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Gong R, Roth RW, Hull K, Rashid H, Vandergrift WA, Parashos A, Sinha N, Davis KA, Bonilha L, Gleichgerrcht E. Quantifying hubness to predict surgical outcomes in epilepsy: Assessing resection-hub alignment in interictal intracranial EEG networks. Epilepsia 2024. [PMID: 39305470 DOI: 10.1111/epi.18128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 09/05/2024] [Accepted: 09/05/2024] [Indexed: 09/25/2024]
Abstract
OBJECTIVE Intracranial EEG can identify epilepsy-related networks in patients with focal epilepsy; however, the association between network organization and post-surgical seizure outcomes remains unclear. Hubness serves as a critical metric to assess network organization by identifying brain regions that are highly influential to other regions. In this study, we tested the hypothesis that favorable post-operative seizure outcomes are associated with the surgical removal of interictal network hubs, measured by the novel metric "Resection-Hub Alignment Degree (RHAD)." METHODS We analyzed Phase II interictal intracranial EEG from 69 patients with epilepsy who were seizure-free (n = 45) and non-seizure-free (n = 24) 1 year post-operatively. Connectivity matrices were constructed from intracranial EEG recordings using imaginary coherence in various frequency bands, and centrality metrics were applied to identify network hubs. The RHAD metric quantified the congruence between hubs and resected/ablated areas. We used a logistic regression model, incorporating other clinical factors, and evaluated the association of this alignment regarding post-surgical seizure outcomes. RESULTS There was a significant difference in RHAD in fast gamma (80-200 Hz) interictal network between patients with favorable and unfavorable surgical outcomes (p = .025). This finding remained similar across network definitions (i.e., channel-based or region-based network) and centrality measurements (Eigenvector, Closeness, and PageRank). The alignment between surgically removed areas and other commonly used clinical quantitative measures (seizure-onset zone, irritative zone, high-frequency oscillations zone) did not reveal significant differences in post-operative outcomes. This finding suggests that the hubness measurement may offer better predictive performance and finer-grained network analysis. In addition, the RHAD metric showed explanatory validity both alone (area under the curve [AUC] = .66) and in combination with surgical therapy type (resection vs ablation, AUC = .71). SIGNIFICANCE Our findings underscore the role of network hub surgical removal, measured through the RHAD metric of interictal intracranial EEG high gamma networks, in enhancing our understanding of seizure outcomes in epilepsy surgery.
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Affiliation(s)
- Ruxue Gong
- Department of Neurology, School of Medicine, Emory University, Atlanta, Georgia, USA
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Rebecca W Roth
- Department of Neurology, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Kaitlyn Hull
- Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Haris Rashid
- Department of Neurology, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - William A Vandergrift
- Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Alexandra Parashos
- Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Nishant Sinha
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Kathryn A Davis
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Leonardo Bonilha
- Department of Neurology, School of Medicine, University of South Carolina, Columbia, South Carolina, USA
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Sheikh SR, McKee ZA, Ghosn S, Jeong KS, Kattan M, Burgess RC, Jehi L, Saab CY. Machine learning algorithm for predicting seizure control after temporal lobe resection using peri-ictal electroencephalography. Sci Rep 2024; 14:21771. [PMID: 39294238 PMCID: PMC11410994 DOI: 10.1038/s41598-024-72249-7] [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: 07/23/2024] [Accepted: 09/05/2024] [Indexed: 09/20/2024] Open
Abstract
Brain resection is curative for a subset of patients with drug resistant epilepsy but up to half will fail to achieve sustained seizure freedom in the long term. There is a critical need for accurate prediction tools to identify patients likely to have recurrent postoperative seizures. Results from preclinical models and intracranial EEG in humans suggest that the window of time immediately before and after a seizure ("peri-ictal") represents a unique brain state with implications for clinical outcome prediction. Using a dataset of 294 patients who underwent temporal lobe resection for seizures, we show that machine learning classifiers can make accurate predictions of postoperative seizure outcome using 5 min of peri-ictal scalp EEG data that is part of universal presurgical evaluation (AUC 0.98, out-of-group testing accuracy > 90%). This is the first approach to seizure outcome prediction that employs a routine non-invasive preoperative study (scalp EEG) with accuracy range likely to translate into a clinical tool. Decision curve analysis (DCA) shows that compared to the prevalent clinical-variable based nomogram, use of the EEG-augmented approach could decrease the rate of unsuccessful brain resections by 20%.
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Affiliation(s)
- Shehryar R Sheikh
- Department of Neurosurgery, Cleveland Clinic, Cleveland, OH, USA.
- Department of Molecular Medicine, Cleveland Clinic, Cleveland, OH, USA.
| | | | - Samer Ghosn
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, OH, USA
| | - Ki-Soo Jeong
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, OH, USA
- Department of Biomedical Engineering, Brown University, Providence, RI, USA
| | - Michael Kattan
- Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA
| | - Richard C Burgess
- Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Lara Jehi
- Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
- Center for Computational Life Sciences, Cleveland Clinic, Cleveland, OH, USA
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Carl Y Saab
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, OH, USA
- Department of Biomedical Engineering, Brown University, Providence, RI, USA
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
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3
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Gong R, Roth RW, Chang AJ, Sinha N, Parashos A, Davis KA, Kuzniecky R, Bonilha L, Gleichgerrcht E. EEG Ictal Power Dynamics, Function-Structure Associations, and Epilepsy Surgical Outcomes. Neurology 2024; 102:e209451. [PMID: 38820468 DOI: 10.1212/wnl.0000000000209451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Postoperative seizure control in drug-resistant temporal lobe epilepsy (TLE) remains variable, and the causes for this variability are not well understood. One contributing factor could be the extensive spread of synchronized ictal activity across networks. Our study used novel quantifiable assessments from intracranial EEG (iEEG) to test this hypothesis and investigated how the spread of seizures is determined by underlying structural network topological properties. METHODS We evaluated iEEG data from 157 seizures in 27 patients with TLE: 100 seizures from 17 patients with postoperative seizure control (Engel score I) vs 57 seizures from 10 patients with unfavorable surgical outcomes (Engel score II-IV). We introduced a quantifiable method to measure seizure power dynamics within anatomical regions, refining existing seizure imaging frameworks and minimizing reliance on subjective human decision-making. Time-frequency power representations were obtained in 6 frequency bands ranging from theta to gamma. Ictal power spectrums were normalized against a baseline clip taken at least 6 hours away from ictal events. Electrodes' time-frequency power spectrums were then mapped onto individual T1-weighted MRIs and grouped based on a standard brain atlas. We compared spatiotemporal dynamics for seizures between groups with favorable and unfavorable surgical outcomes. This comparison included examining the range of activated brain regions and the spreading rate of ictal activities. We then evaluated whether regional iEEG power values were a function of fractional anisotropy (FA) from diffusion tensor imaging across regions over time. RESULTS Seizures from patients with unfavorable outcomes exhibited significantly higher maximum activation sizes in various frequency bands. Notably, we provided quantifiable evidence that in seizures associated with unfavorable surgical outcomes, the spread of beta-band power across brain regions is significantly faster, detectable as early as the first second after seizure onset. There was a significant correlation between beta power during seizures and FA in the corresponding areas, particularly in the unfavorable outcome group. Our findings further suggest that integrating structural and functional features could improve the prediction of epilepsy surgical outcomes. DISCUSSION Our findings suggest that ictal iEEG power dynamics and the structural-functional relationship are mechanistic factors associated with surgical outcomes in TLE.
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Affiliation(s)
- Ruxue Gong
- From the Department of Neurology (R.G., R.W.R., E.G.), School of Medicine, Emory University, Atlanta, GA; Department of Neurology (A.J.C., A.P.), Medical University of South Carolina, Charleston; Department of Neurology (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Department of Neurology (R.K.), Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY; and Department of Neurology (L.B.), School of Medicine, University of South Carolina, Columbia
| | - Rebecca W Roth
- From the Department of Neurology (R.G., R.W.R., E.G.), School of Medicine, Emory University, Atlanta, GA; Department of Neurology (A.J.C., A.P.), Medical University of South Carolina, Charleston; Department of Neurology (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Department of Neurology (R.K.), Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY; and Department of Neurology (L.B.), School of Medicine, University of South Carolina, Columbia
| | - Allen J Chang
- From the Department of Neurology (R.G., R.W.R., E.G.), School of Medicine, Emory University, Atlanta, GA; Department of Neurology (A.J.C., A.P.), Medical University of South Carolina, Charleston; Department of Neurology (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Department of Neurology (R.K.), Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY; and Department of Neurology (L.B.), School of Medicine, University of South Carolina, Columbia
| | - Nishant Sinha
- From the Department of Neurology (R.G., R.W.R., E.G.), School of Medicine, Emory University, Atlanta, GA; Department of Neurology (A.J.C., A.P.), Medical University of South Carolina, Charleston; Department of Neurology (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Department of Neurology (R.K.), Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY; and Department of Neurology (L.B.), School of Medicine, University of South Carolina, Columbia
| | - Alexandra Parashos
- From the Department of Neurology (R.G., R.W.R., E.G.), School of Medicine, Emory University, Atlanta, GA; Department of Neurology (A.J.C., A.P.), Medical University of South Carolina, Charleston; Department of Neurology (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Department of Neurology (R.K.), Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY; and Department of Neurology (L.B.), School of Medicine, University of South Carolina, Columbia
| | - Kathryn A Davis
- From the Department of Neurology (R.G., R.W.R., E.G.), School of Medicine, Emory University, Atlanta, GA; Department of Neurology (A.J.C., A.P.), Medical University of South Carolina, Charleston; Department of Neurology (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Department of Neurology (R.K.), Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY; and Department of Neurology (L.B.), School of Medicine, University of South Carolina, Columbia
| | - Ruben Kuzniecky
- From the Department of Neurology (R.G., R.W.R., E.G.), School of Medicine, Emory University, Atlanta, GA; Department of Neurology (A.J.C., A.P.), Medical University of South Carolina, Charleston; Department of Neurology (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Department of Neurology (R.K.), Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY; and Department of Neurology (L.B.), School of Medicine, University of South Carolina, Columbia
| | - Leonardo Bonilha
- From the Department of Neurology (R.G., R.W.R., E.G.), School of Medicine, Emory University, Atlanta, GA; Department of Neurology (A.J.C., A.P.), Medical University of South Carolina, Charleston; Department of Neurology (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Department of Neurology (R.K.), Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY; and Department of Neurology (L.B.), School of Medicine, University of South Carolina, Columbia
| | - Ezequiel Gleichgerrcht
- From the Department of Neurology (R.G., R.W.R., E.G.), School of Medicine, Emory University, Atlanta, GA; Department of Neurology (A.J.C., A.P.), Medical University of South Carolina, Charleston; Department of Neurology (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Department of Neurology (R.K.), Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY; and Department of Neurology (L.B.), School of Medicine, University of South Carolina, Columbia
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Li H, Meng Q, Liu Y, Wu H, Dong Y, Ren Y, Zhang J, Du C, Dong S, Liu X, Zhang H. The value of ictal scalp EEG in focal epilepsies surgery: a retrospective analysis. Neurol Sci 2024:10.1007/s10072-024-07657-8. [PMID: 38902569 DOI: 10.1007/s10072-024-07657-8] [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: 01/11/2024] [Accepted: 06/14/2024] [Indexed: 06/22/2024]
Abstract
OBJECTIVE To describe the association between preoperative ictal scalp electroencephalogram (EEG) results and surgical outcomes in patients with focal epilepsies. METHODS The data of consecutive patients with focal epilepsies who received surgical treatments at our center from January 2012 to December 2021 were retrospectively analyzed. RESULTS Our data showed that 44.2% (322/729) of patients had ictal EEG recorded on video EEG monitoring during preoperative evaluation, of which 60.6% (195/322) had a concordant ictal EEG results. No significant difference of surgery outcomes between patients with and without ictal EEG was discovered. Among MRI-negative patients, those with concordant ictal EEG had a significantly better outcome than those without ictal EEG (75.7% vs. 43.8%, p = 0.024). Further logistic regression analysis showed that concordant ictal EEG was an independent predictor for a favorable outcome (OR = 4.430, 95%CI 1.175-16.694, p = 0.028). Among MRI-positive patients, those with extra-temporal lesions and discordant ictal EEG results had a worse outcome compared to those without an ictal EEG result (44.7% vs. 68.8%, p = 0.005). Further logistic regression analysis showed that discordant ictal EEG was an independent predictor of worse outcome (OR = 0.387, 95%CI 0.186-0.807, p = 0.011) in these patients. Furthermore, our data indicated that the number of seizures was not associated with the concordance rates of the ictal EEG, nor the surgical outcomes. CONCLUSIONS The value of ictal scalp EEG for epilepsy surgery varies widely among patients. A concordant ictal EEG predicts a good surgical outcome in MRI-negative patients, whereas a discordant ictal EEG predicts a poor postoperative outcome in lesional extratemporal lobe epilepsy.
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Affiliation(s)
- Huanfa Li
- Department of Neurosurgery, Comprehensive Epilepsy Center, The First Affiliated Hospital of Xi'an JiaoTong University, No.277, Yanta West Road, Xi'an, 710061, China
- Clinical Research Center for Refractory Epilepsy of Shaanxi Province, Xi'an, 710061, China
| | - Qiang Meng
- Department of Neurosurgery, Comprehensive Epilepsy Center, The First Affiliated Hospital of Xi'an JiaoTong University, No.277, Yanta West Road, Xi'an, 710061, China
- Clinical Research Center for Refractory Epilepsy of Shaanxi Province, Xi'an, 710061, China
| | - Yong Liu
- Department of Neurosurgery, Comprehensive Epilepsy Center, The First Affiliated Hospital of Xi'an JiaoTong University, No.277, Yanta West Road, Xi'an, 710061, China
- Clinical Research Center for Refractory Epilepsy of Shaanxi Province, Xi'an, 710061, China
| | - Hao Wu
- Department of Neurosurgery, Comprehensive Epilepsy Center, The First Affiliated Hospital of Xi'an JiaoTong University, No.277, Yanta West Road, Xi'an, 710061, China
- Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
- Center for Mitochondrial Biology and Medicine, The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Yicong Dong
- Department of Neurosurgery, Comprehensive Epilepsy Center, The First Affiliated Hospital of Xi'an JiaoTong University, No.277, Yanta West Road, Xi'an, 710061, China
- Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
| | - Yutao Ren
- Department of Neurosurgery, Comprehensive Epilepsy Center, The First Affiliated Hospital of Xi'an JiaoTong University, No.277, Yanta West Road, Xi'an, 710061, China
| | - Jiale Zhang
- Department of Neurosurgery, Comprehensive Epilepsy Center, The First Affiliated Hospital of Xi'an JiaoTong University, No.277, Yanta West Road, Xi'an, 710061, China
| | - Changwang Du
- Department of Neurosurgery, Comprehensive Epilepsy Center, The First Affiliated Hospital of Xi'an JiaoTong University, No.277, Yanta West Road, Xi'an, 710061, China
- Clinical Research Center for Refractory Epilepsy of Shaanxi Province, Xi'an, 710061, China
| | - Shan Dong
- Department of Neurosurgery, Comprehensive Epilepsy Center, The First Affiliated Hospital of Xi'an JiaoTong University, No.277, Yanta West Road, Xi'an, 710061, China
- Clinical Research Center for Refractory Epilepsy of Shaanxi Province, Xi'an, 710061, China
| | - Xiaofang Liu
- Department of Neurosurgery, Comprehensive Epilepsy Center, The First Affiliated Hospital of Xi'an JiaoTong University, No.277, Yanta West Road, Xi'an, 710061, China
- Clinical Research Center for Refractory Epilepsy of Shaanxi Province, Xi'an, 710061, China
| | - Hua Zhang
- Department of Neurosurgery, Comprehensive Epilepsy Center, The First Affiliated Hospital of Xi'an JiaoTong University, No.277, Yanta West Road, Xi'an, 710061, China.
- Clinical Research Center for Refractory Epilepsy of Shaanxi Province, Xi'an, 710061, China.
- Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.
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Mercier M, Pepi C, Carfi-Pavia G, De Benedictis A, Espagnet MCR, Pirani G, Vigevano F, Marras CE, Specchio N, De Palma L. The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach. Sci Rep 2024; 14:10887. [PMID: 38740844 PMCID: PMC11091060 DOI: 10.1038/s41598-024-60622-5] [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: 10/06/2023] [Accepted: 04/25/2024] [Indexed: 05/16/2024] Open
Abstract
Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome. We included 123 paediatric patients who underwent epilepsy surgery at Bambino Gesù Children Hospital (January 2009-April 2020). All patients had long term video-EEG monitoring. We analysed 1-min scalp interictal EEG (wakefulness and sleep) and extracted 13 linear and non-linear EEG features (power spectral density (PSD), Hjorth, approximate entropy, permutation entropy, Lyapunov and Hurst value). We used a logistic regression (LR) as feature selection process. To quantify the correlation between EEG features and surgical outcome we used an artificial neural network (ANN) model with 18 architectures. LR revealed a significant correlation between PSD of alpha band (sleep), Mobility index (sleep) and the Hurst value (sleep and awake) with outcome. The fifty-four ANN models gave a range of accuracy (46-65%) in predicting outcome. Within the fifty-four ANN models, we found a higher accuracy (64.8% ± 7.6%) in seizure outcome prediction, using features selected by LR. The combination of PSD of alpha band, mobility and the Hurst value positively correlate with good surgical outcome.
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Affiliation(s)
- Mattia Mercier
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
- Department of Physiology, Behavioural Neuroscience PhD Program, Sapienza University, Rome, Italy
| | - Chiara Pepi
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
| | - Giusy Carfi-Pavia
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
| | - Alessandro De Benedictis
- Neurosurgery Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, 00165, Rome, Italy
| | | | - Greta Pirani
- Department of Mechanical and Aerospace Engineering - DIMA, Sapienza University of Rome, Rome, Italy
| | - Federico Vigevano
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
| | - Carlo Efisio Marras
- Neurosurgery Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, 00165, Rome, Italy
| | - Nicola Specchio
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy.
| | - Luca De Palma
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
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Casseb RF, de Campos BM, Loos WS, Barbosa MER, Alvim MKM, Paulino GCL, Pucci F, Worrell S, de Souza RM, Jehi L, Cendes F. Fully automatic segmentation of brain lacunas resulting from resective surgery using a 3D deep learning model. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.11.16.23298572. [PMID: 38014004 PMCID: PMC10680896 DOI: 10.1101/2023.11.16.23298572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
The rapid and constant development of deep learning (DL) strategies is pushing forward the quality of object segmentation in images from diverse fields of interest. In particular, these algorithms can be very helpful in delineating brain abnormalities (lesions, tumors, lacunas, etc), enabling the extraction of information such as volume and location, that can inform doctors or feed predictive models. Here, we describe ResectVol DL, a fully automatic tool developed to segment resective lacunas in brain images of patients with epilepsy. ResectVol DL relies on the nnU-Net framework that leverages the 3D U-Net deep learning architecture. T1-weighted MRI datasets from 120 patients (57 women; 31.5 ± 15.9 years old at surgery) were used to train (n=78) and test (n=48) our tool. Manual segmentations were carried out by five different raters and were considered as ground truth for performance assessment. We compared ResectVol DL with two other fully automatic methods: ResectVol 1.1.2 and DeepResection, using the Dice similarity coefficient (DSC), Pearson's correlation coefficient, and relative difference to manual segmentation. ResectVol DL presented the highest median DSC (0.92 vs. 0.78 and 0.90), the highest correlation coefficient (0.99 vs. 0.63 and 0.94), and the lowest median relative difference (9 vs. 44 and 12 %). Overall, we demonstrate that ResectVol DL accurately segments brain lacunas, which has the potential to assist in the development of predictive models for postoperative cognitive and seizure outcomes.
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Affiliation(s)
| | | | - Wallace Souza Loos
- Advanced Imaging and Artificial Intelligence Lab, University of Calgary, Calgary, AB, Canada
| | | | | | | | - Francesco Pucci
- Cleveland Clinic Foundation, Cleveland, OH, United States of America
| | - Samuel Worrell
- Cleveland Clinic Foundation, Cleveland, OH, United States of America
| | | | - Lara Jehi
- Cleveland Clinic Foundation, Cleveland, OH, United States of America
| | - Fernando Cendes
- Universidade Estadual de Campinas (UNICAMP), Neuroimaging Laboratory, Campinas, SP, Brazil
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Sheikh S, Jehi L. Predictive models of epilepsy outcomes. Curr Opin Neurol 2024; 37:115-120. [PMID: 38224138 DOI: 10.1097/wco.0000000000001241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
PURPOSE OF REVIEW Multiple complex medical decisions are necessary in the course of a chronic disease like epilepsy. Predictive tools to assist physicians and patients in navigating this complexity have emerged as a necessity and are summarized in this review. RECENT FINDINGS Nomograms and online risk calculators are user-friendly and offer individualized predictions for outcomes ranging from safety of antiseizure medication withdrawal (accuracy 65-73%) to seizure-freedom, naming, mood, and language outcomes of resective epilepsy surgery (accuracy 72-81%). Improving their predictive performance is limited by the nomograms' inability to ingest complex data inputs. Conversely, machine learning offers the potential of multimodal and expansive model inputs achieving human-expert level accuracy in automated scalp electroencephalogram (EEG) interpretation but lagging in predictive performance or requiring validation for other applications. SUMMARY Good to excellent predictive models are now available to guide medical and surgical epilepsy decision-making with nomograms offering individualized predictions and user-friendly tools, and machine learning approaches offering the potential of improved performance. Future research is necessary to bridge the two approaches for optimal translation to clinical care.
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Affiliation(s)
| | - Lara Jehi
- Epilepsy Center, Neurological Institute
- Center for Computational Life Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
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Goel K, Ghadiyaram A, Krishnakumar A, Morden FTC, Higashihara TJ, Harris WB, Shlobin NA, Wang A, Karunungan K, Dubey A, Phillips HW, Weil AG, Fallah A. Hemimegalencephaly: A Systematic Comparison of Functional and Anatomic Hemispherectomy for Drug-Resistant Epilepsy. Neurosurgery 2024; 94:666-678. [PMID: 37975663 DOI: 10.1227/neu.0000000000002759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 09/19/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Hemimegalencephaly (HME) is a rare diffuse malformation of cortical development characterized by unihemispheric hypertrophy, drug-resistant epilepsy (DRE), hemiparesis, and developmental delay. Definitive treatment for HME-related DRE is hemispheric surgery through either anatomic (AH) or functional hemispherectomy (FH). This individual patient data meta-analysis assessed seizure outcomes of AH and FH for HME with pharmacoresistant epilepsy, predictors of Engel I, and efficacy of different FH approaches. METHODS PubMed, Web of Science, and Cumulative Index to Nursing and Allied Health Literature were searched from inception to Jan 13th, 2023, for primary literature reporting seizure outcomes in >3 patients with HME receiving AH or FH. Demographics, neurophysiology findings, and Engel outcome at the last follow-up were extracted. Postsurgical seizure outcomes were compared through 2-tailed t -test and Fisher exact test. Univariate and multivariate Cox regression analyses were performed to identify independent predictors of Engel I outcome. RESULTS Data from 145 patients were extracted from 26 studies, of which 89 underwent FH (22 vertical, 33 lateral), 47 underwent AH, and 9 received an unspecified hemispherectomy with a median last follow-up of 44.0 months (FH cohort) and 45.0 months (AH cohort). Cohorts were similar in preoperative characteristics and at the last follow-up; 77% (n = 66) of the FH cohort and 81% (n = 38) and of the AH cohort were Engel I. On multivariate analysis, only the presence of bilateral ictal electroencephalography abnormalities (hazard ratio = 11.5; P = .002) was significantly associated with faster time-to-seizure recurrence. A number-needed-to-treat analysis to prevent 1 additional case of posthemispherectomy hydrocephalus reveals that FH, compared with AH, was 3. There was no statistical significance for any differences in time-to-seizure recurrence between lateral and vertical FH approaches (hazard ratio = 2.59; P = .101). CONCLUSION We show that hemispheric surgery is a highly effective treatment for HME-related DRE. Unilateral ictal electroencephalography changes and using the FH approach as initial surgical management may result in better outcomes due to significantly lower posthemispherectomy hydrocephalus probability. However, larger HME registries are needed to further delineate the predictors of seizure outcomes.
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Affiliation(s)
- Keshav Goel
- David Geffen School of Medicine at University of California, Los Angeles, Los Angeles , California , USA
| | - Ashwin Ghadiyaram
- Virginia Commonwealth University School of Medicine, Richmond , Virginia , USA
| | - Asha Krishnakumar
- Virginia Commonwealth University School of Medicine, Richmond , Virginia , USA
| | - Frances T C Morden
- John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu , Hawaii , USA
| | - Tate J Higashihara
- John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu , Hawaii , USA
| | - William B Harris
- Department of Neurosurgery, University of Colorado, Boulder , Colorado , USA
| | - Nathan A Shlobin
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago , Illinois , USA
| | - Andrew Wang
- David Geffen School of Medicine at University of California, Los Angeles, Los Angeles , California , USA
| | - Krystal Karunungan
- David Geffen School of Medicine at University of California, Los Angeles, Los Angeles , California , USA
| | - Anwesha Dubey
- David Geffen School of Medicine at University of California, Los Angeles, Los Angeles , California , USA
| | - H Westley Phillips
- Department of Neurosurgery, Children's Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Alexander G Weil
- Division of Neurosurgery, Department of Surgery, Sainte-Justine University Hospital Centre, Montréal , Québec , Canada
- Division of Neurosurgery, Department of Surgery, University of Montreal Hospital Centre (CHUM), Montréal , Québec , Canada
- Brain and Development Research Axis, Sainte-Justine Research Center, Montréal , Québec , Canada
- Department of Neuroscience, University of Montreal, Montréal , Québec , Canada
| | - Aria Fallah
- David Geffen School of Medicine at University of California, Los Angeles, Los Angeles , California , USA
- Department of Neurosurgery, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles , California , USA
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9
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Hadady L, Sperling MR, Alcala-Zermeno JL, French JA, Dugan P, Jehi L, Fabó D, Klivényi P, Rubboli G, Beniczky S. Prediction tools and risk stratification in epilepsy surgery. Epilepsia 2024; 65:414-421. [PMID: 38060351 DOI: 10.1111/epi.17851] [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: 08/08/2023] [Revised: 12/03/2023] [Accepted: 12/05/2023] [Indexed: 12/17/2023]
Abstract
OBJECTIVE This study was undertaken to conduct external validation of previously published epilepsy surgery prediction tools using a large independent multicenter dataset and to assess whether these tools can stratify patients for being operated on and for becoming free of disabling seizures (International League Against Epilepsy stage 1 and 2). METHODS We analyzed a dataset of 1562 patients, not used for tool development. We applied two scales: Epilepsy Surgery Grading Scale (ESGS) and Seizure Freedom Score (SFS); and two versions of Epilepsy Surgery Nomogram (ESN): the original version and the modified version, which included electroencephalographic data. For the ESNs, we used calibration curves and concordance indexes. We stratified the patients into three tiers for assessing the chances of attaining freedom from disabling seizures after surgery: high (ESGS = 1, SFS = 3-4, ESNs > 70%), moderate (ESGS = 2, SFS = 2, ESNs = 40%-70%), and low (ESGS = 2, SFS = 0-1, ESNs < 40%). We compared the three tiers as stratified by these tools, concerning the proportion of patients who were operated on, and for the proportion of patients who became free of disabling seizures. RESULTS The concordance indexes for the various versions of the nomograms were between .56 and .69. Both scales (ESGS, SFS) and nomograms accurately stratified the patients for becoming free of disabling seizures, with significant differences among the three tiers (p < .05). In addition, ESGS and the modified ESN accurately stratified the patients for having been offered surgery, with significant difference among the three tiers (p < .05). SIGNIFICANCE ESGS and the modified ESN (at thresholds of 40% and 70%) stratify patients undergoing presurgical evaluation into three tiers, with high, moderate, and low chance for favorable outcome, with significant differences between the groups concerning having surgery and becoming free of disabling seizures. Stratifying patients for epilepsy surgery has the potential to help select the optimal candidates in underprivileged areas and better allocate resources in developed countries.
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Affiliation(s)
- Levente Hadady
- Department of Neurology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary
| | - Michael R Sperling
- Department of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Juan Luis Alcala-Zermeno
- Department of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Jacqueline A French
- Department of Neurology, New York University Grossman School of Medicine, New York, New York, USA
| | - Patricia Dugan
- Department of Neurology, New York University Grossman School of Medicine, New York, New York, USA
| | - Lara Jehi
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, USA
- Center for Computational Life Sciences, Cleveland, Ohio, USA
| | - Dániel Fabó
- Department of Neurology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary
- Department of Neurology, National Institute of Clinical Neurosciences, Budapest, Hungary
| | - Péter Klivényi
- Department of Neurology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary
| | - Guido Rubboli
- Department of Neurology, Danish Epilepsy Center, Dianalund, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Sándor Beniczky
- Department of Neurology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary
- Department of Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark
- Department of Clinical Medicine, Aarhus University and Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
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10
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Wissel BD, Greiner HM, Glauser TA, Pestian JP, Ficker DM, Cavitt JL, Estofan L, Holland-Bouley KD, Mangano FT, Szczesniak RD, Dexheimer JW. Early Identification of Candidates for Epilepsy Surgery: A Multicenter, Machine Learning, Prospective Validation Study. Neurology 2024; 102:e208048. [PMID: 38315952 PMCID: PMC10890832 DOI: 10.1212/wnl.0000000000208048] [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: 06/29/2023] [Accepted: 10/13/2023] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Epilepsy surgery is often delayed. We previously developed machine learning (ML) models to identify candidates for resective epilepsy surgery earlier in the disease course. In this study, we report the prospective validation. METHODS In this multicenter, prospective, longitudinal cohort study, random forest models were validated at a pediatric epilepsy center consisting of 2 hospitals and 14 outpatient neurology clinic sites and an adult epilepsy center with 2 hospitals and 27 outpatient neurology clinic sites. The models used neurology visit notes, EEG and MRI reports, visit patterns, hospitalizations, and medication, laboratory, and procedure orders to identify candidates for surgery. The models were trained on historical data up to May 10, 2019. Patients with an ICD-10 diagnosis of epilepsy who visited from May 11, 2019, to May 10, 2020, were screened by the algorithm and assigned surgical candidacy scores. The primary outcome was area under the curve (AUC), which was calculated by comparing scores from patients who underwent epilepsy surgery before November 10, 2020, against scores from nonsurgical patients. Nonsurgical patients' charts were reviewed to determine whether patients with high scores were more likely to be missed surgical candidates. Delay to surgery was defined as the time between the first visit that a surgical candidate was identified by the algorithm and the date of the surgery. RESULTS A total of 5,285 pediatric and 5,782 adult patients were included to train the ML algorithms. During the study period, 41 children and 23 adults underwent resective epilepsy surgery. In the pediatric cohort, AUC was 0.91 (95% CI 0.87-0.94), positive predictive value (PPV) was 0.08 (0.05-0.10), and negative predictive value (NPV) was 1.00 (0.99-1.00). In the adult cohort, AUC was 0.91 (0.86-0.97), PPV was 0.07 (0.04-0.11), and NPV was 1.00 (0.99-1.00). The models first identified patients at a median of 2.1 years (interquartile range [IQR]: 1.2-4.9 years, maximum: 11.1 years) before their surgery and 1.3 years (IQR: 0.3-4.0 years, maximum: 10.1 years) before their presurgical evaluations. DISCUSSION ML algorithms can identify surgical candidates earlier in the disease course. Even at specialized epilepsy centers, there is room to shorten the time to surgery. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that a machine learning algorithm can accurately distinguish patients with epilepsy who require resective surgery from those who do not.
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Affiliation(s)
- Benjamin D Wissel
- From the Division of Biomedical Informatics (B.D.W., J.P.P., J.W.D.), Cincinnati Children's Hospital Medical Center; Department of Pediatrics (H.M.G., T.A.G., J.P.P., K.D.H.-B., F.T.M., R.D.S., J.W.D.), University of Cincinnati College of Medicine; Division of Neurology (H.M.G., T.A.G., K.D.H.-B.), Cincinnati Children's Hospital Medical Center; Department of Neurology and Rehabilitation Medicine (D.M.F., J.L.C., L.E.), University of Cincinnati; Division of Neurosurgery (F.T.M.); Division of Biostatistics and Epidemiology (R.D.S.); and Division of Emergency Medicine (J.W.D.), Cincinnati Children's Hospital Medical Center, OH
| | - Hansel M Greiner
- From the Division of Biomedical Informatics (B.D.W., J.P.P., J.W.D.), Cincinnati Children's Hospital Medical Center; Department of Pediatrics (H.M.G., T.A.G., J.P.P., K.D.H.-B., F.T.M., R.D.S., J.W.D.), University of Cincinnati College of Medicine; Division of Neurology (H.M.G., T.A.G., K.D.H.-B.), Cincinnati Children's Hospital Medical Center; Department of Neurology and Rehabilitation Medicine (D.M.F., J.L.C., L.E.), University of Cincinnati; Division of Neurosurgery (F.T.M.); Division of Biostatistics and Epidemiology (R.D.S.); and Division of Emergency Medicine (J.W.D.), Cincinnati Children's Hospital Medical Center, OH
| | - Tracy A Glauser
- From the Division of Biomedical Informatics (B.D.W., J.P.P., J.W.D.), Cincinnati Children's Hospital Medical Center; Department of Pediatrics (H.M.G., T.A.G., J.P.P., K.D.H.-B., F.T.M., R.D.S., J.W.D.), University of Cincinnati College of Medicine; Division of Neurology (H.M.G., T.A.G., K.D.H.-B.), Cincinnati Children's Hospital Medical Center; Department of Neurology and Rehabilitation Medicine (D.M.F., J.L.C., L.E.), University of Cincinnati; Division of Neurosurgery (F.T.M.); Division of Biostatistics and Epidemiology (R.D.S.); and Division of Emergency Medicine (J.W.D.), Cincinnati Children's Hospital Medical Center, OH
| | - John P Pestian
- From the Division of Biomedical Informatics (B.D.W., J.P.P., J.W.D.), Cincinnati Children's Hospital Medical Center; Department of Pediatrics (H.M.G., T.A.G., J.P.P., K.D.H.-B., F.T.M., R.D.S., J.W.D.), University of Cincinnati College of Medicine; Division of Neurology (H.M.G., T.A.G., K.D.H.-B.), Cincinnati Children's Hospital Medical Center; Department of Neurology and Rehabilitation Medicine (D.M.F., J.L.C., L.E.), University of Cincinnati; Division of Neurosurgery (F.T.M.); Division of Biostatistics and Epidemiology (R.D.S.); and Division of Emergency Medicine (J.W.D.), Cincinnati Children's Hospital Medical Center, OH
| | - David M Ficker
- From the Division of Biomedical Informatics (B.D.W., J.P.P., J.W.D.), Cincinnati Children's Hospital Medical Center; Department of Pediatrics (H.M.G., T.A.G., J.P.P., K.D.H.-B., F.T.M., R.D.S., J.W.D.), University of Cincinnati College of Medicine; Division of Neurology (H.M.G., T.A.G., K.D.H.-B.), Cincinnati Children's Hospital Medical Center; Department of Neurology and Rehabilitation Medicine (D.M.F., J.L.C., L.E.), University of Cincinnati; Division of Neurosurgery (F.T.M.); Division of Biostatistics and Epidemiology (R.D.S.); and Division of Emergency Medicine (J.W.D.), Cincinnati Children's Hospital Medical Center, OH
| | - Jennifer L Cavitt
- From the Division of Biomedical Informatics (B.D.W., J.P.P., J.W.D.), Cincinnati Children's Hospital Medical Center; Department of Pediatrics (H.M.G., T.A.G., J.P.P., K.D.H.-B., F.T.M., R.D.S., J.W.D.), University of Cincinnati College of Medicine; Division of Neurology (H.M.G., T.A.G., K.D.H.-B.), Cincinnati Children's Hospital Medical Center; Department of Neurology and Rehabilitation Medicine (D.M.F., J.L.C., L.E.), University of Cincinnati; Division of Neurosurgery (F.T.M.); Division of Biostatistics and Epidemiology (R.D.S.); and Division of Emergency Medicine (J.W.D.), Cincinnati Children's Hospital Medical Center, OH
| | - Leonel Estofan
- From the Division of Biomedical Informatics (B.D.W., J.P.P., J.W.D.), Cincinnati Children's Hospital Medical Center; Department of Pediatrics (H.M.G., T.A.G., J.P.P., K.D.H.-B., F.T.M., R.D.S., J.W.D.), University of Cincinnati College of Medicine; Division of Neurology (H.M.G., T.A.G., K.D.H.-B.), Cincinnati Children's Hospital Medical Center; Department of Neurology and Rehabilitation Medicine (D.M.F., J.L.C., L.E.), University of Cincinnati; Division of Neurosurgery (F.T.M.); Division of Biostatistics and Epidemiology (R.D.S.); and Division of Emergency Medicine (J.W.D.), Cincinnati Children's Hospital Medical Center, OH
| | - Katherine D Holland-Bouley
- From the Division of Biomedical Informatics (B.D.W., J.P.P., J.W.D.), Cincinnati Children's Hospital Medical Center; Department of Pediatrics (H.M.G., T.A.G., J.P.P., K.D.H.-B., F.T.M., R.D.S., J.W.D.), University of Cincinnati College of Medicine; Division of Neurology (H.M.G., T.A.G., K.D.H.-B.), Cincinnati Children's Hospital Medical Center; Department of Neurology and Rehabilitation Medicine (D.M.F., J.L.C., L.E.), University of Cincinnati; Division of Neurosurgery (F.T.M.); Division of Biostatistics and Epidemiology (R.D.S.); and Division of Emergency Medicine (J.W.D.), Cincinnati Children's Hospital Medical Center, OH
| | - Francesco T Mangano
- From the Division of Biomedical Informatics (B.D.W., J.P.P., J.W.D.), Cincinnati Children's Hospital Medical Center; Department of Pediatrics (H.M.G., T.A.G., J.P.P., K.D.H.-B., F.T.M., R.D.S., J.W.D.), University of Cincinnati College of Medicine; Division of Neurology (H.M.G., T.A.G., K.D.H.-B.), Cincinnati Children's Hospital Medical Center; Department of Neurology and Rehabilitation Medicine (D.M.F., J.L.C., L.E.), University of Cincinnati; Division of Neurosurgery (F.T.M.); Division of Biostatistics and Epidemiology (R.D.S.); and Division of Emergency Medicine (J.W.D.), Cincinnati Children's Hospital Medical Center, OH
| | - Rhonda D Szczesniak
- From the Division of Biomedical Informatics (B.D.W., J.P.P., J.W.D.), Cincinnati Children's Hospital Medical Center; Department of Pediatrics (H.M.G., T.A.G., J.P.P., K.D.H.-B., F.T.M., R.D.S., J.W.D.), University of Cincinnati College of Medicine; Division of Neurology (H.M.G., T.A.G., K.D.H.-B.), Cincinnati Children's Hospital Medical Center; Department of Neurology and Rehabilitation Medicine (D.M.F., J.L.C., L.E.), University of Cincinnati; Division of Neurosurgery (F.T.M.); Division of Biostatistics and Epidemiology (R.D.S.); and Division of Emergency Medicine (J.W.D.), Cincinnati Children's Hospital Medical Center, OH
| | - Judith W Dexheimer
- From the Division of Biomedical Informatics (B.D.W., J.P.P., J.W.D.), Cincinnati Children's Hospital Medical Center; Department of Pediatrics (H.M.G., T.A.G., J.P.P., K.D.H.-B., F.T.M., R.D.S., J.W.D.), University of Cincinnati College of Medicine; Division of Neurology (H.M.G., T.A.G., K.D.H.-B.), Cincinnati Children's Hospital Medical Center; Department of Neurology and Rehabilitation Medicine (D.M.F., J.L.C., L.E.), University of Cincinnati; Division of Neurosurgery (F.T.M.); Division of Biostatistics and Epidemiology (R.D.S.); and Division of Emergency Medicine (J.W.D.), Cincinnati Children's Hospital Medical Center, OH
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11
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Poghosyan V, Algethami H, Alshahrani A, Asiri S, Aldosari MM. Association Between Magnetoencephalography-Localized Epileptogenic Zone, Surgical Resection Volume, and Postsurgical Seizure Outcome. J Clin Neurophysiol 2024:00004691-990000000-00118. [PMID: 38194636 DOI: 10.1097/wnp.0000000000001069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2024] Open
Abstract
PURPOSE Surgical resection of magnetoencephalography (MEG) dipole clusters, reconstructed from interictal epileptiform discharges, is associated with favorable seizure outcomes. However, the relation of MEG cluster resection to the surgical resection volume is not known nor is it clear whether this association is direct and causal, or it may be mediated by the resection volume or other predictive factors. This study aims to clarify these open questions and assess the diagnostic accuracy of MEG in our center. METHODS We performed a retrospective cohort study of 68 patients with drug-resistant epilepsy who underwent MEG followed by resective epilepsy surgery and had at least 12 months of postsurgical follow-up. RESULTS Good seizure outcomes were associated with monofocal localization (χ2 = 6.94, P = 0.001; diagnostic odds ratio = 10.2) and complete resection of MEG clusters (χ2 = 22.1, P < 0.001; diagnostic odds ratio = 42.5). Resection volumes in patients with and without removal of MEG clusters were not significantly different (t = 0.18, P = 0.86; removed: M = 20,118 mm3, SD = 10,257; not removed: M = 19,566 mm3, SD = 10,703). Logistic regression showed that removal of MEG clusters predicts seizure-free outcome independent of the resection volume and other prognostic factors (P < 0.001). CONCLUSIONS Complete resection of MEG clusters leads to favorable seizure outcomes without affecting the volume of surgical resection and independent of other prognostic factors. MEG can localize the epileptogenic zone with high accuracy. MEG interictal epileptiform discharges mapping should be used whenever feasible to improve postsurgical seizure outcomes.
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Affiliation(s)
- Vahe Poghosyan
- Department of Neurophysiology, National Neuroscience Institute, King Fahad Medical City, Riyadh, K.S.A.; and
| | - Hanin Algethami
- Department of Neurology, National Neuroscience Institute, King Fahad Medical City, Riyadh, K.S.A
| | - Ashwaq Alshahrani
- Department of Neurology, National Neuroscience Institute, King Fahad Medical City, Riyadh, K.S.A
| | - Safiyyah Asiri
- Department of Neurology, National Neuroscience Institute, King Fahad Medical City, Riyadh, K.S.A
| | - Mubarak M Aldosari
- Department of Neurology, National Neuroscience Institute, King Fahad Medical City, Riyadh, K.S.A
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12
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Beaulieu-Jones BK, Villamar MF, Scordis P, Bartmann AP, Ali W, Wissel BD, Alsentzer E, de Jong J, Patra A, Kohane I. Predicting seizure recurrence after an initial seizure-like episode from routine clinical notes using large language models: a retrospective cohort study. Lancet Digit Health 2023; 5:e882-e894. [PMID: 38000873 PMCID: PMC10695164 DOI: 10.1016/s2589-7500(23)00179-6] [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: 04/10/2023] [Revised: 08/08/2023] [Accepted: 08/31/2023] [Indexed: 11/26/2023]
Abstract
BACKGROUND The evaluation and management of first-time seizure-like events in children can be difficult because these episodes are not always directly observed and might be epileptic seizures or other conditions (seizure mimics). We aimed to evaluate whether machine learning models using real-world data could predict seizure recurrence after an initial seizure-like event. METHODS This retrospective cohort study compared models trained and evaluated on two separate datasets between Jan 1, 2010, and Jan 1, 2020: electronic medical records (EMRs) at Boston Children's Hospital and de-identified, patient-level, administrative claims data from the IBM MarketScan research database. The study population comprised patients with an initial diagnosis of either epilepsy or convulsions before the age of 21 years, based on International Classification of Diseases, Clinical Modification (ICD-CM) codes. We compared machine learning-based predictive modelling using structured data (logistic regression and XGBoost) with emerging techniques in natural language processing by use of large language models. FINDINGS The primary cohort comprised 14 021 patients at Boston Children's Hospital matching inclusion criteria with an initial seizure-like event and the comparison cohort comprised 15 062 patients within the IBM MarketScan research database. Seizure recurrence based on a composite expert-derived definition occurred in 57% of patients at Boston Children's Hospital and 63% of patients within IBM MarketScan. Large language models with additional domain-specific and location-specific pre-training on patients excluded from the study (F1-score 0·826 [95% CI 0·817-0·835], AUC 0·897 [95% CI 0·875-0·913]) performed best. All large language models, including the base model without additional pre-training (F1-score 0·739 [95% CI 0·738-0·741], AUROC 0·846 [95% CI 0·826-0·861]) outperformed models trained with structured data. With structured data only, XGBoost outperformed logistic regression and XGBoost models trained with the Boston Children's Hospital EMR (logistic regression: F1-score 0·650 [95% CI 0·643-0·657], AUC 0·694 [95% CI 0·685-0·705], XGBoost: F1-score 0·679 [0·676-0·683], AUC 0·725 [0·717-0·734]) performed similarly to models trained on the IBM MarketScan database (logistic regression: F1-score 0·596 [0·590-0·601], AUC 0·670 [0·664-0·675], XGBoost: F1-score 0·678 [0·668-0·687], AUC 0·710 [0·703-0·714]). INTERPRETATION Physician's clinical notes about an initial seizure-like event include substantial signals for prediction of seizure recurrence, and additional domain-specific and location-specific pre-training can significantly improve the performance of clinical large language models, even for specialised cohorts. FUNDING UCB, National Institute of Neurological Disorders and Stroke (US National Institutes of Health).
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Affiliation(s)
- Brett K Beaulieu-Jones
- Department of Medicine, University of Chicago, Chicago, IL, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| | - Mauricio F Villamar
- Department of Neurology, The Warren Alpert Medical School of Brown University, Providence, RI, USA
| | | | | | | | - Benjamin D Wissel
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Emily Alsentzer
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | | | | | - Isaac Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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13
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Mir A, Jallul T, Alotaibi F, Amer F, Najjar A, Alhazmi R, Al Faraidy M, Alharbi A, Aldurayhim F, Barnawi Z, Fallatah B, Ali M, Almuhaish H, Almolani F, Suwailem A, Tuli M, Naim A, Hassan S, Hedgcock B, Bostanji G, Bashir S, AlBaradie R. Outcomes of resective surgery in pediatric patients with drug-resistant epilepsy: A single-center study from the Eastern Mediterranean Region. Epilepsia Open 2023; 8:930-945. [PMID: 37162422 PMCID: PMC10472393 DOI: 10.1002/epi4.12761] [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: 02/02/2023] [Accepted: 05/07/2023] [Indexed: 05/11/2023] Open
Abstract
OBJECTIVE Epilepsy surgery is widely accepted as an effective therapeutic option for carefully selected patients with drug-resistant epilepsy (DRE). There is limited data on the outcome of epilepsy surgery, especially in pediatric patients from the Eastern Mediterranean region. Hence, we performed a retrospective study examining the outcomes of resective surgery in 53 pediatric patients with focal DRE. METHODS Patients with focal DRE who had undergone epilepsy surgery were included in the present study. All patients underwent a comprehensive presurgical evaluation. Postoperative seizure outcomes were classified using the Engel Epilepsy Surgery Outcome Scale. RESULTS After surgery, 33 patients (62.2%) were Class I according to the Engel classification of surgical outcomes; eight patients (15.0%) were Class II, 11 (20.7%) were Class III, and one (1.8%) was Class IV. The relationships of presurgical, surgical, and postsurgical clinical variables to seizure outcomes were compared. Older age at seizure onset, older age at the time of surgery, the presence of focal to bilateral tonic-clonic seizures, seizure duration over 2 minutes, unsuccessful treatment with three or fewer antiseizure medications, lesions confined to one lobe (as demonstrated via magnetic resonance imaging [MRI]), surgical site in the temporal lobe, and histopathology including developmental tumors were significantly linked to an Engel Class I outcome. A univariate analysis of excellent surgical outcomes showed that lateralized semiology, localized interictal and ictal electroencephalogram (EEG) discharges, lateralized single-photon emission computed tomography and positron emission tomography findings, and temporal lobe resections were significantly related to excellent seizure outcomes. SIGNIFICANCE The results of our study are encouraging and similar to those found in other centers around the world. Epilepsy surgery remains an underutilized treatment for children with DRE and should be offered early.
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Affiliation(s)
- Ali Mir
- Department of Pediatric NeurologyKing Fahad Specialist HospitalDammamSaudi Arabia
| | - Tarek Jallul
- Department of NeurosurgeryKing Fahad Specialist HospitalDammamSaudi Arabia
| | - Faisal Alotaibi
- Neuroscience CentreKing Faisal Specialist Hospital and Research CenterRiyadhSaudi Arabia
| | - Fawzia Amer
- Department of Pediatric NeurologyKing Fahad Specialist HospitalDammamSaudi Arabia
- Department of Pediatric Neurology and MetabolicCairo University Children HospitalCairoEgypt
| | - Ahmed Najjar
- Department of NeurosurgeryKing Fahad Specialist HospitalDammamSaudi Arabia
- Department of Surgery, College of MedicineTaibah UniversityAlmadinah AlmunawwarahSaudi Arabia
| | - Rami Alhazmi
- Department of Medical ImagingKing Fahad Specialist HospitalDammamSaudi Arabia
| | - Mona Al Faraidy
- Anesthesia DepartmentKing Fahad Specialist HospitalDammamSaudi Arabia
| | - Alanoud Alharbi
- Department of Pediatric NeurologyKing Fahad Specialist HospitalDammamSaudi Arabia
| | - Fatimah Aldurayhim
- Department of Pediatric NeurologyKing Fahad Specialist HospitalDammamSaudi Arabia
| | - Zakia Barnawi
- Department of NeurosurgeryKing Fahad Specialist HospitalDammamSaudi Arabia
| | - Bassam Fallatah
- Department of NeurosurgeryKing Fahad Specialist HospitalDammamSaudi Arabia
| | - Mona Ali
- Department of Pediatric NeurologyKing Fahad Specialist HospitalDammamSaudi Arabia
| | - Husam Almuhaish
- Department of Medical ImagingKing Fahad Specialist HospitalDammamSaudi Arabia
| | - Fadhel Almolani
- Department of Medical ImagingKing Fahad Specialist HospitalDammamSaudi Arabia
| | - Abdullah Suwailem
- Department of Medical ImagingKing Fahad Specialist HospitalDammamSaudi Arabia
| | - Mahmoud Tuli
- Department of Medical ImagingKing Fahad Specialist HospitalDammamSaudi Arabia
| | - Abdulrahman Naim
- Department of Medical ImagingKing Fahad Specialist HospitalDammamSaudi Arabia
| | - Suad Hassan
- Department of Mental HealthKing Fahad Specialist HospitalDammamSaudi Arabia
| | - Brent Hedgcock
- Department of NeurophysiologyKing Fahad Specialist HospitalDammamSaudi Arabia
| | - Ghadah Bostanji
- Department of Social WorkKing Fahad Specialist HospitalDammamSaudi Arabia
| | - Shahid Bashir
- Neuroscience CentreKing Fahad Specialist HospitalDammamSaudi Arabia
| | - Raidah AlBaradie
- Department of Pediatric NeurologyKing Fahad Specialist HospitalDammamSaudi Arabia
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14
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Janiukstyte V, Owen TW, Chaudhary UJ, Diehl B, Lemieux L, Duncan JS, de Tisi J, Wang Y, Taylor PN. Normative brain mapping using scalp EEG and potential clinical application. Sci Rep 2023; 13:13442. [PMID: 37596291 PMCID: PMC10439201 DOI: 10.1038/s41598-023-39700-7] [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: 04/06/2023] [Accepted: 07/29/2023] [Indexed: 08/20/2023] Open
Abstract
A normative electrographic activity map could be a powerful resource to understand normal brain function and identify abnormal activity. Here, we present a normative brain map using scalp EEG in terms of relative band power. In this exploratory study we investigate its temporal stability, its similarity to other imaging modalities, and explore a potential clinical application. We constructed scalp EEG normative maps of brain dynamics from 17 healthy controls using source-localised resting-state scalp recordings. We then correlated these maps with those acquired from MEG and intracranial EEG to investigate their similarity. Lastly, we use the normative maps to lateralise abnormal regions in epilepsy. Spatial patterns of band powers were broadly consistent with previous literature and stable across recordings. Scalp EEG normative maps were most similar to other modalities in the alpha band, and relatively similar across most bands. Towards a clinical application in epilepsy, we found abnormal temporal regions ipsilateral to the epileptogenic hemisphere. Scalp EEG relative band power normative maps are spatially stable across time, in keeping with MEG and intracranial EEG results. Normative mapping is feasible and may be potentially clinically useful in epilepsy. Future studies with larger sample sizes and high-density EEG are now required for validation.
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Affiliation(s)
- Vytene Janiukstyte
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, NE4 5DG, UK
| | - Thomas W Owen
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, NE4 5DG, UK
| | - Umair J Chaudhary
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK
| | - Louis Lemieux
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK
| | - Yujiang Wang
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, NE4 5DG, UK
- Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, NE2 4HH, UK
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK
| | - Peter N Taylor
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, NE4 5DG, UK.
- Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, NE2 4HH, UK.
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK.
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15
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Qiu P, Chen X, Xiao C, Zhang M, Wang H, Wang C, Li D, Liu J, Chen Y, Liu L, Zhao Q. Emerging glyco-risk prediction model to forecast response to immune checkpoint inhibitors in colorectal cancer. J Cancer Res Clin Oncol 2023; 149:6411-6434. [PMID: 36757621 DOI: 10.1007/s00432-023-04626-0] [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: 11/11/2022] [Accepted: 01/29/2023] [Indexed: 02/10/2023]
Abstract
BACKGROUND Aberrant glycosylation is one of the most common post-translational modifications leading to heterogeneity in colorectal cancer (CRC). This study aims to construct a risk prediction model based on glycosyltransferase to forecast the response to immune checkpoint inhibitors in CRC patients. METHODS Based on the TCGA dataset and glycosyltransferase genes, the NMF algorithm and WGCNA were used to identify molecular subtypes and co-expressed genes, respectively. Lasso and multivariate COX regression were used to identify prognostic glycosyltransferase genes and construct a glyco-risk prediction model in CRC patients. Univariate and multivariate Cox regression, Kaplan-Meier, and ROC curves were applied to further verify the prognostic performance of the model in CRC patients in the training and validation sets. We compared the responsiveness of immunotherapy and chemotherapy between the two groups. In vitro experiments and clinical specimens verified the specific function of the key glycosyltransferase genes in CRC. RESULTS The CRC cohort was divided into two subtypes with prominent differences in survival based on the well-robust seven-gene glyco-risk prediction model (composed of ALG1L2, HAS1, PYGL, COLGALT2, B3GNT4, POFUT2, and GALNT7). The nomograms based on the risk model could predict the prognosis of CRC patients independently of other clinicopathologic characteristics. Our prediction model showed a better overall prediction performance than other models. Compared with the low-risk group, the high-risk CRC patients showed a lower immune infiltration state, but a higher TMB and a lower response to anti-PD-1, anti-PD-L1, and anti-CTLA-4 therapy. Clinical specimen validation showed an obvious difference in the expression of seven glycosyltransferase genes between the low- and high-risk groups. Significant reduction in POFUT2 expression in high-risk groups was associated with reduced N-glycans production. CONCLUSION Our study constructed a robust glyco-risk prediction model that could provide direction for immunotherapy and chemotherapy in CRC patients, which could help clinicians make personalized treatment decisions.
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Affiliation(s)
- Peishan Qiu
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
- Hubei Clinical Center and Key Lab of Intestinal & Colorectal Diseases, Wuhan, 430071, China
| | - Xiaoyu Chen
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
- Hubei Clinical Center and Key Lab of Intestinal & Colorectal Diseases, Wuhan, 430071, China
| | - Cong Xiao
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
- Hubei Clinical Center and Key Lab of Intestinal & Colorectal Diseases, Wuhan, 430071, China
| | - Meng Zhang
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
- Hubei Clinical Center and Key Lab of Intestinal & Colorectal Diseases, Wuhan, 430071, China
| | - Haizhou Wang
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
- Hubei Clinical Center and Key Lab of Intestinal & Colorectal Diseases, Wuhan, 430071, China
| | - Chun Wang
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
- Hubei Clinical Center and Key Lab of Intestinal & Colorectal Diseases, Wuhan, 430071, China
| | - Daojiang Li
- Department of General Surgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Jing Liu
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
- Hubei Clinical Center and Key Lab of Intestinal & Colorectal Diseases, Wuhan, 430071, China
| | - Yuhua Chen
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
- Hubei Clinical Center and Key Lab of Intestinal & Colorectal Diseases, Wuhan, 430071, China.
| | - Lan Liu
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
- Hubei Clinical Center and Key Lab of Intestinal & Colorectal Diseases, Wuhan, 430071, China.
| | - Qiu Zhao
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
- Hubei Clinical Center and Key Lab of Intestinal & Colorectal Diseases, Wuhan, 430071, China.
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16
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Janiukstyte V, Owen TW, Chaudhary UJ, Diehl B, Lemieux L, Duncan JS, de Tisi J, Wang Y, Taylor PN. Normative brain mapping using scalp EEG and potential clinical application. ARXIV 2023:arXiv:2304.03204v1. [PMID: 37064533 PMCID: PMC10104182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
A normative electrographic activity map could be a powerful resource to understand normal brain function and identify abnormal activity. Here, we present a normative brain map using scalp EEG in terms of relative band power. In this exploratory study we investigate its temporal stability, its similarity to other imaging modalities, and explore a potential clinical application. We constructed scalp EEG normative maps of brain dynamics from 17 healthy controls using source-localised resting-state scalp recordings. We then correlated these maps with those acquired from MEG and intracranial EEG to investigate their similarity. Lastly, we use the normative maps to lateralise abnormal regions in epilepsy. Spatial patterns of band powers were broadly consistent with previous literature and stable across recordings. Scalp EEG normative maps were most similar to other modalities in the alpha band, and relatively similar across most bands. Towards a clinical application in epilepsy, we found abnormal temporal regions ipsilateral to the epileptogenic hemisphere. Scalp EEG relative band power normative maps are spatially stable across time, in keeping with MEG and intracranial EEG results. Normative mapping is feasible and may be potentially clinically useful in epilepsy. Future studies with larger sample sizes and high-density EEG are now required for validation.
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Affiliation(s)
- Vytene Janiukstyte
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom, NE4 5DG
| | - Thomas W Owen
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom, NE4 5DG
| | - Umair J Chaudhary
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, United Kingdom, WC1N 3BG
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, United Kingdom, WC1N 3BG
| | - Louis Lemieux
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, United Kingdom, WC1N 3BG
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, United Kingdom, WC1N 3BG
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, United Kingdom, WC1N 3BG
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom, NE4 5DG
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom, NE2 4HH
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, United Kingdom, WC1N 3BG
| | - Peter N Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom, NE4 5DG
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom, NE2 4HH
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, United Kingdom, WC1N 3BG
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17
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Miron G, Müller PM, Holtkamp M, Meisel C. Prediction of epilepsy surgery outcome using foramen ovale EEG - A machine learning approach. Epilepsy Res 2023; 191:107111. [PMID: 36857943 DOI: 10.1016/j.eplepsyres.2023.107111] [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: 12/01/2022] [Revised: 01/20/2023] [Accepted: 02/16/2023] [Indexed: 02/19/2023]
Abstract
INTRODUCTION Patients with drug-resistant focal epilepsy may benefit from ablative or resective surgery. In presurgical work-up, intracranial EEG markers have been shown to be useful in identification of the seizure onset zone and prediction of post-surgical seizure freedom. However, in most cases, implantation of depth or subdural electrodes is performed, exposing patients to increased risks of complications. METHODS We analysed EEG data recorded from a minimally invasive approach utilizing foramen ovale (FO) and epidural peg electrodes using a supervised machine learning approach to predict post-surgical seizure freedom. Power-spectral EEG features were incorporated in a logistic regression model predicting one-year post-surgical seizure freedom. The prediction model was validated using repeated 5-fold cross-validation and compared to outcome prediction based on clinical and scalp EEG variables. RESULTS Forty-seven patients (26 patients with post-surgical 1-year seizure freedom) were included in the study, with 31 having FO and 27 patients having peg onset seizures. The area under the receiver-operating curve for post-surgical seizure freedom (Engel 1A) prediction in patients with FO onset seizures was 0.74 ± 0.23 using electrophysiology features, compared to 0.66 ± 0.22 for predictions based on clinical and scalp EEG variables (p < 0.001). The most important features for prediction were spectral power in the gamma and high gamma ranges. EEG data from peg electrodes was not informative in predicting post-surgical outcomes. CONCLUSION In this hypothesis-generating study, a data-driven approach based on EEG features derived from FO electrodes recordings outperformed the predictive ability based solely on clinical and scalp EEG variables. Pending validation in future studies, this method may provide valuable post-surgical prognostic information while minimizing risks of more invasive diagnostic approaches.
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Affiliation(s)
- Gadi Miron
- Epilepsy-Center Berlin-Brandenburg, Institute for Diagnostics of Epilepsy, Berlin, Germany; Epilepsy-Center Berlin-Brandenburg, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
| | - Paul Manuel Müller
- Computational Neurology, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Berlin Institute of Health, Berlin, Germany
| | - Martin Holtkamp
- Epilepsy-Center Berlin-Brandenburg, Institute for Diagnostics of Epilepsy, Berlin, Germany; Epilepsy-Center Berlin-Brandenburg, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Christian Meisel
- Computational Neurology, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Berlin Institute of Health, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany; Center for Stroke Research Berlin, Berlin, Germany; NeuroCure Cluster of Excellence, Charité - Universitätsmedizin Berlin, Berlin, Germany
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18
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Abstract
Brain surgery offers the best chance of seizure-freedom for patients with focal drug-resistant epilepsy, but only 50% achieve sustained seizure-freedom. With the explosion of data collected during routine presurgical evaluations and recent advances in computational science, we now have a tremendous potential to achieve precision epilepsy surgery: a data-driven tailoring of surgical planning. This review highlights the clinical need, the relevant computational science focusing on machine learning, and discusses some specific applications in epilepsy surgery.
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Affiliation(s)
- Lara Jehi
- Cleveland Clinic Ringgold Standard Institution, Cleveland, OH, USA
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19
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Castro-Villablanca F, Moeller F, Pujar S, D'Arco F, Scott RC, Tahir MZ, Tisdall M, Cross JH, Eltze C. Seizure outcome determinants in children after surgery for single unilateral lesions on magnetic resonance imaging: Role of preoperative ictal and interictal electroencephalography. Epilepsia 2022; 63:3168-3179. [PMID: 36177545 DOI: 10.1111/epi.17425] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 09/21/2022] [Accepted: 09/26/2022] [Indexed: 01/11/2023]
Abstract
OBJECTIVE To determine whether an ictal electroencephalographic (EEG) recording as part of presurgical evaluation of children with a demarcated single unilateral magnetic resonance imaging (MRI) lesion is indispensable for surgical decision-making, we investigated the relationship of interictal/ictal EEG and seizure semiology with seizure-free outcome. METHODS Data were obtained retrospectively from consecutive patients (≤18 years old) undergoing epilepsy surgery with a single unilateral MRI lesion at our institution over a 6-year period. Video-telemetry EEG (VT-EEG) was classified as concordant or nonconcordant/noninformative in relation to the MRI lesion location. The odds of seizure-free outcome associated with nonconcordant versus concordant for semiology, interictal EEG, and ictal EEG were compared separately. Multivariate logistic regression was conducted to correct for confounding variables. RESULTS After a median follow-up of 26 months (interquartile range = 17-37.5), 73 (69%) of 117 children enrolled were seizure-free. Histopathological diagnoses included low-grade epilepsy-associated tumors, n = 46 (39%); focal cortical dysplasia (FCD), n = 33 (28%); mesial temporal sclerosis (MTS), n = 23 (20%); polymicrogyria, n = 3 (3%); and nondiagnostic findings/gliosis, n = 12 (10%). The odds of seizure freedom were lower with a nonconcordant interictal EEG (odds ratio [OR] = .227, 95% confidence interval [CI] = .079-.646, p = .006) and nonconcordant ictal EEG (OR = .359, 95% CI = .15-.878, p = .035). In the multivariate logistic regression model, factors predicting lower odds for seizure-free outcome were developmental delay/intellectual disability and higher number of antiseizure medications tried, with a nonsignificant trend for "nonconcordant interictal EEG." In the combined subgroup of patients with FCD and tumors (n = 79), there was no significant relationship of VT-EEG factors and seizure outcomes, whereas in children with MTS and acquired lesions (n = 25), a nonconcordant EEG was associated with poorer seizure outcomes (p = .003). SIGNIFICANCE An ictal EEG may not be mandatory for presurgical evaluation, particularly when a well-defined single unilateral MRI lesion has been identified and the interictal EEG is concordant.
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Affiliation(s)
- Felipe Castro-Villablanca
- Neurology/Epilepsy Department, Great Ormond Street Hospital for Children, NHS Foundation Trust, London, UK.,Department of Pediatrics, University of Chile, Santiago, Chile
| | - Friederike Moeller
- Department of Clinical Neurophysiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.,Developmental Neurosciences Unit, University College London-Great Ormond Street Institute of Child Health, London, UK
| | - Suresh Pujar
- Neurology/Epilepsy Department, Great Ormond Street Hospital for Children, NHS Foundation Trust, London, UK.,Developmental Neurosciences Unit, University College London-Great Ormond Street Institute of Child Health, London, UK
| | - Felice D'Arco
- Developmental Neurosciences Unit, University College London-Great Ormond Street Institute of Child Health, London, UK.,Department of Neuroradiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
| | - Rod C Scott
- Neurology/Epilepsy Department, Great Ormond Street Hospital for Children, NHS Foundation Trust, London, UK.,Developmental Neurosciences Unit, University College London-Great Ormond Street Institute of Child Health, London, UK.,Divsion of Neurology, A. I. DuPont Hospital for Children, Wilmington, Delaware, USA
| | - M Zubair Tahir
- Department of Neurosurgery, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
| | - Martin Tisdall
- Developmental Neurosciences Unit, University College London-Great Ormond Street Institute of Child Health, London, UK.,Department of Neurosurgery, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
| | - J Helen Cross
- Neurology/Epilepsy Department, Great Ormond Street Hospital for Children, NHS Foundation Trust, London, UK.,Developmental Neurosciences Unit, University College London-Great Ormond Street Institute of Child Health, London, UK
| | - Christin Eltze
- Neurology/Epilepsy Department, Great Ormond Street Hospital for Children, NHS Foundation Trust, London, UK.,Developmental Neurosciences Unit, University College London-Great Ormond Street Institute of Child Health, London, UK
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20
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Miron G, Müller PM, Holtkamp M. Diagnostic and prognostic value of EEG patterns recorded on foramen ovale and epidural peg electrodes. Clin Neurophysiol 2022; 143:107-115. [DOI: 10.1016/j.clinph.2022.08.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 07/28/2022] [Accepted: 08/17/2022] [Indexed: 11/03/2022]
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21
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Shaaban S, Kakisaka Y, Belal T, Jin K, Osawa S, Tominaga T, Elmenshawi I, Nakasato N. Distribution of postictal slowing has an additional yield to interictal epileptiform discharge in predicting surgical outcomes in temporal lobe epilepsy. Epilepsia Open 2022; 7:802-809. [PMID: 36225084 PMCID: PMC9712469 DOI: 10.1002/epi4.12660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 10/07/2022] [Indexed: 12/30/2022] Open
Abstract
OBJECTIVE To investigate whether the slowing of bilateral postictal scalp electroencephalography (EEG) after focal impaired awareness seizures is associated with poor seizure outcomes after temporal lobe epilepsy (TLE) surgery. METHODS This retrospective cohort study was conducted in the Department of Epileptology, Tohoku University Hospital from 2010 to 2020. The study included 42 patients with TLE who underwent a detailed presurgical evaluation and sequential resective surgery for the unilateral probable epileptogenic temporal lobe with 1 year or more of follow-up. We reviewed the interictal epileptiform distribution and those of the ictal and postictal epochs of the first focal impaired awareness seizure recorded in presurgical scalp EEG. We classified patients either with postoperative seizure-free status (Engel I) as group A or those with seizure persistence (Engel II-IV) as group B. RESULTS Of 42 patients, 29 (69%) were classified into group A. Compared with group B, group A had a lower number of bilateral postictal polymorphic delta activity (PPDA) (10.3%: 61.5%) and bilateral interictal epileptiform discharges (IEDs) (13.8%: 69.2%) (P = 0.003, P = 0.001, respectively). A combined analysis of bilateral PPDA and IEDs per individual patient showed significantly more frequent seizure persistence after surgery (P < 0.0001) than a single analysis of bilateral IEDs or PPDA alone (P = 0.001). The regression analysis revealed that bilaterally distributed PPDA or IEDs had 13.50 or 13.72 times higher odds of persisting seizures within 1 year of surgery (95% confidence interval: 1.90-95.88; 2.12-88.87, respectively) (P = 0.009, 0.006). SIGNIFICANCE The results of this study revealed that the bilateral distribution of PPDA was associated with poor postoperative seizure outcomes in patients with TLE, as well as bilateral IEDs. Additionally, the concomitant bilateral distribution of interictal and postictal changes is a strong indicator of poor surgical outcomes.
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Affiliation(s)
- Sally Shaaban
- Department of EpileptologyTohoku University Graduate School of MedicineSendaiMiyagiJapan,Department of Neurology, Mansoura faculty of medicineMansouraDakahliaEgypt
| | - Yosuke Kakisaka
- Department of EpileptologyTohoku University Graduate School of MedicineSendaiMiyagiJapan
| | - Tamer Belal
- Department of Neurology, Mansoura faculty of medicineMansouraDakahliaEgypt
| | - Kazutaka Jin
- Department of EpileptologyTohoku University Graduate School of MedicineSendaiMiyagiJapan
| | - Shin‐ichiro Osawa
- Department of EpileptologyTohoku University Graduate School of MedicineSendaiMiyagiJapan,Department of NeurosurgeryTohoku University Graduate School of MedicineSendaiMiyagiJapan
| | - Teiji Tominaga
- Department of EpileptologyTohoku University Graduate School of MedicineSendaiMiyagiJapan,Department of NeurosurgeryTohoku University Graduate School of MedicineSendaiMiyagiJapan
| | - Ibrahim Elmenshawi
- Department of Neurology, Mansoura faculty of medicineMansouraDakahliaEgypt
| | - Nobukazu Nakasato
- Department of EpileptologyTohoku University Graduate School of MedicineSendaiMiyagiJapan
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22
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Englot DJ. VEP for VIP Care: Patient-Specific Modeling in Epilepsy Surgery. Epilepsy Curr 2022; 22:297-299. [DOI: 10.1177/15357597221123459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
[Box: see text]
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Affiliation(s)
- Dario J. Englot
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
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23
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Ma J, Wang Z, Cheng T, Hu Y, Qin X, Wang W, Yu G, Liu Q, Ji T, Xie H, Zha D, Wang S, Yang Z, Liu X, Cai L, Jiang Y, Hao H, Wang J, Li L, Wu Y. A prediction model integrating synchronization biomarkers and clinical features to identify responders to vagus nerve stimulation among pediatric patients with drug-resistant epilepsy. CNS Neurosci Ther 2022; 28:1838-1848. [PMID: 35894770 PMCID: PMC9532924 DOI: 10.1111/cns.13923] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 07/06/2022] [Accepted: 07/08/2022] [Indexed: 12/01/2022] Open
Abstract
Aims Vagus nerve stimulation (VNS) is a neuromodulation therapy for children with drug‐resistant epilepsy (DRE). The efficacy of VNS is heterogeneous. A prediction model is needed to predict the efficacy before implantation. Methods We collected data from children with DRE who underwent VNS implantation and received regular programming for at least 1 year. Preoperative clinical information and scalp video electroencephalography (EEG) were available in 88 children. Synchronization features, including phase lag index (PLI), weighted phase lag index (wPLI), and phase‐locking value (PLV), were compared between responders and non‐responders. We further adapted a support vector machine (SVM) classifier selected from 25 clinical and 18 synchronization features to build a prediction model for efficacy in a discovery cohort (n = 70) and was tested in an independent validation cohort (n = 18). Results In the discovery cohort, the average interictal awake PLI in the high beta band was significantly higher in responders than non‐responders (p < 0.05). The SVM classifier generated from integrating both clinical and synchronization features had the best prediction efficacy, demonstrating an accuracy of 75.7%, precision of 80.8% and area under the receiver operating characteristic (AUC) of 0.766 on 10‐fold cross‐validation. In the validation cohort, the prediction model demonstrated an accuracy of 61.1%. Conclusion This study established the first prediction model integrating clinical and baseline synchronization features for preoperative VNS responder screening among children with DRE. With further optimization of the model, we hope to provide an effective and convenient method for identifying responders before VNS implantation.
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Affiliation(s)
- Jiayi Ma
- Department of Pediatrics, Peking University First Hospital, Beijing, China
| | - Zhiyan Wang
- National Engineering laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Tungyang Cheng
- National Engineering laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Yingbing Hu
- National Engineering laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Xiaoya Qin
- National Engineering laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Wen Wang
- Pediatric Epilepsy Center, Peking University First Hospital, Beijing, China
| | - Guojing Yu
- Pediatric Epilepsy Center, Peking University First Hospital, Beijing, China
| | - Qingzhu Liu
- Pediatric Epilepsy Center, Peking University First Hospital, Beijing, China
| | - Taoyun Ji
- Department of Pediatrics, Peking University First Hospital, Beijing, China.,Pediatric Epilepsy Center, Peking University First Hospital, Beijing, China
| | - Han Xie
- Department of Pediatrics, Peking University First Hospital, Beijing, China
| | - Daqi Zha
- National Engineering laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Shuang Wang
- Pediatric Epilepsy Center, Peking University First Hospital, Beijing, China
| | - Zhixian Yang
- Department of Pediatrics, Peking University First Hospital, Beijing, China
| | - Xiaoyan Liu
- Department of Pediatrics, Peking University First Hospital, Beijing, China.,Pediatric Epilepsy Center, Peking University First Hospital, Beijing, China
| | - Lixin Cai
- Pediatric Epilepsy Center, Peking University First Hospital, Beijing, China
| | - Yuwu Jiang
- Department of Pediatrics, Peking University First Hospital, Beijing, China.,Pediatric Epilepsy Center, Peking University First Hospital, Beijing, China
| | - Hongwei Hao
- National Engineering laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Jing Wang
- Beijing Key Laboratory of Epilepsy Research, Department of Neurology, Center of Epilepsy, Beijing Institute for Brain Disorders, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Luming Li
- National Engineering laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China.,Precision Medicine & Healthcare Research Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China.,IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China.,Institute of Epilepsy, Beijing Institute for Brain Disorders, Beijing, China
| | - Ye Wu
- Department of Pediatrics, Peking University First Hospital, Beijing, China.,Pediatric Epilepsy Center, Peking University First Hospital, Beijing, China
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Interictal sleep recordings during presurgical evaluation: Bidirectional perspectives on sleep related network functioning. Rev Neurol (Paris) 2022; 178:703-713. [DOI: 10.1016/j.neurol.2022.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 03/08/2022] [Accepted: 03/08/2022] [Indexed: 11/23/2022]
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Varatharajah Y, Joseph B, Brinkmann B, Morita-Sherman M, Fitzgerald Z, Vegh D, Nair D, Burgess R, Cendes F, Jehi L, Worrell G. Quantitative Analysis of Visually Reviewed Normal Scalp EEG Predicts Seizure Freedom Following Anterior Temporal Lobectomy. Epilepsia 2022; 63:1630-1642. [PMID: 35416285 PMCID: PMC9283304 DOI: 10.1111/epi.17257] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 04/10/2022] [Accepted: 04/11/2022] [Indexed: 11/28/2022]
Abstract
Objective Anterior temporal lobectomy (ATL) is a widely performed and successful intervention for drug‐resistant temporal lobe epilepsy (TLE). However, up to one third of patients experience seizure recurrence within 1 year after ATL. Despite the extensive literature on presurgical electroencephalography (EEG) and magnetic resonance imaging (MRI) abnormalities to prognosticate seizure freedom following ATL, the value of quantitative analysis of visually reviewed normal interictal EEG in such prognostication remains unclear. In this retrospective multicenter study, we investigate whether machine learning analysis of normal interictal scalp EEG studies can inform the prediction of postoperative seizure freedom outcomes in patients who have undergone ATL. Methods We analyzed normal presurgical scalp EEG recordings from 41 Mayo Clinic (MC) and 23 Cleveland Clinic (CC) patients. We used an unbiased automated algorithm to extract eyes closed awake epochs from scalp EEG studies that were free of any epileptiform activity and then extracted spectral EEG features representing (a) spectral power and (b) interhemispheric spectral coherence in frequencies between 1 and 25 Hz across several brain regions. We analyzed the differences between the seizure‐free and non–seizure‐free patients and employed a Naïve Bayes classifier using multiple spectral features to predict surgery outcomes. We trained the classifier using a leave‐one‐patient‐out cross‐validation scheme within the MC data set and then tested using the out‐of‐sample CC data set. Finally, we compared the predictive performance of normal scalp EEG‐derived features against MRI abnormalities. Results We found that several spectral power and coherence features showed significant differences correlated with surgical outcomes and that they were most pronounced in the 10–25 Hz range. The Naïve Bayes classification based on those features predicted 1‐year seizure freedom following ATL with area under the curve (AUC) values of 0.78 and 0.76 for the MC and CC data sets, respectively. Subsequent analyses revealed that (a) interhemispheric spectral coherence features in the 10–25 Hz range provided better predictability than other combinations and (b) normal scalp EEG‐derived features provided superior and potentially distinct predictive value when compared with MRI abnormalities (>10% higher F1 score). Significance These results support that quantitative analysis of even a normal presurgical scalp EEG may help prognosticate seizure freedom following ATL in patients with drug‐resistant TLE. Although the mechanism for this result is not known, the scalp EEG spectral and coherence properties predicting seizure freedom may represent activity arising from the neocortex or the networks responsible for temporal lobe seizure generation within vs outside the margins of an ATL.
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Affiliation(s)
- Yogatheesan Varatharajah
- Department of Bioengineering, University of Illinois, Urbana, IL, 61801, USA.,Departments of Neurology and Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA
| | - Boney Joseph
- Departments of Neurology and Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA
| | - Benjamin Brinkmann
- Departments of Neurology and Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA
| | | | | | - Deborah Vegh
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, 44195, USA
| | - Dileep Nair
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, 44195, USA
| | - Richard Burgess
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, 44195, USA
| | - Fernando Cendes
- Department of Neurology, University of Campinas UNICAMP, Campinas, Brazil
| | - Lara Jehi
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, 44195, USA
| | - Gregory Worrell
- Departments of Neurology and Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA
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26
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Hoffmann L, Blümcke I. Neuropathology and epilepsy surgery. Curr Opin Neurol 2022; 35:202-207. [PMID: 35067500 DOI: 10.1097/wco.0000000000001030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Neurosurgical treatment of patients suffering from drug-resistant focal epilepsy is recognized as a successful, yet underutilized medical treatment option. By searching PubMed for articles published between January 2020 and September 2021 with the broad search terms 'neuropathology' AND 'epilepsy surgery', this review highlights the active field of etiology-based epilepsy research in human tissue. RECENT FINDINGS All papers addressing the most common epileptogenic human brain disease entities, i.e. focal cortical dysplasia (FCD), brain tumors or hippocampal sclerosis, and written in English language were eligible for our review. We can conclude from this review that etiology-based studies are of foremost interest for (1) the development of prediction models for postsurgical seizure outcome; (2) decipher genetic and molecular alterations to better define disease entities and underlying molecular pathomechanisms, and (3) the translation of human tissue-derived biomarker into clinically useful diagnostics or novel therapeutic targets in the near future. SUMMARY Highlighting FCD brain somatic gain-of-function variants in mammalian target of Rapamycin are a leading pathway to better classify FCD. An integrated genotype-phenotype analysis enables to classify the broad spectrum of low-grade and epilepsy-associated brain tumors. Further DNA-methylation-based disease classification will increase the mechanistic understanding and diagnostic precision of difficult to classify pathologies in the future.
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Affiliation(s)
- Lucas Hoffmann
- Department of Neuropathology, University Hospital Erlangen, Erlangen, Germany
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Astner-Rohracher A, Zimmermann G, Avigdor T, Abdallah C, Barot N, Brázdil M, Doležalová I, Gotman J, Hall JA, Ikeda K, Kahane P, Kalss G, Kokkinos V, Leitinger M, Mindruta I, Minotti L, Mizera MM, Oane I, Richardson M, Schuele SU, Trinka E, Urban A, Whatley B, Dubeau F, Frauscher B. Development and Validation of the 5-SENSE Score to Predict Focality of the Seizure-Onset Zone as Assessed by Stereoelectroencephalography. JAMA Neurol 2021; 79:70-79. [PMID: 34870697 PMCID: PMC8649918 DOI: 10.1001/jamaneurol.2021.4405] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Importance Stereoelectroencephalography (SEEG) has become the criterion standard in case of inconclusive noninvasive presurgical epilepsy workup. However, up to 40% of patients are subsequently not offered surgery because the seizure-onset zone is less focal than expected or cannot be identified. Objective To predict focality of the seizure-onset zone in SEEG, the 5-point 5-SENSE score was developed and validated. Design, Setting, and Participants This was a monocentric cohort study for score development followed by multicenter validation with patient selection intervals between February 2002 to October 2018 and May 2002 to December 2019. The minimum follow-up period was 1 year. Patients with drug-resistant epilepsy undergoing SEEG at the Montreal Neurological Institute were analyzed to identify a focal seizure-onset zone. Selection criteria were 2 or more seizures in electroencephalography and availability of complete neuropsychological and neuroimaging data sets. For validation, patients from 9 epilepsy centers meeting these criteria were included. Analysis took place between May and July 2021. Main Outcomes and Measures Based on SEEG, patients were grouped as focal and nonfocal seizure-onset zone. Demographic, clinical, electroencephalography, neuroimaging, and neuropsychology data were analyzed, and a multiple logistic regression model for developing a score to predict SEEG focality was created and validated in an independent sample. Results A total of 128 patients (57 women [44.5%]; median [range] age, 31 [13-58] years) were analyzed for score development and 207 patients (97 women [46.9%]; median [range] age, 32 [16-70] years) were analyzed for validation. The score comprised the following 5 predictive variables: focal lesion on structural magnetic resonance imaging, absence of bilateral independent spikes in scalp electroencephalography, localizing neuropsychological deficit, strongly localizing semiology, and regional ictal scalp electroencephalography onset. The 5-SENSE score had an optimal mean (SD) probability cutoff for identifying a focal seizure-onset zone of 37.6 (3.5). Area under the curve, specificity, and sensitivity were 0.83, 76.3% (95% CI, 66.7-85.8), and 83.3% (95% CI, 72.30-94.1), respectively. Validation showed 76.0% (95% CI, 67.5-84.0) specificity and 52.3% (95% CI, 43.0-61.5) sensitivity. Conclusions and Relevance High specificity in score development and validation confirms that the 5-SENSE score predicts patients where SEEG is unlikely to identify a focal seizure-onset zone. It is a simple and useful tool for assisting clinicians to reduce unnecessary invasive diagnostic burden on patients and overutilization of limited health care resources.
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Affiliation(s)
- Alexandra Astner-Rohracher
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology, Christian Doppler University Hospital, Centre for Cognitive Neuroscience Paracelsus Medical University Hospital Salzburg, affiliated Member of the Epicare Reference Network, Salzburg, Austria
| | - Georg Zimmermann
- Team Biostatistics and Big Medical Data, IDA Lab Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Tamir Avigdor
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Chifaou Abdallah
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Nirav Barot
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Milan Brázdil
- Department of Neurology, Faculty of Medicine, Masaryk University and St Ann's University Hospital, Brno, Czech Republic
| | - Irena Doležalová
- Department of Neurology, Faculty of Medicine, Masaryk University and St Ann's University Hospital, Brno, Czech Republic
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jeffery Alan Hall
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Kirsten Ikeda
- Dalhousie University and Hospital, Division of Neurology, Halifax, Nova Scotia, Canada
| | - Philippe Kahane
- CHU Grenoble-Alpes, Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, Grenoble, France
| | - Gudrun Kalss
- Department of Neurology, Christian Doppler University Hospital, Centre for Cognitive Neuroscience Paracelsus Medical University Hospital Salzburg, affiliated Member of the Epicare Reference Network, Salzburg, Austria
| | | | - Markus Leitinger
- Department of Neurology, Christian Doppler University Hospital, Centre for Cognitive Neuroscience Paracelsus Medical University Hospital Salzburg, affiliated Member of the Epicare Reference Network, Salzburg, Austria
| | - Ioana Mindruta
- Neurology Department, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
| | - Lorella Minotti
- CHU Grenoble-Alpes, Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, Grenoble, France
| | | | - Irina Oane
- Neurology Department, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
| | - Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Boston
| | | | - Eugen Trinka
- Department of Neurology, Christian Doppler University Hospital, Centre for Cognitive Neuroscience Paracelsus Medical University Hospital Salzburg, affiliated Member of the Epicare Reference Network, Salzburg, Austria.,Neuroscience Institute, Christian Doppler University Hospital, Centre for Cognitive Neuroscience Paracelsus Medical University Hospital Salzburg, Salzburg, Austria.,Karl Landsteiner Institute for Neurorehabilitation and Space Neurology, Salzburg, Austria.,Department of Public Health, Health Services Research and Health Technology Assessment, University for Health Sciences, Medical Informatics and Technology (UMIT), Hall in Tirol, Austria
| | - Alexandra Urban
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Benjamin Whatley
- Dalhousie University and Hospital, Division of Neurology, Halifax, Nova Scotia, Canada
| | - François Dubeau
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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