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Piskin D, Müller R, Büchel D, Lehmann T, Baumeister J. Behavioral and cortical dynamics underlying superior accuracy in short-distance passes. Behav Brain Res 2024; 471:115120. [PMID: 38905733 DOI: 10.1016/j.bbr.2024.115120] [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: 04/06/2024] [Revised: 06/10/2024] [Accepted: 06/19/2024] [Indexed: 06/23/2024]
Abstract
Improved pass accuracy is a prominent determinant of success in football. It demands an effective interaction of complex behavioral and cortical dynamics. Exploring differences in the ability to sustain an accurate pass behavior in a stable setting and the associated cortical dynamics at different expertise levels may provide an insight into skilled strategies contributing to superior accuracy in football. The aim of this study is to compare trial-to-trial variability of pass biomechanics and the corresponding cortical dynamics during short-distance passes between novices and experienced football players. Thirty participants (15 novices, 15 football players) performed 90 short-distance passes. The intertrial variability of pass biomechanics (foot acceleration, range of hip flexion, knee flexion and foot rotation) was assessed by means of multiscale entropy. The task-related cortical dynamics were analyzed via source-derived event-related spectral perturbations. Experienced players demonstrated higher accuracy and overall lower entropy values across multiple time scales which was significant for hip flexion. The electroencephalography data revealed group differences in parieto-occipital alpha desynchronization and frontal theta synchronization in successive phases of passes. The current findings suggest that experienced football players may show a skilled ability to recruit and retain pass biomechanics promoting higher accuracy, whereas novices may show an explorative behavior with higher spatial variability. This difference may be associated with distinctive visuospatial and attentional strategies acquired with expertise in football. Our study provides an insight into expertise-specific behavioral and cortical dynamics of superior accuracy in football and a basis for its prospective investigation in enriched contexts.
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Affiliation(s)
- Daghan Piskin
- Department Sports & Health, Exercise Science & Neuroscience Unit, Paderborn University, Paderborn 33098, Germany.
| | - Romina Müller
- Department Sports & Health, Exercise Science & Neuroscience Unit, Paderborn University, Paderborn 33098, Germany
| | - Daniel Büchel
- Department Sports & Health, Exercise Science & Neuroscience Unit, Paderborn University, Paderborn 33098, Germany
| | - Tim Lehmann
- Department Sports & Health, Exercise Science & Neuroscience Unit, Paderborn University, Paderborn 33098, Germany
| | - Jochen Baumeister
- Department Sports & Health, Exercise Science & Neuroscience Unit, Paderborn University, Paderborn 33098, Germany
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Ye H, Chen C, Weiss SA, Wang S. Pathological and Physiological High-frequency Oscillations on Electroencephalography in Patients with Epilepsy. Neurosci Bull 2024; 40:609-620. [PMID: 37999861 PMCID: PMC11127900 DOI: 10.1007/s12264-023-01150-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 09/28/2023] [Indexed: 11/25/2023] Open
Abstract
High-frequency oscillations (HFOs) encompass ripples (80 Hz-200 Hz) and fast ripples (200 Hz-600 Hz), serving as a promising biomarker for localizing the epileptogenic zone in epilepsy. Spontaneous fast ripples are always pathological, while ripples may be physiological or pathological. Distinguishing physiological from pathological ripples is important not only for designating epileptogenic brain regions, but also for investigations that study ripples in the context of memory encoding, consolidation, and recall in patients with epilepsy. Many studies have sought to identify distinguishing features between pathological and physiological ripples over the past two decades. Physiological and pathological ripples differ with respect to their spatial location, cellular mechanisms, morphology, and coupling with background electroencephalographic activity. Retrospective studies have demonstrated that differentiating between pathological and physiological ripples can improve surgical outcome prediction. In this review, we summarize the characteristics, differences, and applications of pathological and physiological HFOs and discuss strategies for their clinical translation.
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Affiliation(s)
- Hongyi Ye
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China
| | - Cong Chen
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China
| | - Shennan A Weiss
- Department of Neurology, State University of New York Downstate, Brooklyn, NY, 11203, USA
- Department of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, NY, 11203, USA
- Department of Neurology, New York City Health + Hospitals/Kings County, Brooklyn, NY, 11203, USA
| | - Shuang Wang
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China.
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Monsoor T, Zhang Y, Daida A, Oana S, Lu Q, Hussain SA, Fallah A, Sankar R, Staba RJ, Speier W, Roychowdhury V, Nariai H. Optimizing detection and deep learning-based classification of pathological high-frequency oscillations in epilepsy. Clin Neurophysiol 2023; 154:129-140. [PMID: 37603979 PMCID: PMC10861270 DOI: 10.1016/j.clinph.2023.07.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 06/30/2023] [Accepted: 07/26/2023] [Indexed: 08/23/2023]
Abstract
OBJECTIVE This study aimed to explore sensitive detection methods for pathological high-frequency oscillations (HFOs) to improve seizure outcomes in epilepsy surgery. METHODS We analyzed interictal HFOs (80-500 Hz) in 15 children with medication-resistant focal epilepsy who underwent chronic intracranial electroencephalogram via subdural grids. The HFOs were assessed using the short-term energy (STE) and Montreal Neurological Institute (MNI) detectors and examined for spike association and time-frequency plot characteristics. A deep learning (DL)-based classification was applied to purify pathological HFOs. Postoperative seizure outcomes were correlated with HFO-resection ratios to determine the optimal HFO detection method. RESULTS The MNI detector identified a higher percentage of pathological HFOs than the STE detector, but some pathological HFOs were detected only by the STE detector. HFOs detected by both detectors had the highest spike association rate. The Union detector, which detects HFOs identified by either the MNI or STE detector, outperformed other detectors in predicting postoperative seizure outcomes using HFO-resection ratios before and after DL-based purification. CONCLUSIONS HFOs detected by standard automated detectors displayed different signal and morphological characteristics. DL-based classification effectively purified pathological HFOs. SIGNIFICANCE Enhancing the detection and classification methods of HFOs will improve their utility in predicting postoperative seizure outcomes.
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Affiliation(s)
- Tonmoy Monsoor
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Yipeng Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Atsuro Daida
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Shingo Oana
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Qiujing Lu
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Shaun A Hussain
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Aria Fallah
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Raman Sankar
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA; The UCLA Children's Discovery and Innovation Institute, Los Angeles, CA, USA
| | - Richard J Staba
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - William Speier
- Department of Bioengineering, University of California, Los Angeles, CA, USA; Department of Radiological Sciences, University of California, Los Angeles, CA, USA
| | - Vwani Roychowdhury
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Hiroki Nariai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA; The UCLA Children's Discovery and Innovation Institute, Los Angeles, CA, USA.
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Monsoor T, Zhang Y, Daida A, Oana S, Lu Q, Hussain SA, Fallah A, Sankar R, Staba RJ, Speier W, Roychowdhury V, Nariai H. Optimizing Detection and Deep Learning-based Classification of Pathological High-Frequency Oscillations in Epilepsy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.13.23288435. [PMID: 37131743 PMCID: PMC10153337 DOI: 10.1101/2023.04.13.23288435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Objective This study aimed to explore sensitive detection methods and deep learning (DL)-based classification for pathological high-frequency oscillations (HFOs). Methods We analyzed interictal HFOs (80-500 Hz) in 15 children with medication-resistant focal epilepsy who underwent resection after chronic intracranial electroencephalogram via subdural grids. The HFOs were assessed using the short-term energy (STE) and Montreal Neurological Institute (MNI) detectors and examined for pathological features based on spike association and time-frequency plot characteristics. A DL-based classification was applied to purify pathological HFOs. Postoperative seizure outcomes were correlated with HFO-resection ratios to determine the optimal HFO detection method. Results The MNI detector identified a higher percentage of pathological HFOs than the STE detector, but some pathological HFOs were detected only by the STE detector. HFOs detected by both detectors exhibited the most pathological features. The Union detector, which detects HFOs identified by either the MNI or STE detector, outperformed other detectors in predicting postoperative seizure outcomes using HFO-resection ratios before and after DL-based purification. Conclusions HFOs detected by standard automated detectors displayed different signal and morphological characteristics. DL-based classification effectively purified pathological HFOs. Significance Enhancing the detection and classification methods of HFOs will improve their utility in predicting postoperative seizure outcomes. HIGHLIGHTS HFOs detected by the MNI detector showed different traits and higher pathological bias than those detected by the STE detectorHFOs detected by both MNI and STE detectors (the Intersection HFOs) were deemed the most pathologicalA deep learning-based classification was able to distill pathological HFOs, regard-less of the initial HFO detection methods.
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Li D, Zhang X, Liu R, Long M, Zhou S, Lin J, Zhang L. Kainic acid induced hyperexcitability in thalamic reticular nucleus that initiates an inflammatory response through the HMGB1/TLR4 pathway. Neurotoxicology 2023; 95:94-106. [PMID: 36669621 DOI: 10.1016/j.neuro.2023.01.007] [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: 10/24/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 01/20/2023]
Abstract
OBJECTIVE To explore the relationship between the proinflammatory factor high-mobility group box 1 (HMGB1) and glutamatergic alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors in the development of epilepsy. METHODS Thalamic reticular nucleus (TRN) slices were treated with kainic acid (KA) to simulate seizures. Action potentials and spontaneous inhibitory postsynaptic currents (sIPSCs) were recorded within TRN slices using whole-cell patch clamp techniques. The translocation of HMGB1 was detected by immunofluorescence. The HMGB1/TLR4 signaling pathway and its downstream inflammatory factors (IL-1β and NF-κB) were detected by RTPCR, Western blot and ELISA. RESULTS KA-evoked spikings were observed in TRN slices and blocked by perampanel. sIPSCs in the TRN were enhanced by KA and reduced by perampanel. The translocation of HMGB1 in the TRN was promoted by KA and inhibited by perampanel. The expression of the HMGB1/TLR4 signaling pathway was promoted by KA and suppressed by perampanel. CONCLUSION KA induced hyperexcitability activates the HMGB1/TLR4 pathway, which potentially leading to neuroinflammation in epilepsy.
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Affiliation(s)
- Dongbin Li
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China; First Department of Neurology and Neuroscience Center, Heilongjiang Provincial Hospital, Harbin, China
| | - Xiaosi Zhang
- Metro-Medic Clinic, 1538 sherbrooke Ouest, suite 100, Montreal, QC H3G 1L5, Canada
| | - Ruoshi Liu
- Department of Neurology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Meixin Long
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Shanshan Zhou
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Jinghan Lin
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Liming Zhang
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China.
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Jeong JW, Lee MH, Kuroda N, Sakakura K, O'Hara N, Juhasz C, Asano E. Multi-Scale Deep Learning of Clinically Acquired Multi-Modal MRI Improves the Localization of Seizure Onset Zone in Children With Drug-Resistant Epilepsy. IEEE J Biomed Health Inform 2022; 26:5529-5539. [PMID: 35925854 PMCID: PMC9710730 DOI: 10.1109/jbhi.2022.3196330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The present study investigates the effectiveness of a deep learning neural network for non-invasively localizing the seizure onset zone (SOZ) using multi-modal MRI data that are clinically acquired from children with drug-resistant epilepsy. A cortical parcellation was applied to localize the SOZ in cortical nodes of the epileptogenic hemisphere. At each node, the laminar surface analysis was followed to sample 1) the relative intensity of gray matter and white matter in multi-modal MRI and 2) the neighboring white matter connectivity using diffusion tractography edge strengths. A cross-validation was employed to train and test all layers of a multi-scale residual neural network (msResNet) that can classify SOZ node in an end-to-end fashion. A prediction probability of a given node belonging to the SOZ class was proposed as a non-invasive MRI marker of seizure onset likelihood. In an independent validation cohort, the proposed MRI marker provided a very large effect size of Cohen's d = 1.21 between SOZ and non-SOZ, and classified SOZ with a balanced accuracy of 0.75 in lesional and 0.67 in non-lesional MRI groups. The subsequent multi-variate logistic regression found the incorporation of the proposed MRI marker into interictal intracranial EEG (iEEG) markers further improves the differentiation between the epileptogenic focus (defined as SOZ resected during surgery) and non-epileptogenic sites (i.e., non-SOZ sites preserved during surgery) up to 15 % in non-lesional MRI group, suggesting that the proposed MRI marker could improve the localization of epileptogenic foci for successful pediatric epilepsy surgery.
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Sonoda M, Rothermel R, Carlson A, Jeong JW, Lee MH, Hayashi T, Luat AF, Sood S, Asano E. Naming-related spectral responses predict neuropsychological outcome after epilepsy surgery. Brain 2022; 145:517-530. [PMID: 35313351 PMCID: PMC9014727 DOI: 10.1093/brain/awab318] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 07/14/2021] [Accepted: 07/28/2021] [Indexed: 11/12/2022] Open
Abstract
This prospective study determined the use of intracranially recorded spectral responses during naming tasks in predicting neuropsychological performance following epilepsy surgery. We recruited 65 patients with drug-resistant focal epilepsy who underwent preoperative neuropsychological assessment and intracranial EEG recording. The Clinical Evaluation of Language Fundamentals evaluated the baseline and postoperative language function. During extra-operative intracranial EEG recording, we assigned patients to undergo auditory and picture naming tasks. Time-frequency analysis determined the spatiotemporal characteristics of naming-related amplitude modulations, including high gamma augmentation at 70-110 Hz. We surgically removed the presumed epileptogenic zone based on the intracranial EEG and MRI abnormalities while maximally preserving the eloquent areas defined by electrical stimulation mapping. The multivariate regression model incorporating auditory naming-related high gamma augmentation predicted the postoperative changes in Core Language Score with r2 of 0.37 and in Expressive Language Index with r2 of 0.32. Independently of the effects of epilepsy and neuroimaging profiles, higher high gamma augmentation at the resected language-dominant hemispheric area predicted a more severe postoperative decline in Core Language Score and Expressive Language Index. Conversely, the model incorporating picture naming-related high gamma augmentation predicted the change in Receptive Language Index with an r2 of 0.50. Higher high gamma augmentation independently predicted a more severe postoperative decline in Receptive Language Index. Ancillary regression analysis indicated that naming-related low gamma augmentation and alpha/beta attenuation likewise independently predicted a more severe Core Language Score decline. The machine learning-based prediction model suggested that naming-related high gamma augmentation, among all spectral responses used as predictors, most strongly contributed to the improved prediction of patients showing a >5-point Core Language Score decline (reflecting the lower 25th percentile among patients). We generated the model-based atlas visualizing sites, which, if resected, would lead to such a language decline. With a 5-fold cross-validation procedure, the auditory naming-based model predicted patients who had such a postoperative language decline with an accuracy of 0.80. The model indicated that virtual resection of an electrical stimulation mapping-defined language site would have increased the relative risk of the Core Language Score decline by 5.28 (95% confidence interval: 3.47-8.02). Especially, that of an electrical stimulation mapping-defined receptive language site would have maximized it to 15.90 (95% confidence interval: 9.59-26.33). In summary, naming-related spectral responses predict neuropsychological outcomes after epilepsy surgery. We have provided our prediction model as an open-source material, which will indicate the postoperative language function of future patients and facilitate external validation at tertiary epilepsy centres.
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Affiliation(s)
- Masaki Sonoda
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Department of Neurosurgery, Yokohama City University, Yokohama, Kanagawa 2360004, Japan
| | - Robert Rothermel
- Department of Psychiatry, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Alanna Carlson
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Department of Psychiatry, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Jeong-Won Jeong
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Department of Neurology, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Min-Hee Lee
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Takahiro Hayashi
- Department of Neurosurgery, Yokohama City University, Yokohama, Kanagawa 2360004, Japan
| | - Aimee F Luat
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Department of Neurology, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Department of Pediatrics, Central Michigan University, Mount Pleasant, MI 48858, USA
| | - Sandeep Sood
- Department of Neurosurgery, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Eishi Asano
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Department of Neurology, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Correspondence to: Eishi Asano, MD, PhD, MS (CRDSA) Division of Pediatric Neurology, Children’s Hospital of Michigan Wayne State University. 3901 Beaubien St., Detroit, MI 48201, USA E-mail:
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Sonoda M, Silverstein BH, Jeong JW, Sugiura A, Nakai Y, Mitsuhashi T, Rothermel R, Luat AF, Sood S, Asano E. Six-dimensional dynamic tractography atlas of language connectivity in the developing brain. Brain 2021; 144:3340-3354. [PMID: 34849596 PMCID: PMC8677551 DOI: 10.1093/brain/awab225] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 05/23/2021] [Accepted: 06/05/2021] [Indexed: 11/12/2022] Open
Abstract
During a verbal conversation, our brain moves through a series of complex linguistic processing stages: sound decoding, semantic comprehension, retrieval of semantically coherent words, and overt production of speech outputs. Each process is thought to be supported by a network consisting of local and long-range connections bridging between major cortical areas. Both temporal and extratemporal lobe regions have functional compartments responsible for distinct language domains, including the perception and production of phonological and semantic components. This study provides quantitative evidence of how directly connected inter-lobar neocortical networks support distinct stages of linguistic processing across brain development. Novel six-dimensional tractography was used to intuitively visualize the strength and temporal dynamics of direct inter-lobar effective connectivity between cortical areas activated during each linguistic processing stage. We analysed 3401 non-epileptic intracranial electrode sites from 37 children with focal epilepsy (aged 5-20 years) who underwent extra-operative electrocorticography recording. Principal component analysis of auditory naming-related high-gamma modulations determined the relative involvement of each cortical area during each linguistic processing stage. To quantify direct effective connectivity, we delivered single-pulse electrical stimulation to 488 temporal and 1581 extratemporal lobe sites and measured the early cortico-cortical spectral responses at distant electrodes. Mixed model analyses determined the effects of naming-related high-gamma co-augmentation between connecting regions, age, and cerebral hemisphere on the strength of effective connectivity independent of epilepsy-related factors. Direct effective connectivity was strongest between extratemporal and temporal lobe site pairs, which were simultaneously activated between sentence offset and verbal response onset (i.e. response preparation period); this connectivity was approximately twice more robust than that with temporal lobe sites activated during stimulus listening or overt response. Conversely, extratemporal lobe sites activated during overt response were equally connected with temporal lobe language sites. Older age was associated with increased strength of inter-lobar effective connectivity especially between those activated during response preparation. The arcuate fasciculus supported approximately two-thirds of the direct effective connectivity pathways from temporal to extratemporal auditory language-related areas but only up to half of those in the opposite direction. The uncinate fasciculus consisted of <2% of those in the temporal-to-extratemporal direction and up to 6% of those in the opposite direction. We, for the first time, provided an atlas which quantifies and animates the strength, dynamics, and direction specificity of inter-lobar neural communications between language areas via the white matter pathways. Language-related effective connectivity may be strengthened in an age-dependent manner even after the age of 5.
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Affiliation(s)
- Masaki Sonoda
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Department of Neurosurgery, Yokohama City University, Yokohama, Kanagawa 2360004, Japan
| | - Brian H Silverstein
- Translational Neuroscience Program, Wayne State University, Detroit, MI 48201, USA
| | - Jeong-Won Jeong
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Translational Neuroscience Program, Wayne State University, Detroit, MI 48201, USA
- Department of Neurology, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Ayaka Sugiura
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Yasuo Nakai
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Department of Neurological Surgery, Wakayama Medical University, Wakayama, Wakayama 6418509, Japan
| | - Takumi Mitsuhashi
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Department of Neurosurgery, Juntendo University, School of Medicine, Tokyo, 1138421, Japan
| | - Robert Rothermel
- Department of Psychiatry, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Aimee F Luat
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Department of Neurology, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Department of Pediatrics, Central Michigan University, Mount Pleasant, MI 48858, USA
| | - Sandeep Sood
- Department of Neurosurgery, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Eishi Asano
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Translational Neuroscience Program, Wayne State University, Detroit, MI 48201, USA
- Department of Neurology, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
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Zhang Y, Lu Q, Monsoor T, Hussain SA, Qiao JX, Salamon N, Fallah A, Sim MS, Asano E, Sankar R, Staba RJ, Engel J, Speier W, Roychowdhury V, Nariai H. Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach. Brain Commun 2021; 4:fcab267. [PMID: 35169696 PMCID: PMC8833577 DOI: 10.1093/braincomms/fcab267] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Revised: 09/29/2021] [Accepted: 10/04/2021] [Indexed: 11/12/2022] Open
Abstract
Intracranially recorded interictal high-frequency oscillations have been proposed as a promising spatial biomarker of the epileptogenic zone. However, its visual verification is time-consuming and exhibits poor inter-rater reliability. Furthermore, no method is currently available to distinguish high-frequency oscillations generated from the epileptogenic zone (epileptogenic high-frequency oscillations) from those generated from other areas (non-epileptogenic high-frequency oscillations). To address these issues, we constructed a deep learning-based algorithm using chronic intracranial EEG data via subdural grids from 19 children with medication-resistant neocortical epilepsy to: (i) replicate human expert annotation of artefacts and high-frequency oscillations with or without spikes, and (ii) discover epileptogenic high-frequency oscillations by designing a novel weakly supervised model. The ‘purification power’ of deep learning is then used to automatically relabel the high-frequency oscillations to distill epileptogenic high-frequency oscillations. Using 12 958 annotated high-frequency oscillation events from 19 patients, the model achieved 96.3% accuracy on artefact detection (F1 score = 96.8%) and 86.5% accuracy on classifying high-frequency oscillations with or without spikes (F1 score = 80.8%) using patient-wise cross-validation. Based on the algorithm trained from 84 602 high-frequency oscillation events from nine patients who achieved seizure-freedom after resection, the majority of such discovered epileptogenic high-frequency oscillations were found to be ones with spikes (78.6%, P < 0.001). While the resection ratio of detected high-frequency oscillations (number of resected events/number of detected events) did not correlate significantly with post-operative seizure freedom (the area under the curve = 0.76, P = 0.06), the resection ratio of epileptogenic high-frequency oscillations positively correlated with post-operative seizure freedom (the area under the curve = 0.87, P = 0.01). We discovered that epileptogenic high-frequency oscillations had a higher signal intensity associated with ripple (80–250 Hz) and fast ripple (250–500 Hz) bands at the high-frequency oscillation onset and with a lower frequency band throughout the event time window (the inverted T-shaped), compared to non-epileptogenic high-frequency oscillations. We then designed perturbations on the input of the trained model for non-epileptogenic high-frequency oscillations to determine the model’s decision-making logic. The model confidence significantly increased towards epileptogenic high-frequency oscillations by the artificial introduction of the inverted T-shaped signal template (mean probability increase: 0.285, P < 0.001), and by the artificial insertion of spike-like signals into the time domain (mean probability increase: 0.452, P < 0.001). With this deep learning-based framework, we reliably replicated high-frequency oscillation classification tasks by human experts. Using a reverse engineering technique, we distinguished epileptogenic high-frequency oscillations from others and identified its salient features that aligned with current knowledge.
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Affiliation(s)
- Yipeng Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA
| | - Qiujing Lu
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA
| | - Tonmoy Monsoor
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA
| | - Shaun A. Hussain
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Joe X. Qiao
- Division of Neuroradiology, Department of Radiology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Noriko Salamon
- Division of Neuroradiology, Department of Radiology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Aria Fallah
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Myung Shin Sim
- Department of Medicine, Statistics Core, University of California, Los Angeles, CA 90095, USA
| | - Eishi Asano
- Department of Pediatrics and Neurology, Children’s Hospital of Michigan, Wayne State University School of Medicine, Detroit, MI 48201, USA
| | - Raman Sankar
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA 90095, USA
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
- The UCLA Children’s Discovery and Innovation Institute, Los Angeles, CA, USA
| | - Richard J. Staba
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Jerome Engel
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
- Department of Neurobiology, University of California, Los Angeles, CA 90095, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA 90095, USA
- The Brain Research Institute, University of California, Los Angeles, CA 90095, USA
| | - William Speier
- Department of Radiological Sciences, University of California, Los Angeles, CA 90095, USA
- Department of Bioengineering, University of California, Los Angeles, CA 90095, USA
| | - Vwani Roychowdhury
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA
| | - Hiroki Nariai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA 90095, USA
- The UCLA Children’s Discovery and Innovation Institute, Los Angeles, CA, USA
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10
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Weiss SA, Staba RJ, Sharan A, Wu C, Rubinstein D, Das S, Waldman Z, Orosz I, Worrell G, Engel J, Sperling MR. Accuracy of high-frequency oscillations recorded intraoperatively for classification of epileptogenic regions. Sci Rep 2021; 11:21388. [PMID: 34725412 PMCID: PMC8560764 DOI: 10.1038/s41598-021-00894-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 10/19/2021] [Indexed: 11/10/2022] Open
Abstract
To see whether acute intraoperative recordings using stereo EEG (SEEG) electrodes can replace prolonged interictal intracranial EEG (iEEG) recording, making the process more efficient and safer, 10 min of iEEG were recorded following electrode implantation in 16 anesthetized patients, and 1-2 days later during non-rapid eye movement (REM) sleep. Ripples on oscillations (RonO, 80-250 Hz), ripples on spikes (RonS), sharp-spikes, fast RonO (fRonO, 250-600 Hz), and fast RonS (fRonS) were semi-automatically detected. HFO power and frequency were compared between the conditions using a generalized linear mixed-effects model. HFO rates were compared using a two-way repeated measures ANOVA with anesthesia type and SOZ as factors. A receiver-operating characteristic (ROC) curve analysis quantified seizure onset zone (SOZ) classification accuracy, and the scalar product was used to assess spatial reliability. Resection of contacts with the highest rate of events was compared with outcome. During sleep, all HFOs, except fRonO, were larger in amplitude compared to intraoperatively (p < 0.01). HFO frequency was also affected (p < 0.01). Anesthesia selection affected HFO and sharp-spike rates. In both conditions combined, sharp-spikes and all HFO subtypes were increased in the SOZ (p < 0.01). However, the increases were larger during the sleep recordings (p < 0.05). The area under the ROC curves for SOZ classification were significantly smaller for intraoperative sharp-spikes, fRonO, and fRonS rates (p < 0.05). HFOs and spikes were only significantly spatially reliable for a subset of the patients (p < 0.05). A failure to resect fRonO areas in the sleep recordings trended the most sensitive and accurate for predicting failure. In summary, HFO morphology is altered by anesthesia. Intraoperative SEEG recordings exhibit increased rates of HFOs in the SOZ, but their spatial distribution can differ from sleep recordings. Recording these biomarkers during non-REM sleep offers a more accurate delineation of the SOZ and possibly the epileptogenic zone.
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Affiliation(s)
- Shennan A Weiss
- Department of Neurology, State University of New York Downstate, Brooklyn, NY, 11203, USA.,Department of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, NY, 11203, USA.,Department of Neurology, New York City Health + Hospitals/Kings County, Brooklyn, NY, USA
| | - Richard J Staba
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Ashwini Sharan
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Chengyuan Wu
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Daniel Rubinstein
- Department of Neurology and Neuroscience, Thomas Jefferson University, 901 Walnut St. Suite 400, Philadelphia, PA, 19107, USA
| | - Sandhitsu Das
- Penn Image Computing & Science Lab, University of Pennsylvania, Philadelphia, PA, 19143, USA
| | - Zachary Waldman
- Department of Neurology and Neuroscience, Thomas Jefferson University, 901 Walnut St. Suite 400, Philadelphia, PA, 19107, USA
| | - Iren Orosz
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Gregory Worrell
- Department of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL), Rochester, USA.,Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA
| | - Jerome Engel
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA.,Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA.,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA.,Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Michael R Sperling
- Department of Neurology and Neuroscience, Thomas Jefferson University, 901 Walnut St. Suite 400, Philadelphia, PA, 19107, USA.
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11
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Spontaneous modulations of high-frequency cortical activity. Clin Neurophysiol 2021; 132:2391-2403. [PMID: 34454266 DOI: 10.1016/j.clinph.2021.06.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 05/15/2021] [Accepted: 06/01/2021] [Indexed: 11/20/2022]
Abstract
OBJECTIVE We clarified the clinical and mechanistic significance of physiological modulations of high-frequency broadband cortical activity associated with spontaneous saccadic eye movements during a resting state. METHODS We studied 30 patients who underwent epilepsy surgery following extraoperative electrocorticography and electrooculography recordings. We determined whether high-gamma activity at 70-110 Hz preceding saccade onset would predict upcoming ocular behaviors. We assessed how accurately the model incorporating saccade-related high-gamma modulations would localize the primary visual cortex defined by electrical stimulation. RESULTS The dynamic atlas demonstrated transient high-gamma suppression in the striatal cortex before saccade onset and high-gamma augmentation subsequently involving the widespread posterior brain regions. More intense striatal high-gamma suppression predicted the upcoming saccade directed to the ipsilateral side and lasting longer in duration. The bagged-tree-ensemble model demonstrated that intense saccade-related high-gamma modulations localized the visual cortex with an accuracy of 95%. CONCLUSIONS We successfully animated the neural dynamics supporting saccadic suppression, a principal mechanism minimizing the perception of blurred vision during rapid eye movements. The primary visual cortex per se may prepare actively in advance for massive image motion expected during upcoming prolonged saccades. SIGNIFICANCE Measuring saccade-related electrocorticographic signals may help localize the visual cortex and avoid misperceiving physiological high-frequency activity as epileptogenic.
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12
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Kuroda N, Sonoda M, Miyakoshi M, Nariai H, Jeong JW, Motoi H, Luat AF, Sood S, Asano E. Objective interictal electrophysiology biomarkers optimize prediction of epilepsy surgery outcome. Brain Commun 2021; 3:fcab042. [PMID: 33959709 PMCID: PMC8088817 DOI: 10.1093/braincomms/fcab042] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 01/09/2021] [Accepted: 02/02/2021] [Indexed: 12/12/2022] Open
Abstract
Researchers have looked for rapidly- and objectively-measurable electrophysiology biomarkers that accurately localize the epileptogenic zone. Promising candidates include interictal high-frequency oscillation and phase-amplitude coupling. Investigators have independently created the toolboxes that compute the high-frequency oscillation rate and the severity of phase-amplitude coupling. This study of 135 patients determined what toolboxes and analytic approaches would optimally classify patients achieving post-operative seizure control. Four different detector toolboxes computed the rate of high-frequency oscillation at ≥80 Hz at intracranial EEG channels. Another toolbox calculated the modulation index reflecting the strength of phase-amplitude coupling between high-frequency oscillation and slow-wave at 3–4 Hz. We defined the completeness of resection of interictally-abnormal regions as the subtraction of high-frequency oscillation rate (or modulation index) averaged across all preserved sites from that averaged across all resected sites. We computed the outcome classification accuracy of the logistic regression-based standard model considering clinical, ictal intracranial EEG and neuroimaging variables alone. We then determined how well the incorporation of high-frequency oscillation/modulation index would improve the standard model mentioned above. To assess the anatomical variability across non-epileptic sites, we generated the normative atlas of detector-specific high-frequency oscillation and modulation index. Each atlas allowed us to compute the statistical deviation of high-frequency oscillation/modulation index from the non-epileptic mean. We determined whether the model accuracy would be improved by incorporating absolute or normalized high-frequency oscillation/modulation index as a biomarker assessing interictally-abnormal regions. We finally determined whether the model accuracy would be improved by selectively incorporating high-frequency oscillation verified to have high-frequency oscillatory components unattributable to a high-pass filtering effect. Ninety-five patients achieved successful seizure control, defined as International League against Epilepsy class 1 outcome. Multivariate logistic regression analysis demonstrated that complete resection of interictally-abnormal regions additively increased the chance of success. The model accuracy was further improved by incorporating z-score normalized high-frequency oscillation/modulation index or selective incorporation of verified high-frequency oscillation. The standard model had a classification accuracy of 0.75. Incorporation of normalized high-frequency oscillation/modulation index or verified high-frequency oscillation improved the classification accuracy up to 0.82. These outcome prediction models survived the cross-validation process and demonstrated an agreement between the model-based likelihood of success and the observed success on an individual basis. Interictal high-frequency oscillation and modulation index had a comparably additive utility in epilepsy presurgical evaluation. Our empirical data support the theoretical notion that the prediction of post-operative seizure outcomes can be optimized with the consideration of both interictal and ictal abnormalities.
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Affiliation(s)
- Naoto Kuroda
- Department of Paediatrics, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA.,Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai 9808575, Japan
| | - Masaki Sonoda
- Department of Paediatrics, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA.,Department of Neurosurgery, Yokohama City University, Yokohama 2360004, Japan
| | - Makoto Miyakoshi
- Swartz Centre for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, USA
| | - Hiroki Nariai
- Division of Paediatric Neurology, Department of Paediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Jeong-Won Jeong
- Department of Paediatrics, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA.,Department of Neurology, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA
| | - Hirotaka Motoi
- Department of Paediatrics, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA.,Department of Paediatrics, Yokohama City University Medical Centre, Yokohama 2320024, Japan
| | - Aimee F Luat
- Department of Paediatrics, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA.,Department of Neurology, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA
| | - Sandeep Sood
- Department of Neurosurgery, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA
| | - Eishi Asano
- Department of Paediatrics, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA.,Department of Neurology, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA
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13
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Iwaki H, Sonoda M, Osawa SI, Silverstein BH, Mitsuhashi T, Ukishiro K, Takayama Y, Kambara T, Kakinuma K, Suzuki K, Tominaga T, Nakasato N, Iwasaki M, Asano E. Your verbal questions beginning with 'what' will rapidly deactivate the left prefrontal cortex of listeners. Sci Rep 2021; 11:5257. [PMID: 33664359 PMCID: PMC7933162 DOI: 10.1038/s41598-021-84610-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 02/15/2021] [Indexed: 12/31/2022] Open
Abstract
The left prefrontal cortex is essential for verbal communication. It remains uncertain at what timing, to what extent, and what type of phrase initiates left-hemispheric dominant prefrontal activation during comprehension of spoken sentences. We clarified this issue by measuring event-related high-gamma activity during a task to respond to three-phrase questions configured in different orders. Questions beginning with a wh-interrogative deactivated the left posterior prefrontal cortex right after the 1st phrase offset and the anterior prefrontal cortex after the 2nd phrase offset. Left prefrontal high-gamma activity augmented subsequently and maximized around the 3rd phrase offset. Conversely, questions starting with a concrete phrase deactivated the right orbitofrontal region and then activated the left posterior prefrontal cortex after the 1st phrase offset. Regardless of sentence types, high-gamma activity emerged earlier, by one phrase, in the left posterior prefrontal than anterior prefrontal region. Sentences beginning with a wh-interrogative may initially deactivate the left prefrontal cortex to prioritize the bottom-up processing of upcoming auditory information. A concrete phrase may obliterate the inhibitory function of the right orbitofrontal region and facilitate top-down lexical prediction by the left prefrontal cortex. The left anterior prefrontal regions may be recruited for semantic integration of multiple concrete phrases.
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Affiliation(s)
- Hirotaka Iwaki
- Department of Pediatrics, Children's Hospital of Michigan, Wayne State University, Detroit, MI, 48201, USA.,Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, 9808575, Japan
| | - Masaki Sonoda
- Department of Pediatrics, Children's Hospital of Michigan, Wayne State University, Detroit, MI, 48201, USA.,Department of Neurosurgery, Graduate School of Medicine, Yokohama City University, Kanagawa, 2360004, Japan
| | - Shin-Ichiro Osawa
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, 9808575, Japan.
| | - Brian H Silverstein
- Translational Neuroscience Program, Wayne State University, Detroit, MI, 48201, USA
| | - Takumi Mitsuhashi
- Department of Pediatrics, Children's Hospital of Michigan, Wayne State University, Detroit, MI, 48201, USA.,Department of Neurosurgery, School of Medicine, Juntendo University, Tokyo, 1138421, Japan
| | - Kazushi Ukishiro
- Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, 9808575, Japan.,Department of Neurosurgery, Graduate School of Medicine, Yokohama City University, Kanagawa, 2360004, Japan
| | - Yutaro Takayama
- Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, 9808575, Japan.,Department of Neurosurgery, Graduate School of Medicine, Yokohama City University, Kanagawa, 2360004, Japan.,Department of Neurosurgery, National Center of Neurology and Psychiatry, National Center Hospital, Tokyo, 1878551, Japan
| | - Toshimune Kambara
- Department of Pediatrics, Children's Hospital of Michigan, Wayne State University, Detroit, MI, 48201, USA.,Department of Psychology, Hiroshima University, Hiroshima, 7398524, Japan
| | - Kazuo Kakinuma
- Department of Behavioral Neurology and Cognitive Neuroscience, Tohoku University Graduate School of Medicine, Sendai, 9808575, Japan
| | - Kyoko Suzuki
- Department of Behavioral Neurology and Cognitive Neuroscience, Tohoku University Graduate School of Medicine, Sendai, 9808575, Japan
| | - Teiji Tominaga
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, 9808575, Japan
| | - Nobukazu Nakasato
- Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, 9808575, Japan
| | - Masaki Iwasaki
- Department of Neurosurgery, National Center of Neurology and Psychiatry, National Center Hospital, Tokyo, 1878551, Japan.
| | - Eishi Asano
- Department of Pediatrics, Children's Hospital of Michigan, Wayne State University, Detroit, MI, 48201, USA. .,Department of Neurology, Children's Hospital of Michigan, Wayne State University, Detroit, MI, 48201, USA.
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14
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Abstract
PURPOSE OF REVIEW Epilepsy surgery is the therapy of choice for 30-40% of people with focal drug-resistant epilepsy. Currently only ∼60% of well selected patients become postsurgically seizure-free underlining the need for better tools to identify the epileptogenic zone. This article reviews the latest neurophysiological advances for EZ localization with emphasis on ictal EZ identification, interictal EZ markers, and noninvasive neurophysiological mapping procedures. RECENT FINDINGS We will review methods for computerized EZ assessment, summarize computational network approaches for outcome prediction and individualized surgical planning. We will discuss electrical stimulation as an option to reduce the time needed for presurgical work-up. We will summarize recent research regarding high-frequency oscillations, connectivity measures, and combinations of multiple markers using machine learning. This latter was shown to outperform single markers. The role of NREM sleep for best identification of the EZ interictally will be discussed. We will summarize recent large-scale studies using electrical or magnetic source imaging for clinical decision-making. SUMMARY New approaches based on technical advancements paired with artificial intelligence are on the horizon for better EZ identification. They are ultimately expected to result in a more efficient, less invasive, and less time-demanding presurgical investigation.
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15
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Demuru M, Zweiphenning W, van Blooijs D, Van Eijsden P, Leijten F, Zijlmans M, Kalitzin S. Validation of virtual resection on intraoperative interictal data acquired during epilepsy surgery. J Neural Eng 2020; 17. [PMID: 33086212 DOI: 10.1088/1741-2552/abc3a8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 10/21/2020] [Indexed: 11/11/2022]
Abstract
OBJECTIVE A 'Virtual resection' consists of computationally simulating the effect of an actual resection on the brain. We validated two functional connectivity based virtual resection methods with the actual connectivity measured using post-resection intraoperative recordings. METHODS A non-linear association index was applied to pre-resection recordings from 11 extra-temporal focal epilepsy patients. We computed two virtual resection strategies: first, a 'naive' one obtained by simply removing from the connectivity matrix the electrodes that were resected; second, a virtual resection with partialization accounting for the influence of resected electrodes on not-resected electrodes. We validated the virtual resections with two analysis: 1) We tested with a Kolmogorov-Smirnov test if the distributions of connectivity values after the virtual resections differed from the actual post-resection connectivity distribution; 2) we tested if the overall effect of the resection measured by contrasting pre-resection and post-resection connectivity values is detectable with the virtual resection approach using a Kolmogorv-Smirnov test. RESULTS The estimation of post-resection connectivity values did not succeed for both methods. In the second analysis, the naive method failed completely to detect the effect found between pre-resection and post-resection connectivity distributions, while the partialization method agreed with post-resection measurements in detecting a drop connectivity compared to pre-resection recordings. CONCLUSION Our findings suggest that the partialization technique is superior to the naive method in detecting the overall effect after the resection. SIGNIFICANCE We pointed out how a realistic validation based on actual post-resection recordings reveals that virtual resection methods are not yet mature to inform the clinical decision-making.
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Affiliation(s)
- Matteo Demuru
- Research, SEIN, Hoofddorp, Noord-Holland, NETHERLANDS
| | - Willemiek Zweiphenning
- Neurology and Neurosurgery, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, Utrecht, NETHERLANDS
| | - Dorien van Blooijs
- Neurology and Neurosurgery, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, Utrecht, NETHERLANDS
| | - Pieter Van Eijsden
- Neurology and Neurosurgery, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, Utrecht, NETHERLANDS
| | - Frans Leijten
- Neurology and Neurosurgery, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, Utrecht, NETHERLANDS
| | - Maeike Zijlmans
- Neurology and Neurosurgery, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, Utrecht, NETHERLANDS
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16
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Höller Y, Nardone R. Quantitative EEG biomarkers for epilepsy and their relation to chemical biomarkers. Adv Clin Chem 2020; 102:271-336. [PMID: 34044912 DOI: 10.1016/bs.acc.2020.08.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The electroencephalogram (EEG) is the most important method to diagnose epilepsy. In clinical settings, it is evaluated by experts who identify patterns visually. Quantitative EEG is the application of digital signal processing to clinical recordings in order to automatize diagnostic procedures, and to make patterns visible that are hidden to the human eye. The EEG is related to chemical biomarkers, as electrical activity is based on chemical signals. The most well-known chemical biomarkers are blood laboratory tests to identify seizures after they have happened. However, research on chemical biomarkers is much less extensive than research on quantitative EEG, and combined studies are rarely published, but highly warranted. Quantitative EEG is as old as the EEG itself, but still, the methods are not yet standard in clinical practice. The most evident application is an automation of manual work, but also a quantitative description and localization of interictal epileptiform events as well as seizures can reveal important hints for diagnosis and contribute to presurgical evaluation. In addition, the assessment of network characteristics and entropy measures were found to reveal important insights into epileptic brain activity. Application scenarios of quantitative EEG in epilepsy include seizure prediction, pharmaco-EEG, treatment monitoring, evaluation of cognition, and neurofeedback. The main challenges to quantitative EEG are poor reliability and poor generalizability of measures, as well as the need for individualization of procedures. A main hindrance for quantitative EEG to enter clinical routine is also that training is not yet part of standard curricula for clinical neurophysiologists.
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Affiliation(s)
- Yvonne Höller
- Faculty of Psychology, University of Akureyri, Akureyri, Iceland.
| | - Raffaele Nardone
- Department of Neurology, Franz Tappeiner Hospital, Merano, Italy; Spinal Cord Injury and Tissue Regeneration Center, Salzburg, Austria; Department of Neurology, Christian Doppler Klinik, Paracelsus Medical University, Salzburg, Austria
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17
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Tsuchiya H, Endoh F, Akiyama T, Matsuhashi M, Kobayashi K. Longitudinal correspondence of epilepsy and scalp EEG fast (40-200 Hz) oscillations in pediatric patients with tuberous sclerosis complex. Brain Dev 2020; 42:663-674. [PMID: 32631641 DOI: 10.1016/j.braindev.2020.06.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Revised: 05/29/2020] [Accepted: 06/03/2020] [Indexed: 01/07/2023]
Abstract
INTRODUCTION Epilepsy associated with tuberous sclerosis complex (TSC) has very complex clinical characteristics. Scalp electroencephalogram (EEG) fast (40-200 Hz) oscillations (FOs) were recently suggested to indicate epilepsy severity. Epileptic FOs may undergo age-dependent longitudinal change in individual patients, however, and the typical pattern of such change is not yet fully clarified. We therefore investigated the age-related correspondence between clinical courses and FOs in pediatric patients with TSC-associated epilepsy. SUBJECTS AND METHODS FOs were semi-automatically detected from scalp sleep EEG data recorded from 23 children (15 boys, 8 girls; initial data obtained at <10 years of age) with TSC-associated epilepsy. RESULTS The number of FOs per patient that were associated with spikes was significantly greater than that of FOs unassociated with spikes (median 145 and 5, respectively; p = 0.0001 by the Wilcoxon signed-rank test). In the eight patients who had West syndrome (WS) in infancy, FOs associated with spikes were abundant during the WS period prior to adrenocorticotropic hormone therapy, with significantly greater numbers of FOs compared to the post-WS period (median 242 and 0, respectively; p = 0.0078). As there was no such time-dependent difference regarding FOs unassociated with spikes, FOs associated with spikes were identified as epileptic. The detected FOs included both gamma and ripple oscillations with no consistent age-dependent shifts in dominant frequency. There were no apparent age-related changes in FO duration. CONCLUSIONS Epileptic scalp FOs are confirmed to correspond to severity of epileptic encephalopathy, particularly in WS, even during the long-term evolutional courses of TSC-associated epilepsy.
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Affiliation(s)
- Hiroki Tsuchiya
- Department of Child Neurology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences and Okayama University Hospital, Okayama, Japan.
| | - Fumika Endoh
- Department of Child Neurology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences and Okayama University Hospital, Okayama, Japan; Department of Child Neurology, NHO Minami-Okayama Medical Center, Okayama, Okayama, Japan
| | - Tomoyuki Akiyama
- Department of Child Neurology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences and Okayama University Hospital, Okayama, Japan
| | - Masao Matsuhashi
- Department of Epilepsy, Movement Disorders and Physiology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Katsuhiro Kobayashi
- Department of Child Neurology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences and Okayama University Hospital, Okayama, Japan
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18
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Minthe A, Janzarik WG, Lachner-Piza D, Reinacher P, Schulze-Bonhage A, Dümpelmann M, Jacobs J. Stable high frequency background EEG activity distinguishes epileptic from healthy brain regions. Brain Commun 2020; 2:fcaa107. [PMID: 32954347 PMCID: PMC7475693 DOI: 10.1093/braincomms/fcaa107] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 05/06/2020] [Accepted: 05/15/2020] [Indexed: 11/24/2022] Open
Abstract
High-frequency oscillations are markers of epileptic tissue. Recently, different patterns of EEG background activity were described from which high-frequency oscillations occur: high-frequency oscillations with continuously oscillating background were found to be primarily physiological, those from quiet background were linked to epileptic tissue. It is unclear, whether these interactions remain stable over several days and during different sleep-wake stages. High-frequency oscillation patterns (oscillatory vs. quiet background) were analysed in 23 patients implanted with depth and subdural grid electrodes. Pattern scoring was performed on every channel in 10 s intervals in three separate day- and night-time EEG segments. An entropy value, measuring variability of patterns per channel, was calculated. A low entropy value indicated a stable occurrence of the same pattern in one channel, whereas a high value indicated pattern instability. Differences in pattern distribution and entropy were analysed for 143 280 10 s intervals with allocated patterns from inside and outside the seizure onset zone, different electrode types and brain regions. We found a strong association between high-frequency oscillations out of quiet background activity, and channels of the seizure onset zone (35.2% inside versus 9.7% outside the seizure onset zone, P < 0.001), no association was found for high-frequency oscillations from continuous oscillatory background (P = 0.563). The type of background activity remained stable over the same brain region over several days and was independent of sleep stage and recording technique. Stability of background activity was significantly higher in channels of the seizure onset zone (entropy mean value 0.56 ± 0.39 versus 0.64 ± 0.41; P < 0.001). This was especially true for the presumed epileptic high-frequency oscillations out of quiet background (0.57 ± 0.39 inside versus 0.72 ± 0.37 outside the seizure onset zone; P < 0.001). In contrast, presumed physiological high-frequency oscillations from continuous oscillatory backgrounds were significantly more stable outside the seizure onset zone (0.72 ± 0.45 versus 0.48 ± 0.53; P < 0.001). The overall low entropy values suggest that interactions between high-frequency oscillations and background activity are a stable phenomenon specific to the function of brain regions. High-frequency oscillations occurring from a quiet background are strongly linked to the seizure onset zone whereas high-frequency oscillations from an oscillatory background are not. Pattern stability suggests distinct underlying mechanisms. Analysing short time segments of high-frequency oscillations and background activity could help distinguishing epileptic from physiologically active brain regions.
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Affiliation(s)
- Annika Minthe
- Department of Neuropediatrics and Muscular Diseases, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Wibke G Janzarik
- Department of Neuropediatrics and Muscular Diseases, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Daniel Lachner-Piza
- Department of Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Peter Reinacher
- Division of Stereotactic and Functional Neurosurgery, Department of Neurosurgery, Clinic for Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Department of Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Matthias Dümpelmann
- Department of Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Julia Jacobs
- Department of Neuropediatrics and Muscular Diseases, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany.,Department of Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
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19
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Andrews JP, Ammanuel S, Kleen J, Khambhati AN, Knowlton R, Chang EF. Early seizure spread and epilepsy surgery: A systematic review. Epilepsia 2020; 61:2163-2172. [PMID: 32944952 DOI: 10.1111/epi.16668] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 08/03/2020] [Accepted: 08/04/2020] [Indexed: 01/03/2023]
Abstract
OBJECTIVE A fundamental question in epilepsy surgery is how to delineate the margins of cortex that must be resected to result in seizure freedom. Whether and which areas showing seizure activity early in ictus must be removed to avoid postoperative recurrence of seizures is an area of ongoing research. Seizure spread dynamics in the initial seconds of ictus are often correlated with postoperative outcome; there is neither a consensus definition of early spread nor a concise summary of the existing literature linking seizure spread to postsurgical seizure outcomes. The present study is intended to summarize the literature that links seizure spread to postoperative seizure outcome and to provide a framework for quantitative assessment of early seizure spread. METHODS A systematic review was carried out according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A Medline search identified clinical studies reporting data on seizure spread measured by intracranial electrodes, having at least 10 subjects and reporting at least 1-year postoperative outcome in the English literature from 1990 to 2019. Studies were evaluated regarding support for a primary hypothesis: Areas of early seizure spread represent cortex with seizure-generating potential. RESULTS The search yielded 4562 studies: 15 studies met inclusion criteria and 7 studies supported the primary hypothesis. The methods and metrics used to describe seizure spread were heterogenous. The timeframe of seizure spread associated with seizure outcome ranged from 1-14 seconds, with large, well-designed, retrospective studies pointing to 3-10 seconds as most likely to provide meaningful correlates of postoperative seizure freedom. SIGNIFICANCE The complex correlation between electrophysiologic seizure spread and the potential for seizure generation needs further elucidation. Prospective cohort studies or trials are needed to evaluate epilepsy surgery targeting cortex involved in the first 3-10 seconds of ictus.
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Affiliation(s)
- John P Andrews
- Department of Neurological Surgery, School of Medicine, University of California-San Francisco, San Francisco, California, USA
| | - Simon Ammanuel
- Department of Neurological Surgery, School of Medicine, University of California-San Francisco, San Francisco, California, USA
| | - Jonathan Kleen
- Department of Neurology, School of Medicine, University of California-San Francisco, San Francisco, California, USA
| | - Ankit N Khambhati
- Department of Neurological Surgery, School of Medicine, University of California-San Francisco, San Francisco, California, USA
| | - Robert Knowlton
- Department of Neurology, School of Medicine, University of California-San Francisco, San Francisco, California, USA
| | - Edward F Chang
- Department of Neurological Surgery, School of Medicine, University of California-San Francisco, San Francisco, California, USA
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20
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Demuru M, Kalitzin S, Zweiphenning W, van Blooijs D, Van't Klooster M, Van Eijsden P, Leijten F, Zijlmans M. The value of intra-operative electrographic biomarkers for tailoring during epilepsy surgery: from group-level to patient-level analysis. Sci Rep 2020; 10:14654. [PMID: 32887896 PMCID: PMC7474097 DOI: 10.1038/s41598-020-71359-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 04/23/2020] [Indexed: 01/08/2023] Open
Abstract
Signal analysis biomarkers, in an intra-operative setting, may be complementary tools to guide and tailor the resection in drug-resistant focal epilepsy patients. Effective assessment of biomarker performances are needed to evaluate their clinical usefulness and translation. We defined a realistic ground-truth scenario and compared the effectiveness of different biomarkers alone and combined to localize epileptogenic tissue during surgery. We investigated the performances of univariate, bivariate and multivariate signal biomarkers applied to 1 min inter-ictal intra-operative electrocorticography to discriminate between epileptogenic and non-epileptogenic locations in 47 drug-resistant people with epilepsy (temporal and extra-temporal) who had been seizure-free one year after the operation. The best result using a single biomarker was obtained using the phase-amplitude coupling measure for which the epileptogenic tissue was localized in 17 out of 47 patients. Combining the whole set of biomarkers provided an improvement of the performances: 27 out of 47 patients. Repeating the analysis only on the temporal-lobe resections we detected the epileptogenic tissue in 29 out of 30 combining all the biomarkers. We suggest that the assessment of biomarker performances on a ground-truth scenario is required to have a proper estimate on how biomarkers translate into clinical use. Phase-amplitude coupling seems the best performing single biomarker and combining biomarkers improves localization of epileptogenic tissue. Performance achieved is not adequate as a tool in the operation theater yet, but it can improve the understanding of pathophysiological process.
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Affiliation(s)
- Matteo Demuru
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands.
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Stiliyan Kalitzin
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Willemiek Zweiphenning
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Dorien van Blooijs
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Maryse Van't Klooster
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Pieter Van Eijsden
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Frans Leijten
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Maeike Zijlmans
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
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21
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Remakanthakurup Sindhu K, Staba R, Lopour BA. Trends in the use of automated algorithms for the detection of high-frequency oscillations associated with human epilepsy. Epilepsia 2020; 61:1553-1569. [PMID: 32729943 DOI: 10.1111/epi.16622] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 06/17/2020] [Accepted: 06/29/2020] [Indexed: 12/11/2022]
Abstract
High-frequency oscillations (HFOs) in intracranial electroencephalography (EEG) are a promising biomarker of the epileptogenic zone and tool for surgical planning. Many studies have shown that a high rate of HFOs (number per minute) is correlated with the seizure-onset zone, and complete removal of HFO-generating brain regions has been associated with seizure-free outcome after surgery. In order to use HFOs as a biomarker, these transient events must first be detected in electrophysiological data. Because visual detection of HFOs is time-consuming and subject to low interrater reliability, many automated algorithms have been developed, and they are being used increasingly for such studies. However, there is little guidance on how to select an algorithm, implement it in a clinical setting, and validate the performance. Therefore, we aim to review automated HFO detection algorithms, focusing on conceptual similarities and differences between them. We summarize the standard steps for data pre-processing, as well as post-processing strategies for rejection of false-positive detections. We also detail four methods for algorithm testing and validation, and we describe the specific goal achieved by each one. We briefly review direct comparisons of automated algorithms applied to the same data set, emphasizing the importance of optimizing detection parameters. Then, to assess trends in the use of automated algorithms and their potential for use in clinical studies, we review evidence for the relationship between automatically detected HFOs and surgical outcome. We conclude with practical recommendations and propose standards for the selection, implementation, and validation of automated HFO-detection algorithms.
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Affiliation(s)
| | | | - Beth A Lopour
- Biomedical Engineering, UC Irvine, Irvine, California, USA
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22
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Gerner N, Thomschewski A, Marcu A, Trinka E, Höller Y. Pitfalls in Scalp High-Frequency Oscillation Detection From Long-Term EEG Monitoring. Front Neurol 2020; 11:432. [PMID: 32582002 PMCID: PMC7280487 DOI: 10.3389/fneur.2020.00432] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 04/23/2020] [Indexed: 11/17/2022] Open
Abstract
Aims: Intracranially recorded high-frequency oscillations (>80 Hz) are considered a candidate epilepsy biomarker. Recent studies claimed their detectability on the scalp surface. We aimed to investigate the applicability of high-frequency oscillation analysis to routine surface EEG obtained at an epilepsy monitoring unit. Methods: We retrospectively analyzed surface EEGs of 18 patients with focal epilepsy and six controls, recorded during sleep under maximal medication withdrawal. As a proof of principle, the occurrence of motor task-related events during wakefulness was analyzed in a subsample of six patients with seizure- or syncope-related motor symptoms. Ripples (80-250 Hz) and fast ripples (>250 Hz) were identified by semi-automatic detection. Using semi-parametric statistics, differences in spontaneous and task-related occurrence rates were examined within subjects and between diagnostic groups considering the factors diagnosis, brain region, ripple type, and task condition. Results: We detected high-frequency oscillations in 17 out of 18 patients and in four out of six controls. Results did not show statistically significant differences in the mean rates of event occurrences, neither regarding the laterality of the epileptic focus, nor with respect to active and inactive task conditions, or the moving hand laterality. Significant differences in general spontaneous incidence [WTS(1) = 9.594; p = 0.005] that indicated higher rates of fast ripples compared to ripples, notably in patients with epilepsy compared to the control group, may be explained by variations in data quality. Conclusion: The current analysis methods are prone to biases. A common agreement on a standard operating procedure is needed to ensure reliable and economic detection of high-frequency oscillations.
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Affiliation(s)
- Nathalie Gerner
- Department of Neurology, Christian-Doppler Medical Centre, Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria,Department of Mathematics, Paris-Lodron University of Salzburg, Salzburg, Austria
| | - Aljoscha Thomschewski
- Department of Neurology, Christian-Doppler Medical Centre, Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria,Department of Mathematics, Paris-Lodron University of Salzburg, Salzburg, Austria,*Correspondence: Aljoscha Thomschewski
| | - Adrian Marcu
- Department of Neurology, Christian-Doppler Medical Centre, Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria
| | - Eugen Trinka
- Department of Neurology, Christian-Doppler Medical Centre, Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria
| | - Yvonne Höller
- Department of Neurology, Christian-Doppler Medical Centre, Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria,Department of Psychology, University of Akureyri, Akureyri, Iceland
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23
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Salami P, Peled N, Nadalin JK, Martinet LE, Kramer MA, Lee JW, Cash SS. Seizure onset location shapes dynamics of initiation. Clin Neurophysiol 2020; 131:1782-1797. [PMID: 32512346 DOI: 10.1016/j.clinph.2020.04.168] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 03/24/2020] [Accepted: 04/13/2020] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Ictal electrographic patterns are widely thought to reflect underlying neural mechanisms of seizures. Here we studied the degree to which seizure patterns are consistent in a given patient, relate to particular brain regions and if two candidate biomarkers (high-frequency oscillations, HFOs; infraslow activity, ISA) and network activity, as assessed with cross-frequency interactions, can discriminate between seizure types. METHODS We analyzed temporal changes in low and high frequency oscillations recorded during seizures, as well as phase-amplitude coupling (PAC) to monitor the interactions between delta/theta and ripple/fast ripple frequency bands at seizure onset. RESULTS Seizures of multiple electrographic patterns were observed in a given patient and brain region. While there was an increase in HFO rate across different electrographic patterns, there are specific relationships between types of HFO activity and onset region. Similarly, changes in PAC dynamics were more closely related to seizure onset region than they were to electrographic patterns while ISA was a poor indicator for seizure onset. CONCLUSIONS Our findings suggest that the onset region sculpts neurodynamics at seizure initiation and that unique features of the cytoarchitecture and/or connectivity of that region play a significant role in determining seizure mechanism. SIGNIFICANCE To learn how seizures are initiated, researchers would do well to consider other aspects of their manifestation, in addition to their electrographic patterns. Examination of onset pattern in conjunction with the interactions between different oscillatory frequencies in the context of different brain regions might be more informative and lead to more reliable clinical inference as well as novel therapeutic approaches.
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Affiliation(s)
- Pariya Salami
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
| | - Noam Peled
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jessica K Nadalin
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA
| | - Louis-Emmanuel Martinet
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Mark A Kramer
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA
| | - Jong W Lee
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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24
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Abstract
During the presurgical evaluation of patients with focal refractory epilepsies, the spatial mapping of the seizure onset zone (SOZ) and seizure propagation networks critically depends on the use of different features extracted from the intracranial electroencephalogram (IEEG). The identification of the SOZ is usually based on visual inspection by highly qualified neurophysiologists. However, quantitative IEEG analyses have recently been developed by exploiting signal and image characteristics in order to improve and expedite the SOZ detection. Here, the authors briefly review some of the latest methods proposed by different research groups and then present the recent implementation in Brainstorm software.
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