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Shibata T, Tsuchiya H, Akiyama M, Akiyama T, Kobayashi K. Modulation index predicts the effect of ethosuximide on developmental and epileptic encephalopathy with spike-and-wave activation in sleep. Epilepsy Res 2024; 202:107359. [PMID: 38582072 DOI: 10.1016/j.eplepsyres.2024.107359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 03/27/2024] [Accepted: 04/03/2024] [Indexed: 04/08/2024]
Abstract
PURPOSE In developmental and epileptic encephalopathy with spike-and-wave activation in sleep (DEE-SWAS), the thalamocortical network is suggested to play an important role in the pathophysiology of the progression from focal epilepsy to DEE-SWAS. Ethosuximide (ESM) exerts effects by blocking T-type calcium channels in thalamic neurons. With the thalamocortical network in mind, we studied the prediction of ESM effectiveness in DEE-SWAS treatment using phase-amplitude coupling (PAC) analysis. METHODS We retrospectively enrolled children with DEE-SWAS who had an electroencephalogram (EEG) recorded between January 2009 and September 2022 and were prescribed ESM at Okayama University Hospital. Only patients whose EEG showed continuous spike-and-wave during sleep were included. We extracted 5-min non-rapid eye movement sleep stage N2 segments from EEG recorded before starting ESM. We calculated the modulation index (MI) as the measure of PAC in pair combination comprising one of two fast oscillation types (gamma, 40-80 Hz; ripples, 80-150 Hz) and one of five slow-wave bands (delta, 0.5-1, 1-2, 2-3, and 3-4 Hz; theta, 4-8 Hz), and compared it between ESM responders and non-responders. RESULTS We identified 20 children with a diagnosis of DEE-SWAS who took ESM. Fifteen were ESM responders. Regarding gamma oscillations, significant differences were seen only in MI with 0.5-1 Hz slow waves in the frontal pole and occipital regions. Regarding ripples, ESM responders had significantly higher MI in coupling with all slow waves in the frontal pole region, 0.5-1, 3-4, and 4-8 Hz slow waves in the frontal region, 3-4 Hz slow waves in the parietal region, 0.5-1, 2-3, 3-4, and 4-8 Hz slow waves in the occipital region, and 3-4 Hz slow waves in the anterior-temporal region. SIGNIFICANCE High MI in a wider area of the brain may represent the epileptic network mediated by the thalamus in DEE-SWAS and may be a predictor of ESM effectiveness.
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Affiliation(s)
- Takashi Shibata
- Department of Child Neurology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences and Okayama University Hospital, Okayama, Japan.
| | - Hiroki Tsuchiya
- Department of Child Neurology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences and Okayama University Hospital, Okayama, Japan
| | - Mari Akiyama
- Department of Child Neurology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences and Okayama University Hospital, Okayama, Japan
| | - Tomoyuki Akiyama
- Department of Child Neurology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences and Okayama University Hospital, Okayama, 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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Alsallom F, Simon MV. Pediatric Intraoperative Neurophysiologic Mapping and Monitoring in Brain Surgery. J Clin Neurophysiol 2024; 41:96-107. [PMID: 38306217 DOI: 10.1097/wnp.0000000000001054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2024] Open
Abstract
SUMMARY Similar to adults, children undergoing brain surgery can significantly benefit from intraoperative neurophysiologic mapping and monitoring. Although young brains present the advantage of increased plasticity, during procedures in close proximity to eloquent regions, the risk of irreversible neurological compromise remains and can be lowered further by these techniques. More so, pathologies specific to the pediatric population, such as neurodevelopmental lesions, often result in medically refractory epilepsy. Thus, their successful surgical treatment also relies on accurate demarcation and resection of the epileptogenic zone, processes in which intraoperative electrocorticography is often employed. However, stemming from the development and maturation of the central and peripheral nervous systems as the child grows, intraoperative neurophysiologic testing in this population poses methodologic and interpretative challenges even to experienced clinical neurophysiologists. For example, it is difficult to perform awake craniotomies and language testing in the majority of pediatric patients. In addition, children may be more prone to intraoperative seizures and exhibit afterdischarges more frequently during functional mapping using electrical cortical stimulation because of high stimulation thresholds needed to depolarize immature cortex. Moreover, choice of anesthetic regimen and doses may be different in pediatric patients, as is the effect of these drugs on immature brain; these factors add additional complexity in terms of interpretation and analysis of neurophysiologic recordings. Below, we are describing the modalities commonly used during intraoperative neurophysiologic testing in pediatric brain surgery, with emphasis on age-specific clinical indications, methodology, and challenges.
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Affiliation(s)
- Faisal Alsallom
- King Fahad Medical City, KFMC Neurosciences Center, Riyadh, Saudi Arabia; and
| | - Mirela V Simon
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, U.S.A
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Miao Y, Iimura Y, Sugano H, Fukumori K, Tanaka T. Seizure onset zone identification using phase-amplitude coupling and multiple machine learning approaches for interictal electrocorticogram. Cogn Neurodyn 2023; 17:1591-1607. [PMID: 37969944 PMCID: PMC10640557 DOI: 10.1007/s11571-022-09915-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/26/2022] [Accepted: 11/17/2022] [Indexed: 12/12/2022] Open
Abstract
Automatic seizure onset zone (SOZ) localization using interictal electrocorticogram (ECoG) improves the diagnosis and treatment of patients with medically refractory epilepsy. This study aimed to investigate the characteristics of phase-amplitude coupling (PAC) extracted from interictal ECoG and the feasibility of PAC serving as a promising biomarker for SOZ identification. We employed the mean vector length modulation index approach on the 20-s ECoG window to calculate PAC features between low-frequency rhythms (0.5-24 Hz) and high frequency oscillations (HFOs) (80-560 Hz). We used statistical measures to test the significant difference in PAC between the SOZ and non-seizure onset zone (NSOZ). To overcome the drawback of handcraft feature engineering, we established novel machine learning models to learn automatically the characteristics of the obtained PAC features and classify them to identify the SOZ. Besides, to handle imbalanced dataset classification, we introduced novel feature-wise/class-wise re-weighting strategies in conjunction with classifiers. In addition, we proposed a time-series nest cross-validation to provide more accurate and unbiased evaluations for this model. Seven patients with focal cortical dysplasia were included in this study. The experiment results not only showed that a significant coupling at band pairs of slow waves and HFOs exists in the SOZ when compared with the NSOZ, but also indicated the effectiveness of the PAC features and the proposed models in achieving better classification performance .
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Affiliation(s)
- Yao Miao
- Tokyo University of Agriculture and Technology, Tokyo, Japan
| | - Yasushi Iimura
- Department of Neurosurgery, Juntendo University School of Medicine, Tokyo, Japan
| | - Hidenori Sugano
- Department of Neurosurgery, Juntendo University School of Medicine, Tokyo, Japan
| | - Kosuke Fukumori
- Tokyo University of Agriculture and Technology, Tokyo, Japan
| | - Toshihisa Tanaka
- Tokyo University of Agriculture and Technology, Tokyo, Japan
- Department of Neurosurgery, Juntendo University School of Medicine, Tokyo, Japan
- RIKEN Center for Brain Science, Saitama, Japan
- RIKEN Center for Advanced Intelligent Project, Tokyo, Japan
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Dahal R, Tamura K, Pan DS, Sasaki R, Takeshima Y, Matsuda R, Yamada S, Nishimura F, Nakagawa I, Park YS, Hayashi H, Kawaguchi M, Nakase H. Effect of Sevoflurane Anesthesia on Intraoperative Spikes, High-Frequency Oscillations, and Phase-Amplitude Coupling in MRI-Normal Hippocampus. J Clin Neurophysiol 2023:00004691-990000000-00107. [PMID: 37934075 DOI: 10.1097/wnp.0000000000001031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023] Open
Abstract
INTRODUCTION The purpose of this study was to determine the effect of sevoflurane anesthesia on spikes, high-frequency oscillations (HFOs), and phase-amplitude coupling using a modulation index in MRI-normal hippocampus, with the aim of evaluating the utility of intraoperative electrocorticography in identifying the epileptogenic hippocampus during sevoflurane administration. METHODS Eleven patients with intractable temporal lobe epilepsy with a normal hippocampus on MRI underwent extra-operative electrocorticography evaluation. Patients were assigned to the Ictal (+) or Ictal (-) group depending on whether the parahippocampal gyrus was included in the seizure onset zone. Intraoperative electrocorticography was performed under 0.5 and 1.5 minimum alveolar concentration of sevoflurane. The rates of spikes, ripples, fast ripples (FRs), ripples on spikes, FRs on spikes, and MI HFO(3-4 Hz) were evaluated. RESULTS During the intraoperative electrocorticography procedure, sevoflurane administration was found to significantly increase the rate of spikes, ripples on spikes, fast ripples on spikes, and MI HFO(3-4 Hz) in the Ictal (+) group (P < 0.01). By contrast, the Ictal (-) group exhibited a paradoxical increase in the rate of ripples and fast ripple (P < 0.05). CONCLUSIONS Our findings indicate that the administration of sevoflurane during intraoperative electrocorticography in patients with MRI-normal hippocampus can lead to a dose-dependent enhancement of epileptic biomarkers (spikes, ripples on spikes, fast ripples on spikes, and MI (HFO 3-4)) in the epileptogenic hippocampus, while paradoxically increasing the rate of ripples and fast ripple in the nonepileptogenic hippocampus. These results have significant implications for the identification of the MRI-normal hippocampus that requires surgical intervention and preservation of the nonepileptogenic hippocampus.
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Affiliation(s)
- Riju Dahal
- Department of Neurosurgery, Nara Medical University, Kashihara, Nara, Japan
| | - Kentaro Tamura
- Department of Neurosurgery, Nara Medical University, Kashihara, Nara, Japan
| | - Dong-Sheng Pan
- Department of Neurosurgery, General Hospital of Northern Theater Command, Shenyang, China; and
| | - Ryota Sasaki
- Department of Neurosurgery, Nara Medical University, Kashihara, Nara, Japan
| | - Yasuhiro Takeshima
- Department of Neurosurgery, Nara Medical University, Kashihara, Nara, Japan
| | - Ryosuke Matsuda
- Department of Neurosurgery, Nara Medical University, Kashihara, Nara, Japan
| | - Shuichi Yamada
- Department of Neurosurgery, Nara Medical University, Kashihara, Nara, Japan
| | - Fumihiko Nishimura
- Department of Neurosurgery, Nara Medical University, Kashihara, Nara, Japan
| | - Ichiro Nakagawa
- Department of Neurosurgery, Nara Medical University, Kashihara, Nara, Japan
| | - Young-Soo Park
- Department of Neurosurgery, Nara Medical University, Kashihara, Nara, Japan
| | - Hironobu Hayashi
- Department of Anesthesiology, Nara Medical University, Kashihara, Nara, Japan
| | - Masahiko Kawaguchi
- Department of Anesthesiology, Nara Medical University, Kashihara, Nara, Japan
| | - Hiroyuki Nakase
- Department of Neurosurgery, Nara Medical University, Kashihara, Nara, Japan
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Sakakura K, Kuroda N, Sonoda M, Mitsuhashi T, Firestone E, Luat AF, Marupudi NI, Sood S, Asano E. Developmental atlas of phase-amplitude coupling between physiologic high-frequency oscillations and slow waves. Nat Commun 2023; 14:6435. [PMID: 37833252 PMCID: PMC10575956 DOI: 10.1038/s41467-023-42091-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
We investigated the developmental changes in high-frequency oscillation (HFO) and Modulation Index (MI) - the coupling measure between HFO and slow-wave phase. We generated normative brain atlases, using subdural EEG signals from 8251 nonepileptic electrode sites in 114 patients (ages 1.0-41.5 years) who achieved seizure control following resective epilepsy surgery. We observed a higher MI in the occipital lobe across all ages, and occipital MI increased notably during early childhood. The cortical areas exhibiting MI co-growth were connected via the vertical occipital fasciculi and posterior callosal fibers. While occipital HFO rate showed no significant age-association, the temporal, frontal, and parietal lobes exhibited an age-inversed HFO rate. Assessment of 1006 seizure onset sites revealed that z-score normalized MI and HFO rate were higher at seizure onset versus nonepileptic electrode sites. We have publicly shared our intracranial EEG data to enable investigators to validate MI and HFO-centric presurgical evaluations to identify the epileptogenic zone.
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Affiliation(s)
- Kazuki Sakakura
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA
- Department of Neurosurgery, Rush University Medical Center, Chicago, IL, 60612, USA
- Department of Neurosurgery, University of Tsukuba, Tsukuba, 3058575, Japan
| | - Naoto Kuroda
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, 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, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA
- Department of Neurosurgery, Yokohama City University, Yokohama-shi, 2360004, 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, Tokyo, 1138421, Japan
| | - Ethan Firestone
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA
- Department of Physiology, 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
| | - Neena I Marupudi
- Department of Neurosurgery, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, 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.
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Hannan S, Thomas J, Jaber K, El Kosseifi C, Ho A, Abdallah C, Avigdor T, Gotman J, Frauscher B. The Differing Effects of Sleep on Ictal and Interictal Network Dynamics in Drug-Resistant Epilepsy. Ann Neurol 2023. [PMID: 37712215 DOI: 10.1002/ana.26796] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/14/2023] [Accepted: 09/11/2023] [Indexed: 09/16/2023]
Abstract
OBJECTIVE Sleep has important influences on focal interictal epileptiform discharges (IEDs), and the rates and spatial extent of IEDs are increased in non-rapid eye movement (NREM) sleep. In contrast, the influence of sleep on seizures is less clear, and its effects on seizure topography are poorly documented. We evaluated the influences of NREM sleep on ictal spatiotemporal dynamics and contrasted these with interictal network dynamics. METHODS We included patients with drug-resistant focal epilepsy who underwent continuous intracranial electroencephalography (iEEG) with depth electrodes. Patients were selected if they had 1 to 3 seizures from each vigilance state, wakefulness and NREM sleep, within a 48-hour window, and under the same antiseizure medication. A 10-minute epoch of the interictal iEEG was selected per state, and IEDs were detected automatically. A total of 25 patients (13 women; aged 32.5 ± 7.1 years) were included. RESULTS The seizure onset pattern, duration, spatiotemporal propagation, and latency of ictal high-frequency activity did not differ significantly between wakefulness and NREM sleep (all p > 0.05). In contrast, IED rates and spatial distribution were increased in NREM compared with wakefulness (p < 0.001, Cliff's d = 0.48 and 0.49). The spatial overlap between vigilance states was higher for seizures (57.1 ± 40.1%) than IEDs (41.7 ± 46.2%; p = 0.001, Cliff's d = 0.51). INTERPRETATION In contrast to its effects on IEDs, NREM sleep does not affect ictal spatiotemporal dynamics. This suggests that once the brain surpasses the seizure threshold, it will follow the underlying epileptic network irrespective of the vigilance state. These findings offer valuable insights into neural network dynamics in epilepsy and have important clinical implications for localizing seizure foci. ANN NEUROL 2023.
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Affiliation(s)
- Sana Hannan
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
- Department of Biomedical and Life Sciences, Lancaster University, Lancaster, United Kingdom
| | - John Thomas
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Kassem Jaber
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Charbel El Kosseifi
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Alyssa Ho
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Chifaou Abdallah
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Tamir Avigdor
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
- Department of Neurology, Duke University Medical Center, Durham, North Carolina, USA
- Analytical Neurophysiology Lab, Duke University Medical Center, Durham, North Carolina, USA
- Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, North Carolina, USA
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Dellavale D, Bonini F, Pizzo F, Makhalova J, Wendling F, Badier JM, Bartolomei F, Bénar CG. Spontaneous fast-ultradian dynamics of polymorphic interictal events in drug-resistant focal epilepsy. Epilepsia 2023; 64:2027-2043. [PMID: 37199673 DOI: 10.1111/epi.17655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 05/16/2023] [Accepted: 05/16/2023] [Indexed: 05/19/2023]
Abstract
OBJECTIVE We studied the rate dynamics of interictal events occurring over fast-ultradian time scales, as commonly examined in clinics to guide surgical planning in epilepsy. METHODS Stereo-electroencephalography (SEEG) traces of 35 patients with good surgical outcome (Engel I) were analyzed. For this we developed a general data mining method aimed at clustering the plethora of transient waveform shapes including interictal epileptiform discharges (IEDs) and assessed the temporal fluctuations in the capability of mapping the epileptogenic zone (EZ) of each type of event. RESULTS We found that the fast-ultradian dynamics of the IED rate may effectively impair the precision of EZ identification, and appear to occur spontaneously, that is, not triggered by or exclusively associated with a particular cognitive task, wakefulness, sleep, seizure occurrence, post-ictal state, or antiepileptic drug withdrawal. Propagation of IEDs from the EZ to the propagation zone (PZ) could explain the observed fast-ultradian fluctuations in a reduced fraction of the analyzed patients, suggesting that other factors like the excitability of the epileptogenic tissue could play a more relevant role. A novel link was found between the fast-ultradian dynamics of the overall rate of polymorphic events and the rate of specific IEDs subtypes. We exploited this feature to estimate in each patient the 5 min interictal epoch for near-optimal EZ and resected-zone (RZ) localization. This approach produces at the population level a better EZ/RZ classification when compared to both (1) the whole time series available in each patient (p = .084 for EZ, p < .001 for RZ, Wilcoxon signed-rank test) and (2) 5 min epochs sampled randomly from the interictal recordings of each patient (p < .05 for EZ, p < .001 for RZ, 105 random samplings). SIGNIFICANCE Our results highlight the relevance of the fast-ultradian IED dynamics in mapping the EZ, and show how this dynamics can be estimated prospectively to inform surgical planning in epilepsy.
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Affiliation(s)
- Damián Dellavale
- Institut de Neurosciences des Systèmes (INS, UMR1106), Aix Marseille Université, INSERM, Marseille, France
- Centro Atómico Bariloche and Instituto Balseiro, Comisión Nacional de Energía Atómica (CNEA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Cuyo (UNCUYO), Río Negro, San Carlos de Bariloche, Argentina
| | - Francesca Bonini
- Institut de Neurosciences des Systèmes (INS, UMR1106), Aix Marseille Université, INSERM, Marseille, France
- Epileptology and Cerebral Rhythmology, APHM, Timone Hospital, Marseille, France
| | - Francesca Pizzo
- Institut de Neurosciences des Systèmes (INS, UMR1106), Aix Marseille Université, INSERM, Marseille, France
- Epileptology and Cerebral Rhythmology, APHM, Timone Hospital, Marseille, France
| | - Julia Makhalova
- Institut de Neurosciences des Systèmes (INS, UMR1106), Aix Marseille Université, INSERM, Marseille, France
- Epileptology and Cerebral Rhythmology, APHM, Timone Hospital, Marseille, France
| | | | - Jean-Michel Badier
- Institut de Neurosciences des Systèmes (INS, UMR1106), Aix Marseille Université, INSERM, Marseille, France
| | - Fabrice Bartolomei
- Institut de Neurosciences des Systèmes (INS, UMR1106), Aix Marseille Université, INSERM, Marseille, France
- Epileptology and Cerebral Rhythmology, APHM, Timone Hospital, Marseille, France
| | - Christian-George Bénar
- Institut de Neurosciences des Systèmes (INS, UMR1106), Aix Marseille Université, INSERM, Marseille, France
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Ricci L, Tamilia E, Mercier M, Pepi C, Carfì-Pavia G, De Benedictis A, Assenza G, Di Lazzaro V, Vigevano F, Specchio N, de Palma L. Phase-amplitude coupling between low- and high-frequency activities as preoperative biomarker of focal cortical dysplasia subtypes. Clin Neurophysiol 2023; 150:40-48. [PMID: 37002979 DOI: 10.1016/j.clinph.2023.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 02/08/2023] [Accepted: 03/02/2023] [Indexed: 04/01/2023]
Abstract
OBJECTIVE To evaluate whether ictal phase-amplitude coupling (PAC) between high-frequency activity and low-frequency activity could be used as a preoperative biomarker of Focal Cortical Dysplasia (FCD) subtypes. We hypothesize that FCD seizures present unique PAC characteristics that may be linked to their specific histopathological features. METHODS We retrospectively examined 12 children with FCD and refractory epilepsy who underwent successful epilepsy surgery. We identified ictal onsets recorded with stereo-EEG. We estimated the strength of PAC between low-frequencies and high-frequencies for each seizure by means of modulation index. Generalized mixed effect models and receiver operating characteristic (ROC) curve analysis were used to test the association between ictal PAC and FCD subtypes. RESULTS Ictal PAC was significantly higher in patients with FCD type II compared to type I, only on SOZ-electrodes (p < 0.005). No differences in ictal PAC were found on non-SOZ electrodes. Pre-ictal PAC registered on SOZ electrodes predicted FCD histopathology with a classification accuracy > 0.9 (p < 0.05). CONCLUSIONS The correlations between histopathology and neurophysiology provide evidence for the contribution of ictal PAC as a preoperative biomarker of FCD subtypes. SIGNIFICANCE Developed into a proper clinical application, such a technique may help improve clinical management and facilitate the prediction of surgical outcome in patients with FCD undergoing stereo-EEG monitoring.
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Yeh CH, Zhang C, Shi W, Lo MT, Tinkhauser G, Oswal A. Cross-Frequency Coupling and Intelligent Neuromodulation. Cyborg Bionic Syst 2023; 4:0034. [PMID: 37266026 PMCID: PMC10231647 DOI: 10.34133/cbsystems.0034] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 05/02/2023] [Indexed: 06/03/2023] Open
Abstract
Cross-frequency coupling (CFC) reflects (nonlinear) interactions between signals of different frequencies. Evidence from both patient and healthy participant studies suggests that CFC plays an essential role in neuronal computation, interregional interaction, and disease pathophysiology. The present review discusses methodological advances and challenges in the computation of CFC with particular emphasis on potential solutions to spurious coupling, inferring intrinsic rhythms in a targeted frequency band, and causal interferences. We specifically focus on the literature exploring CFC in the context of cognition/memory tasks, sleep, and neurological disorders, such as Alzheimer's disease, epilepsy, and Parkinson's disease. Furthermore, we highlight the implication of CFC in the context and for the optimization of invasive and noninvasive neuromodulation and rehabilitation. Mainly, CFC could support advancing the understanding of the neurophysiology of cognition and motor control, serve as a biomarker for disease symptoms, and leverage the optimization of therapeutic interventions, e.g., closed-loop brain stimulation. Despite the evident advantages of CFC as an investigative and translational tool in neuroscience, further methodological improvements are required to facilitate practical and correct use in cyborg and bionic systems in the field.
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Affiliation(s)
- Chien-Hung Yeh
- School of Information and Electronics,
Beijing Institute of Technology, Beijing, China
| | - Chuting Zhang
- School of Information and Electronics,
Beijing Institute of Technology, Beijing, China
| | - Wenbin Shi
- School of Information and Electronics,
Beijing Institute of Technology, Beijing, China
| | - Men-Tzung Lo
- Department of Biomedical Sciences and Engineering,
National Central University, Taoyuan, Taiwan
| | - Gerd Tinkhauser
- Department of Neurology,
Bern University Hospital and University of Bern, Bern, Switzerland
| | - Ashwini Oswal
- MRC Brain Network Dynamics Unit,
University of Oxford, Oxford, UK
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10
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Jiang X, Liu X, Liu Y, Wang Q, Li B, Zhang L. Epileptic seizures detection and the analysis of optimal seizure prediction horizon based on frequency and phase analysis. Front Neurosci 2023; 17:1191683. [PMID: 37260846 PMCID: PMC10228742 DOI: 10.3389/fnins.2023.1191683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 04/14/2023] [Indexed: 06/02/2023] Open
Abstract
Changes in the frequency composition of the human electroencephalogram are associated with the transitions to epileptic seizures. Cross-frequency coupling (CFC) is a measure of neural oscillations in different frequency bands and brain areas, and specifically phase-amplitude coupling (PAC), a form of CFC, can be used to characterize these dynamic transitions. In this study, we propose a method for seizure detection and prediction based on frequency domain analysis and PAC combined with machine learning. We analyzed two databases, the Siena Scalp EEG database and the CHB-MIT database, and used the frequency features and modulation index (MI) for time-dependent quantification. The extracted features were fed to a random forest classifier for classification and prediction. The seizure prediction horizon (SPH) was also analyzed based on the highest-performing band to maximize the time for intervention and treatment while ensuring the accuracy of the prediction. Under comprehensive consideration, the results demonstrate that better performance could be achieved at an interval length of 5 min with an average accuracy of 85.71% and 95.87% for the Siena Scalp EEG database and the CHB-MIT database, respectively. As for the adult database, the combination of PAC analysis and classification can be of significant help for seizure detection and prediction. It suggests that the rarely used SPH also has a major impact on seizure detection and prediction and further explorations for the application of PAC are needed.
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Affiliation(s)
- Ximiao Jiang
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Xiaotong Liu
- Department of Dynamics and Control, Beihang University, Beijing, China
| | - Youjun Liu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Qingyun Wang
- Department of Dynamics and Control, Beihang University, Beijing, China
| | - Bao Li
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Liyuan Zhang
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
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11
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Frauscher B, Bénar CG, Engel JJ, Grova C, Jacobs J, Kahane P, Wiebe S, Zjilmans M, Dubeau F. Neurophysiology, Neuropsychology, and Epilepsy, in 2022: Hills We Have Climbed and Hills Ahead. Neurophysiology in epilepsy. Epilepsy Behav 2023; 143:109221. [PMID: 37119580 DOI: 10.1016/j.yebeh.2023.109221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 05/01/2023]
Abstract
Since the discovery of the human electroencephalogram (EEG), neurophysiology techniques have become indispensable tools in our armamentarium to localize epileptic seizures. New signal analysis techniques and the prospects of artificial intelligence and big data will offer unprecedented opportunities to further advance the field in the near future, ultimately resulting in improved quality of life for many patients with drug-resistant epilepsy. This article summarizes selected presentations from Day 1 of the two-day symposium "Neurophysiology, Neuropsychology, Epilepsy, 2022: Hills We Have Climbed and the Hills Ahead". Day 1 was dedicated to highlighting and honoring the work of Dr. Jean Gotman, a pioneer in EEG, intracranial EEG, simultaneous EEG/ functional magnetic resonance imaging, and signal analysis of epilepsy. The program focused on two main research directions of Dr. Gotman, and was dedicated to "High-frequency oscillations, a new biomarker of epilepsy" and "Probing the epileptic focus from inside and outside". All talks were presented by colleagues and former trainees of Dr. Gotman. The extended summaries provide an overview of historical and current work in the neurophysiology of epilepsy with emphasis on novel EEG biomarkers of epilepsy and source imaging and concluded with an outlook on the future of epilepsy research, and what is needed to bring the field to the next level.
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Affiliation(s)
- B Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
| | - C G Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - J Jr Engel
- David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - C Grova
- Multimodal Functional Imaging Lab, PERFORM Centre, Department of Physics, Concordia University, Montreal, QC, Canada; Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, QC, Canada; Montreal Neurological Institute and Hospital, Neurology and Neurosurgery Department, McGill University, Montreal, QC, Canada
| | - J Jacobs
- Department of Pediatric and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - P Kahane
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institute Neurosciences, Department of Neurology, 38000 Grenoble, France
| | - S Wiebe
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - M Zjilmans
- Stichting Epilepsie Instellingen Nederland, The Netherlands; Brain Center, University Medical Center Utrecht, The Netherlands
| | - F Dubeau
- Montreal Neurological Institute and Hospital, Neurology and Neurosurgery Department, McGill University, Montreal, QC, Canada
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12
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Ono H, Sonoda M, Sakakura K, Kitazawa Y, Mitsuhashi T, Firestone E, Jeong JW, Luat AF, Marupudi NI, Sood S, Asano E. Dynamic cortical and tractography atlases of proactive and reactive alpha and high-gamma activities. Brain Commun 2023; 5:fcad111. [PMID: 37228850 PMCID: PMC10204271 DOI: 10.1093/braincomms/fcad111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 10/15/2022] [Accepted: 04/03/2023] [Indexed: 05/27/2023] Open
Abstract
Alpha waves-posterior dominant rhythms at 8-12 Hz reactive to eye opening and closure-are among the most fundamental EEG findings in clinical practice and research since Hans Berger first documented them in the early 20th century. Yet, the exact network dynamics of alpha waves in regard to eye movements remains unknown. High-gamma activity at 70-110 Hz is also reactive to eye movements and a summary measure of local cortical activation supporting sensorimotor or cognitive function. We aimed to build the first-ever brain atlases directly visualizing the network dynamics of eye movement-related alpha and high-gamma modulations, at cortical and white matter levels. We studied 28 patients (age: 5-20 years) who underwent intracranial EEG and electro-oculography recordings. We measured alpha and high-gamma modulations at 2167 electrode sites outside the seizure onset zone, interictal spike-generating areas and MRI-visible structural lesions. Dynamic tractography animated white matter streamlines modulated significantly and simultaneously beyond chance, on a millisecond scale. Before eye-closure onset, significant alpha augmentation occurred at the occipital and frontal cortices. After eye-closure onset, alpha-based functional connectivity was strengthened, while high gamma-based connectivity was weakened extensively in both intra-hemispheric and inter-hemispheric pathways involving the central visual areas. The inferior fronto-occipital fasciculus supported the strengthened alpha co-augmentation-based functional connectivity between occipital and frontal lobe regions, whereas the posterior corpus callosum supported the inter-hemispheric functional connectivity between the occipital lobes. After eye-opening offset, significant high-gamma augmentation and alpha attenuation occurred at occipital, fusiform and inferior parietal cortices. High gamma co-augmentation-based functional connectivity was strengthened, whereas alpha-based connectivity was weakened in the posterior inter-hemispheric and intra-hemispheric white matter pathways involving central and peripheral visual areas. Our results do not support the notion that eye closure-related alpha augmentation uniformly reflects feedforward or feedback rhythms propagating from lower to higher order visual cortex, or vice versa. Rather, proactive and reactive alpha waves involve extensive, distinct white matter networks that include the frontal lobe cortices, along with low- and high-order visual areas. High-gamma co-attenuation coupled to alpha co-augmentation in shared brain circuitry after eye closure supports the notion of an idling role for alpha waves during eye closure. These normative dynamic tractography atlases may improve understanding of the significance of EEG alpha waves in assessing the functional integrity of brain networks in clinical practice; they also may help elucidate the effects of eye movements on task-related brain network measures observed in cognitive neuroscience research.
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Affiliation(s)
- Hiroya Ono
- Department of Pediatrics, Children’s Hospital of Michigan, Wayne State University, Detroit, MI 48201, USA
- Department of Pediatric Neurology, National Center of Neurology and Psychiatry, Joint Graduate School of Tohoku University, Tokyo 1878551, Japan
- Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - 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, Yokohama 2360004, Japan
| | - Kazuki Sakakura
- Department of Pediatrics, Children’s Hospital of Michigan, Wayne State University, Detroit, MI 48201, USA
- Department of Neurosurgery, University of Tsukuba, Tsukuba 3058575, Japan
| | - Yu Kitazawa
- Department of Pediatrics, Children’s Hospital of Michigan, Wayne State University, Detroit, MI 48201, USA
- Department of Neurology and Stroke Medicine, Yokohama City University, Yokohama, Kanagawa 2360004, Japan
| | - Takumi Mitsuhashi
- Department of Pediatrics, Children’s Hospital of Michigan, Wayne State University, Detroit, MI 48201, USA
- Department of Neurosurgery, Juntendo University, School of Medicine, Tokyo 1138421, Japan
| | - Ethan Firestone
- Department of Physiology, Wayne State University, Detroit, MI 48201, USA
| | - Jeong-Won Jeong
- 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
| | - Aimee F Luat
- 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
- Department of Pediatrics, Central Michigan University, Mount Pleasant, MI 48858, USA
| | - Neena I Marupudi
- Department of Neurosurgery, Children’s Hospital of Michigan, Wayne State University, Detroit, MI 48201, USA
| | - Sandeep Sood
- Department of Neurosurgery, Children’s Hospital of Michigan, Wayne State University, Detroit, MI 48201, USA
| | - 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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13
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Firestone E, Sonoda M, Kuroda N, Sakakura K, Jeong J, Lee M, Wada K, Takayama Y, Iijima K, Iwasaki M, Miyazaki T, Asano E. Sevoflurane-induced high-frequency oscillations, effective connectivity and intraoperative classification of epileptic brain areas. Clin Neurophysiol 2023. [PMID: 36989866 DOI: 10.1016/j.clinph.2023.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 02/27/2023] [Accepted: 03/02/2023] [Indexed: 03/18/2023]
Abstract
OBJECTIVE To determine how sevoflurane anesthesia modulates intraoperative epilepsy biomarkers on electrocorticography, including high-frequency oscillation (HFO) effective connectivity (EC), and to investigate their relation to epileptogenicity and anatomical white matter. METHODS We studied eight pediatric drug-resistant focal epilepsy patients who achieved seizure control after invasive monitoring and resective surgery. We visualized spatial distributions of the electrocorticography biomarkers at an oxygen baseline, three time-points while sevoflurane was increasing, and at a plateau of 2 minimum alveolar concentration (MAC) sevoflurane. HFO EC was combined with diffusion-weighted imaging, in dynamic tractography. RESULTS Intraoperative HFO EC diffusely increased as a function of sevoflurane concentration, although most in epileptogenic sites (defined as those included in the resection); their ability to classify epileptogenicity was optimized at sevoflurane 2 MAC. HFO EC could be visualized on major white matter tracts, as a function of sevoflurane level. CONCLUSIONS The results strengthened the hypothesis that sevoflurane-activated HFO biomarkers may help intraoperatively localize the epileptogenic zone. SIGNIFICANCE Our results help characterize how HFOs at non-epileptogenic and epileptogenic networks respond to sevoflurane. It may be warranted to establish a normative HFO atlas incorporating the modifying effects of sevoflurane and major white matter pathways, as critical reference in epilepsy presurgical evaluation.
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14
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Conrad EC, Revell AY, Greenblatt AS, Gallagher RS, Pattnaik AR, Hartmann N, Gugger JJ, Shinohara RT, Litt B, Marsh ED, Davis KA. Spike patterns surrounding sleep and seizures localize the seizure-onset zone in focal epilepsy. Epilepsia 2023; 64:754-768. [PMID: 36484572 PMCID: PMC10045742 DOI: 10.1111/epi.17482] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 12/08/2022] [Accepted: 12/08/2022] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Interictal spikes help localize seizure generators as part of surgical planning for drug-resistant epilepsy. However, there are often multiple spike populations whose frequencies change over time, influenced by brain state. Understanding state changes in spike rates will improve our ability to use spikes for surgical planning. Our goal was to determine the effect of sleep and seizures on interictal spikes, and to use sleep and seizure-related changes in spikes to localize the seizure-onset zone (SOZ). METHODS We performed a retrospective analysis of intracranial electroencephalography (EEG) data from patients with focal epilepsy. We automatically detected interictal spikes and we classified different time periods as awake or asleep based on the ratio of alpha to delta power, with a secondary analysis using the recently published SleepSEEG algorithm. We analyzed spike rates surrounding sleep and seizures. We developed a model to localize the SOZ using state-dependent spike rates. RESULTS We analyzed data from 101 patients (54 women, age range 16-69). The normalized alpha-delta power ratio accurately classified wake from sleep periods (area under the curve = .90). Spikes were more frequent in sleep than wakefulness and in the post-ictal compared to the pre-ictal state. Patients with temporal lobe epilepsy had a greater wake-to-sleep and pre- to post-ictal spike rate increase compared to patients with extra-temporal epilepsy. A machine-learning classifier incorporating state-dependent spike rates accurately identified the SOZ (area under the curve = .83). Spike rates tended to be higher and better localize the seizure-onset zone in non-rapid eye movement (NREM) sleep than in wake or REM sleep. SIGNIFICANCE The change in spike rates surrounding sleep and seizures differs between temporal and extra-temporal lobe epilepsy. Spikes are more frequent and better localize the SOZ in sleep, particularly in NREM sleep. Quantitative analysis of spikes may provide useful ancillary data to localize the SOZ and improve surgical planning.
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Affiliation(s)
- Erin C. Conrad
- Department of Neurology, University of Pennsylvania, Philadelphia, PA
| | - Andrew Y. Revell
- Medical Scientist Training Program, University of Pennsylvania, Philadelphia, PA
| | | | - Ryan S. Gallagher
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Akash R. Pattnaik
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA
| | - Nicole Hartmann
- Department of Neurology, University of Pennsylvania, Philadelphia, PA
| | - James J. Gugger
- Department of Neurology, University of Pennsylvania, Philadelphia, PA
| | - Russell T. Shinohara
- Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA
- Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA
| | - Brian Litt
- Department of Neurology, University of Pennsylvania, Philadelphia, PA
| | - Eric D. Marsh
- Department of Neurology, University of Pennsylvania, Philadelphia, PA
- Division of Child Neurology, Department of Biostatistics, University of Pennsylvania, Epidemiology, & Informatics, Philadelphi Department of Biostatistics, University of Pennsylvania, Epidemiology, & Informatics, Philadelphi Pediatric Epilepsy Program, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Kathryn A. Davis
- Department of Neurology, University of Pennsylvania, Philadelphia, PA
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15
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Islam MR, Zhao X, Miao Y, Sugano H, Tanaka T. Epileptic seizure focus detection from interictal electroencephalogram: a survey. Cogn Neurodyn 2023; 17:1-23. [PMID: 36704629 DOI: 10.1007/s11571-022-09816-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 04/15/2022] [Accepted: 04/21/2022] [Indexed: 01/29/2023] Open
Abstract
Electroencephalogram (EEG) is one of most effective clinical diagnosis modalities for the localization of epileptic focus. Most current AI solutions use this modality to analyze the EEG signals in an automated manner to identify the epileptic seizure focus. To develop AI system for identifying the epileptic focus, there are many recently-published AI solutions based on biomarkers or statistic features that utilize interictal EEGs. In this review, we survey these solutions and find that they can be divided into three main categories: (i) those that use of biomarkers in EEG signals, including high-frequency oscillation, phase-amplitude coupling, and interictal epileptiform discharges, (ii) others that utilize feature-extraction methods, and (iii) solutions based upon neural networks (an end-to-end approach). We provide a detailed description of seizure focus with clinical diagnosis methods, a summary of the public datasets that seek to reduce the research gap in epilepsy, recent novel performance evaluation criteria used to evaluate the AI systems, and guidelines on when and how to use them. This review also suggests a number of future research challenges that must be overcome in order to design more efficient computer-aided solutions to epilepsy focus detection.
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16
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Togawa J, Matsumoto R, Usami K, Matsuhashi M, Inouchi M, Kobayashi K, Hitomi T, Nakae T, Shimotake A, Yamao Y, Kikuchi T, Yoshida K, Kunieda T, Miyamoto S, Takahashi R, Ikeda A. Enhanced phase-amplitude coupling of human electrocorticography selectively in the posterior cortical region during rapid eye movement sleep. Cereb Cortex 2022; 33:486-496. [PMID: 35288751 DOI: 10.1093/cercor/bhac079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 01/31/2022] [Accepted: 02/02/2022] [Indexed: 01/17/2023] Open
Abstract
The spatiotemporal dynamics of interaction between slow (delta or infraslow) waves and fast (gamma) activities during wakefulness and sleep are yet to be elucidated in human electrocorticography (ECoG). We evaluated phase-amplitude coupling (PAC), which reflects neuronal coding in information processing, using ECoG in 11 patients with intractable focal epilepsy. PAC was observed between slow waves of 0.5-0.6 Hz and gamma activities, not only during light sleep and slow-wave sleep (SWS) but even during wakefulness and rapid eye movement (REM) sleep. While PAC was high over a large region during SWS, it was stronger in the posterior cortical region around the temporoparietal junction than in the frontal cortical region during REM sleep. PAC tended to be higher in the posterior cortical region than in the frontal cortical region even during wakefulness. Our findings suggest that the posterior cortical region has a functional role in REM sleep and may contribute to the maintenance of the dreaming experience.
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Affiliation(s)
- Jumpei Togawa
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan.,Department of Respiratory Care and Sleep Control Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Riki Matsumoto
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan.,Divison of Neurology, Kobe University Graduate School of Medicine, Kobe 650-0017, Japan
| | - Kiyohide Usami
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Masao Matsuhashi
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Morito Inouchi
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan.,Department of Neurology, National Hospital Organization Kyoto Medical Center, Kyoto 612-8555, Japan
| | - Katsuya Kobayashi
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Takefumi Hitomi
- Department of Clinical Laboratory Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Takuro Nakae
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan.,Department of Neurosurgery, Shiga General Hospital, Moriyama, Shiga 524-8524, Japan
| | - Akihiro Shimotake
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Yukihiro Yamao
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Takayuki Kikuchi
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Kazumichi Yoshida
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Takeharu Kunieda
- Department of Neurosurgery, Ehime University Graduate School of Medicine, To-on, Ehime 791-0295, Japan
| | - Susumu Miyamoto
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Ryosuke Takahashi
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Akio Ikeda
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
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17
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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] [What about the content of this article? (0)] [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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18
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Li X, Zhang H, Lai H, Wang J, Wang W, Yang X. High-Frequency Oscillations and Epileptogenic Network. Curr Neuropharmacol 2022; 20:1687-1703. [PMID: 34503414 PMCID: PMC9881061 DOI: 10.2174/1570159x19666210908165641] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/26/2021] [Accepted: 08/31/2021] [Indexed: 11/22/2022] Open
Abstract
Epilepsy is a network disease caused by aberrant neocortical large-scale connectivity spanning regions on the scale of several centimeters. High-frequency oscillations, characterized by the 80-600 Hz signals in electroencephalography, have been proven to be a promising biomarker of epilepsy that can be used in assessing the severity and susceptibility of epilepsy as well as the location of the epileptogenic zone. However, the presence of a high-frequency oscillation network remains a topic of debate as high-frequency oscillations have been previously thought to be incapable of propagation, and the relationship between high-frequency oscillations and the epileptogenic network has rarely been discussed. Some recent studies reported that high-frequency oscillations may behave like networks that are closely relevant to the epileptogenic network. Pathological highfrequency oscillations are network-driven phenomena and elucidate epileptogenic network development; high-frequency oscillations show different characteristics coincident with the epileptogenic network dynamics, and cross-frequency coupling between high-frequency oscillations and other signals may mediate the generation and propagation of abnormal discharges across the network.
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Affiliation(s)
- Xiaonan Li
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
| | | | | | - Jiaoyang Wang
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
| | - Wei Wang
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
| | - Xiaofeng Yang
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China,Address correspondence to this author at the Bioland Laboratory, Guangzhou, China; Tel: 86+ 18515855127; E-mail:
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Tamrakar S, Iimura Y, Suzuki H, Mitsuhashi T, Ueda T, Nishioka K, Karagiozov K, Nakajima M, Miao Y, Tanaka T, Sugano H. Higher phase-amplitude coupling between ripple and slow oscillations indicates the distribution of epileptogenicity in temporal lobe epilepsy with hippocampal sclerosis. Seizure 2022; 100:1-7. [DOI: 10.1016/j.seizure.2022.05.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 05/27/2022] [Accepted: 05/28/2022] [Indexed: 10/18/2022] Open
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20
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Orihara A, Inaji M, Fujii S, Fujimoto SH, Hara K, Maehara T. Validity of intraoperative ECoG in the parahippocampal gyrus as an indicator of hippocampal epileptogenicity. Epilepsy Res 2022. [DOI: 10.1016/j.eplepsyres.2022.106950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 05/01/2022] [Accepted: 05/25/2022] [Indexed: 11/20/2022]
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21
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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: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Billardello R, Ntolkeras G, Chericoni A, Madsen JR, Papadelis C, Pearl PL, Grant PE, Taffoni F, Tamilia E. Novel User-Friendly Application for MRI Segmentation of Brain Resection following Epilepsy Surgery. Diagnostics (Basel) 2022; 12:diagnostics12041017. [PMID: 35454065 PMCID: PMC9032020 DOI: 10.3390/diagnostics12041017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 04/10/2022] [Accepted: 04/13/2022] [Indexed: 11/16/2022] Open
Abstract
Delineation of resected brain cavities on magnetic resonance images (MRIs) of epilepsy surgery patients is essential for neuroimaging/neurophysiology studies investigating biomarkers of the epileptogenic zone. The gold standard to delineate the resection on MRI remains manual slice-by-slice tracing by experts. Here, we proposed and validated a semiautomated MRI segmentation pipeline, generating an accurate model of the resection and its anatomical labeling, and developed a graphical user interface (GUI) for user-friendly usage. We retrieved pre- and postoperative MRIs from 35 patients who had focal epilepsy surgery, implemented a region-growing algorithm to delineate the resection on postoperative MRIs and tested its performance while varying different tuning parameters. Similarity between our output and hand-drawn gold standards was evaluated via dice similarity coefficient (DSC; range: 0-1). Additionally, the best segmentation pipeline was trained to provide an automated anatomical report of the resection (based on presurgical brain atlas). We found that the best-performing set of parameters presented DSC of 0.83 (0.72-0.85), high robustness to seed-selection variability and anatomical accuracy of 90% to the clinical postoperative MRI report. We presented a novel user-friendly open-source GUI that implements a semiautomated segmentation pipeline specifically optimized to generate resection models and their anatomical reports from epilepsy surgery patients, while minimizing user interaction.
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Affiliation(s)
- Roberto Billardello
- Fetal Neonatal Neuroimaging and Developmental Science Center (FNNDSC), Newborn Medicine Division, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA; (G.N.); (A.C.); (P.E.G.)
- Advanced Robotics and Human-Centered Technologies-CREO Lab, Università Campus Bio-Medico di Roma, 00128 Rome, Italy;
- Correspondence: (R.B.); (E.T.)
| | - Georgios Ntolkeras
- Fetal Neonatal Neuroimaging and Developmental Science Center (FNNDSC), Newborn Medicine Division, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA; (G.N.); (A.C.); (P.E.G.)
- Baystate Children’s Hospital, Springfield, MA 01199, USA
| | - Assia Chericoni
- Fetal Neonatal Neuroimaging and Developmental Science Center (FNNDSC), Newborn Medicine Division, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA; (G.N.); (A.C.); (P.E.G.)
- Advanced Robotics and Human-Centered Technologies-CREO Lab, Università Campus Bio-Medico di Roma, 00128 Rome, Italy;
| | - Joseph R. Madsen
- Epilepsy Surgery Program, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
| | - Christos Papadelis
- Jane and John Justin Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX 76104, USA;
| | - Phillip L. Pearl
- Division of Epilepsy and Clinical Neurophysiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
| | - Patricia Ellen Grant
- Fetal Neonatal Neuroimaging and Developmental Science Center (FNNDSC), Newborn Medicine Division, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA; (G.N.); (A.C.); (P.E.G.)
| | - Fabrizio Taffoni
- Advanced Robotics and Human-Centered Technologies-CREO Lab, Università Campus Bio-Medico di Roma, 00128 Rome, Italy;
| | - Eleonora Tamilia
- Fetal Neonatal Neuroimaging and Developmental Science Center (FNNDSC), Newborn Medicine Division, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA; (G.N.); (A.C.); (P.E.G.)
- Correspondence: (R.B.); (E.T.)
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23
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Fujita Y, Yanagisawa T, Fukuma R, Ura N, Oshino S, Kishima H. Abnormal phase-amplitude coupling characterizes the interictal state in epilepsy. J Neural Eng 2022; 19. [PMID: 35385832 DOI: 10.1088/1741-2552/ac64c4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 04/05/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Diagnosing epilepsy still requires visual interpretation of electroencephalography and magnetoencephalography (MEG) by specialists, which prevents quantification and standardization of diagnosis. Previous studies proposed automated diagnosis by combining various features from electroencephalography and MEG, such as relative power (Power) and functional connectivity. However, the usefulness of interictal phase-amplitude coupling (PAC) in diagnosing epilepsy is still unknown. We hypothesized that resting-state PAC would be different for patients with epilepsy in the interictal state and for healthy participants such that it would improve discrimination between the groups. METHODS We obtained resting-state MEG and magnetic resonance imaging in 90 patients with epilepsy during their preoperative evaluation and in 90 healthy participants. We used the cortical currents estimated from MEG and magnetic resonance imaging to calculate Power in the δ (1-3 Hz), θ (4-7 Hz), α (8-13 Hz), β (13-30 Hz), low γ (35-55 Hz), and high γ (65-90 Hz) bands and functional connectivity in the θ band. PAC was evaluated using the synchronization index (SI) for eight frequency band pairs: the phases of δ, θ, α, and β and the amplitudes of low and high γ. First, we compared the mean SI values for the patients with epilepsy and the healthy participants. Then, using features such as PAC, Power, functional connectivity, and features extracted by deep learning individually or combined, we tested whether PAC improves discrimination accuracy for the two groups. RESULTS The mean SI values were significantly different for the patients with epilepsy and the healthy participants. The SI value difference was highest for θ/low γ in the temporal lobe. Discrimination accuracy was the highest, at 90%, using the combination of PAC and deep learning. SIGNIFICANCE Abnormal PAC characterized the patients with epilepsy in the interictal state compared with the healthy participants, potentially improving the discrimination of epilepsy.
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Affiliation(s)
- Yuya Fujita
- Institute for Advanced co-creation studies, Osaka University, 2-2 Yamadaoka Suita Osaka Japan, Suita, 565-0871, JAPAN
| | - Takufumi Yanagisawa
- Institute for Advanced co-creation studies, Osaka University, 2-2 Yamadaoka Suita Osaka Japan, Suita, 565-0871, JAPAN
| | - Ryohei Fukuma
- Institute for Advanced co-creation studies, Osaka University, 2-2 Yamadaoka Suita Osaka Japan, Suita, 565-0871, JAPAN
| | - Natsuko Ura
- Institute for Advanced co-creation studies, Osaka University, 2-2 Yamadaoka Suita Osaka Japan, Suita, 565-0871, JAPAN
| | - Satoru Oshino
- Department of Neurosurgery, Osaka University Faculty of Medicine Graduate School of Medicine, 2-2 Yamadaoka, suita, Osaka, Japan, Osaka University Graduate School of Medicine, Dept of Neurosurgery, Osaka, Osaka, 5670871, JAPAN
| | - Haruhiko Kishima
- Department of neurosurgery, Osaka University, 2-2, Yamadaoka, Suita, Suita, Osaka, 5650871, JAPAN
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Nunez MD, Charupanit K, Sen-Gupta I, Lopour BA, Lin JJ. Beyond rates: time-varying dynamics of high frequency oscillations as a biomarker of the seizure onset zone. J Neural Eng 2022; 19:10.1088/1741-2552/ac520f. [PMID: 35120337 PMCID: PMC9258635 DOI: 10.1088/1741-2552/ac520f] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 02/04/2022] [Indexed: 11/11/2022]
Abstract
Objective. High frequency oscillations (HFOs) recorded by intracranial electrodes have generated excitement for their potential to help localize epileptic tissue for surgical resection. However, the number of HFOs per minute (i.e. the HFO 'rate') is not stable over the duration of intracranial recordings; for example, the rate of HFOs increases during periods of slow-wave sleep. Moreover, HFOs that are predictive of epileptic tissue may occur in oscillatory patterns due to phase coupling with lower frequencies. Therefore, we sought to further characterize between-seizure (i.e. 'interictal') HFO dynamics both within and outside the seizure onset zone (SOZ).Approach. Using long-term intracranial EEG (mean duration 10.3 h) from 16 patients, we automatically detected HFOs using a new algorithm. We then fit a hierarchical negative binomial model to the HFO counts. To account for differences in HFO dynamics and rates between sleep and wakefulness, we also fit a mixture model to the same data that included the ability to switch between two discrete brain states that were automatically determined during the fitting process. The ability to predict the SOZ by model parameters describing HFO dynamics (i.e. clumping coefficients and coefficients of variation) was assessed using receiver operating characteristic curves.Main results. Parameters that described HFO dynamics were predictive of SOZ. In fact, these parameters were found to be more consistently predictive than HFO rate. Using concurrent scalp EEG in two patients, we show that the model-found brain states corresponded to (1) non-REM sleep and (2) awake and rapid eye movement sleep. However the brain state most likely corresponding to slow-wave sleep in the second model improved SOZ prediction compared to the first model for only some patients.Significance. This work suggests that delineation of SOZ with interictal data can be improved by the inclusion of time-varying HFO dynamics.
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Affiliation(s)
- Michael D. Nunez
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands,Department of Biomedical Engineering, University of California, Irvine CA, USA,Corresponding author (Michael D. Nunez), (Beth A. Lopour)
| | - Krit Charupanit
- Department of Biomedical Engineering, University of California, Irvine CA, USA,Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand
| | - Indranil Sen-Gupta
- Neurology, University of California Irvine Medical Center, Orange CA, USA
| | - Beth A. Lopour
- Department of Biomedical Engineering, University of California, Irvine CA, USA,Corresponding author (Michael D. Nunez), (Beth A. Lopour)
| | - Jack J. Lin
- Department of Neurology, University of California, Irvine CA, USA
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25
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Wada K, Sonoda M, Firestone E, Sakakura K, Kuroda N, Takayama Y, Iijima K, Iwasaki M, Mihara T, Goto T, Asano E, Miyazaki T. Sevoflurane-based enhancement of phase-amplitude coupling and localization of the epileptogenic zone. Clin Neurophysiol 2022; 134:1-8. [PMID: 34922194 PMCID: PMC8766927 DOI: 10.1016/j.clinph.2021.11.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 10/05/2021] [Accepted: 11/03/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Phase-amplitude coupling between high-frequency (≥150 Hz) and delta (3-4 Hz) oscillations - modulation index (MI) - is a promising, objective biomarker of epileptogenicity. We determined whether sevoflurane anesthesia preferentially enhances this metric within the epileptogenic zone. METHODS This is an observational study of intraoperative electrocorticography data from 621 electrodes chronically implanted into eight patients with drug-resistant, focal epilepsy. All patients were anesthetized with sevoflurane during resective surgery, which subsequently resulted in seizure control. We classified 'removed' and 'retained' brain sites as epileptogenic and non-epileptogenic, respectively. Mixed model analysis determined which anesthetic stage optimized MI-based classification of epileptogenic sites. RESULTS MI increased as a function of anesthetic stage, ranging from baseline (i.e., oxygen alone) to 2.0 minimum alveolar concentration (MAC) of sevoflurane, preferentially at sites showing higher initial MI values. This phenomenon was accentuated just prior to sevoflurane reaching 2.0 MAC, at which time, the odds of a site being classified as epileptogenic were enhanced by 86.6 times for every increase of 1.0 MI. CONCLUSIONS Intraoperative MI best localized the epileptogenic zone immediately before sevoflurane reaching 2.0 MAC in this small cohort of patients. SIGNIFICANCE Prospective, large cohort studies are warranted to determine whether sevoflurane anesthesia can reduce the need for extraoperative, invasive evaluation.
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Affiliation(s)
- Keiko Wada
- Department of Anesthesiology, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Tokyo 1878551, Japan,Department of Anesthesiology and Critical Care, Yokohama City University Graduate School of Medicine, Yokohama, 2360004, Japan
| | - 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 Graduate School of Medicine, Yokohama 2360004, Japan
| | - Ethan Firestone
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA,Department of Physiology, Wayne State University, Detroit, MI 48201, USA
| | - Kazuki Sakakura
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA,Department of Neurosurgery, University of Tsukuba, Tsukuba, 3058575, Japan
| | - Naoto Kuroda
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA,Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai 9808575, Japan
| | - Yutaro Takayama
- Department of Neurosurgery, Yokohama City University Graduate School of Medicine, Yokohama 2360004, Japan,Department of Neurosurgery, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Tokyo 1878551, Japan
| | - Keiya Iijima
- Department of Neurosurgery, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Tokyo 1878551, Japan
| | - Masaki Iwasaki
- Department of Neurosurgery, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Tokyo 1878551, Japan
| | - Takahiro Mihara
- Department of Anesthesiology and Critical Care, Yokohama City University Graduate School of Medicine, Yokohama, 2360004, Japan,Department of Health Data Science, Yokohama City University Graduate School of Data Science, Yokohama, 2360027, Japan
| | - Takahisa Goto
- Department of Anesthesiology and Critical Care, Yokohama City University Graduate School of Medicine, Yokohama, 2360004, Japan
| | - 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,E.A. and T.M. share the senior authorship. Corresponding Authors: Eishi Asano, M.D., Ph.D., M.S. (C.R.D.S.A.), Address: Division of Pediatric Neurology, Children’s Hospital of Michigan, Wayne State University. 3901 Beaubien St., Detroit, MI, 48201, USA, Phone: +1-313-745-5547, FAX: +1-313-745-9435, and Tomoyuki Miyazaki, M.D., Ph.D., Address: Department of Physiology/Anesthesiology, Yokohama City University Graduate School of Medicine. 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, Japan, Phone: +81-45-787-2918, FAX: +81-45-787-2917,
| | - Tomoyuki Miyazaki
- Department of Anesthesiology and Critical Care, Yokohama City University Graduate School of Medicine, Yokohama, 2360004, Japan,Department of Physiology, Yokohama City University Graduate School of Medicine, Yokohama 2360004, Japan,E.A. and T.M. share the senior authorship. Corresponding Authors: Eishi Asano, M.D., Ph.D., M.S. (C.R.D.S.A.), Address: Division of Pediatric Neurology, Children’s Hospital of Michigan, Wayne State University. 3901 Beaubien St., Detroit, MI, 48201, USA, Phone: +1-313-745-5547, FAX: +1-313-745-9435, and Tomoyuki Miyazaki, M.D., Ph.D., Address: Department of Physiology/Anesthesiology, Yokohama City University Graduate School of Medicine. 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, Japan, Phone: +81-45-787-2918, FAX: +81-45-787-2917,
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26
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Ali R, Gollwitzer S, Reindl C, Hamer H, Coras R, Blümcke I, Buchfelder M, Hastreiter P, Rampp S. Phase-Amplitude Coupling measures for determination of the epileptic network: A methodological comparison. J Neurosci Methods 2022; 370:109484. [DOI: 10.1016/j.jneumeth.2022.109484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 12/29/2021] [Accepted: 01/18/2022] [Indexed: 12/01/2022]
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27
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Liu X, Han F, Fu R, Wang Q, Luan G. Epileptogenic Zone Location of Temporal Lobe Epilepsy by Cross-Frequency Coupling Analysis. Front Neurol 2021; 12:764821. [PMID: 34867749 PMCID: PMC8636749 DOI: 10.3389/fneur.2021.764821] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 10/12/2021] [Indexed: 12/02/2022] Open
Abstract
Epilepsy is a chronic brain disease with dysfunctional brain networks, and electroencephalography (EEG) is an important tool for epileptogenic zone (EZ) identification, with rich information about frequencies. Different frequency oscillations have different contributions to brain function, and cross-frequency coupling (CFC) has been found to exist within brain regions. Cross-channel and inter-channel analysis should be both focused because they help to analyze how epilepsy networks change and also localize the EZ. In this paper, we analyzed long-term stereo-electroencephalography (SEEG) data from 17 patients with temporal lobe epilepsy. Single-channel and cross-channel CFC features were combined to establish functional brain networks, and the network characteristics under different periods and the localization of EZ were analyzed. It was observed that theta–gamma phase amplitude coupling (PAC) within the electrodes in the seizure region increased during the ictal (p < 0.05). Theta–gamma and delta–gamma PAC of cross-channel were enhanced in the early and mid-late ictal, respectively. It was also found that there was a strong cross-frequency coupling state between channels of EZ in the functional network during the ictal, along with a more regular network than interictal. The accuracy rate of EZ localization was 82.4%. Overall, the combination of single-channel and multi-channel cross-band coupling analysis can help identify seizures and localize EZ for temporal lobe epilepsy. Rhythmic coupling reveals a relationship between the functional network and the seizure status of epilepsy.
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Affiliation(s)
- Xiaotong Liu
- Department of Dynamics and Control, Beihang University, Beijing, China
| | - Fang Han
- College of Information Science and Technology, Donghua University, Shanghai, China
| | - Rui Fu
- Department of Dynamics and Control, Beihang University, Beijing, China
| | - Qingyun Wang
- Department of Dynamics and Control, Beihang University, Beijing, China
| | - Guoming Luan
- Beijing Key Laboratory of Epilepsy, Sanbo Brain Hospital, Capital Medical University, Beijing, China
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28
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Sun Y, Ren G, Ren J, Wang Q. High-frequency oscillations detected by electroencephalography as biomarkers to evaluate treatment outcome, mirror pathological severity and predict susceptibility to epilepsy. Acta Epileptologica 2021. [DOI: 10.1186/s42494-021-00063-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
AbstractHigh-frequency oscillations (HFOs) in the electroencephalography (EEG) have been extensively investigated as a potential biomarker of epileptogenic zones. The understanding of the role of HFOs in epilepsy has been advanced considerably over the past decade, and the use of scalp EEG facilitates recordings of HFOs. HFOs were initially applied in large scale in epilepsy surgery and are now being utilized in other applications. In this review, we summarize applications of HFOs in 3 subtopics: (1) HFOs as biomarkers to evaluate epilepsy treatment outcome; (2) HFOs as biomarkers to measure seizure propensity; (3) HFOs as biomarkers to reflect the pathological severity of epilepsy. Nevertheless, knowledge regarding the above clinical applications of HFOs remains limited at present. Further validation through prospective studies is required for its reliable application in the clinical management of individual epileptic patients.
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29
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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: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Miao Y, Iimura Y, Sugano H, Fukumori K, Shoji T, Tanaka T. Seizure Onset Zone Identification Based on Phase-Amplitude Coupling of Interictal Electrocorticogram. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:587-590. [PMID: 34891362 DOI: 10.1109/embc46164.2021.9630941] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Presurgical localization from interictal electrocorticogram (ECoG) and resection of seizure onset zone (SOZ) are difficult processes to achieve seizure freedom. Recently, high frequency oscillations (HFOs) have been recognized as reliable biomarkers for epilepsy surgery which has a relation with the phase of low frequency activities in ECoG. Considering the recent valid biomarker for epilepsy surgery, we hypothesize that the approach of coupling between HFOs and low frequency phases differs SOZ from non-seizure onset zone (NSOZ). This study proposes phase-amplitude coupling (PAC) method to identify SOZ by measuring whether the amplitude of HFOs is coupled with a phase at 2-34 Hz in ECoG. Besides, three machine learning models for PAC-based features are designed for SOZ detection. Four patients with focal cortical dysplasia (FCD) are examined to observe efficiency. Experimental results indicate that the mode of coupling is a potential feature to detect SOZ.Clinical relevance- This suggests the PAC feature between low frequency phase and HFO amplitude may be used as a candidate biomarker to detect SOZ.
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Stovall T, Hunt B, Glynn S, Stacey WC, Gliske SV. Interictal high frequency background activity as a biomarker of epileptogenic tissue. Brain Commun 2021; 3:fcab188. [PMID: 34704026 PMCID: PMC8417455 DOI: 10.1093/braincomms/fcab188] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 06/17/2021] [Accepted: 06/28/2021] [Indexed: 11/16/2022] Open
Abstract
High frequency oscillations (HFOs) are very brief events that are a well-established biomarker of the epileptogenic zone (EZ) but are rare and comprise only a tiny fraction of the total recorded EEG. We hypothesize that the interictal high frequency ‘background’ data, which has received little attention but represents the majority of the EEG record, also may contain additional, novel information for identifying the EZ. We analysed intracranial EEG (30–500 Hz frequency range) acquired from 24 patients who underwent resective surgery. We computed 38 quantitative features based on all usable, interictal data (63–307 h per subject), excluding all detected HFOs. We assessed association between each feature and the seizure onset zone (SOZ) and resected volume (RV) using logistic regression. A pathology score per channel was also created via principle component analysis and logistic regression, using hold-out-one-patient cross-validation to avoid in-sample training. Association of the pathology score with the SOZ and RV was quantified using an asymmetry measure. Many features were associated with the SOZ: 23/38 features had odds ratios >1.3 or <0.7 and 17/38 had odds ratios different than zero with high significance (P < 0.001/39, logistic regression with Bonferroni Correction). The pathology score, the rate of HFOs, and their channel-wise product were each strongly associated with the SOZ [median asymmetry ≥0.44, good surgery outcome patients; median asymmetry ≥0.40, patients with other outcomes; 95% confidence interval (CI) > 0.27 in both cases]. The pathology score and the channel-wise product also had higher asymmetry with respect to the SOZ than the HFO rate alone (median difference in asymmetry ≥0.18, 95% CI >0.05). These results support that the high frequency background data contains useful information for determining the EZ, distinct and complementary to information from detected HFOs. The concordance between the high frequency activity pathology score and the rate of HFOs appears to be a better biomarker of epileptic tissue than either measure alone.
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Affiliation(s)
- Truman Stovall
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Brian Hunt
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA
| | - Simon Glynn
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA
| | - William C Stacey
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.,Department of Neurology, University of Michigan, Ann Arbor, MI, USA
| | - Stephen V Gliske
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.,Department of Neurology, University of Michigan, Ann Arbor, MI, USA.,Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE, USA
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Ma H, Wang Z, Li C, Chen J, Wang Y. Phase-Amplitude Coupling and Epileptogenic Zone Localization of Frontal Epilepsy Based on Intracranial EEG. Front Neurol 2021; 12:718683. [PMID: 34566860 PMCID: PMC8458805 DOI: 10.3389/fneur.2021.718683] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 08/17/2021] [Indexed: 11/28/2022] Open
Abstract
Objective: This study aimed to explore the characteristics of phase-amplitude coupling in patients with frontal epilepsy based on their electrocorticography data, in order to identify the localization of epileptic regions and further guide clinical resection surgery. Methods: We adopted the modulation index based on the Kullback-Leibler distance, phase-amplitude coupling co-modulogram, and time-varying phase-amplitude modulogram to explore the temporal-spatial patterns and characterization of PAC strength during the period from inter- seizure to post-seizure. Taking the resected area as the gold standard, the epileptogenic zone was located based on MI values of 7 different seizure periods, and the accuracy of localization was measured by the area under the receiver operating curve. Results: (1) The PAC in the inter- and pre-seizure periods was weak and paroxysmal, but strong PAC channels were confined more to the seizure-onset zone and resection region. PAC during the seizure period was intense and persistent, but gradually deviated from the seizure-onset zone. (2) The characteristics of coupling strength of the inter- and pre-seizure EEG can be used to accurately locate the epileptogenic zone, which is better than that in periods after the beginning of a seizure. (3) In an epileptic seizure, the preferred phases of coupling were usually in the rising branches at the pre- and early-seizure stages, while those in the middle- and terminal-seizure were usually in the falling branch. We thus speculate that the coupling occurred in the rising branch can promote the recruitment of abnormal discharge, while the coupling occurred in the falling branch can inhibit the abnormal discharge. Conclusion: The findings suggest that the phase-amplitude coupling during inter- and pre-seizure is a promising marker of epileptic focus location. The preferred phase of coupling changed regularly with the time of epileptic seizure, suggesting that the surge and suppression of abnormal discharges are related to different phases.
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Affiliation(s)
- Huijuan Ma
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Zeyu Wang
- Department of Biomedical Engineering, Shenyan University of Technology, Shenyang, China
| | - Chunsheng Li
- Department of Biomedical Engineering, Shenyan University of Technology, Shenyang, China
| | - Jia Chen
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neuromodulation, Beijing, China.,Center of Epilepsy, Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Yuping Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neuromodulation, Beijing, China.,Center of Epilepsy, Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
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Migliorelli C, Romero S, Bachiller A, Aparicio J, Alonso JF, Mañanas MA, Antonio-Arce VS. Improving the ripple classification in focal pediatric epilepsy: identifying pathological high-frequency oscillations by Gaussian mixture model clustering. J Neural Eng 2021; 18. [PMID: 34384061 DOI: 10.1088/1741-2552/ac1d31] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 08/12/2021] [Indexed: 11/11/2022]
Abstract
Objective. High-frequency oscillations (HFOs) have emerged as a promising clinical biomarker for presurgical evaluation in childhood epilepsy. HFOs are commonly classified in stereo-encephalography as ripples (80-200 Hz) and fast ripples (200-500 Hz). Ripples are less specific and not so directly associated with epileptogenic activity because of their physiological and pathological origin. The aim of this paper is to distinguish HFOs in the ripple band and to improve the evaluation of the epileptogenic zone (EZ).Approach. This study constitutes a novel modeling approach evaluated in ten patients from Sant Joan de Deu Pediatric Hospital (Barcelona, Spain), with clearly-defined seizure onset zones (SOZ) during presurgical evaluation. A subject-by-subject basis analysis is proposed: a probabilistic Gaussian mixture model (GMM) based on the combination of specific ripple features is applied for estimating physiological and pathological ripple subpopulations.Main Results. Clear pathological and physiological ripples are identified. Features differ considerably among patients showing within-subject variability, suggesting that individual models are more appropriate than a traditional whole-population approach. The difference in rates inside and outside the SOZ for pathological ripples is significantly higher than when considering all the ripples. These significant differences also appear in signal segments without epileptiform activity. Pathological ripple rates show a sharp decline from SOZ to non-SOZ contacts and a gradual decrease with distance.Significance. This novel individual GMM approach improves ripple classification and helps to refine the delineation of the EZ, as well as being appropriate to investigate the interaction of epileptogenic and propagation networks.
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Affiliation(s)
- Carolina Migliorelli
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain.,Universitat Politecnica de Catalunya, Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Barcelona, Spain.,Institut de recerca pediatrica Hospital Sant Joan de Déu, Barcelona, Spain
| | - Sergio Romero
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain.,Universitat Politecnica de Catalunya, Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Barcelona, Spain.,Institut de recerca pediatrica Hospital Sant Joan de Déu, Barcelona, Spain
| | - Alejandro Bachiller
- Universitat Politecnica de Catalunya, Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Barcelona, Spain.,Institut de recerca pediatrica Hospital Sant Joan de Déu, Barcelona, Spain
| | - Javier Aparicio
- Universitary Hospital Sant Joan de Déu, Epilepsy Unit. Department of Neuropediatrics (member of the European Reference Network for rare and complex epilepsies EpiCARE), Barcelona, Spain
| | - Joan F Alonso
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain.,Universitat Politecnica de Catalunya, Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Barcelona, Spain.,Institut de recerca pediatrica Hospital Sant Joan de Déu, Barcelona, Spain
| | - Miguel A Mañanas
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain.,Universitat Politecnica de Catalunya, Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Barcelona, Spain.,Institut de recerca pediatrica Hospital Sant Joan de Déu, Barcelona, Spain
| | - Victoria San Antonio-Arce
- Universitary Hospital Sant Joan de Déu, Epilepsy Unit. Department of Neuropediatrics (member of the European Reference Network for rare and complex epilepsies EpiCARE), Barcelona, Spain.,Freiburg Epilepsy Center, Medical Center-University of Freiburg, Faculty of Medicine (member of the European Reference Network for rare and complex epilepsies EpiCARE), Freiburg, Germany
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Li C, Sohrabpour A, Jiang H, He B. High-Frequency Hubs of the Ictal Cross-Frequency Coupling Network Predict Surgical Outcome in Epilepsy Patients. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1290-1299. [PMID: 34191730 DOI: 10.1109/tnsre.2021.3093703] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Seizure generation is thought to be a process driven by epileptogenic networks; thus, network analysis tools can help determine the efficacy of epilepsy treatment. Studies have suggested that low-frequency (LF) to high-frequency (HF) cross-frequency coupling (CFC) is a useful biomarker for localizing epileptogenic tissues. However, it remains unclear whether the LF or HF coordinated CFC network hubs are more critical in determining the treatment outcome. We hypothesize that HF hubs are primarily responsible for seizure dynamics. Stereo-electroencephalography (SEEG) recordings of 36 seizures from 16 intractable epilepsy patients were analyzed. We propose a new approach to model the temporal-spatial-spectral dynamics of CFC networks. Graph measures are then used to characterize the HF and LF hubs. In the patient group with Engel Class (EC) I outcome, the strength of HF hubs was significantly higher inside the resected regions during the early and middle stages of seizure, while such a significant difference was not observed in the EC III group and only for the early stage in the EC II group. For the LF hubs, a significant difference was identified at the late stage and only in the EC I group. Our findings suggest that HF hubs increase at early and middle stages of the ictal interval while LF hubs increase activity at the late stages. In addition, HF hubs can predict treatment outcomes more precisely, compared to the LF hubs of the CFC network. The proposed method promises to identify more accurate targets for surgical interventions or neuromodulation therapies.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Suzuki H, Otsubo H, Yokota N, Nishijima S, Go C, Carter Snead O, Ochi A, Rutka JT, Moharir M. Epileptogenic modulation index and synchronization in hypsarrhythmia of West syndrome secondary to perinatal arterial ischemic stroke. Clin Neurophysiol 2021; 132:1185-1193. [PMID: 33674213 DOI: 10.1016/j.clinph.2020.12.028] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 11/30/2020] [Accepted: 12/14/2020] [Indexed: 11/24/2022]
Abstract
OBJECTIVE Perinatal arterial ischemic stroke (PAIS) is associated with epileptic spasms of West syndrome (WS) and long term Focal epilepsy (FE). The mechanism of epileptogenic network generation causing hypsarrhythmia of WS is unknown. We hypothesized that Modulation index (MI) [strength of phase-amplitude coupling] and Synchronization likelihood (SL) [degree of connectivity] could interrogate the epileptogenic network in hypsarrhythmia of WS secondary to PAIS. METHODS We analyzed interictal scalp electroencephalography (EEG) in 10 WS and 11 FE patients with unilateral PAIS. MI between gamma (30-70 Hz) and slow waves (3-4 Hz) was calculated to measure phase-amplitude coupling. SL between electrode pairs was analyzed in 9-frequency bands (5-delta, theta, alpha, beta, gamma) to examine inter- and intra-hemispheric connectivity. RESULTS MI was higher in affected hemispheres in WS (p = 0.006); no differences observed in FE. Inter-hemispheric SL of 3-delta, theta, alpha, beta, gamma bands was significantly higher in WS (p < 0.001). In WS, modified Z-Score of intra-hemispheric SL values in 3-delta, theta, alpha, beta and gamma in the affected hemispheres were significantly higher than those in the unaffected hemispheres (p < 0.001) as well as 0.5-4 Hz (p = 0.004). CONCLUSIONS The significantly higher modulation in affected hemisphere and stronger inter- and intra-hemispheric connectivity generate hypsarrhythmia of WS secondary to PAIS. SIGNIFICANCE Epileptogenic cortical-subcortical transcallosal networks from affected hemisphere post-PAIS provokes infantile spasms.
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Affiliation(s)
- Hiroharu Suzuki
- Division of Neurology, Department of Pediatrics, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada; Department of Neurosurgery, Juntendo University, 2-1-1, Hongo, Bunkyo-ku, Tokyo 113-8421, Japan.
| | - Hiroshi Otsubo
- Division of Neurology, Department of Pediatrics, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada.
| | - Nanako Yokota
- Division of Neurology, Department of Pediatrics, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada.
| | - Sakura Nishijima
- Division of Neurology, Department of Pediatrics, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada.
| | - Cristina Go
- Division of Neurology, Department of Pediatrics, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada.
| | - O Carter Snead
- Division of Neurology, Department of Pediatrics, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada.
| | - Ayako Ochi
- Division of Neurology, Department of Pediatrics, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada.
| | - James T Rutka
- Division of Neurosurgery, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada.
| | - Mahendranath Moharir
- Division of Neurology, Department of Pediatrics, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada; Children's Stroke Program, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada.
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37
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Uda T, Kuki I, Inoue T, Kunihiro N, Suzuki H, Uda H, Kawashima T, Nakajo K, Nakanishi Y, Maruyama S, Shibata T, Ogawa H, Okazaki S, Kawawaki H, Ohata K, Goto T, Otsubo H. Phase-amplitude coupling of interictal fast activities modulated by slow waves on scalp EEG and its correlation with seizure outcomes of disconnection surgery in children with intractable nonlesional epileptic spasms. J Neurosurg Pediatr 2021; 27:572-580. [PMID: 33636702 DOI: 10.3171/2020.9.peds20520] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 09/04/2020] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Epileptic spasms (ESs) are classified as focal, generalized, or unknown onset ESs. The classification of ESs and surgery in patients without lesions apparent on MRI is challenging. Total corpus callosotomy (TCC) is a surgical option for diagnosis of the lateralization and possible treatment for ESs. This study investigated phase-amplitude coupling (PAC) of fast activity modulated by slow waves on scalp electroencephalography (EEG) to evaluate the strength of the modulation index (MI) before and after disconnection surgery in children with intractable nonlesional ESs. The authors hypothesize that a decreased MI due to surgery correlates with good seizure outcomes. METHODS The authors studied 10 children with ESs without lesions on MRI who underwent disconnection surgeries. Scalp EEG was obtained before and after surgery. The authors collected 20 epochs of 3 minutes each during non-rapid eye movement sleep. The MI of the gamma (30-70 Hz) amplitude and delta (0.5-4 Hz) phase was obtained in each electrode. MIs for each electrode were averaged in 4 brain areas (left/right, anterior/posterior quadrants) and evaluated to determine the correlation with seizure outcomes. RESULTS The median age at first surgery was 2.3 years (range 10 months-9.1 years). Two patients with focal onset ESs underwent anterior quadrant disconnection (AQD). TCC alone was performed in 5 patients with generalized or unknown onset ESs. Two patients achieved seizure freedom. Three patients had residual generalized onset ESs. Disconnection surgeries in addition to TCC consisted of TCC + posterior quadrant disconnection (PQD) (1 patient); TCC + AQD + PQD (1 patient); and TCC + AQD + hemispherotomy (1 patient). Seven patients became seizure free with a mean follow-up period of 28 months (range 5-54 months). After TCC, MIs in 4 quadrants were significantly lower in the 2 seizure-free patients than in the 6 patients with residual ESs (p < 0.001). After all 15 disconnection surgeries in 10 patients, MIs in the 13 target quadrants for each disconnection surgery that resulted in freedom from seizures were significantly lower than in the 26 target quadrants in patients with residual ESs (p < 0.001). CONCLUSIONS In children with nonlesional ESs, PAC for scalp EEG before and after disconnection surgery may be a surrogate marker for control of ESs. The MI may indicate epileptogenic neuronal modulation of the interhemispheric corpus callosum and intrahemispheric subcortical network for ESs. TCC may be a therapeutic option to disconnect the interhemispheric modulation of epileptic networks.
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Affiliation(s)
- Takehiro Uda
- 1Department of Neurosurgery, Osaka City University Graduate School of Medicine.,Departments of2Pediatric Neurosurgery and
| | - Ichiro Kuki
- 3Pediatric Neurology, Osaka City General Hospital, Osaka, Japan; and
| | - Takeshi Inoue
- 3Pediatric Neurology, Osaka City General Hospital, Osaka, Japan; and
| | | | - Hiroharu Suzuki
- 4Department of Neurology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Hiroshi Uda
- 1Department of Neurosurgery, Osaka City University Graduate School of Medicine.,Departments of2Pediatric Neurosurgery and
| | - Toshiyuki Kawashima
- 1Department of Neurosurgery, Osaka City University Graduate School of Medicine
| | - Kosuke Nakajo
- 1Department of Neurosurgery, Osaka City University Graduate School of Medicine
| | | | - Shinsuke Maruyama
- 4Department of Neurology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Takashi Shibata
- 4Department of Neurology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Hiroshi Ogawa
- 4Department of Neurology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Shin Okazaki
- 3Pediatric Neurology, Osaka City General Hospital, Osaka, Japan; and
| | - Hisashi Kawawaki
- 3Pediatric Neurology, Osaka City General Hospital, Osaka, Japan; and
| | - Kenji Ohata
- 1Department of Neurosurgery, Osaka City University Graduate School of Medicine
| | - Takeo Goto
- 1Department of Neurosurgery, Osaka City University Graduate School of Medicine
| | - Hiroshi Otsubo
- 4Department of Neurology, The Hospital for Sick Children, Toronto, Ontario, Canada
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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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Goodale SE, González HFJ, Johnson GW, Gupta K, Rodriguez WJ, Shults R, Rogers BP, Rolston JD, Dawant BM, Morgan VL, Englot DJ. Resting-State SEEG May Help Localize Epileptogenic Brain Regions. Neurosurgery 2020; 86:792-801. [PMID: 31814011 DOI: 10.1093/neuros/nyz351] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 06/18/2019] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Stereotactic electroencephalography (SEEG) is a minimally invasive neurosurgical method to localize epileptogenic brain regions in epilepsy but requires days in the hospital with interventions to trigger several seizures. OBJECTIVE To make initial progress in the development of network analysis methods to identify epileptogenic brain regions using brief, resting-state SEEG data segments, without requiring seizure recordings. METHODS In a cohort of 15 adult focal epilepsy patients undergoing SEEG, we evaluated functional connectivity (alpha-band imaginary coherence) across sampled regions using brief (2 min) resting-state data segments. Bootstrapped logistic regression was used to generate a model to predict epileptogenicity of individual regions. RESULTS Compared to nonepileptogenic structures, we found increased functional connectivity within epileptogenic regions (P < .05) and between epileptogenic areas and other structures (P < .01, paired t-tests, corrected). Epileptogenic areas also demonstrated higher clustering coefficient (P < .01) and betweenness centrality (P < .01), and greater decay of functional connectivity with distance (P < .05, paired t-tests, corrected). Our functional connectivity model to predict epileptogenicity of individual regions demonstrated an area under the curve of 0.78 and accuracy of 80.4%. CONCLUSION Our study represents a preliminary step towards defining resting-state SEEG functional connectivity patterns to help localize epileptogenic brain regions ahead of neurosurgical treatment without requiring seizure recordings.
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Affiliation(s)
- Sarah E Goodale
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
| | - Hernán F J González
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
| | - Graham W Johnson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
| | - Kanupriya Gupta
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee
| | - William J Rodriguez
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee
| | - Robert Shults
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Baxter P Rogers
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - John D Rolston
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah
| | - Benoit M Dawant
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee.,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee
| | - Victoria L Morgan
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee.,Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Dario J Englot
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
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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: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Nariai H, Hussain SA, Bernardo D, Motoi H, Sonoda M, Kuroda N, Asano E, Nguyen JC, Elashoff D, Sankar R, Bragin A, Staba RJ, Wu JY. Scalp EEG interictal high frequency oscillations as an objective biomarker of infantile spasms. Clin Neurophysiol 2020; 131:2527-2536. [PMID: 32927206 DOI: 10.1016/j.clinph.2020.08.013] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 07/25/2020] [Accepted: 08/04/2020] [Indexed: 12/21/2022]
Abstract
OBJECTIVE To investigate the diagnostic utility of high frequency oscillations (HFOs) via scalp electroencephalogram (EEG) in infantile spasms. METHODS We retrospectively analyzed interictal slow-wave sleep EEGs sampled at 2,000 Hz recorded from 30 consecutive patients who were suspected of having infantile spasms. We measured the rate of HFOs (80-500 Hz) and the strength of the cross-frequency coupling between HFOs and slow-wave activity (SWA) at 3-4 Hz and 0.5-1 Hz as quantified with modulation indices (MIs). RESULTS Twenty-three patients (77%) exhibited active spasms during the overnight EEG recording. Although the HFOs were detected in all children, increased HFO rate and MIs correlated with the presence of active spasms (p < 0.001 by HFO rate; p < 0.01 by MIs at 3-4 Hz; p = 0.02 by MIs at 0.5-1 Hz). The presence of active spasms was predicted by the logistic regression models incorporating HFO-related metrics (AUC: 0.80-0.98) better than that incorporating hypsarrhythmia (AUC: 0.61). The predictive performance of the best model remained favorable (87.5% accuracy) after a cross-validation procedure. CONCLUSIONS Increased rate of HFOs and coupling between HFOs and SWA are associated with active epileptic spasms. SIGNIFICANCE Scalp-recorded HFOs may serve as an objective EEG biomarker for active epileptic spasms.
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Affiliation(s)
- Hiroki Nariai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, California, USA.
| | - Shaun A Hussain
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, California, USA
| | - Danilo Bernardo
- Department of Neurology, Division of Epilepsy, University of California, San Francisco, San Francisco, CA, USA
| | - Hirotaka Motoi
- Department of Pediatrics and Neurology, Children's Hospital of Michigan, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Masaki Sonoda
- Department of Pediatrics and Neurology, Children's Hospital of Michigan, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Naoto Kuroda
- Department of Pediatrics and Neurology, Children's Hospital of Michigan, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Eishi Asano
- Department of Pediatrics and Neurology, Children's Hospital of Michigan, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Jimmy C Nguyen
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, California, USA
| | - David Elashoff
- Department of Medicine, Statistics Core, University of California, Los Angeles, Los Angeles, California, USA
| | - Raman Sankar
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, California, USA
| | - Anatol Bragin
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, California, USA
| | - Richard J Staba
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, California, USA
| | - Joyce Y Wu
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, California, USA
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Ren G, Yan J, Sun Y, Ren J, Dai J, Mei S, Li Y, Wang X, Yang X, Wang Q. Association Between Interictal High-Frequency Oscillations and Slow Wave in Refractory Focal Epilepsy With Good Surgical Outcome. Front Hum Neurosci 2020; 14:335. [PMID: 33005137 PMCID: PMC7479180 DOI: 10.3389/fnhum.2020.00335] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Accepted: 07/29/2020] [Indexed: 11/13/2022] Open
Abstract
High-frequency oscillations (HFOs) have been proposed as a promising biomarker of the epileptogenic zone (EZ). But accurate delineation of EZ based on HFOs is still challenging. Our study compared HFOs from EZ and non-EZ on the basis of their associations with interictal slow waves, aiming at exploring a new way to localize EZ. Nineteen medically intractable epilepsy patients with good surgical outcome were included. Five minute interictal intracranial electroencephalography (EEG) epochs of slow-wave sleep were randomly selected; then ripples (80–200 Hz), fast ripples (FRs; 200–500 Hz), and slow waves (0.1–4 Hz) were automatically analyzed. The EZ and non-EZ were identified by resection range during the surgeries. We found that both ripples and FRs superimposed more frequently on slow waves in EZ than in non-EZ (P < 0.01). Although ripples preferred to occur on the down state of slow waves in both two groups, ripples in EZ tended to be closer to the down-state peak of slow wave than in non-EZ (-174 vs. -231 ms, P = 0.008). As for FR, no statistical difference was found between the two groups (P = 0.430). Additionally, slow wave-containing ripples in EZ had a steeper slope (1.7 vs. 1.5 μV/ms, P < 0.001) and wider distribution ratio (32.3 vs. 30.1%, P < 0.001) than those in the non-EZ. But for slow wave-containing FR, only a steeper slope (1.7 vs. 1.4 μV/ms, P < 0.001) was observed. Our study innovatively compared the different features of association between HFOs and slow wave in EZ and non-EZ from refractory focal epilepsy with good surgical outcome, proposing a new method to localize EZ and facilitating the surgical plan.
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Affiliation(s)
- Guoping Ren
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jiaqing Yan
- College of Electrical and Control Engineering, North China University of Technology, Beijing, China
| | - Yueqian Sun
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Laboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Capital Medical University, Ministry of Science and Technology, Beijing, China
| | - Jiechuan Ren
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jindong Dai
- Department of Functional Neurosurgery, Beijing Haidian Hospital, Beijing, China
| | - Shanshan Mei
- Department of Functional Neurosurgery, Beijing Haidian Hospital, Beijing, China
| | - Yunlin Li
- Department of Functional Neurosurgery, Beijing Haidian Hospital, Beijing, China
| | - Xiaofei Wang
- Department of Functional Neurosurgery, Beijing Haidian Hospital, Beijing, China
| | - Xiaofeng Yang
- Laboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Capital Medical University, Ministry of Science and Technology, Beijing, China.,Neuroelectrophysiological Laboratory, Xuanwu Hospital, Capital Medical University, Beijing, China.,Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Qun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Laboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Capital Medical University, Ministry of Science and Technology, Beijing, China
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Shibata T, Otsubo H. Phase-amplitude coupling of delta brush unveiling neuronal modulation development in the neonatal brain. Neurosci Lett 2020; 735:135211. [PMID: 32593774 DOI: 10.1016/j.neulet.2020.135211] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 06/15/2020] [Accepted: 06/24/2020] [Indexed: 10/24/2022]
Abstract
INTRODUCTION Delta brushes are an indicator of brain maturity on a neonatal EEG. We investigated phase-amplitude coupling (PAC) between slow delta waves and superimposed alpha-beta activity in delta brushes to elucidate the spatiotemporal developments of the delta brush with post-menstrual weeks (PMW). METHODS The subjects were 18 neurologically intact patients (seven girls). We analyzed EEG within 42 PMW. Patients were divided into four age groups as follows: PMW ≤30w; 31-34 w; 35-38 w; and 39-42 w. We selected up to three epochs of 2-minute EEG segments including delta brushes. We calculated the modulation index (MI), direct mean vector length (dMVL), and mean of phase angle of coupling by PAC between slow waves (0.5-1.5 Hz) and fast activities (8-25 Hz) in four regions (F: Fp1 and Fp2, C: C3 and C4, T: T3 and T4, O: O1 and O2). RESULTS We collected data from 18 patients and 31 epochs between 29 and 42 PMW, which comprised one, four, five, and eight patients, and two, seven, eight, and 14 epochs in the ≤30w, 31-34 w, 35-38 w, and 39-42 w groups, respectively. There were significant differences in the dMVL between the four regions in age groups ≤30w (P = 0.033) and 31-34w (0.017). Both MI and dMVL showed that delta brushes became higher in the occipital region from 32 to 36 PMW. The mean phase angle of coupling concentrated around either 0° or 180° for all age groups. CONCLUSIONS PAC analysis revealed the spatiotemporal relations of alpha-beta activities that are modulated by slow delta waves in neonatal delta brushes. The delta brushes appeared to be at a maximum around 32-36 PMW with the predominant occipital distribution. The PAC of the delta brush might represent the cortical neuronal fast activity that is modulated by slow delta waves of subcortical regions during a particular neonatal period.
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Affiliation(s)
- Takashi Shibata
- Division of Neurology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Hiroshi Otsubo
- Division of Neurology, The Hospital for Sick Children, Toronto, Ontario, Canada.
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Wang Y, Zhou D, Yang X, Xu X, Ren L, Yu T, Zhou W, Shao X, Yang Z, Wang S, Cao D, Liu C, Kwan SY, Xiang J. Expert consensus on clinical applications of high-frequency oscillations in epilepsy. Acta Epileptologica 2020. [DOI: 10.1186/s42494-020-00018-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
AbstractStudies in animal models of epilepsy and pre-surgical patients have unanimously found a strong correlation between high-frequency oscillations (HFOs, > 80 Hz) and the epileptogenic zone, suggesting that HFOs can be a potential biomarker of epileptogenicity and epileptogenesis. This consensus includes the definition and standard detection techniques of HFOs, the localizing value of pathological HFOs for epileptic foci, and different ways to distinguish physiological from epileptic HFOs. The latest clinical applications of HFOs in epilepsy and the related findings are also discussed. HFOs will advance our understanding of the pathophysiology of epilepsy.
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Dellavale D, Urdapilleta E, Cámpora N, Velarde OM, Kochen S, Mato G. Two types of ictal phase-amplitude couplings in epilepsy patients revealed by spectral harmonicity of intracerebral EEG recordings. Clin Neurophysiol 2020; 131:1866-1885. [PMID: 32580114 DOI: 10.1016/j.clinph.2020.04.160] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 03/27/2020] [Accepted: 04/05/2020] [Indexed: 01/04/2023]
Abstract
OBJECTIVE Spectral harmonicity of the ictal activity was analyzed regarding two clinically relevant aspects, (1) as a confounding factor producing 'spurious' phase-amplitude couplings (PAC) which may lead to wrong conclusions about the underlying ictal mechanisms, and (2) its role in how good PAC is in correspondence to the seizure onset zone (SOZ) classification performed by the epileptologists. METHODS PAC patterns observed in intracerebral electroencephalography (iEEG) recordings were retrospectively studied during seizures of seven patients with pharmacoresistant focal epilepsy. The time locked index (TLI) measure was introduced to quantify the degree of harmonicity between frequency bands associated to the emergence of PAC during epileptic seizures. RESULTS (1) Harmonic and non harmonic PAC patterns coexist during the seizure dynamics in iEEG recordings with macroelectrodes. (2) Harmonic PAC patterns are an emergent property of the periodic non sinusoidal waveform constituting the epileptiform activity. (3) The TLI metric allows to distinguish the non harmonic PAC pattern, which has been previously associated with the ictal core through the paroxysmal depolarizing shifts mechanism of seizure propagation. CONCLUSIONS Our results suggest that the spectral harmonicity of the ictal activity plays a relevant role in the visual analysis of the iEEG recordings performed by the epileptologists to define the SOZ, and that it should be considered for the proper interpretation of ictal mechanisms. SIGNIFICANCE The proposed harmonicity analysis can be used to improve the delineation of the SOZ by reliably identifying non harmonic PAC patterns emerging from fully recruited cortical and subcortical areas.
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Affiliation(s)
- Damián Dellavale
- Centro Atómico Bariloche and Instituto Balseiro, Comisión Nacional de Energía Atómica (CNEA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Cuyo (UNCUYO), Av. E. Bustillo 9500, R8402AGP San Carlos de Bariloche, Río Negro, Argentina.
| | - Eugenio Urdapilleta
- Centro Atómico Bariloche and Instituto Balseiro, Comisión Nacional de Energía Atómica (CNEA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Cuyo (UNCUYO), Av. E. Bustillo 9500, R8402AGP San Carlos de Bariloche, Río Negro, Argentina
| | - Nuria Cámpora
- Neurosciences and Complex Systems Unit (EnyS), El Cruce "N. Kirchner" Hosp., UNAJ, Epilepsy Center, "R. Mejía" Hosp., Faculty of Medicine, UBA, CONICET, Argentina
| | - Osvaldo Matías Velarde
- Centro Atómico Bariloche and Instituto Balseiro, Comisión Nacional de Energía Atómica (CNEA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Cuyo (UNCUYO), Av. E. Bustillo 9500, R8402AGP San Carlos de Bariloche, Río Negro, Argentina
| | - Silvia Kochen
- Neurosciences and Complex Systems Unit (EnyS), El Cruce "N. Kirchner" Hosp., UNAJ, Epilepsy Center, "R. Mejía" Hosp., Faculty of Medicine, UBA, CONICET, Argentina.
| | - Germán Mato
- Centro Atómico Bariloche and Instituto Balseiro, Comisión Nacional de Energía Atómica (CNEA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Cuyo (UNCUYO), Av. E. Bustillo 9500, R8402AGP San Carlos de Bariloche, Río Negro, Argentina.
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Silverstein BH, Asano E, Sugiura A, Sonoda M, Lee MH, Jeong JW. Dynamic tractography: Integrating cortico-cortical evoked potentials and diffusion imaging. Neuroimage 2020; 215:116763. [PMID: 32294537 DOI: 10.1016/j.neuroimage.2020.116763] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 03/02/2020] [Accepted: 03/17/2020] [Indexed: 02/03/2023] Open
Abstract
INTRODUCTION Cortico-cortical evoked potentials (CCEPs) are utilized to identify effective networks in the human brain. Following single-pulse electrical stimulation of cortical electrodes, evoked responses are recorded from distant cortical areas. A negative deflection (N1) which occurs 10-50 ms post-stimulus is considered to be a marker for direct cortico-cortical connectivity. However, with CCEPs alone it is not possible to observe the white matter pathways that conduct the signal or accurately predict N1 amplitude and latency at downstream recoding sites. Here, we develop a new approach, termed "dynamic tractography," which integrates CCEP data with diffusion-weighted imaging (DWI) data collected from the same patients. This innovative method allows greater insights into cortico-cortical networks than provided by each method alone and may improve the understanding of large-scale networks that support cognitive functions. The dynamic tractography model produces several fundamental hypotheses which we investigate: 1) DWI-based pathlength predicts N1 latency; 2) DWI-based pathlength negatively predicts N1 voltage; and 3) fractional anisotropy (FA) along the white matter path predicts N1 propagation velocity. METHODS Twenty-three neurosurgical patients with drug-resistant epilepsy underwent both extraoperative CCEP recordings and preoperative DWI scans. Subdural grids of 3 mm diameter electrodes were used for stimulation and recording, with 98-128 eligible electrodes per patient. CCEPs were elicited by trains of 1 Hz stimuli with an intensity of 5 mA and recorded at a sample rate of 1 kHz. N1 peak and latency were defined as the maximum of a negative deflection within 10-50 ms post-stimulus with a z-score > 5 relative to baseline. Electrodes and DWI were coregistered to construct electrode connectomes for white matter quantification. RESULTS Clinical variables (age, sex, number of anti-epileptic drugs, handedness, and stimulated hemisphere) did not correlate with the key outcome measures (N1 peak amplitude, latency, velocity, or DWI pathlength). All subjects and electrodes were therefore pooled into a group-level analysis to determine overall patterns. As hypothesized, DWI path length positively predicted N1 latency (R2 = 0.81, β = 1.51, p = 4.76e-16) and negatively predicted N1 voltage (R2 = 0.79, β = -0.094, p = 9.30e-15), while FA predicted N1 propagation velocity (R2 = 0.35, β = 48.0, p = 0.001). CONCLUSION We have demonstrated that the strength and timing of the CCEP N1 is dependent on the properties of the underlying white matter network. Integrated CCEP and DWI visualization allows robust localization of intact axonal pathways which effectively interconnect eloquent cortex.
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Affiliation(s)
- Brian H Silverstein
- Translational Neuroscience Program, Wayne State University, Detroit, MI, USA
| | - Eishi Asano
- Translational Neuroscience Program, Wayne State University, Detroit, MI, USA; Dept. of Pediatrics, Wayne State University, Children's Hospital of Michigan, Detroit, MI, USA; Dept. of Neurology, Wayne State University, Children's Hospital of Michigan, Detroit, MI, USA
| | - Ayaka Sugiura
- Dept. of Pediatrics, Wayne State University, Children's Hospital of Michigan, Detroit, MI, USA
| | - Masaki Sonoda
- Dept. of Pediatrics, Wayne State University, Children's Hospital of Michigan, Detroit, MI, USA
| | - Min-Hee Lee
- Dept. of Pediatrics, Wayne State University, Children's Hospital of Michigan, Detroit, MI, USA; Translational Imaging Laboratory, Wayne State University, Detroit, MI, USA
| | - Jeong-Won Jeong
- Translational Neuroscience Program, Wayne State University, Detroit, MI, USA; Dept. of Pediatrics, Wayne State University, Children's Hospital of Michigan, Detroit, MI, USA; Dept. of Neurology, Wayne State University, Children's Hospital of Michigan, Detroit, MI, USA; Translational Imaging Laboratory, Wayne State University, Detroit, MI, USA.
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47
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Bernardo D, Nariai H, Hussain SA, Sankar R, Wu JY. Interictal scalp fast ripple occurrence and high frequency oscillation slow wave coupling in epileptic spasms. Clin Neurophysiol 2020; 131:1433-43. [PMID: 32387963 DOI: 10.1016/j.clinph.2020.03.025] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 02/27/2020] [Accepted: 03/12/2020] [Indexed: 12/26/2022]
Abstract
OBJECTIVE Intracranial high frequency oscillation (HFO) occurrence rate (OR) and slow wave activity (SWA) coupling are potential markers of epileptogenicity in epileptic spasms (ES). Scalp ripple (R) detection and SWA coupling have been described in ES; however, the feasibility of scalp fast ripple (FR) detection and measurement of scalp FR coupling to SWA is not known. We evaluated interictal scalp R and FR OR and SWA coupling in pre-treatment EEG in children with short-term treatment-refractory ES compared to short-term treatment non-refractory ES. METHODS We retrospectively identified children with ES and identified HFOs using a semi-automated HFO detector on pre-treatment scalp EEG during sleep. We evaluated HFO OR and event-triggered modulation index (MI) to quantify R (100-250 Hz) and FR (250-600 Hz) coupling strength with different SWA passbands (0.5-1, 1-2, 2-3, 3-4, and 4-8 Hz). We used HFO phasor transform and circular statistics to evaluate phase coupling angle distributions. RESULTS We identified 15 children with ES with pre-treatment EEG recorded at 2000 Hz. Thirteen out of 15 patients had HFOs and were included for analysis. There were six treatment responders and seven nonresponders three months after treatment initiation. Responders and nonresponders were similar in age (6.1 vs 7.2 mo), ES diagnosis duration (0.7 vs 2.6 mo), and HFO OR (R: 1.07 vs 2.30/min, FR: 0.43 vs 1.96/min). No differences between responders and nonresponders were seen in HFO MI at different SWA. Coupling of R and FR to 2-3 Hz SWA demonstrated increased incidence rate ratio in nonresponders relative to responders at distinct phase coupling angle distributions. CONCLUSIONS This study demonstrates the feasibility of interictal scalp R and FR detection and quantification of scalp R and FR coupling to SWA in ES. SIGNIFICANCE HFO phase coupling with SWA may be useful as a marker of potential treatment refractoriness in patients with ES.
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Cámpora NE, Mininni CJ, Kochen S, Lew SE. Seizure localization using pre ictal phase-amplitude coupling in intracranial electroencephalography. Sci Rep 2019; 9:20022. [PMID: 31882956 PMCID: PMC6934586 DOI: 10.1038/s41598-019-56548-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 11/27/2019] [Indexed: 11/09/2022] Open
Abstract
Understanding changes in brain rhythms provides useful information to predict the onset of a seizure and to localize its onset zone in epileptic patients. Brain rhythms dynamics in general, and phase-amplitude coupling in particular, are known to be drastically altered during epileptic seizures. However, the neural processes that take place before a seizure are not well understood. We analysed the phase-amplitude coupling dynamics of stereoelectroencephalography recordings (30 seizures, 5 patients) before and after seizure onset. Electrodes near the seizure onset zone showed higher phase-amplitude coupling. Immediately before the beginning of the seizure, phase-amplitude coupling dropped to values similar to the observed in electrodes far from the seizure onset zone. Thus, our results bring accurate information to detect epileptic events during pre-ictal periods and to delimit the zone of seizure onset in patients undergoing epilepsy surgery.
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Affiliation(s)
- Nuria E Cámpora
- Instituto de Ingeniería Biomédica, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Camilo J Mininni
- Instituto de Biología y Medicina Experimental - CONICET, Buenos Aires, Argentina
| | - Silvia Kochen
- Centro de Epilepsia, Hospital Ramos Mejía. Estudio en Neurociencias y Sistemas Complejos (ENyS),CONICET. Universidad de Buenos Aires, Buenos Aires, Argentina.
| | - Sergio E Lew
- Instituto de Ingeniería Biomédica, Universidad de Buenos Aires, Buenos Aires, Argentina.
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Mooij AH, Frauscher B, Gotman J, Huiskamp GJM. A skew-based method for identifying intracranial EEG channels with epileptic activity without detecting spikes, ripples, or fast ripples. Clin Neurophysiol 2019; 131:183-192. [PMID: 31805492 DOI: 10.1016/j.clinph.2019.10.025] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 09/11/2019] [Accepted: 10/16/2019] [Indexed: 02/04/2023]
Abstract
OBJECTIVE To develop a method for identifying intracranial EEG (iEEG) channels with epileptic activity without the need to detect spikes, ripples, or fast ripples. METHODS We compared the skew of the distribution of power values from five minutes non-rapid eye movement stage N3 sleep for the 5-80 Hz, 80-250 Hz (ripple), and 250-500 Hz (fast ripple) bands of epileptic (located in seizure-onset or irritative zone) and non-epileptic iEEG channels recorded in patients with drug-resistant focal epilepsy. We optimized settings in 120 bipolar channels from 10 patients, compared the results to 120 channels from another 10 patients, and applied the method to channels of 12 individual patients. RESULTS The distribution of power values was more skewed in epileptic than in non-epileptic channels in all three frequency bands. The differences in skew were correlated with the presence of spikes, ripples, and fast ripples. When classifying epileptic and non-epileptic channels, the mean accuracy over 12 patients was 0.82 (sensitivity: 0.76, specificity: 0.91). CONCLUSIONS The 'skew method' can distinguish epileptic from non-epileptic channels with good accuracy and, in particular, high specificity. SIGNIFICANCE This is an easy-to-apply method that circumvents the need to visually mark or automatically detect interictal epileptic events.
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Affiliation(s)
- Anne H Mooij
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A 2B4, Canada
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A 2B4, Canada
| | - Geertjan J M Huiskamp
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
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Motoi H, Jeong JW, Juhász C, Miyakoshi M, Nakai Y, Sugiura A, Luat AF, Sood S, Asano E. Quantitative analysis of intracranial electrocorticography signals using the concept of statistical parametric mapping. Sci Rep 2019; 9:17385. [PMID: 31758022 PMCID: PMC6874664 DOI: 10.1038/s41598-019-53749-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 11/04/2019] [Indexed: 11/23/2022] Open
Abstract
Statistical parametric mapping (SPM) is a technique with which one can delineate brain activity statistically deviated from the normative mean, and has been commonly employed in noninvasive neuroimaging and EEG studies. Using the concept of SPM, we developed a novel technique for quantification of the statistical deviation of an intracranial electrocorticography (ECoG) measure from the nonepileptic mean. We validated this technique using data previously collected from 123 patients with drug-resistant epilepsy who underwent resective epilepsy surgery. We determined how the measurement of statistical deviation of modulation index (MI) from the non-epileptic mean (rated by z-score) improved the performance of seizure outcome classification model solely based on conventional clinical, seizure onset zone (SOZ), and neuroimaging variables. Here, MI is a summary measure quantifying the strength of in-situ coupling between high-frequency activity at >150 Hz and slow wave at 3-4 Hz. We initially generated a normative MI atlas showing the mean and standard deviation of slow-wave sleep MI of neighboring non-epileptic channels of 47 patients, whose ECoG sampling involved all four lobes. We then calculated 'MI z-score' at each electrode site. SOZ had a greater 'MI z-score' compared to non-SOZ in the remaining 76 patients. Subsequent multivariate logistic regression analysis and receiver operating characteristic analysis to the combined data of all patients revealed that the full regression model incorporating all predictor variables, including SOZ and 'MI z-score', best classified the seizure outcome with sensitivity/specificity of 0.86/0.76. The model excluding 'MI z-score' worsened its sensitivity/specificity to 0.86/0.48. Furthermore, the leave-one-out analysis successfully cross-validated the full regression model. Measurement of statistical deviation of MI from the non-epileptic mean on invasive recording is technically feasible. Our analytical technique can be used to evaluate the utility of ECoG biomarkers in epilepsy presurgical evaluation.
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Affiliation(s)
- Hirotaka Motoi
- Department of Pediatrics, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA
- Department of Pediatrics, Yokohama City University Medical Center, Yokohama, 2320024, Japan
| | - Jeong-Won Jeong
- Department of Pediatrics, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA
- Department of Neurology, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA
| | - Csaba Juhász
- Department of Pediatrics, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA
- Department of Neurology, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA
- Department of Neurosurgery, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA
| | - Makoto Miyakoshi
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA, 92093, USA
| | - Yasuo Nakai
- Department of Pediatrics, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA
| | - Ayaka Sugiura
- Department of Pediatrics, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA
| | - Aimee F Luat
- Department of Pediatrics, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA
- Department of Neurology, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA
| | - Sandeep Sood
- Department of Neurosurgery, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA
| | - Eishi Asano
- Department of Pediatrics, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA.
- Department of Neurology, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA.
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