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Wei Z, Wang X, Liu C, Feng Y, Gan Y, Shi Y, Wang X, Liu Y, Deng Y. Microstate-based brain network dynamics distinguishing temporal lobe epilepsy patients: A machine learning approach. Neuroimage 2024; 296:120683. [PMID: 38880308 DOI: 10.1016/j.neuroimage.2024.120683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 06/02/2024] [Accepted: 06/10/2024] [Indexed: 06/18/2024] Open
Abstract
Temporal lobe epilepsy (TLE) stands as the predominant adult focal epilepsy syndrome, characterized by dysfunctional intrinsic brain dynamics. However, the precise mechanisms underlying seizures in these patients remain elusive. Our study encompassed 116 TLE patients compared with 51 healthy controls. Employing microstate analysis, we assessed brain dynamic disparities between TLE patients and healthy controls, as well as between drug-resistant epilepsy (DRE) and drug-sensitive epilepsy (DSE) patients. We constructed dynamic functional connectivity networks based on microstates and quantified their spatial and temporal variability. Utilizing these brain network features, we developed machine learning models to discriminate between TLE patients and healthy controls, and between DRE and DSE patients. Temporal dynamics in TLE patients exhibited significant acceleration compared to healthy controls, along with heightened synchronization and instability in brain networks. Moreover, DRE patients displayed notably lower spatial variability in certain parts of microstate B, E and F dynamic functional connectivity networks, while temporal variability in certain parts of microstate E and G dynamic functional connectivity networks was markedly higher in DRE patients compared to DSE patients. The machine learning model based on these spatiotemporal metrics effectively differentiated TLE patients from healthy controls and discerned DRE from DSE patients. The accelerated microstate dynamics and disrupted microstate sequences observed in TLE patients mirror highly unstable intrinsic brain dynamics, potentially underlying abnormal discharges. Additionally, the presence of highly synchronized and unstable activities in brain networks of DRE patients signifies the establishment of stable epileptogenic networks, contributing to the poor responsiveness to antiseizure medications. The model based on spatiotemporal metrics demonstrated robust predictive performance, accurately distinguishing both TLE patients from healthy controls and DRE patients from DSE patients.
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Affiliation(s)
- Zihan Wei
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China
| | - Xinpei Wang
- School of Aerospace Medicine, Fourth Military Medical University, Xi'an, China
| | - Chao Liu
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China
| | - Yan Feng
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China; Xi'an Medical University, Xi'an 710021, PR China
| | - Yajing Gan
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China
| | - Yuqing Shi
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China; Xi'an Medical University, Xi'an 710021, PR China
| | - Xiaoli Wang
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China
| | - Yonghong Liu
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China
| | - Yanchun Deng
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China.
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Harlow TJ, Marquez SM, Bressler S, Read HL. Individualized Closed-Loop Acoustic Stimulation Suggests an Alpha Phase Dependence of Sound Evoked and Induced Brain Activity Measured with EEG Recordings. eNeuro 2024; 11:ENEURO.0511-23.2024. [PMID: 38834300 PMCID: PMC11181104 DOI: 10.1523/eneuro.0511-23.2024] [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: 12/06/2023] [Revised: 04/25/2024] [Accepted: 05/20/2024] [Indexed: 06/06/2024] Open
Abstract
Following repetitive visual stimulation, post hoc phase analysis finds that visually evoked response magnitudes vary with the cortical alpha oscillation phase that temporally coincides with sensory stimulus. This approach has not successfully revealed an alpha phase dependence for auditory evoked or induced responses. Here, we test the feasibility of tracking alpha with scalp electroencephalogram (EEG) recordings and play sounds phase-locked to individualized alpha phases in real-time using a novel end-point corrected Hilbert transform (ecHT) algorithm implemented on a research device. Based on prior work, we hypothesize that sound-evoked and induced responses vary with the alpha phase at sound onset and the alpha phase that coincides with the early sound-evoked response potential (ERP) measured with EEG. Thus, we use each subject's individualized alpha frequency (IAF) and individual auditory ERP latency to define target trough and peak alpha phases that allow an early component of the auditory ERP to align to the estimated poststimulus peak and trough phases, respectively. With this closed-loop and individualized approach, we find opposing alpha phase-dependent effects on the auditory ERP and alpha oscillations that follow stimulus onset. Trough and peak phase-locked sounds result in distinct evoked and induced post-stimulus alpha level and frequency modulations. Though additional studies are needed to localize the sources underlying these phase-dependent effects, these results suggest a general principle for alpha phase-dependence of sensory processing that includes the auditory system. Moreover, this study demonstrates the feasibility of using individualized neurophysiological indices to deliver automated, closed-loop, phase-locked auditory stimulation.
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Affiliation(s)
- Tylor J Harlow
- Department of Psychological Sciences, University of Connecticut, Storrs, Connecticut 06269
- Brain-Computer Interface Core, University of Connecticut, Storrs, Connecticut 06269
- Institute of Brain and Cognitive Science (IBACS), University of Connecticut, Storrs, Connecticut 06269
| | - Samantha M Marquez
- Department of Psychological Sciences, University of Connecticut, Storrs, Connecticut 06269
| | - Scott Bressler
- Elemind Technologies, Inc., Cambridge, Massachusetts 02139
| | - Heather L Read
- Department of Psychological Sciences, University of Connecticut, Storrs, Connecticut 06269
- Brain-Computer Interface Core, University of Connecticut, Storrs, Connecticut 06269
- Institute of Brain and Cognitive Science (IBACS), University of Connecticut, Storrs, Connecticut 06269
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269
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3
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Tait L, Staniaszek LE, Galizia E, Martin-Lopez D, Walker MC, Azeez AAA, Meiklejohn K, Allen D, Price C, Georgiou S, Bagary M, Khalsa S, Manfredonia F, Tittensor P, Lawthom C, Howes BB, Shankar R, Terry JR, Woldman W. Estimating the likelihood of epilepsy from clinically noncontributory electroencephalograms using computational analysis: A retrospective, multisite case-control study. Epilepsia 2024. [PMID: 38780578 DOI: 10.1111/epi.18024] [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: 09/19/2023] [Revised: 05/09/2024] [Accepted: 05/09/2024] [Indexed: 05/25/2024]
Abstract
OBJECTIVE This study was undertaken to validate a set of candidate biomarkers of seizure susceptibility in a retrospective, multisite case-control study, and to determine the robustness of these biomarkers derived from routinely collected electroencephalography (EEG) within a large cohort (both epilepsy and common alternative conditions such as nonepileptic attack disorder). METHODS The database consisted of 814 EEG recordings from 648 subjects, collected from eight National Health Service sites across the UK. Clinically noncontributory EEG recordings were identified by an experienced clinical scientist (N = 281; 152 alternative conditions, 129 epilepsy). Eight computational markers (spectral [n = 2], network-based [n = 4], and model-based [n = 2]) were calculated within each recording. Ensemble-based classifiers were developed using a two-tier cross-validation approach. We used standard regression methods to assess whether potential confounding variables (e.g., age, gender, treatment status, comorbidity) impacted model performance. RESULTS We found levels of balanced accuracy of 68% across the cohort with clinically noncontributory normal EEGs (sensitivity =61%, specificity =75%, positive predictive value =55%, negative predictive value =79%, diagnostic odds ratio =4.64, area under receiver operated characteristics curve =.72). Group level analysis found no evidence suggesting any of the potential confounding variables significantly impacted the overall performance. SIGNIFICANCE These results provide evidence that the set of biomarkers could provide additional value to clinical decision-making, providing the foundation for a decision support tool that could reduce diagnostic delay and misdiagnosis rates. Future work should therefore assess the change in diagnostic yield and time to diagnosis when utilizing these biomarkers in carefully designed prospective studies.
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Affiliation(s)
- Luke Tait
- Cardiff University, Cardiff, UK
- University of Birmingham, Birmingham
| | - Lydia E Staniaszek
- University Hospitals Bristol and Weston National Health Service Foundation Trust, Bristol, UK
- Neuronostics, Bristol, UK
| | - Elizabeth Galizia
- St. George's Hospital National Health Service Foundation Trust, London, UK
| | - David Martin-Lopez
- St. George's Hospital National Health Service Foundation Trust, London, UK
- Kingston Hospital National Health Service Foundation Trust, Kingston, UK
| | - Matthew C Walker
- University College London, London, UK
- University College London Hospitals, London, UK
| | | | - Kay Meiklejohn
- Neuronostics, Bristol, UK
- University Hospital Southampton National Health Service Foundation Trust, Southampton, UK
| | - David Allen
- University Hospital Southampton National Health Service Foundation Trust, Southampton, UK
| | - Chris Price
- Royal Devon and Exeter National Health Service Foundation Trust, Exeter, UK
| | - Sophie Georgiou
- Royal Devon and Exeter National Health Service Foundation Trust, Exeter, UK
| | - Manny Bagary
- Birmingham and Solihull Mental Health National Health Service Foundation Trust, Birmingham, UK
| | - Sakh Khalsa
- Birmingham and Solihull Mental Health National Health Service Foundation Trust, Birmingham, UK
| | | | - Phil Tittensor
- Royal Wolverhampton National Health Service Trust, Wolverhampton, UK
- University of Wolverhampton, Wolverhampton, UK
| | | | | | - Rohit Shankar
- University of Plymouth, Plymouth, UK
- Cornwall Partnership National Health Service Foundation Trust, Bodmin, UK
| | - John R Terry
- University of Birmingham, Birmingham
- Neuronostics, Bristol, UK
| | - Wessel Woldman
- University of Birmingham, Birmingham
- Neuronostics, Bristol, UK
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Zhong J, Tan G, Wang H, Chen Y. Excessively increased thalamocortical connectivity and poor initial antiseizure medication response in epilepsy patients. Front Neurol 2023; 14:1153563. [PMID: 37396772 PMCID: PMC10312096 DOI: 10.3389/fneur.2023.1153563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 05/25/2023] [Indexed: 07/04/2023] Open
Abstract
Objectives The network mechanism underlying the initial response to antiseizure medication in epilepsy has not been revealed yet. Given the central role of the thalamus in the brain network, we conducted a case-control study to investigate the association between thalamic connectivity and medication response. Methods We recruited 39 patients with newly diagnosed and medication-naïve epilepsy of genetic or unknown etiology, including 26 with a good response (GR group) and 13 with a poor response (PR group), and 26 matched healthy participants (control group). We measured the gray matter density (GMD) and the amplitude of low-frequency fluctuation (ALFF) of bilateral thalami. We then set each thalamus as the seed region of interest (ROI) to calculate voxel-wise functional connectivity (FC) and assessed ROI-wise effective connectivity (EC) between the thalamus and targeted regions. Results We found no significant difference between groups in the GMD or ALFF of bilateral thalami. However, we observed that the FC values of several circuits connecting the left thalamus and the cortical areas, including the bilateral Rolandic operculum, the left insula, the left postcentral gyrus, the left supramarginal gyrus, and the left superior temporal gyrus, differed among groups (False Discovery Rate correction, P < 0.05), with a higher value in the PR group than in the GR group and/or the control group (Bonferroni correction, P < 0.05). Similarly, both the outflow and the inflow EC in each thalamocortical circuit were higher in the PR group than in the GR group and the control group, although these differences did not remain statistically significant after applying the Bonferroni correction (P < 0.05). The FC showed a positive correlation with the corresponding outflow and inflow ECs for each circuit. Conclusion Our finding suggested that patients with stronger thalamocortical connectivity, potentially driven by both thalamic outflowing and inflowing information, may be more likely to respond poorly to initial antiseizure medication.
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Affiliation(s)
- Jiyuan Zhong
- International Medical College of Chongqing Medical University, Chongqing, China
| | - Ge Tan
- Epilepsy Center, Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Haijiao Wang
- Epilepsy Center, Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
- Department of Neurology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Yangmei Chen
- Department of Neurology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Wang K, Xie F, Liu C, Wang G, Zhang M, He J, Tan L, Tang H, Chen F, Xiao B, Song Y, Long L. Shared functional network abnormality in patients with temporal lobe epilepsy and their siblings. CNS Neurosci Ther 2023; 29:1109-1119. [PMID: 36647843 PMCID: PMC10018100 DOI: 10.1111/cns.14087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 12/07/2022] [Accepted: 12/26/2022] [Indexed: 01/18/2023] Open
Abstract
AIM Temporal lobe epilepsy is a neurological network disease in which genetics played a greater role than previously appreciated. This study aimed to explore shared functional network abnormalities in patients with sporadic temporal lobe epilepsy and their unaffected siblings. METHODS Fifty-eight patients with sporadic temporal lobe epilepsy, 13 unaffected siblings, and 30 healthy controls participated in this cross-sectional study. We examined the task-based whole-brain functional network topology and the effective functional connectivity between networks identified by group-independent component analysis. RESULTS We observed increased global efficiency, decreased clustering coefficiency, and decreased small-worldness in patients and siblings (p < 0.05, false discovery rate-corrected). The effective network connectivity from the ventral attention network to the limbic system was impaired (p < 0.001, false discovery rate-corrected). These features had higher prevalence in unaffected siblings than in normal population and was not correlated with disease burden. In addition, topological abnormalities had a high intraclass correlation between patients and their siblings. CONCLUSION Patients with temporal lobe epilepsy and their unaffected siblings showed shared topological functional disturbance and the effective functional network connectivity impairment. These abnormalities may contribute to the pathogenesis that promotes the susceptibility of seizures and language decline in temporal lobe epilepsy.
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Affiliation(s)
- Kangrun Wang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,Clinical Research Center for Epileptic disease of Hunan Province, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Fangfang Xie
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Chaorong Liu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Ge Wang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Min Zhang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Jialinzi He
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Langzi Tan
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Haiyun Tang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Fenghua Chen
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Bo Xiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,Clinical Research Center for Epileptic disease of Hunan Province, Xiangya Hospital, Central South University, Changsha, China
| | - Yanmin Song
- Department of Emergency, Xiangya Hospital, Central South University, Changsha, China
| | - Lili Long
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,Clinical Research Center for Epileptic disease of Hunan Province, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
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6
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Ponomareva NV, Andreeva TV, Protasova M, Konovalov RN, Krotenkova MV, Kolesnikova EP, Malina DD, Kanavets EV, Mitrofanov AA, Fokin VF, Illarioshkin SN, Rogaev EI. Genetic association of apolipoprotein E genotype with EEG alpha rhythm slowing and functional brain network alterations during normal aging. Front Neurosci 2022; 16:931173. [PMID: 35979332 PMCID: PMC9376365 DOI: 10.3389/fnins.2022.931173] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/27/2022] [Indexed: 12/02/2022] Open
Abstract
The ε4 allele of the apolipoprotein E (APOE4+) genotype is a major genetic risk factor for Alzheimer’s disease (AD), but the mechanisms underlying its influence remain incompletely understood. The study aimed to investigate the possible effect of the APOE genotype on spontaneous electroencephalogram (EEG) alpha characteristics, resting-state functional MRI (fMRI) connectivity (rsFC) in large brain networks and the interrelation of alpha rhythm and rsFC characteristics in non-demented adults during aging. We examined the EEG alpha subband’s relative power, individual alpha peak frequency (IAPF), and fMRI rsFC in non-demented volunteers (age range 26–79 years) stratified by the APOE genotype. The presence of the APOE4+ genotype was associated with lower IAPF and lower relative power of the 11–13 Hz alpha subbands. The age related decrease in EEG IAPF was more pronounced in the APOE4+ carriers than in the APOE4+ non-carriers (APOE4-). The APOE4+ carriers had a stronger fMRI positive rsFC of the interhemispheric regions of the frontoparietal, lateral visual and salience networks than the APOE4– individuals. In contrast, the negative rsFC in the network between the left hippocampus and the right posterior parietal cortex was reduced in the APOE4+ carriers compared to the non-carriers. Alpha rhythm slowing was associated with the dysfunction of hippocampal networks. Our results show that in adults without dementia APOE4+ genotype is associated with alpha rhythm slowing and that this slowing is age-dependent. Our data suggest predominant alterations of inhibitory processes in large-scale brain network of non-demented APOE4+ carriers. Moreover, dysfunction of large-scale hippocampal network can influence APOE-related alpha rhythm vulnerability.
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Affiliation(s)
- Natalya V. Ponomareva
- Research Center of Neurology, Moscow, Russia
- Center for Genetics and Life Science, Sirius University of Science and Technology, Sochi, Russia
- *Correspondence: Natalya V. Ponomareva,
| | - Tatiana V. Andreeva
- Center for Genetics and Life Science, Sirius University of Science and Technology, Sochi, Russia
- Vavilov Institute of General Genetics, Russian Academy of Sciences (RAS), Moscow, Russia
| | - Maria Protasova
- Center for Genetics and Life Science, Sirius University of Science and Technology, Sochi, Russia
- Vavilov Institute of General Genetics, Russian Academy of Sciences (RAS), Moscow, Russia
| | | | | | | | | | | | | | | | | | - Evgeny I. Rogaev
- Center for Genetics and Life Science, Sirius University of Science and Technology, Sochi, Russia
- Vavilov Institute of General Genetics, Russian Academy of Sciences (RAS), Moscow, Russia
- Brudnick Neuropsychiatric Research Institute (BNRI), University of Massachusetts Medical School, Worcester, MA, United States
- Evgeny I. Rogaev,
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Shakeshaft A, Laiou P, Abela E, Stavropoulos I, Richardson MP, Pal DK, Howell A, Hyde A, McQueen A, Duran A, Gaurav A, Collingwood A, Kitching A, Shakeshaft A, Papathanasiou A, Clough A, Gribbin A, Swain A, Needle A, Hall A, Smith A, Macleod A, Chhibda A, Fonferko-Shadrach B, Camara B, Petrova B, Stuart C, Hamilton C, Peacey C, Campbell C, Cotter C, Edwards C, Picton C, Busby C, Quamina C, Waite C, West C, Ng CC, Giavasi C, Backhouse C, Holliday C, Mewies C, Thow C, Egginton D, Dickerson D, Rice D, Mullan D, Daly D, Mcaleer D, Gardella E, Stephen E, Irvine E, Sacre E, Lin F, Castle G, Mackay G, Salim H, Cock H, Collier H, Cockerill H, Navarra H, Mhandu H, Crudgington H, Hayes I, Stavropoulos I, Daglish J, Smith J, Bartholomew J, Cotta J, Ceballos JP, Natarajan J, Crooks J, Quirk J, Bland J, Sidebottom J, Gesche J, Glenton J, Henry J, Davis J, Ball J, Selmer KK, Rhodes K, Holroyd K, Lim KS, O’Brien K, Thrasyvoulou L, Makawa L, Charles L, Richardson L, Nelson L, Walding L, Woodhead L, Ehiorobo L, Hawkins L, Adams L, Connon M, Home M, Baker M, Mencias M, Richardson MP, Sargent M, Syvertsen M, Milner M, Recto M, Chang M, O'Donoghue M, Young M, Ray M, Panjwani N, Ghaus N, Sudarsan N, Said N, Pickrell O, Easton P, Frattaroli P, McAlinden P, Harrison R, Swingler R, Wane R, Ramsay R, Møller RS, McDowall R, Clegg R, Uka S, White S, Truscott S, Francis S, Tittensor S, Sharman SJ, Chung SK, Patel S, Ellawela S, Begum S, Kempson S, Raj S, Bayley S, Warriner S, Kilroy S, MacFarlane S, Brown T, Samakomva T, Nortcliffe T, Calder V, Collins V, Parker V, Richmond V, Stern W, Haslam Z, Šobíšková Z, Agrawal A, Whiting A, Pratico A, Desurkar A, Saraswatula A, MacDonald B, Fong CY, Beier CP, Andrade D, Pauldhas D, Greenberg DA, Deekollu D, Pal DK, Jayachandran D, Lozsadi D, Galizia E, Scott F, Rubboli G, Angus-Leppan H, Talvik I, Takon I, Zarubova J, Koht J, Aram J, Lanyon K, Irwin K, Hamandi K, Yeung L, Strug LJ, Rees M, Reuber M, Kirkpatrick M, Taylor M, Maguire M, Koutroumanidis M, Khan M, Moran N, Striano P, Bala P, Bharat R, Pandey R, Mohanraj R, Thomas R, Belderbos R, Slaght SJ, Delamont S, Sastry S, Mariguddi S, Kumar S, Kumar S, Majeed T, Jegathasan U, Whitehouse W. Heterogeneity of resting-state EEG features in juvenile myoclonic epilepsy and controls. Brain Commun 2022; 4:fcac180. [PMID: 35873918 PMCID: PMC9301584 DOI: 10.1093/braincomms/fcac180] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 05/18/2022] [Accepted: 07/07/2022] [Indexed: 11/12/2022] Open
Abstract
Abnormal EEG features are a hallmark of epilepsy, and abnormal frequency and network features are apparent in EEGs from people with idiopathic generalized epilepsy in both ictal and interictal states. Here, we characterize differences in the resting-state EEG of individuals with juvenile myoclonic epilepsy and assess factors influencing the heterogeneity of EEG features. We collected EEG data from 147 participants with juvenile myoclonic epilepsy through the Biology of Juvenile Myoclonic Epilepsy study. Ninety-five control EEGs were acquired from two independent studies [Chowdhury et al. (2014) and EU-AIMS Longitudinal European Autism Project]. We extracted frequency and functional network-based features from 10 to 20 s epochs of resting-state EEG, including relative power spectral density, peak alpha frequency, network topology measures and brain network ictogenicity: a computational measure of the propensity of networks to generate seizure dynamics. We tested for differences between epilepsy and control EEGs using univariate, multivariable and receiver operating curve analysis. In addition, we explored the heterogeneity of EEG features within and between cohorts by testing for associations with potentially influential factors such as age, sex, epoch length and time, as well as testing for associations with clinical phenotypes including anti-seizure medication, and seizure characteristics in the epilepsy cohort. P-values were corrected for multiple comparisons. Univariate analysis showed significant differences in power spectral density in delta (2-5 Hz) (P = 0.0007, hedges' g = 0.55) and low-alpha (6-9 Hz) (P = 2.9 × 10-8, g = 0.80) frequency bands, peak alpha frequency (P = 0.000007, g = 0.66), functional network mean degree (P = 0.0006, g = 0.48) and brain network ictogenicity (P = 0.00006, g = 0.56) between epilepsy and controls. Since age (P = 0.009) and epoch length (P = 1.7 × 10-8) differed between the two groups and were potential confounders, we controlled for these covariates in multivariable analysis where disparities in EEG features between epilepsy and controls remained. Receiver operating curve analysis showed low-alpha power spectral density was optimal at distinguishing epilepsy from controls, with an area under the curve of 0.72. Lower average normalized clustering coefficient and shorter average normalized path length were associated with poorer seizure control in epilepsy patients. To conclude, individuals with juvenile myoclonic epilepsy have increased power of neural oscillatory activity at low-alpha frequencies, and increased brain network ictogenicity compared with controls, supporting evidence from studies in other epilepsies with considerable external validity. In addition, the impact of confounders on different frequency-based and network-based EEG features observed in this study highlights the need for careful consideration and control of these factors in future EEG research in idiopathic generalized epilepsy particularly for their use as biomarkers.
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Affiliation(s)
- Amy Shakeshaft
- Department of Basic & Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK,MRC Centre for Neurodevelopmental Disorders, King’s College London, London, UK
| | - Petroula Laiou
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Eugenio Abela
- Department of Basic & Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | | | - Mark P Richardson
- Correspondence may also be addressed to: Professor Mark P Richardson Maurice Wohl Clinical Neurosciences Institute Institute of Psychiatry, Psychology & Neuroscience King’s College London, 5 Cutcombe Road, London SE5 9RX, UK E-mail:
| | - Deb K Pal
- Correspondence to: Professor Deb K Pal Maurice Wohl Clinical Neurosciences Institute Institute of Psychiatry, Psychology & Neuroscience King’s College London 5 Cutcombe Road, London SE5 9RX, UK E-mail:
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8
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Wang G, Wu W, Xu Y, Yang Z, Xiao B, Long L. Imaging Genetics in Epilepsy: Current Knowledge and New Perspectives. Front Mol Neurosci 2022; 15:891621. [PMID: 35706428 PMCID: PMC9189397 DOI: 10.3389/fnmol.2022.891621] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 05/06/2022] [Indexed: 12/11/2022] Open
Abstract
Epilepsy is a neurological network disease with genetics playing a much greater role than was previously appreciated. Unfortunately, the relationship between genetic basis and imaging phenotype is by no means simple. Imaging genetics integrates multidimensional datasets within a unified framework, providing a unique opportunity to pursue a global vision for epilepsy. This review delineates the current knowledge of underlying genetic mechanisms for brain networks in different epilepsy syndromes, particularly from a neural developmental perspective. Further, endophenotypes and their potential value are discussed. Finally, we highlight current challenges and provide perspectives for the future development of imaging genetics in epilepsy.
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Affiliation(s)
- Ge Wang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Wenyue Wu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Yuchen Xu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhuanyi Yang
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Bo Xiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Lili Long
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
- *Correspondence: Lili Long
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Viana PF, Remvig LS, Duun-Henriksen J, Glasstetter M, Dümpelmann M, Nurse ES, Martins IP, Schulze-Bonhage A, Freestone DR, Brinkmann BH, Kjaer TW, Richardson MP. Signal quality and power spectrum analysis of remote ultra long-term subcutaneous EEG. Epilepsia 2021; 62:1820-1828. [PMID: 34250608 DOI: 10.1111/epi.16969] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 06/02/2021] [Accepted: 06/03/2021] [Indexed: 01/09/2023]
Abstract
OBJECTIVE Ultra long-term subcutaneous electroencephalography (sqEEG) monitoring is a new modality with great potential for both health and disease, including epileptic seizure detection and forecasting. However, little is known about the long-term quality and consistency of the sqEEG signal, which is the objective of this study. METHODS The largest multicenter cohort of sqEEG was analyzed, including 14 patients with epilepsy and 12 healthy subjects, implanted with a sqEEG device (24/7 EEG™ SubQ), and recorded from 23 to 230 days (median 42 days), with a median data capture rate of 75% (17.9 hours/day). Median power spectral density plots of each subject were examined for physiological peaks, including at diurnal and nocturnal periods. Long-term temporal trends in signal impedance and power spectral features were investigated with subject-specific linear regression models and group-level linear mixed-effects models. RESULTS sqEEG spectrograms showed an approximate 1/f power distribution. Diurnal peaks in the alpha range (8-13Hz) and nocturnal peaks in the sigma range (12-16Hz) were seen in the majority of subjects. Signal impedances remained low, and frequency band powers were highly stable throughout the recording periods. SIGNIFICANCE The spectral characteristics of minimally invasive, ultra long-term sqEEG are similar to scalp EEG, whereas the signal is highly stationary. Our findings reinforce the suitability of this system for chronic implantation on diverse clinical applications, from seizure detection and forecasting to brain-computer interfaces.
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Affiliation(s)
- Pedro F Viana
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | | | | | - Martin Glasstetter
- Epilepsy Center, Department for Neurosurgery, University Medical Center Freiburg, Freiburg, Germany
| | - Matthias Dümpelmann
- Epilepsy Center, Department for Neurosurgery, University Medical Center Freiburg, Freiburg, Germany
| | - Ewan S Nurse
- Seer Medical Inc, Melbourne, Vic, Australia.,Department of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, Vic, Australia
| | | | - Andreas Schulze-Bonhage
- Epilepsy Center, Department for Neurosurgery, University Medical Center Freiburg, Freiburg, Germany
| | - Dean R Freestone
- Seer Medical Inc, Melbourne, Vic, Australia.,Department of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, Vic, Australia
| | - Benjamin H Brinkmann
- Bioelectronics Neurology and Engineering Laboratory, Department of Neurology, Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Troels W Kjaer
- Department of Neurology, Zealand University Hospital, Roskilde, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Mark P Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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11
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Zhang S, Cao C, Quinn A, Vivekananda U, Zhan S, Liu W, Sun B, Woolrich M, Lu Q, Litvak V. Dynamic analysis on simultaneous iEEG-MEG data via hidden Markov model. Neuroimage 2021; 233:117923. [PMID: 33662572 PMCID: PMC8204269 DOI: 10.1016/j.neuroimage.2021.117923] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 02/17/2021] [Accepted: 02/24/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Intracranial electroencephalography (iEEG) recordings are used for clinical evaluation prior to surgical resection of the focus of epileptic seizures and also provide a window into normal brain function. A major difficulty with interpreting iEEG results at the group level is inconsistent placement of electrodes between subjects making it difficult to select contacts that correspond to the same functional areas. Recent work using time delay embedded hidden Markov model (HMM) applied to magnetoencephalography (MEG) resting data revealed a distinct set of brain states with each state engaging a specific set of cortical regions. Here we use a rare group dataset with simultaneously acquired resting iEEG and MEG to test whether there is correspondence between HMM states and iEEG power changes that would allow classifying iEEG contacts into functional clusters. METHODS Simultaneous MEG-iEEG recordings were performed at rest on 11 patients with epilepsy whose intracranial electrodes were implanted for pre-surgical evaluation. Pre-processed MEG sensor data was projected to source space. Time delay embedded HMM was then applied to MEG time series. At the same time, iEEG time series were analyzed with time-frequency decomposition to obtain spectral power changes with time. To relate MEG and iEEG results, correlations were computed between HMM probability time courses of state activation and iEEG power time course from the mid contact pair for each electrode in equally spaced frequency bins and presented as correlation spectra for the respective states and iEEG channels. Association of iEEG electrodes with HMM states based on significant correlations was compared to that based on the distance to peaks in subject-specific state topographies. RESULTS Five HMM states were inferred from MEG. Two of them corresponded to the left and the right temporal activations and had a spectral signature primarily in the theta/alpha frequency band. All the electrodes had significant correlations with at least one of the states (p < 0.05 uncorrected) and for 27/50 electrodes these survived within-subject FDR correction (q < 0.05). These correlations peaked in the theta/alpha band. There was a highly significant dependence between the association of states and electrodes based on functional correlations and that based on spatial proximity (p = 5.6e-6,χ2 test for independence). Despite the potentially atypical functional anatomy and physiological abnormalities related to epilepsy, HMM model estimated from the patient group was very similar to that estimated from healthy subjects. CONCLUSION Epilepsy does not preclude HMM analysis of interictal data. The resulting group functional states are highly similar to those reported for healthy controls. Power changes recorded with iEEG correlate with HMM state time courses in the alpha-theta band and the presence of this correlation can be related to the spatial location of electrode contacts close to the individual peaks of the corresponding state topographies. Thus, the hypothesized relation between iEEG contacts and HMM states exists and HMM could be further explored as a method for identifying comparable iEEG channels across subjects for the purposes of group analysis.
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Affiliation(s)
- Siqi Zhang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, China; Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, 12 Queen Square, London WC1N 3BG, UK
| | - Chunyan Cao
- Department of Neurosurgery, affiliated Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Andrew Quinn
- Oxford Centre for Human Brain Activity, University of Oxford, Warneford Hospital, Oxford, UK
| | - Umesh Vivekananda
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, 12 Queen Square, London WC1N 3BG, UK; National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Shikun Zhan
- Department of Neurosurgery, affiliated Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Liu
- Department of Neurosurgery, affiliated Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bomin Sun
- Department of Neurosurgery, affiliated Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mark Woolrich
- Oxford Centre for Human Brain Activity, University of Oxford, Warneford Hospital, Oxford, UK
| | - Qing Lu
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, China.
| | - Vladimir Litvak
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, 12 Queen Square, London WC1N 3BG, UK.
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12
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Pegg EJ, Taylor JR, Mohanraj R. Spectral power of interictal EEG in the diagnosis and prognosis of idiopathic generalized epilepsies. Epilepsy Behav 2020; 112:107427. [PMID: 32949965 DOI: 10.1016/j.yebeh.2020.107427] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 08/09/2020] [Accepted: 08/11/2020] [Indexed: 12/19/2022]
Abstract
INTRODUCTION Idiopathic generalized epilepsies (IGE) are characterized by generalized interictal epileptiform discharges (IEDs) on a normal background electroencephalography (EEG). However, the yield of IEDs can be low. Approximately 20% of patients with IGE fail to achieve seizure control with antiepileptic drug (AED) treatment. Currently, there are no reliable prognostic markers for early identification of drug-resistant epilepsy (DRE). We examined spectral power of the interictal EEG in patients with IGE and healthy controls, to identify potential diagnostic and prognostic biomarkers of IGE. METHODS A 64-channel EEG was recorded under standard conditions in patients with well-controlled IGE (WC-IGE, n = 19), drug-resistant IGE (DR-IGE, n = 18), and age-matched controls (n = 20). After preprocessing, fast Fourier transform was performed to obtain 1D frequency spectra for each EEG channel. The 1D spectra (averaged over channels) and 2D topographic maps (averaged over canonical frequency bands) were computed for each participant. Power spectra in the 3 cohorts were compared using one-way analysis of variance (ANOVA), and power spectra images were compared using T-contrast tests. A post hoc analysis compared peak alpha power between the groups. RESULTS Compared with controls, participants with IGE had higher interictal EEG spectral power in the delta band in the midline central region, in the theta band in the midline, in the beta band over the left hemisphere, and in the gamma band over right hemisphere and left central regions. There were no differences in spectral power between cohorts with WC-IGE and DR-IGE. Peak alpha power was lower in WC-IGE and DR-IGE than controls. CONCLUSIONS Electroencephalography spectral power analysis could form part of a clinically useful diagnostic biomarker for IGE; however, it did not correlate with response to AED in this study.
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Affiliation(s)
- Emily J Pegg
- Department of Neurology, Manchester Centre for Clinical Neurosciences, United Kingdom; Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, United Kingdom
| | - Jason R Taylor
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, United Kingdom; Manchester Academic Health Sciences Centre, United Kingdom
| | - Rajiv Mohanraj
- Department of Neurology, Manchester Centre for Clinical Neurosciences, United Kingdom; Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, United Kingdom.
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