-patient dataset (with sevenfold patientwise cross-validation) and robustly rendered accurate localisation of epileptic activity with a lobar concordance of 87.18% against interictal and ictal stereo-electroencephalography findings in a 13-patient dataset. We also showed that the AMSI pipeline accomplishes the necessary processes and delivers objective results within a much shorter time frame (∼12 min) than traditional manual processes (∼4 h).Significance.The AMSI pipeline promises to facilitate increased utilisation of MEG data in the clinical analysis of patients with epilepsy.
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Affiliation(s)
- Li Zheng
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
- Changping Laboratory, Beijing, People's Republic of China
| | - Pan Liao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, People's Republic of China
| | - Xiuwen Wu
- Changping Laboratory, Beijing, People's Republic of China
- Center for Biomedical Engineering, University of Science and Technology of China, Anhui, People's Republic of China
| | - Miao Cao
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
- Changping Laboratory, Beijing, People's Republic of China
| | - Wei Cui
- Center for Biomedical Engineering, University of Science and Technology of China, Anhui, People's Republic of China
| | - Lingxi Lu
- Center for the Cognitive Science of Language, Beijing Language and Culture University, Beijing, People's Republic of China
| | - Hui Xu
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
| | - Linlin Zhu
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
| | - Bingjiang Lyu
- Changping Laboratory, Beijing, People's Republic of China
| | - Xiongfei Wang
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
- Beijing Key Laboratory of Epilepsy, Capital Medical University, Beijing, People's Republic of China
| | - Pengfei Teng
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Jing Wang
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Simon Vogrin
- Department of Neuroimaging, Swinburne University of Technology, Melbourne, Australia
| | - Chris Plummer
- Department of Neuroimaging, Swinburne University of Technology, Melbourne, Australia
| | - Guoming Luan
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
- Beijing Key Laboratory of Epilepsy, Capital Medical University, Beijing, People's Republic of China
| | - Jia-Hong Gao
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
- Changping Laboratory, Beijing, People's Republic of China
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, People's Republic of China
- McGovern Institute for Brain Research, Peking University, Beijing, People's Republic of China
- National Biomedical Imaging Center, Peking University, Beijing, People's Republic of China
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24
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Asadi-Pooya AA, Brigo F, Lattanzi S, Blumcke I. Adult epilepsy. Lancet 2023; 402:412-424. [PMID: 37459868 DOI: 10.1016/s0140-6736(23)01048-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 05/15/2023] [Accepted: 05/19/2023] [Indexed: 07/31/2023]
Abstract
Epilepsy is a common medical condition that affects people of all ages, races, social classes, and geographical regions. Diagnosis of epilepsy remains clinical, and ancillary investigations (electroencephalography, imaging, etc) are of aid to determine the type, cause, and prognosis. Antiseizure medications represent the mainstay of epilepsy treatment: they aim to suppress seizures without adverse events, but they do not affect the underlying predisposition to generate seizures. Currently available antiseizure medications are effective in around two-thirds of patients with epilepsy. Neurosurgical resection is an effective strategy to reach seizure control in selected individuals with drug-resistant focal epilepsy. Non-pharmacological treatments such as palliative surgery (eg, corpus callosotomy), neuromodulation techniques (eg, vagus nerve stimulation), and dietary interventions represent therapeutic options for patients with drug-resistant epilepsy who are not suitable for resective brain surgery.
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Affiliation(s)
- Ali A Asadi-Pooya
- Epilepsy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran; Jefferson Comprehensive Epilepsy Center, Department of Neurology, Thomas Jefferson University, Philadelphia, PA, USA.
| | - Francesco Brigo
- Department of Neurology, Hospital of Merano (SABES-ASDAA), Merano, Italy; Lehrkrankenhaus der Paracelsus Medizinischen Privatuniversität, Salzburg, Austria
| | - Simona Lattanzi
- Neurological Clinic, Department of Experimental and Clinical Medicine, Marche Polytechnic University, Ancona, Italy
| | - Ingmar Blumcke
- Institute of Neuropathology, University Hospitals Erlangen, Erlangen, Germany; Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
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25
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Janca R, Jezdik P, Ebel M, Kalina A, Kudr M, Jahodova A, Krysl D, Mackova K, Straka B, Marusic P, Krsek P. Distinct patterns of interictal intracranial EEG in focal cortical dysplasia type I and II. Clin Neurophysiol 2023; 151:10-17. [PMID: 37121217 DOI: 10.1016/j.clinph.2023.03.360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 03/13/2023] [Accepted: 03/21/2023] [Indexed: 05/02/2023]
Abstract
OBJECTIVE Focal cortical dysplasia (FCD) is the most common malformation causing refractory focal epilepsy. Surgical removal of the entire dysplastic cortex is crucial for achieving a seizure-free outcome. Precise presurgical distinctions between FCD types by neuroimaging are difficult, mainly in patients with normal magnetic resonance imaging findings. However, the FCD type is important for planning the extent of surgical approach and counselling. METHODS This study included patients with focal drug-resistant epilepsy and definite histopathological FCD type I or II diagnoses who underwent intracranial electroencephalography (iEEG). We detected interictal epileptiform discharges (IEDs) and their recruitment into repetitive discharges (RDs) to compare electrophysiological patterns characterizing FCD types. RESULTS Patients with FCD type II had a significantly higher IED rate (p < 0.005), a shorter inter-discharge interval within RD episodes (p < 0.003), sleep influence on decreased RD periodicity (p < 0.036), and longer RD episode duration (p < 0.003) than patients with type I. A Bayesian classifier stratified FCD types with 82% accuracy. CONCLUSION Temporal characteristics of IEDs and RDs reflect the histological findings of FCD subtypes and can differentiate FCD types I and II. SIGNIFICANCE Presurgical prediction of FCD type can help to plan a more tailored surgical approach in patients with normal magnetic resonance findings.
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Affiliation(s)
- Radek Janca
- Faculty of Electrical Engineering, Department of Circuit Theory, Czech Technical University in Prague, Technicka 2, 166 27 Prague, Czech Republic.
| | - Petr Jezdik
- Faculty of Electrical Engineering, Department of Circuit Theory, Czech Technical University in Prague, Technicka 2, 166 27 Prague, Czech Republic
| | - Matyas Ebel
- Department of Paediatric Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006, Prague, Czech Republic(2)
| | - Adam Kalina
- Department of Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006 Prague, Czech Republic(2)
| | - Martin Kudr
- Department of Paediatric Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006, Prague, Czech Republic(2)
| | - Alena Jahodova
- Department of Paediatric Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006, Prague, Czech Republic(2)
| | - David Krysl
- Department of Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006 Prague, Czech Republic(2)
| | - Katerina Mackova
- Faculty of Electrical Engineering, Department of Circuit Theory, Czech Technical University in Prague, Technicka 2, 166 27 Prague, Czech Republic
| | - Barbora Straka
- Department of Paediatric Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006, Prague, Czech Republic(2)
| | - Petr Marusic
- Department of Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006 Prague, Czech Republic(2)
| | - Pavel Krsek
- Department of Paediatric Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006, Prague, Czech Republic(2)
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26
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Rijal S, Corona L, Perry MS, Tamilia E, Madsen JR, Stone SSD, Bolton J, Pearl PL, Papadelis C. Functional connectivity discriminates epileptogenic states and predicts surgical outcome in children with drug resistant epilepsy. Sci Rep 2023; 13:9622. [PMID: 37316544 PMCID: PMC10267141 DOI: 10.1038/s41598-023-36551-0] [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: 10/05/2022] [Accepted: 06/06/2023] [Indexed: 06/16/2023] Open
Abstract
Normal brain functioning emerges from a complex interplay among regions forming networks. In epilepsy, these networks are disrupted causing seizures. Highly connected nodes in these networks are epilepsy surgery targets. Here, we assess whether functional connectivity (FC) using intracranial electroencephalography can quantify brain regions epileptogenicity and predict surgical outcome in children with drug resistant epilepsy (DRE). We computed FC between electrodes on different states (i.e. interictal without spikes, interictal with spikes, pre-ictal, ictal, and post-ictal) and frequency bands. We then estimated the electrodes' nodal strength. We compared nodal strength between states, inside and outside resection for good- (n = 22, Engel I) and poor-outcome (n = 9, Engel II-IV) patients, respectively, and tested their utility to predict the epileptogenic zone and outcome. We observed a hierarchical epileptogenic organization among states for nodal strength: lower FC during interictal and pre-ictal states followed by higher FC during ictal and post-ictal states (p < 0.05). We further observed higher FC inside resection (p < 0.05) for good-outcome patients on different states and bands, and no differences for poor-outcome patients. Resection of nodes with high FC was predictive of outcome (positive and negative predictive values: 47-100%). Our findings suggest that FC can discriminate epileptogenic states and predict outcome in patients with DRE.
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Affiliation(s)
- Sakar Rijal
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, 76010, USA
| | - Ludovica Corona
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, 76010, USA
| | - M Scott Perry
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA
| | - Eleonora Tamilia
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Scellig S D Stone
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Jeffrey Bolton
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Christos Papadelis
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA.
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, 76010, USA.
- School of Medicine, Texas Christian University, Fort Worth, TX, 76129, USA.
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27
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Cuesta P, Bruña R, Shah E, Laohathai C, Garcia-Tarodo S, Funke M, Von Allmen G, Maestú F. An individual data-driven virtual resection model based on epileptic network dynamics in children with intractable epilepsy: a magnetoencephalography interictal activity application. Brain Commun 2023; 5:fcad168. [PMID: 37274829 PMCID: PMC10236945 DOI: 10.1093/braincomms/fcad168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 01/24/2023] [Accepted: 05/23/2023] [Indexed: 06/07/2023] Open
Abstract
Epilepsy surgery continues to be a recommended treatment for intractable (medication-resistant) epilepsy; however, 30-70% of epilepsy surgery patients can continue to have seizures. Surgical failures are often associated with incomplete resection or inaccurate localization of the epileptogenic zone. This retrospective study aims to improve surgical outcome through in silico testing of surgical hypotheses through a personalized computational neurosurgery model created from individualized patient's magnetoencephalography recording and MRI. The framework assesses the extent of the epileptic network and evaluates underlying spike dynamics, resulting in identification of one single brain volume as a candidate for resection. Dynamic-locked networks were utilized for virtual cortical resection. This in silico protocol was tested in a cohort of 24 paediatric patients with focal drug-resistant epilepsy who underwent epilepsy surgery. Of 24 patients who were included in the analysis, 79% (19 of 24) of the models agreed with the patient's clinical surgery outcome and 21% (5 of 24) were considered as model failures (accuracy 0.79, sensitivity 0.77, specificity 0.82). Patients with unsuccessful surgery outcome typically showed a model cluster outside of the resected cavity, while those with successful surgery showed the cluster model within the cavity. Two of the model failures showed the cluster in the vicinity of the resected tissue and either a functional disconnection or lack of precision of the magnetoencephalography-MRI overlapping could explain the results. Two other cases were seizure free for 1 year but developed late recurrence. This is the first study that provides in silico personalized protocol for epilepsy surgery planning using magnetoencephalography spike network analysis. This model could provide complementary information to the traditional pre-surgical assessment methods and increase the proportion of patients achieving seizure-free outcome from surgery.
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Affiliation(s)
- Pablo Cuesta
- Correspondence to: Pablo Cuesta Pza. Ramón y Cajal, s/n. Ciudad Universitaria 28040 Madrid, Spain E-mail:
| | - Ricardo Bruña
- Department of Radiology, Rehabilitation and Physiotherapy, Universidad Complutense de Madrid, Madrid, 28040, Spain
- Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, 28040, Spain
| | - Ekta Shah
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | | | - Stephanie Garcia-Tarodo
- Département de la femme, de l'enfant et de l'adolescent, Hôpital des Enfants - Hôpitaux Universitaires de Genève, Geneva, 1211 Genève 14, Switzerland
| | - Michael Funke
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Gretchen Von Allmen
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Fernando Maestú
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, 28040, Spain
- Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, 28040, Spain
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, 28040, Spain
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28
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Santalucia R, Carapancea E, Vespa S, Germany Morrison E, Ghasemi Baroumand A, Vrielynck P, Fierain A, Joris V, Raftopoulos C, Duprez T, Ferrao Santos S, van Mierlo P, El Tahry R. Clinical added value of interictal automated electrical source imaging in the presurgical evaluation of MRI-negative epilepsy: A real-life experience in 29 consecutive patients. Epilepsy Behav 2023; 143:109229. [PMID: 37148703 DOI: 10.1016/j.yebeh.2023.109229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 04/09/2023] [Accepted: 04/20/2023] [Indexed: 05/08/2023]
Abstract
OBJECTIVE During the presurgical evaluation, manual electrical source imaging (ESI) provides clinically useful information in one-third of the patients but it is time-consuming and requires specific expertise. This prospective study aims to assess the clinical added value of a fully automated ESI analysis in a cohort of patients with MRI-negative epilepsy and describe its diagnostic performance, by evaluating sublobar concordance with stereo-electroencephalography (SEEG) results and surgical resection and outcome. METHODS All consecutive patients referred to the Center for Refractory Epilepsy (CRE) of St-Luc University Hospital (Brussels, Belgium) for presurgical evaluation between 15/01/2019 and 31/12/2020 meeting the inclusion criteria, were recruited to the study. Interictal ESI was realized on low-density long-term EEG monitoring (LD-ESI) and, whenever available, high-density EEG (HD-ESI), using a fully automated analysis (Epilog PreOp, Epilog NV, Ghent, Belgium). The multidisciplinary team (MDT) was asked to formulate hypotheses about the epileptogenic zone (EZ) location at sublobar level and make a decision on further management for each patient at two distinct moments: i) blinded to ESI and ii) after the presentation and clinical interpretation of ESI. Results leading to a change in clinical management were considered contributive. Patients were followed up to assess whether these changes lead to concordant results on stereo-EEG (SEEG) or successful epilepsy surgery. RESULTS Data from all included 29 patients were analyzed. ESI led to a change in the management plan in 12/29 patients (41%). In 9/12 (75%), modifications were related to a change in the plan of the invasive recording. In 8/9 patients, invasive recording was performed. In 6/8 (75%), the intracranial EEG recording confirmed the localization of the ESI at a sublobar level. So far, 5/12 patients, for whom the management plan was changed after ESI, were operated on and have at least one-year postoperative follow-up. In all cases, the EZ identified by ESI was included in the resection zone. Among these patients, 4/5 (80%) are seizure-free (ILAE 1) and one patient experienced a seizure reduction of more than 50% (ILAE 4). CONCLUSIONS In this single-center prospective study, we demonstrated the added value of automated ESI in the presurgical evaluation of MRI-negative cases, especially in helping to plan the implantation of depth electrodes for SEEG, provided that ESI results are integrated into the whole multimodal evaluation and clinically interpreted.
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Affiliation(s)
- Roberto Santalucia
- Cliniques Universitaires Saint-Luc, Paediatric Neurology Unit, Brussels, Belgium; Institute of Neurosciences (IoNS/NEUR), Université Catholique de Louvain (UCL), Brussels, Belgium; Centre Hospitalier Neurologique William Lennox (CHNWL), Clinical Neurophysiology, Ottignies, Belgium; Cliniques Universitaires Saint-Luc, Reference Center for Refractory Epilepsy (CRE), Brussels, Belgium.
| | - Evelina Carapancea
- Institute of Neurosciences (IoNS/NEUR), Université Catholique de Louvain (UCL), Brussels, Belgium
| | - Simone Vespa
- Institute of Neurosciences (IoNS/NEUR), Université Catholique de Louvain (UCL), Brussels, Belgium
| | - Enrique Germany Morrison
- Institute of Neurosciences (IoNS/NEUR), Université Catholique de Louvain (UCL), Brussels, Belgium
| | - Amir Ghasemi Baroumand
- Medical Image and Signal Processing, Ghent University, Ghent, Belgium; Epilog NV, Ghent, Belgium
| | - Pascal Vrielynck
- Centre Hospitalier Neurologique William Lennox (CHNWL), Clinical Neurophysiology, Ottignies, Belgium; Cliniques Universitaires Saint-Luc, Reference Center for Refractory Epilepsy (CRE), Brussels, Belgium
| | - Alexane Fierain
- Centre Hospitalier Neurologique William Lennox (CHNWL), Clinical Neurophysiology, Ottignies, Belgium; Cliniques Universitaires Saint-Luc, Reference Center for Refractory Epilepsy (CRE), Brussels, Belgium; Cliniques Universitaires Saint-Luc, Neurology Unit, Brussels, Belgium
| | - Vincent Joris
- Institute of Neurosciences (IoNS/NEUR), Université Catholique de Louvain (UCL), Brussels, Belgium; Cliniques Universitaires Saint-Luc, Reference Center for Refractory Epilepsy (CRE), Brussels, Belgium; Cliniques Universitaires Saint-Luc, Neurosurgery Unit, Brussels, Belgium
| | - Christian Raftopoulos
- Cliniques Universitaires Saint-Luc, Reference Center for Refractory Epilepsy (CRE), Brussels, Belgium; Cliniques Universitaires Saint-Luc, Neurosurgery Unit, Brussels, Belgium
| | - Thierry Duprez
- Cliniques Universitaires Saint-Luc, Reference Center for Refractory Epilepsy (CRE), Brussels, Belgium; Cliniques Universitaires Saint-Luc, Medical Imaging Department, Neuroradiology Unit, Belgium
| | - Susana Ferrao Santos
- Institute of Neurosciences (IoNS/NEUR), Université Catholique de Louvain (UCL), Brussels, Belgium; Cliniques Universitaires Saint-Luc, Reference Center for Refractory Epilepsy (CRE), Brussels, Belgium; Cliniques Universitaires Saint-Luc, Neurology Unit, Brussels, Belgium
| | - Pieter van Mierlo
- Medical Image and Signal Processing, Ghent University, Ghent, Belgium; Epilog NV, Ghent, Belgium
| | - Riëm El Tahry
- Institute of Neurosciences (IoNS/NEUR), Université Catholique de Louvain (UCL), Brussels, Belgium; Cliniques Universitaires Saint-Luc, Reference Center for Refractory Epilepsy (CRE), Brussels, Belgium; Cliniques Universitaires Saint-Luc, Neurology Unit, Brussels, Belgium; WELBIO Department, WEL Research Institute, Avenue Pasteur 6, 1300 Wavre, Belgium
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29
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Corona L, Tamilia E, Perry MS, Madsen JR, Bolton J, Stone SSD, Stufflebeam SM, Pearl PL, Papadelis C. Non-invasive mapping of epileptogenic networks predicts surgical outcome. Brain 2023; 146:1916-1931. [PMID: 36789500 PMCID: PMC10151194 DOI: 10.1093/brain/awac477] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 11/03/2022] [Accepted: 11/30/2022] [Indexed: 02/16/2023] Open
Abstract
Epilepsy is increasingly considered a disorder of brain networks. Studying these networks with functional connectivity can help identify hubs that facilitate the spread of epileptiform activity. Surgical resection of these hubs may lead patients who suffer from drug-resistant epilepsy to seizure freedom. Here, we aim to map non-invasively epileptogenic networks, through the virtual implantation of sensors estimated with electric and magnetic source imaging, in patients with drug-resistant epilepsy. We hypothesize that highly connected hubs identified non-invasively with source imaging can predict the epileptogenic zone and the surgical outcome better than spikes localized with conventional source localization methods (dipoles). We retrospectively analysed simultaneous high-density electroencephalography (EEG) and magnetoencephalography data recorded from 37 children and young adults with drug-resistant epilepsy who underwent neurosurgery. Using source imaging, we estimated virtual sensors at locations where intracranial EEG contacts were placed. On data with and without spikes, we computed undirected functional connectivity between sensors/contacts using amplitude envelope correlation and phase locking value for physiologically relevant frequency bands. From each functional connectivity matrix, we generated an undirected network containing the strongest connections within sensors/contacts using the minimum spanning tree. For each sensor/contact, we computed graph centrality measures. We compared functional connectivity and their derived graph centrality of sensors/contacts inside resection for good (n = 22, ILAE I) and poor (n = 15, ILAE II-VI) outcome patients, tested their ability to predict the epileptogenic zone in good-outcome patients, examined the association between highly connected hubs removal and surgical outcome and performed leave-one-out cross-validation to support their prognostic value. We also compared the predictive values of functional connectivity with those of dipoles. Finally, we tested the reliability of virtual sensor measures via Spearman's correlation with intracranial EEG at population- and patient-level. We observed higher functional connectivity inside than outside resection (P < 0.05, Wilcoxon signed-rank test) for good-outcome patients, on data with and without spikes across different bands for intracranial EEG and electric/magnetic source imaging and few differences for poor-outcome patients. These functional connectivity measures were predictive of both the epileptogenic zone and outcome (positive and negative predictive values ≥55%, validated using leave-one-out cross-validation) outperforming dipoles on spikes. Significant correlations were found between source imaging and intracranial EEG measures (0.4 ≤ rho ≤ 0.9, P < 0.05). Our findings suggest that virtual implantation of sensors through source imaging can non-invasively identify highly connected hubs in patients with drug-resistant epilepsy, even in the absence of frank epileptiform activity. Surgical resection of these hubs predicts outcome better than dipoles.
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Affiliation(s)
- Ludovica Corona
- Jane and John Justin Institute for Mind Health, Cook Children's Health Care System, Fort Worth, TX 76104, USA
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX 76010, USA
| | - Eleonora Tamilia
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - M Scott Perry
- Jane and John Justin Institute for Mind Health, Cook Children's Health Care System, Fort Worth, TX 76104, USA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jeffrey Bolton
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Scellig S D Stone
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Steve M Stufflebeam
- Athinoula Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Christos Papadelis
- Jane and John Justin Institute for Mind Health, Cook Children's Health Care System, Fort Worth, TX 76104, USA
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX 76010, USA
- School of Medicine, Texas Christian University, Fort Worth, TX 76129, USA
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Kitchigina V, Shubina L. Oscillations in the dentate gyrus as a tool for the performance of the hippocampal functions: Healthy and epileptic brain. Prog Neuropsychopharmacol Biol Psychiatry 2023; 125:110759. [PMID: 37003419 DOI: 10.1016/j.pnpbp.2023.110759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 03/17/2023] [Accepted: 03/28/2023] [Indexed: 04/03/2023]
Abstract
The dentate gyrus (DG) is part of the hippocampal formation and is essential for important cognitive processes such as navigation and memory. The oscillatory activity of the DG network is believed to play a critical role in cognition. DG circuits generate theta, beta, and gamma rhythms, which participate in the specific information processing performed by DG neurons. In the temporal lobe epilepsy (TLE), cognitive abilities are impaired, which may be due to drastic alterations in the DG structure and network activity during epileptogenesis. The theta rhythm and theta coherence are especially vulnerable in dentate circuits; disturbances in DG theta oscillations and their coherence may be responsible for general cognitive impairments observed during epileptogenesis. Some researchers suggested that the vulnerability of DG mossy cells is a key factor in the genesis of TLE, but others did not support this hypothesis. The aim of the review is not only to present the current state of the art in this field of research but to help pave the way for future investigations by highlighting the gaps in our knowledge to completely appreciate the role of DG rhythms in brain functions. Disturbances in oscillatory activity of the DG during TLE development may be a diagnostic marker in the treatment of this disease.
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Affiliation(s)
- Valentina Kitchigina
- Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, Pushchino, Moscow region 142290, Russia.
| | - Liubov Shubina
- Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, Pushchino, Moscow region 142290, Russia
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Mangalore S, Peer S, Khokhar SK, Bharath RD, Kulanthaivelu K, Saini J, Sinha S, Kishore VK, Mundlamuri RC, Asranna A, Lakshminarayanapuram Gopal V, Kenchaiah R, Arimappamagan A, Sadashiva N, Rao MB, Mahadevan A, Rajeswaran J, Kumar K, Thennarasu K. Resting-State Functional MRI/PET Profile as a Potential Alternative to Tri-Modality EEG-MR/PET Imaging: An Exploratory Study in Drug-Refractory Epilepsy. Asian J Neurosurg 2023; 18:53-61. [PMID: 37056888 PMCID: PMC10089745 DOI: 10.1055/s-0043-1760852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023] Open
Abstract
Abstract
Objective The study explores whether the epileptic networks associate with predetermined seizure onset zone (SOZ) identified from other modalities such as electroencephalogram/video electroencephalogram/structural MRI (EEG/VEEG/sMRI) and with the degree of resting-state functional MRI/positron emission tomography (RS-fMRI/PET) coupling. Here, we have analyzed the subgroup of patients who reported having a seizure on the day of scan as postictal cases and compared the findings with interictal cases (seizure-free interval).
Methods We performed independent component analysis (ICA) on RS-fMRI and 20 ICA were hand-labeled as large scale, noise, downstream, and epilepsy networks (Epinets) based on their profile in spatial, time series, and power spectrum domains. We had a total of 43 cases, with 4 cases in the postictal group (100%). Of 39 cases, 14 cases did not yield any Epinet and 25 cases (61%) were analyzed for the final study. The analysis was done patient-wise and correlated with predetermined SOZ.
Results The yield of finding Epinets on RS-fMRI is more during the postictal period than in the interictal period, although PET and RS-fMRI spatial, time series, and power spectral patterns were similar in both these subgroups. Overlaps between large-scale and downstream networks were noted, indicating that epilepsy propagation can involve large-scale cognition networks. Lateralization to SOZ was noted as blood oxygen level–dependent activation and correlated with sMRI/PET findings. Postoperative surgical failure cases showed residual Epinet profile.
Conclusion RS-fMRI may be a viable option for trimodality imaging to obtain simultaneous physiological information at the functional network and metabolic level.
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Steinbrenner M, McDowell A, Centeno M, Moeller F, Perani S, Lorio S, Maziero D, Carmichael DW. Camera-based Prospective Motion Correction in Paediatric Epilepsy Patients Enables EEG-fMRI Localization Even in High-motion States. Brain Topogr 2023; 36:319-337. [PMID: 36939987 PMCID: PMC10164016 DOI: 10.1007/s10548-023-00945-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 02/14/2023] [Indexed: 03/21/2023]
Abstract
BACKGROUND EEG-fMRI is a useful additional test to localize the epileptogenic zone (EZ) particularly in MRI negative cases. However subject motion presents a particular challenge owing to its large effects on both MRI and EEG signal. Traditionally it is assumed that prospective motion correction (PMC) of fMRI precludes EEG artifact correction. METHODS Children undergoing presurgical assessment at Great Ormond Street Hospital were included into the study. PMC of fMRI was done using a commercial system with a Moiré Phase Tracking marker and MR-compatible camera. For retrospective EEG correction both a standard and a motion educated EEG artefact correction (REEGMAS) were compared to each other. RESULTS Ten children underwent simultaneous EEG-fMRI. Overall head movement was high (mean RMS velocity < 1.5 mm/s) and showed high inter- and intra-individual variability. Comparing motion measured by the PMC camera and the (uncorrected residual) motion detected by realignment of fMRI images, there was a five-fold reduction in motion from its prospective correction. Retrospective EEG correction using both standard approaches and REEGMAS allowed the visualization and identification of physiological noise and epileptiform discharges. Seven of 10 children had significant maps, which were concordant with the clinical EZ hypothesis in 6 of these 7. CONCLUSION To our knowledge this is the first application of camera-based PMC for MRI in a pediatric clinical setting. Despite large amount of movement PMC in combination with retrospective EEG correction recovered data and obtained clinically meaningful results during high levels of subject motion. Practical limitations may currently limit the widespread use of this technology.
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Affiliation(s)
- Mirja Steinbrenner
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK.,Department of Neurology and Experimental Neurology, Epilepsy Center Berlin-Brandenburg, Charité-Universitätsmedizin Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Amy McDowell
- Developmental Imaging and Biophysics, UCL Institute of Child Health, University College London, 30 Guilford St, London, WC1N 1EH, UK
| | - Maria Centeno
- Developmental Imaging and Biophysics, UCL Institute of Child Health, University College London, 30 Guilford St, London, WC1N 1EH, UK.,Epilepsy Unit, Neurology Department, Hospital Clinic Barcelona/IDIBAPS, Villarroel 170., Barcelona, 08036, Spain
| | - Friederike Moeller
- Department of Clinical Neurophysiology, Great Ormond Street Hospital, Great Ormond Street, London, WC1N 3JH, UK
| | - Suejen Perani
- Department of Basic and Clinical Neuroscience, KCL Institute of Psychiatry, Psychology & Neuroscience, 16 De Crespigny Park, London, SE5 8AF, UK
| | - Sara Lorio
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK
| | - Danilo Maziero
- Department of Radiation Medicine & Applied Sciences, University of California, San Diego Health, San Diego, CA, USA
| | - David W Carmichael
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK. .,Developmental Imaging and Biophysics, UCL Institute of Child Health, University College London, 30 Guilford St, London, WC1N 1EH, UK.
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Zhao B, McGonigal A, Hu W, Zhang C, Wang X, Mo J, Zhao X, Ai L, Shao X, Zhang K, Zhang J. Interictal HFO and FDG-PET correlation predicts surgical outcome following SEEG. Epilepsia 2023; 64:667-677. [PMID: 36510851 DOI: 10.1111/epi.17485] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/09/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022]
Abstract
OBJECTIVE This study aimed to investigate the quantitative relationship between interictal 18 F-fluorodeoxyglucose-positron emission tomography (FDG-PET) and interictal high-frequency oscillations (HFOs) from stereo-electroencephalography (SEEG) recordings in patients with refractory epilepsy. METHODS We retrospectively included 32 patients. FDG-PET data were quantified through statistical parametric mapping (SPM) t test modeling with normal controls. Interictal SEEG segments with four, 10-min segments were selected randomly. HFO detection and classification procedures were automatically performed. Channel-based HFOs separating ripple (80-250 Hz) and fast ripple (FR; 250-500 Hz) counts were correlated with the surrounding metabolism T score at the individual and group level, respectively. The association was further validated across anatomic seizure origins and sleep vs wake states. We built a joint feature FR × T reflecting the FR and hypometabolism concordance to predict surgical outcomes in 28 patients who underwent surgery. RESULTS We found a negative correlation between interictal FDG-PET and HFOs through the linear mixed-effects model (R2 = .346 and .457 for ripples and FRs, respectively, p < .001); these correlations were generalizable to different epileptogenic-zone lobar localizations and vigilance states. The FR × T inside the resection volume could be used as a predictor for surgical outcomes with an area under the curve of 0.81. SIGNIFICANCE The degree of hypometabolism is associated with HFO generation rate, especially for FRs. This relationship would be meaningful for selection of SEEG candidates and for optimizing SEEG scheme planning. The concordance between FRs and hypometabolism inside the resection volume could provide prognostic information regarding surgical outcome.
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Affiliation(s)
- Baotian Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Aileen McGonigal
- Epilepsy Unit, Neurosciences Centre, Mater Hospital and Mater Research Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiaobin Zhao
- Department of Nuclear Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lin Ai
- Department of Nuclear Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiaoqiu Shao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neurostimulation, Beijing, China
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Lisgaras CP, Oliva A, Mckenzie S, LaFrancois J, Siegelbaum SA, Scharman HE. Hippocampal area CA2 controls seizure dynamics, interictal EEG abnormalities and social comorbidity in mouse models of temporal lobe epilepsy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.15.524149. [PMID: 36711983 PMCID: PMC9882187 DOI: 10.1101/2023.01.15.524149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Temporal lobe epilepsy (TLE) is characterized by spontaneous recurrent seizures, abnormal activity between seizures, and impaired behavior. CA2 pyramidal neurons (PNs) are potentially important because inhibiting them with a chemogenetic approach reduces seizure frequency in a mouse model of TLE. However, whether seizures could be stopped by timing inhibition just as a seizure begins is unclear. Furthermore, whether inhibition would reduce the cortical and motor manifestations of seizures are not clear. Finally, whether interictal EEG abnormalities and TLE comorbidities would be improved are unknown. Therefore, real-time optogenetic silencing of CA2 PNs during seizures, interictal activity and behavior were studied in 2 mouse models of TLE. CA2 silencing significantly reduced seizure duration and time spent in convulsive behavior. Interictal spikes and high frequency oscillations were significantly reduced, and social behavior was improved. Therefore, brief focal silencing of CA2 PNs reduces seizures, their propagation, and convulsive manifestations, improves interictal EEG, and ameliorates social comorbidities. HIGHLIGHTS Real-time CA2 silencing at the onset of seizures reduces seizure durationWhen CA2 silencing reduces seizure activity in hippocampus it also reduces cortical seizure activity and convulsive manifestations of seizuresInterictal spikes and high frequency oscillations are reduced by real-time CA2 silencingReal-time CA2 silencing of high frequency oscillations (>250Hz) rescues social memory deficits of chronic epileptic mice.
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Li Z, Sun W, Duan W, Jiang Y, Chen M, Lin G, Wang Q, Fan Z, Tong Y, Chen L, Li J, Cheng G, Wang C, Li C, Chen L. Guiding Epilepsy Surgery with an LRP1-Targeted SPECT/SERRS Dual-Mode Imaging Probe. ACS APPLIED MATERIALS & INTERFACES 2023; 15:14-25. [PMID: 35588160 DOI: 10.1021/acsami.2c02540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Accurate identification of the resectable epileptic lesion is a precondition of operative intervention to drug-resistant epilepsy (DRE) patients. However, even when multiple diagnostic modalities are combined, epileptic foci cannot be accurately identified in ∼30% of DRE patients. Inflammation-associated low-density lipoprotein receptor-related protein-1 (LRP1) has been validated to be a surrogate target for imaging epileptic foci. Here, we reported an LRP1-targeted dual-mode probe that is capable of providing comprehensive epilepsy information preoperatively with SPECT imaging while intraoperatively delineating epileptic margins in a sensitive high-contrast manner with surface-enhanced resonance Raman scattering (SERRS) imaging. Notably, a novel and universal strategy for constructing self-assembled monolayer (SAM)-based Raman reporters was proposed for boosting the sensitivity, stability, reproducibility, and quantifiability of the SERRS signal. The probe showed high efficacy to penetrate the blood-brain barrier. SPECT imaging showed the probe could delineate the epileptic foci clearly with a high target-to-background ratio (4.11 ± 0.71, 2 h). Further, with the assistance of the probe, attenuated seizure frequency in the epileptic mouse models was achieved by using SPECT together with Raman images before and during operation, respectively. Overall, this work highlights a new strategy to develop a SPECT/SERRS dual-mode probe for comprehensive epilepsy surgery that can overcome the brain shift by the co-registration of preoperative SPECT and SERRS intraoperative images.
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Affiliation(s)
- Zhi Li
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Wanbing Sun
- Department of Neurology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai 200433, China
- Department of Neurology and Research Center of Neurology in Second Affiliated Hospital and Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Wenjia Duan
- Key Laboratory of Smart Drug Delivery, Ministry of Education, State Key Laboratory of Medical Neurobiology, School of Pharmacy, Fudan University, Shanghai 201203, China
| | - Yiqing Jiang
- Key Laboratory of Smart Drug Delivery, Ministry of Education, State Key Laboratory of Medical Neurobiology, School of Pharmacy, Fudan University, Shanghai 201203, China
| | - Ming Chen
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Guorong Lin
- Key Laboratory of Smart Drug Delivery, Ministry of Education, State Key Laboratory of Medical Neurobiology, School of Pharmacy, Fudan University, Shanghai 201203, China
| | - Qinyue Wang
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Zhen Fan
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Yusheng Tong
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Luo Chen
- Key Laboratory of Smart Drug Delivery, Ministry of Education, State Key Laboratory of Medical Neurobiology, School of Pharmacy, Fudan University, Shanghai 201203, China
| | - Jianing Li
- Key Laboratory of Smart Drug Delivery, Ministry of Education, State Key Laboratory of Medical Neurobiology, School of Pharmacy, Fudan University, Shanghai 201203, China
| | - Guangli Cheng
- Key Laboratory of Smart Drug Delivery, Ministry of Education, State Key Laboratory of Medical Neurobiology, School of Pharmacy, Fudan University, Shanghai 201203, China
| | - Cong Wang
- Key Laboratory of Smart Drug Delivery, Ministry of Education, State Key Laboratory of Medical Neurobiology, School of Pharmacy, Fudan University, Shanghai 201203, China
- Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China
| | - Cong Li
- Key Laboratory of Smart Drug Delivery, Ministry of Education, State Key Laboratory of Medical Neurobiology, School of Pharmacy, Fudan University, Shanghai 201203, China
| | - Liang Chen
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai 200032, China
- National Center for Neurological Disorders, Shanghai 200040, China
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Johnson EA, Lee JJ, Hacker CD, Park KY, Rustamov N, Daniel AGS, Shimony JS, Leuthardt EC. Preoperative functional connectivity by magnetic resonance imaging for refractory neocortical epilepsy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.10.23284374. [PMID: 36712003 PMCID: PMC9882436 DOI: 10.1101/2023.01.10.23284374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Objective Patients with refractory epilepsy experience extensive and invasive clinical testing for seizure onset zones treatable by surgical resection. However, surgical resection can fail to provide therapeutic benefit, and patients with neocortical epilepsy have the poorest therapeutic outcomes. This case series studied patients with neocortical epilepsy who were referred for surgical treatment. Prior to surgery, patients volunteered for resting-state functional magnetic resonance imaging (rs-fMRI) in addition to imaging for the clinical standard of care. This work examined the variability of functional connectivity in patients, estimated from rs-fMRI, for associations with surgical outcomes. Methods This work examined pre-operative structural imaging, pre-operative rs-fMRI, and post-operative structural imaging from seven epilepsy patients. Review of the clinical record provided Engel classifications for surgical outcomes. A novel method assessed pre-operative rs-fMRI from patients using comparative rs-fMRI from a large cohort of healthy control subjects and estimated Gibbs distributions for functional connectivity in patients compared to healthy controls. Results Three patients had Engel classification Ia, one patient had Engel classification IIa, and three patients had Engel classification IV. Metrics for variability of functional connectivity, including absolute differences of the functional connectivity of each patient from healthy control averages and probabilistic scores for absolute differences, were higher for patients classified as Engel IV, for whom epilepsy surgery provided no meaningful improvement. Significance This work continues on-going efforts to use rs-fMRI to characterize abnormal functional connectivity in the brain. Preliminary evidence indicates that the topography of variant functional connectivity in epilepsy patients may be clinically relevant for identifying patients unlikely to have favorable outcomes after epilepsy surgery. Widespread topographic variations of functional connectivity also support the hypothesis that epilepsy is a disease of brain resting-state networks.
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Karawun: a software package for assisting evaluation of advances in multimodal imaging for neurosurgical planning and intraoperative neuronavigation. Int J Comput Assist Radiol Surg 2023; 18:171-179. [PMID: 36070033 PMCID: PMC9883338 DOI: 10.1007/s11548-022-02736-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 08/09/2022] [Indexed: 02/01/2023]
Abstract
PURPOSE The neuroimaging research community-which includes a broad range of scientific, medical, statistical, and engineering disciplines-has developed many tools to advance our knowledge of brain structure, function, development, aging, and disease. Past research efforts have clearly shaped clinical practice. However, translation of new methodologies into clinical practice is challenging. Anything that can reduce these barriers has the potential to improve the rate at which research outcomes can contribute to clinical practice. In this article, we introduce Karawun, a file format conversion tool, that has become a key part of our work in translating advances in diffusion imaging acquisition and analysis into neurosurgical practice at our institution. METHODS Karawun links analysis workflows created using open-source neuroimaging software, to Brainlab (Brainlab AG, Munich, Germany), a commercially available surgical planning and navigation suite. Karawun achieves this using DICOM standards supporting representation of 3D structures, including tractography streamlines, and thus offers far more than traditional screenshot or color overlay approaches. RESULTS We show that neurosurgical planning data, created from multimodal imaging data using analysis methods implemented in open-source research software, can be imported into Brainlab. The datasets can be manipulated as if they were created by Brainlab, including 3D visualizations of white matter tracts and other objects. CONCLUSION Clinicians can explore and interact with the results of research neuroimaging pipelines using familiar tools within their standard clinical workflow, understand the impact of the new methods on their practice and provide feedback to methods developers. This capability has been important to the translation of advanced analysis techniques into practice at our institution.
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Dangouloff-Ros V, Jansen JFA, de Jong J, Postma AA, Hoeberigs C, Fillon L, Boisgontier J, Roux CJ, Levy R, Varlet P, Blauwblomme T, Eisermann M, Losito E, Bourgeois M, Chiron C, Nabbout R, Boddaert N, Backes W. Abnormal Spontaneous Blood Oxygenation Level Dependent Fluctuations in Children with Focal Cortical Dysplasias: Initial Findings in Surgically Confirmed Cases. Neuropediatrics 2022; 54:188-196. [PMID: 36223876 DOI: 10.1055/a-1959-9241] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
BACKGROUND Focal cortical dysplasias (FCD) are a frequent cause of drug-resistant epilepsy in children but are often undetected on structural magnetic resonance imaging (MRI). We aimed to measure and validate the variation of resting state functional MRI (rs-fMRI) blood oxygenation level dependent (BOLD) metrics in surgically proven FCDs in children, to assess the potential yield for detecting and understanding these lesions. METHODS We prospectively included pediatric patients with surgically proven FCD with inconclusive structural MRI and healthy controls, who underwent a ten-minute rs-fMRI acquired at 3T. Rs-fMRI data was pre-processed and maps of values of regional homogeneity (ReHo), degree centrality (DC), amplitude of low frequency fluctuations (ALFF) and fractional ALFF (fALFF) were calculated. The variations of BOLD metrics within the to-be-resected areas were analyzed visually, and quantitatively using lateralization indices. BOLD metrics variations were also analyzed in fluorodeoxyglucose-positron emission tomography (FDG-PET) hypometabolic areas. RESULTS We included 7 patients (range: 3-15 years) and 6 aged-matched controls (range: 6-17 years). ReHo lateralization indices were positive in the to-be-resected areas in 4/7 patients, and in 6/7 patients in the additional PET hypometabolic areas. These indices were significantly higher compared to controls in 3/7 and 4/7 patients, respectively. Visual analysis revealed a good spatial correlation between high ReHo areas and MRI structural abnormalities (when present) or PET hypometabolic areas. No consistent variation was seen using DC, ALFF, or fALFF. CONCLUSION Resting-state fMRI metrics, noticeably increase in ReHo, may have potential to help detect MRI-negative FCDs in combination with other morphological and functional techniques, used in clinical practice and epilepsy-surgery screening.
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Affiliation(s)
- Volodia Dangouloff-Ros
- Pediatric Radiology Department, AP-HP, Hôpital Universitaire Necker-Enfants Malades, Paris, France.,Université de Paris, INSERM U1199, Paris, France.,Université de Paris, Institut Imagine, Paris, France.,Department of Radiology, Maastricht University Medical Center, Maastricht, the Netherlands.,School of Mental Health and Neurosciences, Maastricht, the Netherlands
| | - Jacobus F A Jansen
- Department of Radiology, Maastricht University Medical Center, Maastricht, the Netherlands.,School of Mental Health and Neurosciences, Maastricht, the Netherlands
| | - Joost de Jong
- Department of Radiology, Maastricht University Medical Center, Maastricht, the Netherlands.,School of Mental Health and Neurosciences, Maastricht, the Netherlands
| | - Alida A Postma
- Department of Radiology, Maastricht University Medical Center, Maastricht, the Netherlands.,School of Mental Health and Neurosciences, Maastricht, the Netherlands
| | - Christianne Hoeberigs
- Department of Radiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Ludovic Fillon
- Pediatric Radiology Department, AP-HP, Hôpital Universitaire Necker-Enfants Malades, Paris, France.,Université de Paris, INSERM U1199, Paris, France.,Université de Paris, Institut Imagine, Paris, France
| | - Jennifer Boisgontier
- Pediatric Radiology Department, AP-HP, Hôpital Universitaire Necker-Enfants Malades, Paris, France.,Université de Paris, INSERM U1199, Paris, France.,Université de Paris, Institut Imagine, Paris, France
| | - Charles-Joris Roux
- Pediatric Radiology Department, AP-HP, Hôpital Universitaire Necker-Enfants Malades, Paris, France.,Université de Paris, INSERM U1199, Paris, France.,Université de Paris, Institut Imagine, Paris, France
| | - Raphael Levy
- Pediatric Radiology Department, AP-HP, Hôpital Universitaire Necker-Enfants Malades, Paris, France.,Université de Paris, INSERM U1199, Paris, France.,Université de Paris, Institut Imagine, Paris, France
| | - Pascale Varlet
- Neuropathology Department, GHU Paris, Université de Paris, 1 rue Cabanis, Paris
| | - Thomas Blauwblomme
- Pediatric Neurosurgery Department, AP-HP, Hôpital Universitaire Necker-Enfants Malades, Paris, France.,Université de Paris, INSERM U1129, Pediatric Epilepsies and Brain Plasticity, Paris, France
| | - Monika Eisermann
- Université de Paris, INSERM U1129, Pediatric Epilepsies and Brain Plasticity, Paris, France.,Department of Clinical Neurophysiology, Hôpital Universitaire Necker-Enfants Malades, Paris, France
| | - Emma Losito
- Université de Paris, INSERM U1129, Pediatric Epilepsies and Brain Plasticity, Paris, France.,Pediatric Neurology Department, Reference Center for Rare Epilepsies, Hôpital Universitaire Necker-Enfants Malades, Paris, France
| | - Marie Bourgeois
- Pediatric Neurosurgery Department, AP-HP, Hôpital Universitaire Necker-Enfants Malades, Paris, France
| | - Catherine Chiron
- Université de Paris, INSERM U1129, Pediatric Epilepsies and Brain Plasticity, Paris, France.,Pediatric Neurology Department, Reference Center for Rare Epilepsies, Hôpital Universitaire Necker-Enfants Malades, Paris, France.,Department of Nuclear Medicine, SHFJ-CEA, Orsay, France
| | - Rima Nabbout
- Université de Paris, INSERM U1129, Pediatric Epilepsies and Brain Plasticity, Paris, France.,Pediatric Neurology Department, Reference Center for Rare Epilepsies, Hôpital Universitaire Necker-Enfants Malades, Paris, France
| | - Nathalie Boddaert
- Pediatric Radiology Department, AP-HP, Hôpital Universitaire Necker-Enfants Malades, Paris, France.,Université de Paris, INSERM U1199, Paris, France.,Université de Paris, Institut Imagine, Paris, France
| | - Walter Backes
- Department of Radiology, Maastricht University Medical Center, Maastricht, the Netherlands.,School of Mental Health and Neurosciences, Maastricht, the Netherlands
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Cai Z, He B. Ictal source localization from intracranial recordings. Clin Neurophysiol 2022; 144:121-122. [PMID: 36257896 PMCID: PMC9936740 DOI: 10.1016/j.clinph.2022.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 09/27/2022] [Indexed: 11/15/2022]
Affiliation(s)
- Zhengxiang Cai
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
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Kishk NA, Farghaly M, Nawito A, Shamloul RM, Moawad MK. Neuropsychological performance in patients with focal drug-resistant epilepsy and different factors that affect their performance. THE EGYPTIAN JOURNAL OF NEUROLOGY, PSYCHIATRY AND NEUROSURGERY 2022. [DOI: 10.1186/s41983-022-00523-4] [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
Drug-resistant epilepsy (DRE) accounts for nearly 30% of patients with epilepsy, which is associated with high incidence of cognitive comorbidity. The aim of this work was to study the role of neuropsychological assessment in patients with epilepsy, and different factors that affect their performance in patients with multiple factors (focal onset DRE).
Methods
118 patients were recruited from Kasr Alainy hospital, epilepsy outpatient clinic with focal DRE. The patients’ demographic and clinical data were collected, Electroencephalograph (EEG) interictal/ictal (when available), and brain imaging (MRI epilepsy protocol). Neuropsychological assessment by Wechsler Adult Intelligence Scale (WAIS-IV), proposed neurocognitive assessment battery and mood assessment was done. Their performance in neuropsychological assessment was correlated with the collected data. Concordance between different assessment modalities and brain lesion were done.
Results
Among recruited patients, 67.3% of patients showed Full-scale Intelligence Quotient (FSIQ) was less than average. FSIQ score significantly correlated with years of education, and number of anti-seizure medications (ASMs). Neurocognitive assessment battery could achieve cognitive profile of the patients but with poor lateralizing value. Executive function was the most affected cognitive domain. History of status epilepticus significantly affect FSIQ and executive function performance. Fifty-six percent of patients had depression. Among the analyzed factors, FSIQ and lesional brain imaging significantly affected neurocognitive performance of studied patients. Clinical semiology had better concordance in lateralization (74.7%) and localization (69.5%) with brain imaging compared to ictal EEG. Among patients who had ictal EEG recording, 36.4% patients (25% were temporal lobe) had complete concordance, while 38.6% patients had partial concordance.
Conclusions
Among analyzed factors, FSIQ was the most significant determinant of studied population’s neurocognitive performance. Clinical semiology were the best correlated with brain lesion. Complete concordance was best detected at the temporal lobe.
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Perucca P, Gotman J. Delineating the epileptogenic zone: spikes versus oscillations. Lancet Neurol 2022; 21:949-951. [DOI: 10.1016/s1474-4422(22)00396-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 09/13/2022] [Indexed: 10/31/2022]
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42
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Qi L, Xu C, Wang X, Du J, He Q, Wu D, Wang X, Jin G, Wang Q, Chen J, Wang D, Zhang H, Zhang X, Wei P, Shan Y, Cui Z, Wang Y, Shu Y, Zhao G, Yu T, Ren L. Intracranial direct electrical mapping reveals the functional architecture of the human basal ganglia. Commun Biol 2022; 5:1123. [PMID: 36274105 PMCID: PMC9588773 DOI: 10.1038/s42003-022-04084-3] [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: 06/09/2022] [Accepted: 10/07/2022] [Indexed: 11/30/2022] Open
Abstract
The basal ganglia play a key role in integrating a variety of human behaviors through the cortico–basal ganglia–thalamo–cortical loops. Accordingly, basal ganglia disturbances are implicated in a broad range of debilitating neuropsychiatric disorders. Despite accumulating knowledge of the basal ganglia functional organization, the neural substrates and circuitry subserving functions have not been directly mapped in humans. By direct electrical stimulation of distinct basal ganglia regions in 35 refractory epilepsy patients undergoing stereoelectroencephalography recordings, we here offer currently the most complete overview of basal ganglia functional characterization, extending not only to the expected sensorimotor responses, but also to vestibular sensations, autonomic responses, cognitive and multimodal effects. Specifically, some locations identified responses weren’t predicted by the model derived from large-scale meta-analyses. Our work may mark an important step toward understanding the functional architecture of the human basal ganglia and provide mechanistic explanations of non-motor symptoms in brain circuit disorders. Direct electrical stimulation of the basal ganglia using implanted SEEG electrodes produced a variety of motor and non-motor effects in human participants, providing insight into the functional architecture of this key brain region.
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Dimakopoulos V, Mégevand P, Stieglitz LH, Imbach L, Sarnthein J. Information flows from hippocampus to auditory cortex during replay of verbal working memory items. eLife 2022; 11:78677. [PMID: 35960169 PMCID: PMC9374435 DOI: 10.7554/elife.78677] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 07/06/2022] [Indexed: 01/07/2023] Open
Abstract
The maintenance of items in working memory (WM) relies on a widespread network of cortical areas and hippocampus where synchronization between electrophysiological recordings reflects functional coupling. We investigated the direction of information flow between auditory cortex and hippocampus while participants heard and then mentally replayed strings of letters in WM by activating their phonological loop. We recorded local field potentials from the hippocampus, reconstructed beamforming sources of scalp EEG, and – additionally in four participants – recorded from subdural cortical electrodes. When analyzing Granger causality, the information flow was from auditory cortex to hippocampus with a peak in the [4 8] Hz range while participants heard the letters. This flow was subsequently reversed during maintenance while participants maintained the letters in memory. The functional interaction between hippocampus and the cortex and the reversal of information flow provide a physiological basis for the encoding of memory items and their active replay during maintenance. Every day, the brain’s ability to temporarily store and recall information – called working memory – enables us to reason, solve complex problems or to speak. Holding pieces of information in working memory for short periods of times is a skill that relies on communication between neural circuits that span several areas of the brain. The hippocampus, a seahorse-shaped area at the centre of the brain, is well-known for its role in learning and memory. Less clear, however, is how brain regions that process sensory inputs, including visual stimuli and sounds, contribute to working memory. To investigate, Dimakopoulos et al. studied the flow of information between the hippocampus and the auditory cortex, which processes sound. To do so, various types of electrodes were placed on the scalp or surgically implanted in the brains of people with drug-resistant epilepsy. These electrodes measured the brain activity of participants as they read, heard and then mentally replayed strings of up to 8 letters. The electrical signals analysed reflected the flow of information between brain areas. When participants read and heard the sequence of letters, brain signals flowed from the auditory cortex to the hippocampus. The flow of electrical activity was reversed while participants recalled the letters. This pattern was found only in the left side of the brain, as expected for a language related task, and only if participants recalled the letters correctly. This work by Dimakopoulos et al. provides the first evidence of bidirectional communication between brain areas that are active when people memorise and recall information from their working memory. In doing so, it provides a physiological basis for how the brain encodes and replays information stored in working memory, which evidently relies on the interplay between the hippocampus and sensory cortex.
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Affiliation(s)
- Vasileios Dimakopoulos
- Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zurich, Switzerland
| | - Pierre Mégevand
- Département des neurosciences fondamentales, Faculté de médecine, Université de Genève, Genève, Switzerland.,Service de neurologie, Hôpitaux Universitaires de Genève, Geneva, Switzerland, Genève, Switzerland
| | - Lennart H Stieglitz
- Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zurich, Switzerland
| | - Lukas Imbach
- Schweizerisches Epilepsie Zentrum, Klinik Lengg AG, Zurich, Switzerland.,Neuroscience Center Zurich, ETH Zuric, Zurich, Switzerland
| | - Johannes Sarnthein
- Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zurich, Switzerland.,Neuroscience Center Zurich, ETH Zuric, Zurich, Switzerland
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Upadhya D, Attaluri S, Liu Y, Hattiangady B, Castro OW, Shuai B, Dong Y, Zhang SC, Shetty AK. Grafted hPSC-derived GABA-ergic interneurons regulate seizures and specific cognitive function in temporal lobe epilepsy. NPJ Regen Med 2022; 7:38. [PMID: 35915118 PMCID: PMC9343458 DOI: 10.1038/s41536-022-00234-7] [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: 09/24/2021] [Accepted: 06/24/2022] [Indexed: 11/25/2022] Open
Abstract
Interneuron loss/dysfunction contributes to spontaneous recurrent seizures (SRS) in chronic temporal lobe epilepsy (TLE), and interneuron grafting into the epileptic hippocampus reduces SRS and improves cognitive function. This study investigated whether graft-derived gamma-aminobutyric acid positive (GABA-ergic) interneurons directly regulate SRS and cognitive function in a rat model of chronic TLE. Human pluripotent stem cell-derived medial ganglionic eminence-like GABA-ergic progenitors, engineered to express hM4D(Gi), a designer receptor exclusively activated by designer drugs (DREADDs) through CRISPR/Cas9 technology, were grafted into hippocampi of chronically epileptic rats to facilitate the subsequent silencing of graft-derived interneurons. Such grafting substantially reduced SRS and improved hippocampus-dependent cognitive function. Remarkably, silencing of graft-derived interneurons with a designer drug increased SRS and induced location memory impairment but did not affect pattern separation function. Deactivation of DREADDs restored both SRS control and object location memory function. Thus, transplanted GABA-ergic interneurons could directly regulate SRS and specific cognitive functions in TLE.
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Affiliation(s)
- Dinesh Upadhya
- Institute for Regenerative Medicine, Texas A&M Health Science Center College of Medicine, College Station, TX, USA.,Department of Molecular and Cellular Medicine, Texas A&M Health Science Center College of Medicine, College Station, TX, USA.,Research Service, Olin E. Teague Veterans' Medical Center, Central Texas Veterans Health Care System, Temple, TX, USA.,Centre for Molecular Neurosciences, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Sahithi Attaluri
- Institute for Regenerative Medicine, Texas A&M Health Science Center College of Medicine, College Station, TX, USA.,Department of Molecular and Cellular Medicine, Texas A&M Health Science Center College of Medicine, College Station, TX, USA.,Research Service, Olin E. Teague Veterans' Medical Center, Central Texas Veterans Health Care System, Temple, TX, USA
| | - Yan Liu
- Waisman Center, Departments of Neuroscience and Neurology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Bharathi Hattiangady
- Institute for Regenerative Medicine, Texas A&M Health Science Center College of Medicine, College Station, TX, USA.,Department of Molecular and Cellular Medicine, Texas A&M Health Science Center College of Medicine, College Station, TX, USA.,Research Service, Olin E. Teague Veterans' Medical Center, Central Texas Veterans Health Care System, Temple, TX, USA
| | - Olagide W Castro
- Institute for Regenerative Medicine, Texas A&M Health Science Center College of Medicine, College Station, TX, USA.,Department of Molecular and Cellular Medicine, Texas A&M Health Science Center College of Medicine, College Station, TX, USA.,Research Service, Olin E. Teague Veterans' Medical Center, Central Texas Veterans Health Care System, Temple, TX, USA.,Institute of Biological Sciences and Health, Federal Univ of Alagoas (UFAL), Maceio, AL, Brazil
| | - Bing Shuai
- Institute for Regenerative Medicine, Texas A&M Health Science Center College of Medicine, College Station, TX, USA.,Department of Molecular and Cellular Medicine, Texas A&M Health Science Center College of Medicine, College Station, TX, USA.,Research Service, Olin E. Teague Veterans' Medical Center, Central Texas Veterans Health Care System, Temple, TX, USA
| | - Yi Dong
- Waisman Center, Departments of Neuroscience and Neurology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Su-Chun Zhang
- Waisman Center, Departments of Neuroscience and Neurology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Ashok K Shetty
- Institute for Regenerative Medicine, Texas A&M Health Science Center College of Medicine, College Station, TX, USA. .,Department of Molecular and Cellular Medicine, Texas A&M Health Science Center College of Medicine, College Station, TX, USA. .,Research Service, Olin E. Teague Veterans' Medical Center, Central Texas Veterans Health Care System, Temple, TX, USA.
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45
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Transfer Learning from Healthy to Unhealthy Patients for the Automated Classification of Functional Brain Networks in fMRI. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12146925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Functional Magnetic Resonance Imaging (fMRI) is an essential tool for the pre-surgical planning of brain tumor removal, which allows the identification of functional brain networks to preserve the patient’s neurological functions. One fMRI technique used to identify the functional brain network is the resting-state-fMRI (rs-fMRI). This technique is not routinely available because of the necessity to have an expert reviewer who can manually identify each functional network. The lack of sufficient unhealthy data has so far hindered a data-driven approach based on machine learning tools for full automation of this clinical task. In this article, we investigate the possibility of such an approach via the transfer learning method from healthy control data to unhealthy patient data to boost the detection of functional brain networks in rs-fMRI data. The end-to-end deep learning model implemented in this article distinguishes seven principal functional brain networks using fMRI images. The best performance of a 75% correct recognition rate is obtained from the proposed deep learning architecture, which shows its superiority over other machine learning algorithms that were equally tested for this classification task. Based on this best reference model, we demonstrate the possibility of boosting the results of our algorithm with transfer learning from healthy patients to unhealthy patients. This application of the transfer learning technique opens interesting possibilities because healthy control subjects can be easily enrolled for fMRI data acquisition since it is non-invasive. Consequently, this process helps to compensate for the usual small cohort of unhealthy patient data. This transfer learning approach could be extended to other medical imaging modalities and pathology.
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46
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Cross JH, Reilly C, Gutierrez Delicado E, Smith ML, Malmgren K. Epilepsy surgery for children and adolescents: evidence-based but underused. THE LANCET CHILD & ADOLESCENT HEALTH 2022; 6:484-494. [DOI: 10.1016/s2352-4642(22)00098-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 03/12/2022] [Accepted: 03/23/2022] [Indexed: 11/30/2022]
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47
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Paredes-Aragon E, AlKhaldi NA, Ballesteros-Herrera D, Mirsattari SM. Stereo-Encephalographic Presurgical Evaluation of Temporal Lobe Epilepsy: An Evolving Science. Front Neurol 2022; 13:867458. [PMID: 35720095 PMCID: PMC9197919 DOI: 10.3389/fneur.2022.867458] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 04/25/2022] [Indexed: 11/15/2022] Open
Abstract
Drug-resistant epilepsy is present in nearly 30% of patients. Resection of the epileptogenic zone has been found to be the most effective in achieving seizure freedom. The study of temporal lobe epilepsy for surgical treatment is extensive and complex. It involves a multidisciplinary team in decision-making with initial non-invasive studies (Phase I), providing 70% of the required information to elaborate a hypothesis and treatment plans. Select cases present more complexity involving bilateral clinical or electrographic manifestations, have contradicting information, or may involve deeper structures as a part of the epileptogenic zone. These cases are discussed by a multidisciplinary team of experts with a hypothesis for invasive methods of study. Subdural electrodes were once the mainstay of invasive presurgical evaluation and in later years most Comprehensive Epilepsy Centers have shifted to intracranial recordings. The intracranial recording follows original concepts since its development by Bancaud and Talairach, but great advances have been made in the field. Stereo-electroencephalography is a growing field of study, treatment, and establishment of seizure pattern complexities. In this comprehensive review, we explore the indications, usefulness, discoveries in interictal and ictal findings, pitfalls, and advances in the science of presurgical stereo-encephalography for temporal lobe epilepsy.
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Affiliation(s)
- Elma Paredes-Aragon
- Department of Clinical Neurological Sciences, Western University, London, ON, Canada
| | - Norah A AlKhaldi
- Department of Clinical Neurological Sciences, Western University, London, ON, Canada.,Neurology Department, King Fahad Hospital of the University, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Daniel Ballesteros-Herrera
- Neurosurgery Department, National Institute of Neurology and Neurosurgery "Dr. Manuel Velasco Suárez", Mexico City, Mexico
| | - Seyed M Mirsattari
- Department of Clinical Neurological Sciences, Western University, London, ON, Canada.,Departments of Clinical Neurological Sciences, Diagnostic Imaging, Biomedical Imaging and Psychology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
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Kudo K, Morise H, Ranasinghe KG, Mizuiri D, Bhutada AS, Chen J, Findlay A, Kirsch HE, Nagarajan SS. Magnetoencephalography Imaging Reveals Abnormal Information Flow in Temporal Lobe Epilepsy. Brain Connect 2022; 12:362-373. [PMID: 34210170 PMCID: PMC9131359 DOI: 10.1089/brain.2020.0989] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background/Introduction: Widespread network disruption has been hypothesized to be an important predictor of outcomes in patients with refractory temporal lobe epilepsy (TLE). Most studies examining functional network disruption in epilepsy have largely focused on the symmetric bidirectional metrics of the strength of network connections. However, a more complete description of network dysfunction impacts in epilepsy requires an investigation of the potentially more sensitive directional metrics of information flow. Methods: This study describes a whole-brain magnetoencephalography-imaging approach to examine resting-state directional information flow networks, quantified by phase-transfer entropy (PTE), in patients with TLE compared with healthy controls (HCs). Associations between PTE and clinical characteristics of epilepsy syndrome are also investigated. Results: Deficits of information flow were specific to alpha-band frequencies. In alpha band, while HCs exhibit a clear posterior-to-anterior directionality of information flow, in patients with TLE, this pattern of regional information outflow and inflow was significantly altered in the frontal and occipital regions. The changes in information flow within the alpha band in selected brain regions were correlated with interictal spike frequency and duration of epilepsy. Conclusions: Impaired information flow is an important dimension of network dysfunction associated with the pathophysiological mechanisms of TLE.
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Affiliation(s)
- Kiwamu Kudo
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
- Medical Imaging Business Center, Ricoh Company Ltd., Kanazawa, Japan
| | - Hirofumi Morise
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
- Medical Imaging Business Center, Ricoh Company Ltd., Kanazawa, Japan
| | - Kamalini G. Ranasinghe
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, California, USA
| | - Danielle Mizuiri
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Abhishek S. Bhutada
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Jessie Chen
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Anne Findlay
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Heidi E. Kirsch
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
- Epilepsy Center, Department of Neurology, University of California, San Francisco, San Francisco, California, USA
| | - Srikantan S. Nagarajan
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
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49
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Lisgaras CP, Scharfman HE. Robust chronic convulsive seizures, high frequency oscillations, and human seizure onset patterns in an intrahippocampal kainic acid model in mice. Neurobiol Dis 2022; 166:105637. [PMID: 35091040 PMCID: PMC9034729 DOI: 10.1016/j.nbd.2022.105637] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 01/05/2022] [Accepted: 01/22/2022] [Indexed: 01/21/2023] Open
Abstract
Intrahippocampal kainic acid (IHKA) has been widely implemented to simulate temporal lobe epilepsy (TLE), but evidence of robust seizures is usually limited. To resolve this problem, we slightly modified previous methods and show robust seizures are common and frequent in both male and female mice. We employed continuous wideband video-EEG monitoring from 4 recording sites to best demonstrate the seizures. We found many more convulsive seizures than most studies have reported. Mortality was low. Analysis of convulsive seizures at 2-4 and 10-12 wks post-IHKA showed a robust frequency (2-4 per day on average) and duration (typically 20-30 s) at each time. Comparison of the two timepoints showed that seizure burden became more severe in approximately 50% of the animals. We show that almost all convulsive seizures could be characterized as either low-voltage fast or hypersynchronous onset seizures, which has not been reported in a mouse model of epilepsy and is important because these seizure types are found in humans. In addition, we report that high frequency oscillations (>250 Hz) occur, resembling findings from IHKA in rats and TLE patients. Pathology in the hippocampus at the site of IHKA injection was similar to mesial temporal lobe sclerosis and reduced contralaterally. In summary, our methods produce a model of TLE in mice with robust convulsive seizures, and there is variable progression. HFOs are robust also, and seizures have onset patterns and pathology like human TLE. SIGNIFICANCE: Although the IHKA model has been widely used in mice for epilepsy research, there is variation in outcomes, with many studies showing few robust seizures long-term, especially convulsive seizures. We present an implementation of the IHKA model with frequent convulsive seizures that are robust, meaning they are >10 s and associated with complex high frequency rhythmic activity recorded from 2 hippocampal and 2 cortical sites. Seizure onset patterns usually matched the low-voltage fast and hypersynchronous seizures in TLE. Importantly, there is low mortality, and both sexes can be used. We believe our results will advance the ability to use the IHKA model of TLE in mice. The results also have important implications for our understanding of HFOs, progression, and other topics of broad interest to the epilepsy research community. Finally, the results have implications for preclinical drug screening because seizure frequency increased in approximately half of the mice after a 6 wk interval, suggesting that the typical 2 wk period for monitoring seizure frequency is insufficient.
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Affiliation(s)
- Christos Panagiotis Lisgaras
- Departments of Child & Adolescent Psychiatry, Neuroscience & Physiology, and Psychiatry, and the Neuroscience Institute, New York University Langone Health, 550 First Ave., New York, NY 10016, United States of America,Center for Dementia Research, The Nathan Kline Institute for Psychiatric Research, New York State Office of Mental Health, 140 Old Orangeburg Road, Bldg. 35, Orangeburg, NY 10962, United States of America
| | - Helen E. Scharfman
- Departments of Child & Adolescent Psychiatry, Neuroscience & Physiology, and Psychiatry, and the Neuroscience Institute, New York University Langone Health, 550 First Ave., New York, NY 10016, United States of America,Center for Dementia Research, The Nathan Kline Institute for Psychiatric Research, New York State Office of Mental Health, 140 Old Orangeburg Road, Bldg. 35, Orangeburg, NY 10962, United States of America,Corresponding author at: The Nathan Kline Institute, Center for Dementia Research, 140 Old Orangeburg Rd. Bldg. 35, Orangeburg, NY 10962, United States of America. (H.E. Scharfman)
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50
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Multimodal Presurgical Evaluation of Medically Refractory Focal Epilepsy in Adults: An Update for Radiologists. AJR Am J Roentgenol 2022; 219:488-500. [PMID: 35441531 DOI: 10.2214/ajr.22.27588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Surgery is a potentially curative treatment option for patients with medically refractory focal epilepsy. Advanced neuroimaging modalities often improve surgical outcomes by contributing key information during the highly individualized surgical planning process and intraoperative localization. Hence, neuroradiologists play an integral role as part of the multidisciplinary management team. In this review, we initially present the conceptual background and practical framework of the presurgical evaluation process, including a description of the surgical treatment approaches in medically refractory focal epilepsy in adults. This background is followed by an overview of the advanced modalities commonly used during the presurgical workup at level IV epilepsy centers including diffusion imaging techniques, blood oxygen level dependent (BOLD) functional MRI (fMRI), PET, SPECT, and subtraction ictal SPECT, as well as by introductions to 7-T MRI and electrophysiologic techniques including electroencephalography (EEG) and magnetoencephalography (MEG). We also provide illustrative case examples of multimodal neuroimaging including PET/MRI, PET/MRI-DTI, subtraction ictal SPECT, and image-guided stereotactic planning with fMRI-DTI.
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