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von Ellenrieder N, Alrashid M, Myers K, Osterman B, Simard-Tremblay E, Karamchandani J, Guiot MC, Gotman J, Dudley RWR. No association between histopathology and neurophysiology in surgical specimens from pediatric focal epilepsy patients. Epilepsia 2025. [PMID: 40372349 DOI: 10.1111/epi.18459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2025] [Revised: 04/29/2025] [Accepted: 04/30/2025] [Indexed: 05/16/2025]
Abstract
OBJECTIVE Focal epilepsy is caused by focal brain pathologies sometimes with involvement of surrounding or distant tissue. However, within the epileptogenic zone (EZ), which can be resected to cure epilepsy, the contribution of histopathological cells to epileptogenicity remains unknown. We hypothesized that areas showing neurophysiological biomarkers of epileptogenicity would more often contain histopathological cells compared to areas without such biomarkers. METHODS Pediatric epilepsy patients with nonlesional magnetic resonance imaging (MRI), or with lesions with unclear borders, underwent stereoelectroencephalographic (SEEG) exploration followed by resective surgery of the suspected EZ. Tissue specimens were taken from locations where the SEEG contacts had been, using intraoperative MRI-guided precise neuronavigation. Then, we explored the association between histopathology and rates of interictal epileptic discharges, ripples, and fast ripples (FRs), and the channels of the seizure onset zone (SOZ). RESULTS The association between histopathology and ictal/interictal activity was low and not statistically significant in 260 specimens from 20 surgeries. Rates of interictal events were slightly lower for pathological samples than in normal tissue (low effect size, Cliff |d| < .15, p > .1). The classification accuracy of tissue as normal or pathological based on interictal activity/SOZ was low (accuracy ≤ 54%). As a secondary outcome, SEEG events were excellent predictors of surgical outcome, FRs leading to perfect prediction (20/20). SIGNIFICANCE Histopathological tissue initiates epileptogenicity in focal epilepsy. However, our findings suggest that the EZ is not strictly a histopathological entity but a hybrid of abnormal cells and normal-appearing cells. Thus, SEEG events are biomarkers of epileptogenicity and not histopathology. Resecting of electrophysiological biomarkers of epileptogenicity may be more important than resecting all histopathology.
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Affiliation(s)
| | - Mariam Alrashid
- Department of Neurosurgery, King Fahd Hospital of the University, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Kenneth Myers
- Child Health and Human Development Program, Research Institute of McGill University Health Centre, Montreal, Quebec, Canada
- Division of Neurology, Department of Pediatrics, Montreal Children's Hospital, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University Health Centre, Montreal, Quebec, Canada
| | - Bradley Osterman
- Division of Neurology, Department of Pediatrics, Montreal Children's Hospital, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University Health Centre, Montreal, Quebec, Canada
| | - Elisabeth Simard-Tremblay
- Division of Neurology, Department of Pediatrics, Montreal Children's Hospital, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University Health Centre, Montreal, Quebec, Canada
| | - Jason Karamchandani
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
- Department of Pathology, Neurosurgery, and Neurology, McGill University, Montreal, Quebec, Canada
| | - Marie-Christine Guiot
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
- Department of Pathology, Neurosurgery, and Neurology, McGill University, Montreal, Quebec, Canada
- Division of Pathology, McGill University Health Centre, Montreal, Quebec, Canada
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Roy W R Dudley
- Child Health and Human Development Program, Research Institute of McGill University Health Centre, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University Health Centre, Montreal, Quebec, Canada
- Division of Neurosurgery, Department of Pediatric Surgery, Montreal Children's Hospital, Montreal, Quebec, Canada
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Fabbri L, Tamilia E, Matarrese MAG, Tran L, Malik SI, Shahani D, Keator CG, Stufflebeam SM, Pearl PL, Perry MS, Papadelis C. Noninvasive classification of physiological and pathological high frequency oscillations in children. Brain Commun 2025; 7:fcaf170. [PMID: 40370690 PMCID: PMC12077393 DOI: 10.1093/braincomms/fcaf170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2024] [Revised: 03/28/2025] [Accepted: 04/30/2025] [Indexed: 05/16/2025] Open
Abstract
High frequency oscillations have been extensively investigated as interictal biomarkers of epilepsy. Yet, their value is largely debated due to the presence of physiological oscillations, which complicate distinguishing between normal versus abnormal events. So far, this debate has been addressed using intracranial EEG data from patients with drug-resistant epilepsy. Yet, this approach suffers from inability to record control data from healthy subjects and lack of whole brain coverage. Here, we aim to differentiate physiological from pathological high frequency oscillations using non-invasive whole brain electrophysiological recordings from children with drug-resistant epilepsy and typically developing controls. We recorded high-density EEG and magnetoencephalography data from 47 controls (median age: 11 years; 25 females) and 54 children with drug-resistant epilepsy (median age: 14 years, 33 females). We detected high frequency oscillations (in ripple frequency band) semi-automatically and localized their cortical generators through electric or magnetic source imaging. From each ripple, we extracted a set of temporal, morphological, spectral and spatial features. We then compared the features between ripples recorded from the epileptic brain (further distinguished into those from epileptogenic and non-epileptogenic regions) and those recorded from the control group (normal brain). We used these features to cross-validate a Naïve-Bayes algorithm for classifying each ripple recorded from children with epilepsy as coming from an epileptogenic region or not. We observed more high frequency oscillations on EEG than magnetoencephalography recordings (P < 0.001) both in the epilepsy and control groups. Physiological high frequency oscillations (recorded from controls) showed lower power, shorter duration and less variability (in both amplitude and duration) than those recorded from the epilepsy group (P < 0.001). Inter-channel latency of physiological ripples was longer compared to ripples from the epileptogenic regions (P < 0.01), while it was similar to the ripples from non-epileptogenic regions (P > 0.05). Ripples from epileptogenic regions showed larger extent than those from non-epileptogenic regions or from the control group (P < 0.001). The classification model showed an accuracy of 73%, with negative and positive predictive values of 73% and 70% (P < 0.0001), respectively, in classifying high frequency oscillations from the drug-resistant epilepsy group (as either epileptogenic or not). Our study indicates that physiological high frequency oscillations, recorded from the healthy brain, have distinct temporal, morphological, spectral and spatial features compared to those generated by the epileptic brain. The differentiation of pathological from physiological high frequency oscillations through non-invasive full-head techniques may augment the presurgical evaluation process of children with drug-resistant epilepsy and lead to better postsurgical seizure outcomes.
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Affiliation(s)
- Lorenzo Fabbri
- Neuroscience Research, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, Fort Worth, TX 76104, USA
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX 76019, USA
| | - Eleonora Tamilia
- Division of Newborn Medicine, Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Margherita A G Matarrese
- Neuroscience Research, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, Fort Worth, TX 76104, USA
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX 76019, USA
- Research Unit of Intelligent Health Technology for Health and Wellbeing, Department of Engineering, Università Campus Bio-Medico di Roma, Rome 00128, Italy
| | - Linh Tran
- Neuroscience Research, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, Fort Worth, TX 76104, USA
| | - Saleem I Malik
- Neuroscience Research, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, Fort Worth, TX 76104, USA
| | - Dave Shahani
- Neuroscience Research, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, Fort Worth, TX 76104, USA
| | - Cynthia G Keator
- Neuroscience Research, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, Fort Worth, TX 76104, USA
| | - Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital & Harvard Medical School, Charlestown, MA 02129, USA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - M Scott Perry
- Neuroscience Research, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, Fort Worth, TX 76104, USA
| | - Christos Papadelis
- Neuroscience Research, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, Fort Worth, TX 76104, USA
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX 76019, USA
- Burnett School of Medicine, Texas Christian University, Fort Worth, TX 76129, USA
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3
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Qing K, Von Stein E, Yamada L, Fogarty A, Nuyujukian P. Classifying High-Frequency Oscillations by Morphologic Contrast to Background, With Surgical Outcome Correlates. J Clin Neurophysiol 2025; 42:294-303. [PMID: 39354667 DOI: 10.1097/wnp.0000000000001121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/03/2024] Open
Abstract
PURPOSE Ictal high-frequency oscillations (HFOs) are a reliable indicator of a seizure onset zone for intracranial EEG recordings. Interictal HFOs often are also observed and may be a useful biomarker to supplement ictal data, but distinguishing pathologic from physiologic HFOs continues to be a challenging task. We present a method of classifying HFOs based on morphologic contrast to the background. METHODS We retrospectively screened 31 consecutive patients who underwent intracranial recordings for epilepsy at Stanford Medical Center during a 2-year period, and 13 patients met the criteria for inclusion. Interictal EEG data were analyzed using an automated event detector followed by morphologic feature extraction and k-means clustering. Instead of only using event features, the algorithm also incorporated features of the background adjacent to the events. High-frequency oscillations with higher morphologic contrast to the background were labeled as pathologic, and "hotspots" with the most active pathologic HFOs were identified and compared with clinically determined seizure onset zones. RESULTS Clustering with contrast features produced groups with better separation and more consistent boundaries. Eleven of the 13 patients proceeded to surgery, and patients whose hotspots matched seizure onset zones had better outcomes, with 4 out of 5 "match" patients having no disabling seizures at 1+ year postoperatively (Engel I or International League Against Epilepsy Class 1-2), while all "mismatch" patients continued to have disabling seizures (Fisher exact test P -value = 0.015). CONCLUSIONS High-frequency oscillations with higher contrast to background more likely represent paroxysmal bursts of pathologic activity. Patients with HFO hotspots outside of identified seizure onset zones may not respond as well to surgery.
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Affiliation(s)
- Kurt Qing
- Epilepsy, Stanford University, Stanford, CA
- Epilepsy, University of California, Irvine, CA
| | | | - Lisa Yamada
- Bioengineering, Neurosurgery, Electrical Engineering, Stanford University, Stanford, CA; and
| | | | - Paul Nuyujukian
- Bioengineering, Neurosurgery, Electrical Engineering, Stanford University, Stanford, CA; and
- Stanford Bio-X, Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA
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Kitazawa Y, Sakakura K, Uda H, Kuroda N, Ueda R, Firestone E, Lee MH, Jeong JW, Sonoda M, Osawa SI, Ukishiro K, Ishida M, Kakinuma K, Ota S, Takayama Y, Iijima K, Kambara T, Endo H, Suzuki K, Nakasato N, Iwasaki M, Asano E. Visualization of functional and effective connectivity underlying auditory descriptive naming. Clin Neurophysiol 2025; 175:2010729. [PMID: 40349545 DOI: 10.1016/j.clinph.2025.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2024] [Revised: 04/02/2025] [Accepted: 04/09/2025] [Indexed: 05/14/2025]
Abstract
OBJECTIVE We visualized functional and effective connectivity within specific white matter networks in response to auditory descriptive questions. METHODS We investigated 40 Japanese-speaking patients with focal epilepsy and estimated connectivity measures using cortical high-gamma dynamics and MRI tractography. RESULTS Hearing a wh-interrogative at question onset enhanced inter-hemispheric functional connectivity, with left-to-right callosal facilitatory flows between the superior-temporal gyri, contrasted by functional connectivity diminution with right-to-left callosal suppressive flows between dorsolateral prefrontal regions. Processing verbs associated with concrete objects or adverbs increased left intra-hemispheric connectivity, with bidirectional facilitatory flows through extensive white matter pathways. Questions beginning with what, compared to where, induced greater neural engagement in the left posterior inferior-frontal gyrus at question offset, linked to enhanced functional connectivity and bidirectional facilitatory flows to the temporal lobe neocortex via the arcuate fasciculus. During overt responses, inter-hemispheric functional connectivity was enhanced, with bidirectional callosal flows between Rolandic areas, and individuals with higher IQ scores exhibited less prolonged neural engagement in the left posterior middle frontal gyrus. CONCLUSIONS Visualization of directional neural interactions within white matter networks during overt naming is feasible. SIGNIFICANCE Phrase order may influence network dynamics in listeners, even when presented with auditory descriptive questions conveying similar meanings.
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Affiliation(s)
- Yu Kitazawa
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA; Department of Neurology and Stroke Medicine, Yokohama City University, Yokohama, Kanagawa 2360004, Japan
| | - 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, Ibaraki 3058575, Japan; Department of Neurosurgery, Rush University Medical Center, Chicago, IL 60612, USA
| | - Hiroshi Uda
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA; Department of Neurosurgery, Osaka Metropolitan University Graduate School of Medicine, Osaka 5458585, 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
| | - Riyo Ueda
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA; Epilepsy Center, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 1878551, 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
| | - Min-Hee Lee
- Department of Pediatrics, 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
| | - 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
| | - Shin-Ichiro Osawa
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai 9808574, Japan
| | - Kazushi Ukishiro
- Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai 9808575, Japan
| | - Makoto Ishida
- Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai 9808575, Japan
| | - Kazuo Kakinuma
- Department of Behavioral Neurology and Cognitive Neuroscience, Tohoku University Graduate School of Medicine, Sendai 9808575, Japan
| | - Shoko Ota
- Department of Behavioral Neurology and Cognitive Neuroscience, Tohoku University Graduate School of Medicine, Sendai 9808575, Japan
| | - Yutaro Takayama
- Department of Neurosurgery, Yokohama City University, Yokohama, Kanagawa 2360004, Japan; Department of Neurosurgery, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 1878551, Japan
| | - Keiya Iijima
- Department of Neurosurgery, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 1878551, Japan
| | - Toshimune Kambara
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA; Department of Psychology, Hiroshima University, Hiroshima 7398524, Japan
| | - Hidenori Endo
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai 9808574, Japan
| | - Kyoko Suzuki
- Department of Behavioral Neurology and Cognitive Neuroscience, Tohoku University Graduate School of Medicine, Sendai 9808575, Japan
| | - Nobukazu Nakasato
- Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai 9808575, Japan
| | - Masaki Iwasaki
- Department of Neurosurgery, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 1878551, 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; Department of Pediatrics, Central Michigan University, Mt. Pleasant, MI 48858, USA.
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5
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Firestone E, Uda H, Kuroda N, Sakakura K, Sonoda M, Ueda R, Kitazawa Y, Lee MH, Jeong JW, Luat AF, Cools MJ, Sood S, Asano E. Normative high-frequency oscillation phase-amplitude coupling and effective connectivity under sevoflurane. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.03.18.644050. [PMID: 40166237 PMCID: PMC11956958 DOI: 10.1101/2025.03.18.644050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/02/2025]
Abstract
Resective surgery for pediatric drug-resistant focal epilepsy often requires extraoperative intracranial electroencephalography recording to accurately localize the epileptogenic zone. This procedure entails multiple neurosurgeries, intracranial electrode implantation and explantation, and days of invasive inpatient evaluation. There is a need for methods to reduce diagnostic burden and introduce objective epilepsy biomarkers. Our preliminary studies aimed to address these issues by using sevoflurane anesthesia to rapidly and reversibly activate intraoperative phase-amplitude coupling between delta and high-frequency activities, as well as high-frequency activity-based effective connectivity. Phase-amplitude coupling can serve as a proxy for spike-and-wave discharges, and effective connectivity describes the spatiotemporal dynamics of neural information flow among regions. Notably, sevoflurane activated these interictal electrocorticography biomarkers most robustly in areas whose resection led to seizure freedom. However, they were also increased in normative brain regions that did not require removal for seizure control. Before using these electrocorticography biomarkers prospectively to guide resection, we should understand their endogenous distribution and propagation pathways, at different anesthetic stages. In the current study, we highlighted the normative distribution of delta and high-frequency activity phase-amplitude coupling and effective connectivity under sevoflurane. Normative data was derived from nineteen patients, whose ages ranged from four to eighteen years and included eleven males. All achieved seizure control following focal resection. Electrocorticography was recorded at an isoflurane baseline, during stepwise increases in sevoflurane concentration, and also during extraoperative slow-wave sleep without anesthesia. Normative electrode sites were then mapped onto a standard cortical surface for anatomical visualization. Dynamic tractography traced white matter pathways that connected sites with significantly augmented biomarkers. Finally, we analyzed all sites -regardless of normal or abnormal status - to determine whether sevoflurane-enhanced biomarker values could intraoperatively localize the epileptogenic sites. We found that normative electrocorticography biomarkers increased as a function of sevoflurane concentration, especially in bilateral frontal and parietal lobe regions (Bonferroni-corrected p-values <0.05). Callosal fibers directly connected homotopic Rolandic regions exhibiting elevated phase-amplitude coupling. The superior longitudinal fasciculus linked frontal and parietal association cortices showing augmented effective connectivity. Higher biomarker values, particularly at three to four volume percent sevoflurane, characterized epileptogenicity and seizure-onset zone status (Bonferroni-corrected p-values <0.05). Supplementary analysis showed that epileptogenic sites exhibited less augmentation in delta-based effective connectivity. This study helps clarify the normative distribution of, and plausible propagation pathways supporting, sevoflurane enhanced electrocorticographic biomarkers. Future work should confirm that sevoflurane-activated electrocorticography biomarkers can predict postoperative seizure outcomes in larger cohorts, to establish their clinical utility.
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6
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Ding L, Zou Q, Zhu J, Wang Y, Yang Y. Dynamical intracranial EEG functional network controllability localizes the seizure onset zone and predicts the epilepsy surgical outcome. J Neural Eng 2025; 22:026015. [PMID: 40009882 DOI: 10.1088/1741-2552/adba8d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2024] [Accepted: 02/26/2025] [Indexed: 02/28/2025]
Abstract
Objective. Seizure onset zone (SOZ) localization and SOZ resection outcome prediction are critical for the surgical treatment of drug-resistant epilepsy but have mainly relied on manual inspection of intracranial electroencephalography (iEEG) monitoring data, which can be both inaccurate and time-consuming. Therefore, automating SOZ localization and surgical outcome prediction by using appropriate iEEG neural features and machine learning models has become an emerging topic. However, current channel-wise local features, graph-theoretic network features, and system-theoretic network features cannot fully capture the spatial, temporal, and neural dynamical aspects of epilepsy, hindering accurate SOZ localization and surgical outcome prediction.Approach. Here, we develop a method for computing dynamical functional network controllability from multi-channel iEEG signals, which from a control-theoretic viewpoint, has the ability to simultaneously capture the spatial, temporal, functional, and dynamical aspects of epileptic brain networks. We then apply multiple machine learning models to use iEEG functional network controllability for localizing SOZ and predicting surgical outcomes in drug-resistant epilepsy patients and compare with existing neural features. We finally combine iEEG functional network controllability with representative local, graph-theoretic, and system-theoretic features to leverage complementary information for further improving performance.Main results. We find that iEEG functional network controllability at SOZ channels is significantly higher than that of other channels. We further show that machine learning models using iEEG functional network controllability successfully localize SOZ and predict surgical outcomes, significantly outperforming existing local, graph-theoretic, and system-theoretic features. We finally demonstrate that there exists complementary information among different types of neural features and fusing them further improves performance.Significance. Our results suggest that iEEG functional network controllability is an effective feature for automatic SOZ localization and surgical outcome prediction in epilepsy treatment.
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Affiliation(s)
- Ling Ding
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou 310058, People's Republic of China
- Nanhu Brain-computer Interface Institute, Hangzhou 311100, People's Republic of China
| | - Qingyu Zou
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou 310058, People's Republic of China
- Nanhu Brain-computer Interface Institute, Hangzhou 311100, People's Republic of China
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, People's Republic of China
| | - Junming Zhu
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Hangzhou 310058, People's Republic of China
| | - Yueming Wang
- Nanhu Brain-computer Interface Institute, Hangzhou 311100, People's Republic of China
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, People's Republic of China
| | - Yuxiao Yang
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou 310058, People's Republic of China
- Nanhu Brain-computer Interface Institute, Hangzhou 311100, People's Republic of China
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, People's Republic of China
- State Key Laboratory of Brain-machine Intelligence, Hangzhou 310058, People's Republic of China
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Hangzhou 310058, People's Republic of China
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7
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Monsoor T, Kanai S, Daida A, Kuroda N, Sinha P, Oana S, Zhang Y, Liu L, Singh G, Duan C, Sim MS, Fallah A, Speier W, Asano E, Roychowdhury V, Nariai H. Mini-Seizures: Novel Interictal iEEG Biomarker Capturing Synchronization Network Dynamics at the Epileptogenic Zone. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2025:2025.01.31.25321482. [PMID: 39974054 PMCID: PMC11838652 DOI: 10.1101/2025.01.31.25321482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/21/2025]
Abstract
Epilepsy affects 1% of the population, with up to one-third of patients being medication-resistant. Surgery is the only curative treatment, yet over one-third of surgical patients fail to achieve seizure freedom due to the lack of a reliable epileptogenic zone (EZ) biomarker. We introduced and validated mini-seizures, frequent hypersynchronization events at EZ hubs that mirror seizure network dynamics, as a novel interictal EEG biomarker. Using a dynamical networks-based model, we analyzed short interictal intracranial EEG from 159 patients across two institutions. Our model, integrating hypersynchronous network properties and clinical data, successfully identified EZ hubs and accurately predicted one-year postoperative seizure outcomes. Our model (mean F1 score: 87%) outperformed the high-frequency oscillations-based model (mean F1 score: 79%) and seizure onset zone resection-status-based model (current clinical standard) (mean F1 score: 78%), supporting its potential as a robust interictal biomarker for EZ localization. Our findings suggest mini-seizures and seizures exist on a continuum of epileptic events, sharing common network properties. Unlike seizure-based analyses that require 1-2 weeks of monitoring to capture spontaneous seizures, mini-seizures provide a rapid alternative using only brief interictal recordings.
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Affiliation(s)
- Tonmoy Monsoor
- Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Sotaro Kanai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Atsuro Daida
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Naoto Kuroda
- Department of Pediatrics and Neurology, Children’s Hospital of Michigan, Wayne State University School of Medicine, MI, USA
| | - Prateik Sinha
- Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Shingo Oana
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Yipeng Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Lawrence Liu
- Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Gaurav Singh
- Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Chenda Duan
- Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Myung Shin Sim
- Department of Medicine, Statistics Core, University of California, Los Angeles, CA, USA
| | - Aria Fallah
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - William Speier
- Department of Radiological Sciences, University of California, Los Angeles, CA, USA
- Department of Bioengineering, University of California, Los Angeles, CA, USA
| | - Eishi Asano
- Department of Pediatrics and Neurology, Children’s Hospital of Michigan, Wayne State University School of Medicine, MI, USA
| | - Vwani Roychowdhury
- Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Hiroki Nariai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
- The UCLA Children’s Discovery and Innovation Institute, Los Angeles, CA, USA
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8
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Zhang Y, Daida A, Liu L, Kuroda N, Ding Y, Oana S, Kanai S, Monsoor T, Duan C, Hussain SA, Qiao JX, Salamon N, Fallah A, Sim MS, Sankar R, Staba RJ, Engel J, Asano E, Roychowdhury V, Nariai H. Self-Supervised Data-Driven Approach Defines Pathological High-Frequency Oscillations in Human. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.10.24310189. [PMID: 39040207 PMCID: PMC11261948 DOI: 10.1101/2024.07.10.24310189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Objective Interictal high-frequency oscillations (HFOs) are a promising neurophysiological biomarker of the epileptogenic zone (EZ). However, objective criteria for distinguishing pathological from physiological HFOs remain elusive, hindering clinical application. We investigated whether the distinct mechanisms underlying pathological and physiological HFOs are encapsulated in their signal morphology in intracranial EEG (iEEG) recordings and whether this mechanism-driven distinction could be simulated by a deep generative model. Methods In a retrospective cohort of 185 epilepsy patients who underwent iEEG monitoring, we analyzed 686,410 HFOs across 18,265 brain contacts. To learn morphological characteristics, each event was transformed into a time-frequency plot and input into a variational autoencoder. We characterized latent space clusters containing morphologically defined putative pathological HFOs (mpHFOs) using interpretability analysis, including latent space disentanglement and time-domain perturbation. Results mpHFOs showed strong associations with expert-defined spikes and were predominantly located within the seizure onset zone (SOZ). Discovered novel pathological features included high power in the gamma (30-80 Hz) and ripple (>80 Hz) bands centered on the event. These characteristics were consistent across multiple variables, including institution, electrode type, and patient demographics. Predicting 12-month postoperative seizure outcomes using the resection ratio of mpHFOs outperformed unclassified HFOs (F1=0.72 vs. 0.68) and matched current clinical standards using SOZ resection (F1=0.74). Combining mpHFO data with demographic and SOZ resection status further improved prediction accuracy (F1=0.83). Interpretation Our data-driven approach yielded a novel, explainable definition of pathological HFOs, which has the potential to further enhance the clinical use of HFOs for EZ delineation.
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Affiliation(s)
- Yipeng Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Atsuro Daida
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Lawrence Liu
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Naoto Kuroda
- Department of Pediatrics and Neurology, Children's Hospital of Michigan, Wayne State University School of Medicine, Detroit, MI, USA
| | - Yuanyi Ding
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Shingo Oana
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Sotaro Kanai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Tonmoy Monsoor
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Chenda Duan
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Shaun A Hussain
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
- The UCLA Children's Discovery and Innovation Institute, Los Angeles, CA, USA
| | - Joe X Qiao
- Division of Neuroradiology, Department of Radiology, UCLA Medical Center, David Geffen School of Medicine, Los 6 Angeles, CA, USA
| | - Noriko Salamon
- Division of Neuroradiology, Department of Radiology, UCLA Medical Center, David Geffen School of Medicine, Los 6 Angeles, CA, USA
| | - Aria Fallah
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Myung Shin Sim
- Department of Medicine, Statistics Core, University of California, Los Angeles, CA, USA
| | - Raman Sankar
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
- The UCLA Children's Discovery and Innovation Institute, Los Angeles, CA, USA
| | - Richard J Staba
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Jerome Engel
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
- Department of Neurobiology, University of California, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
- The Brain Research Institute, University of California, Los Angeles, CA, USA
| | - Eishi Asano
- Department of Pediatrics and Neurology, Children's Hospital of Michigan, Wayne State University School of Medicine, Detroit, MI, USA
| | - Vwani Roychowdhury
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Hiroki Nariai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
- The UCLA Children's Discovery and Innovation Institute, Los Angeles, CA, USA
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9
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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 2024; 41:589-596. [PMID: 37934075 DOI: 10.1097/wnp.0000000000001031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023] Open
Abstract
PURPOSE 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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10
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Uda H, Kuroda N, Firestone E, Ueda R, Sakakura K, Kitazawa Y, Choromanski D, Cools M, Luat AF, Asano E. Normative atlases of high-frequency oscillation and spike rates under Sevoflurane anesthesia. Clin Neurophysiol 2024; 167:117-130. [PMID: 39307102 PMCID: PMC11588517 DOI: 10.1016/j.clinph.2024.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2024] [Revised: 08/21/2024] [Accepted: 09/07/2024] [Indexed: 11/21/2024]
Abstract
OBJECTIVE We analyzed the dose-dependent effects of Sevoflurane anesthesia on high-frequency oscillations (HFOs) and spike discharges at non-epileptic sites and evaluated their effectiveness in identifying the epileptogenic zone. METHODS We studied 21 children with drug-resistant focal epilepsy who achieved seizure control after focal resective surgery. Open-source detectors quantified HFO and spike rates during extraoperative and intraoperative intracranial EEG recordings performed before resection. We determined under which anesthetic conditions HFO and spike rates differentiated the seizure onset zone (SOZ) within the resected area from non-epileptic sites. RESULTS We analyzed 925 artifact-free electrodes, including 867 at non-epileptic sites and 58 at SOZ sites. Higher Sevoflurane doses significantly increased HFO and spike rates at non-epileptic sites, exhibiting spatial variability among different detectors. These biomarkers were elevated in the SOZ more than in non-epileptic sites under 2-4 vol% Sevoflurane anesthesia, with Cohen's d effect sizes above 3.0 and Mann-Whitney U-Test r effect sizes above 0.5. CONCLUSIONS We provided normative atlases of HFO and spike rates under different Sevoflurane anesthesia conditions. Sevoflurane elevates HFO and spike rates preferentially in the epileptogenic zone. SIGNIFICANCE Assessing the relative severity of biomarker levels across sites may be relevant for localizing the epileptogenic zone under Sevoflurane anesthesia.
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Affiliation(s)
- Hiroshi Uda
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA; Department of Neurosurgery, Osaka Metropolitan University Graduate School of Medicine, Osaka 5458585, 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
| | - Ethan Firestone
- Department of Physiology, Wayne State University, Detroit, MI 48202, USA
| | - Riyo Ueda
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, 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, Rush University Medical Center, Chicago, IL 60612, USA; Department of Neurosurgery, University of Tsukuba, Tsukuba 3058575, Japan
| | - Yu Kitazawa
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA; Department of Neurology and Stroke Medicine, Yokohama City University, Yokohama 2360004, Japan
| | - Dominik Choromanski
- Department of Anesthesiology, Children's Hospital of Michigan, Detroit Medical Center, Detroit, MI 48201, USA
| | - Michael Cools
- Department of Neurosurgery, Children's Hospital of Michigan, Detroit Medical Center, 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, Mt. Pleasant, MI 48858, 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; Department of Pediatrics, Central Michigan University, Mt. Pleasant, MI 48858, USA.
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11
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Zhang R, Rong R, Xu Y, Wang H, Wang X. OxcarNet: sinc convolutional network with temporal and channel attention for prediction of oxcarbazepine monotherapy responses in patients with newly diagnosed epilepsy. J Neural Eng 2024; 21:056019. [PMID: 39250934 DOI: 10.1088/1741-2552/ad788c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Accepted: 09/09/2024] [Indexed: 09/11/2024]
Abstract
Objective.Monotherapy with antiepileptic drugs (AEDs) is the preferred strategy for the initial treatment of epilepsy. However, an inadequate response to the initially prescribed AED is a significant indicator of a poor long-term prognosis, emphasizing the importance of precise prediction of treatment outcomes with the initial AED regimen in patients with epilepsy.Approach. We introduce OxcarNet, an end-to-end neural network framework developed to predict treatment outcomes in patients undergoing oxcarbazepine monotherapy. The proposed predictive model adopts a Sinc Module in its initial layers for adaptive identification of discriminative frequency bands. The derived feature maps are then processed through a Spatial Module, which characterizes the scalp distribution patterns of the electroencephalography (EEG) signals. Subsequently, these features are fed into an attention-enhanced Temporal Module to capture temporal dynamics and discrepancies. A channel module with an attention mechanism is employed to reveal inter-channel dependencies within the output of the Temporal Module, ultimately achieving response prediction. OxcarNet was rigorously evaluated using a proprietary dataset of retrospectively collected EEG data from newly diagnosed epilepsy patients at Nanjing Drum Tower Hospital. This dataset included patients who underwent long-term EEG monitoring in a clinical inpatient setting.Main results.OxcarNet demonstrated exceptional accuracy in predicting treatment outcomes for patients undergoing Oxcarbazepine monotherapy. In the ten-fold cross-validation, the model achieved an accuracy of 97.27%, and in the validation involving unseen patient data, it maintained an accuracy of 89.17%, outperforming six conventional machine learning methods and three generic neural decoding networks. These findings underscore the model's effectiveness in accurately predicting the treatment responses in patients with newly diagnosed epilepsy. The analysis of features extracted by the Sinc filters revealed a predominant concentration of predictive frequencies in the high-frequency range of the gamma band.Significance. The findings of our study offer substantial support and new insights into tailoring early AED selection, enhancing the prediction accuracy for the responses of AEDs.
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Affiliation(s)
- Runkai Zhang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, People's Republic of China
| | - Rong Rong
- Department of Neurology, Nanjing Drum Tower Hospital, Nanjing 210008, Jiangsu, People's Republic of China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital, Nanjing 210008, Jiangsu, People's Republic of China
| | - Haixian Wang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, People's Republic of China
| | - Xiaoyun Wang
- Department of Neurology, Nanjing Drum Tower Hospital, Nanjing 210008, Jiangsu, People's Republic of China
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12
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Ueda R, Sakakura K, Mitsuhashi T, Sonoda M, Firestone E, Kuroda N, Kitazawa Y, Uda H, Luat AF, Johnson EL, Ofen N, Asano E. Cortical and white matter substrates supporting visuospatial working memory. Clin Neurophysiol 2024; 162:9-27. [PMID: 38552414 PMCID: PMC11102300 DOI: 10.1016/j.clinph.2024.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 02/24/2024] [Accepted: 03/11/2024] [Indexed: 05/19/2024]
Abstract
OBJECTIVE In tasks involving new visuospatial information, we rely on working memory, supported by a distributed brain network. We investigated the dynamic interplay between brain regions, including cortical and white matter structures, to understand how neural interactions change with different memory loads and trials, and their subsequent impact on working memory performance. METHODS Patients undertook a task of immediate spatial recall during intracranial EEG monitoring. We charted the dynamics of cortical high-gamma activity and associated functional connectivity modulations in white matter tracts. RESULTS Elevated memory loads were linked to enhanced functional connectivity via occipital longitudinal tracts, yet decreased through arcuate, uncinate, and superior-longitudinal fasciculi. As task familiarity grew, there was increased high-gamma activity in the posterior inferior-frontal gyrus (pIFG) and diminished functional connectivity across a network encompassing frontal, parietal, and temporal lobes. Early pIFG high-gamma activity was predictive of successful recall. Including this metric in a logistic regression model yielded an accuracy of 0.76. CONCLUSIONS Optimizing visuospatial working memory through practice is tied to early pIFG activation and decreased dependence on irrelevant neural pathways. SIGNIFICANCE This study expands our knowledge of human adaptation for visuospatial working memory, showing the spatiotemporal dynamics of cortical network modulations through white matter tracts.
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Affiliation(s)
- Riyo Ueda
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan 48201, USA; National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 1878551, Japan.
| | - Kazuki Sakakura
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan 48201, USA; Department of Neurosurgery, Rush University Medical Center, Chicago, Illinois 60612, USA; Department of Neurosurgery, University of Tsukuba, Tsukuba 3058575, Japan.
| | - Takumi Mitsuhashi
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan 48201, USA; Department of Neurosurgery, Juntendo University, School of Medicine, Tokyo 1138421, Japan.
| | - Masaki Sonoda
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan 48201, USA; Department of Neurosurgery, Yokohama City University, Yokohama 2360004, Japan.
| | - Ethan Firestone
- Department of Physiology, Wayne State University, Detroit, Michigan 48202, USA.
| | - Naoto Kuroda
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan 48201, USA; Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai 9808575, Japan.
| | - Yu Kitazawa
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan 48201, USA; Department of Neurology and Stroke Medicine, Yokohama City University, Yokohama 2360004, Japan.
| | - Hiroshi Uda
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan 48201, USA; Department of Neurosurgery, Osaka Metropolitan University Graduate School of Medicine, Osaka 5458585, Japan.
| | - Aimee F Luat
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan 48201, USA; Department of Neurology, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan 48201, USA; Department of Pediatrics, Central Michigan University, Mt. Pleasant, Michigan 48858, USA.
| | - Elizabeth L Johnson
- Departments of Medical Social Sciences, Pediatrics, and Psychology, Northwestern University, Chicago, Illinois 60611, USA.
| | - Noa Ofen
- Life-Span Cognitive Neuroscience Program, Institute of Gerontology and Merrill Palmer Skillman Institute, Wayne State University, Detroit, Michigan 48202, USA; Department of Psychology, Wayne State University, Detroit, Michigan 48202, USA.
| | - Eishi Asano
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan 48201, USA; Department of Neurology, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan 48201, USA; Translational Neuroscience Program, Wayne State University, Detroit, Michigan 48201, USA.
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13
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Zhang Y, Liu L, Ding Y, Chen X, Monsoor T, Daida A, Oana S, Hussain S, Sankar R, Fallah A, Santana-Gomez C, Engel J, Staba RJ, Speier W, Zhang J, Nariai H, Roychowdhury V. PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application. J Neural Eng 2024; 21:036023. [PMID: 38722308 PMCID: PMC11135143 DOI: 10.1088/1741-2552/ad4916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 04/19/2024] [Accepted: 05/09/2024] [Indexed: 05/29/2024]
Abstract
Objective. This study aims to develop and validate an end-to-end software platform, PyHFO, that streamlines the application of deep learning (DL) methodologies in detecting neurophysiological biomarkers for epileptogenic zones from EEG recordings.Approach. We introduced PyHFO, which enables time-efficient high-frequency oscillation (HFO) detection algorithms like short-term energy and Montreal Neurological Institute and Hospital detectors. It incorporates DL models for artifact and HFO with spike classification, designed to operate efficiently on standard computer hardware.Main results. The validation of PyHFO was conducted on three separate datasets: the first comprised solely of grid/strip electrodes, the second a combination of grid/strip and depth electrodes, and the third derived from rodent studies, which sampled the neocortex and hippocampus using depth electrodes. PyHFO demonstrated an ability to handle datasets efficiently, with optimization techniques enabling it to achieve speeds up to 50 times faster than traditional HFO detection applications. Users have the flexibility to employ our pre-trained DL model or use their EEG data for custom model training.Significance. PyHFO successfully bridges the computational challenge faced in applying DL techniques to EEG data analysis in epilepsy studies, presenting a feasible solution for both clinical and research settings. By offering a user-friendly and computationally efficient platform, PyHFO paves the way for broader adoption of advanced EEG data analysis tools in clinical practice and fosters potential for large-scale research collaborations.
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Affiliation(s)
- Yipeng Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, United States of America
| | - Lawrence Liu
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, United States of America
| | - Yuanyi Ding
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, United States of America
| | - Xin Chen
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, United States of America
| | - Tonmoy Monsoor
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, United States of America
| | - Atsuro Daida
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, United States of America
| | - Shingo Oana
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, United States of America
| | - Shaun Hussain
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, United States of America
| | - Raman Sankar
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, United States of America
| | - Aria Fallah
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, United States of America
| | - Cesar Santana-Gomez
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, United States of America
| | - Jerome Engel
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, United States of America
- Department of Neurobiology, University of California, Los Angeles, CA, United States of America
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, United States of America
| | - Richard J Staba
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, United States of America
| | - William Speier
- Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States of America
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States of America
| | - Jianguo Zhang
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, People’s Republic of China
| | - Hiroki Nariai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, United States of America
| | - Vwani Roychowdhury
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, United States of America
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14
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Ye H, Chen C, Weiss SA, Wang S. Pathological and Physiological High-frequency Oscillations on Electroencephalography in Patients with Epilepsy. Neurosci Bull 2024; 40:609-620. [PMID: 37999861 PMCID: PMC11127900 DOI: 10.1007/s12264-023-01150-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 09/28/2023] [Indexed: 11/25/2023] Open
Abstract
High-frequency oscillations (HFOs) encompass ripples (80 Hz-200 Hz) and fast ripples (200 Hz-600 Hz), serving as a promising biomarker for localizing the epileptogenic zone in epilepsy. Spontaneous fast ripples are always pathological, while ripples may be physiological or pathological. Distinguishing physiological from pathological ripples is important not only for designating epileptogenic brain regions, but also for investigations that study ripples in the context of memory encoding, consolidation, and recall in patients with epilepsy. Many studies have sought to identify distinguishing features between pathological and physiological ripples over the past two decades. Physiological and pathological ripples differ with respect to their spatial location, cellular mechanisms, morphology, and coupling with background electroencephalographic activity. Retrospective studies have demonstrated that differentiating between pathological and physiological ripples can improve surgical outcome prediction. In this review, we summarize the characteristics, differences, and applications of pathological and physiological HFOs and discuss strategies for their clinical translation.
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Affiliation(s)
- Hongyi Ye
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China
| | - Cong Chen
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China
| | - Shennan A Weiss
- Department of Neurology, State University of New York Downstate, Brooklyn, NY, 11203, USA
- Department of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, NY, 11203, USA
- Department of Neurology, New York City Health + Hospitals/Kings County, Brooklyn, NY, 11203, USA
| | - Shuang Wang
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China.
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Casseb RF, de Campos BM, Loos WS, Barbosa MER, Alvim MKM, Paulino GCL, Pucci F, Worrell S, de Souza RM, Jehi L, Cendes F. Fully automatic segmentation of brain lacunas resulting from resective surgery using a 3D deep learning model. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.11.16.23298572. [PMID: 38014004 PMCID: PMC10680896 DOI: 10.1101/2023.11.16.23298572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
The rapid and constant development of deep learning (DL) strategies is pushing forward the quality of object segmentation in images from diverse fields of interest. In particular, these algorithms can be very helpful in delineating brain abnormalities (lesions, tumors, lacunas, etc), enabling the extraction of information such as volume and location, that can inform doctors or feed predictive models. Here, we describe ResectVol DL, a fully automatic tool developed to segment resective lacunas in brain images of patients with epilepsy. ResectVol DL relies on the nnU-Net framework that leverages the 3D U-Net deep learning architecture. T1-weighted MRI datasets from 120 patients (57 women; 31.5 ± 15.9 years old at surgery) were used to train (n=78) and test (n=48) our tool. Manual segmentations were carried out by five different raters and were considered as ground truth for performance assessment. We compared ResectVol DL with two other fully automatic methods: ResectVol 1.1.2 and DeepResection, using the Dice similarity coefficient (DSC), Pearson's correlation coefficient, and relative difference to manual segmentation. ResectVol DL presented the highest median DSC (0.92 vs. 0.78 and 0.90), the highest correlation coefficient (0.99 vs. 0.63 and 0.94), and the lowest median relative difference (9 vs. 44 and 12 %). Overall, we demonstrate that ResectVol DL accurately segments brain lacunas, which has the potential to assist in the development of predictive models for postoperative cognitive and seizure outcomes.
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Affiliation(s)
| | | | - Wallace Souza Loos
- Advanced Imaging and Artificial Intelligence Lab, University of Calgary, Calgary, AB, Canada
| | | | | | | | - Francesco Pucci
- Cleveland Clinic Foundation, Cleveland, OH, United States of America
| | - Samuel Worrell
- Cleveland Clinic Foundation, Cleveland, OH, United States of America
| | | | - Lara Jehi
- Cleveland Clinic Foundation, Cleveland, OH, United States of America
| | - Fernando Cendes
- Universidade Estadual de Campinas (UNICAMP), Neuroimaging Laboratory, Campinas, SP, Brazil
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16
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Gerstl JVE, Kiseleva A, Imbach L, Sarnthein J, Fedele T. High frequency oscillations in relation to interictal spikes in predicting postsurgical seizure freedom. Sci Rep 2023; 13:21313. [PMID: 38042925 PMCID: PMC10693609 DOI: 10.1038/s41598-023-48764-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 11/30/2023] [Indexed: 12/04/2023] Open
Abstract
We evaluate whether interictal spikes, epileptiform HFOs and their co-occurrence (Spike + HFO) were included in the resection area with respect to seizure outcome. We also characterise the relationship between high frequency oscillations (HFOs) and propagating spikes. We analysed intracranial EEG of 20 patients that underwent resective epilepsy surgery. The co-occurrence of ripples and fast ripples was considered an HFO event; the co-occurrence of an interictal spike and HFO was considered a Spike + HFO event. HFO distribution and spike onset were compared in cases of spike propagation. Accuracy in predicting seizure outcome was 85% for HFO, 60% for Spikes, and 79% for Spike + HFO. Sensitivity was 57% for HFO, 71% for Spikes and 67% for Spikes + HFO. Specificity was 100% for HFO, 54% for Spikes and 85% for Spikes + HFO. In 2/2 patients with spike propagation, the spike onset included the HFO area. Combining interictal spikes with HFO had comparable accuracy to HFO. In patients with propagating spikes, HFO rate was maximal at the onset of spike propagation.
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Affiliation(s)
- Jakob V E Gerstl
- University College London Medical School, London, UK
- Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland
| | - Alina Kiseleva
- Institute for Cognitive Neuroscience, HSE University, Myasnitskaya Ulitsa, 20, Moscow, Russian Federation, 101000
| | - Lukas Imbach
- Swiss Epilepsy Center, Klinik Lengg, Zurich, Switzerland
| | - Johannes Sarnthein
- Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland
| | - Tommaso Fedele
- Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland.
- Institute for Cognitive Neuroscience, HSE University, Myasnitskaya Ulitsa, 20, Moscow, Russian Federation, 101000.
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17
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Horsley JJ, Thomas RH, Chowdhury FA, Diehl B, McEvoy AW, Miserocchi A, de Tisi J, Vos SB, Walker MC, Winston GP, Duncan JS, Wang Y, Taylor PN. Complementary structural and functional abnormalities to localise epileptogenic tissue. EBioMedicine 2023; 97:104848. [PMID: 37898096 PMCID: PMC10630610 DOI: 10.1016/j.ebiom.2023.104848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 10/11/2023] [Accepted: 10/11/2023] [Indexed: 10/30/2023] Open
Abstract
BACKGROUND When investigating suitability for epilepsy surgery, people with drug-refractory focal epilepsy may have intracranial EEG (iEEG) electrodes implanted to localise seizure onset. Diffusion-weighted magnetic resonance imaging (dMRI) may be acquired to identify key white matter tracts for surgical avoidance. Here, we investigate whether structural connectivity abnormalities, inferred from dMRI, may be used in conjunction with functional iEEG abnormalities to aid localisation of the epileptogenic zone (EZ), improving surgical outcomes in epilepsy. METHODS We retrospectively investigated data from 43 patients (42% female) with epilepsy who had surgery following iEEG. Twenty-five patients (58%) were free from disabling seizures (ILAE 1 or 2) at one year. Interictal iEEG functional, and dMRI structural connectivity abnormalities were quantified by comparison to a normative map and healthy controls. We explored whether the resection of maximal abnormalities related to improved surgical outcomes, in both modalities individually and concurrently. Additionally, we suggest how connectivity abnormalities may inform the placement of iEEG electrodes pre-surgically using a patient case study. FINDINGS Seizure freedom was 15 times more likely in patients with resection of maximal connectivity and iEEG abnormalities (p = 0.008). Both modalities separately distinguished patient surgical outcome groups and when used simultaneously, a decision tree correctly separated 36 of 43 (84%) patients. INTERPRETATION Our results suggest that both connectivity and iEEG abnormalities may localise epileptogenic tissue, and that these two modalities may provide complementary information in pre-surgical evaluations. FUNDING This research was funded by UKRI, CDT in Cloud Computing for Big Data, NIH, MRC, Wellcome Trust and Epilepsy Research UK.
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Affiliation(s)
- Jonathan J Horsley
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Rhys H Thomas
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Fahmida A Chowdhury
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Andrew W McEvoy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sjoerd B Vos
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Centre for Microscopy, Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia; Centre for Medical Image Computing, Computer Science Department, University College London, London, United Kingdom
| | - Matthew C Walker
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Gavin P Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Division of Neurology, Department of Medicine, Queen's University, Kingston, Canada
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Peter N Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.
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18
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Fawcett J, Davis S, Manford M. Further advances in epilepsy. J Neurol 2023; 270:5655-5670. [PMID: 37458794 DOI: 10.1007/s00415-023-11860-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 06/29/2023] [Accepted: 06/29/2023] [Indexed: 10/15/2023]
Abstract
In 2017, one of us reviewed advances in epilepsy (Manford in J Neurol 264:1811-1824, 2017). The current paper brings that review up to date and gives a slight change in emphasis. Once again, the story is of evolution rather than revolution. In recognition that most of our current medications act on neurotransmitters or ion channels, and not on the underlying changes in connectivity and pathways, they have been renamed as antiseizure (ASM) medications rather than antiepileptic drugs. Cenobamate is the one newly licensed medication for broader use in focal epilepsy but there have been a number of developments for specific disorders. We review new players and look forward to new developments in the light of evolving underlying science. We look at teratogenicity; old villains and new concerns in which clinicians play a vital role in explaining and balancing the risks. Medical treatment of status epilepticus, long without evidence, has benefitted from high-quality trials to inform practice; like buses, several arriving at once. Surgical treatment continues to be refined with improvements in the pre-surgical evaluation of patients, especially with new imaging techniques. Alternatives including stereotactic radiotherapy have received further focus and targets for palliative stimulation techniques have grown in number. Individuals' autonomy and quality of life continue to be the subject of research with refinement of what clinicians can do to help persons with epilepsy (PWE) achieve control. This includes seizure management but extends to broader considerations of human empowerment, needs and desires, which may be aided by emerging technologies such as seizure detection devices. The role of specialist nurses in improving that quality has been reinforced by specific endorsement from the International League against Epilepsy (ILAE).
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Affiliation(s)
- Joanna Fawcett
- Department of Neurology, Royal United Hospital, Bath, UK
| | - Sarah Davis
- Department of Neurology, Royal United Hospital, Bath, UK
| | - Mark Manford
- Department of Neurology, Royal United Hospital, Bath, UK.
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19
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Horsley JJ, Thomas RH, Chowdhury FA, Diehl B, McEvoy AW, Miserocchi A, de Tisi J, Vos SB, Walker MC, Winston GP, Duncan JS, Wang Y, Taylor PN. Complementary structural and functional abnormalities to localise epileptogenic tissue. ARXIV 2023:arXiv:2304.03192v3. [PMID: 37064531 PMCID: PMC10104180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Background When investigating suitability for epilepsy surgery, people with drug-refractory focal epilepsy may have intracranial EEG (iEEG) electrodes implanted to localise seizure onset. Diffusion-weighted magnetic resonance imaging (dMRI) may be acquired to identify key white matter tracts for surgical avoidance. Here, we investigate whether structural connectivity abnormalities, inferred from dMRI, may be used in conjunction with functional iEEG abnormalities to aid localisation of the epileptogenic zone (EZ), improving surgical outcomes in epilepsy. Methods We retrospectively investigated data from 43 patients with epilepsy who had surgery following iEEG. Twenty-five patients (58%) were free from disabling seizures (ILAE 1 or 2) at one year. Interictal iEEG functional, and dMRI structural connectivity abnormalities were quantified by comparison to a normative map and healthy controls. We explored whether the resection of maximal abnormalities related to improved surgical outcomes, in both modalities individually and concurrently. Additionally, we suggest how connectivity abnormalities may inform the placement of iEEG electrodes pre-surgically using a patient case study. Findings Seizure freedom was 15 times more likely in patients with resection of maximal connectivity and iEEG abnormalities (p=0.008). Both modalities separately distinguished patient surgical outcome groups and when used simultaneously, a decision tree correctly separated 36 of 43 (84%) patients. Interpretation Our results suggest that both connectivity and iEEG abnormalities may localise epileptogenic tissue, and that these two modalities may provide complementary information in pre-surgical evaluations. Funding This research was funded by UKRI, CDT in Cloud Computing for Big Data, NIH, MRC, Wellcome Trust and Epilepsy Research UK.
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Affiliation(s)
- Jonathan J. Horsley
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Rhys H. Thomas
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Fahmida A. Chowdhury
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Andrew W. McEvoy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sjoerd B. Vos
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Centre for Microscopy, Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
- Centre for Medical Image Computing, Computer Science Department, University College London, London, United Kingdom
| | - Matthew C. Walker
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Gavin P. Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Division of Neurology, Department of Medicine, Queen’s University, Kingston, Canada
| | - John S. Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Peter N. Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
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20
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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: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [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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21
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Monsoor T, Zhang Y, Daida A, Oana S, Lu Q, Hussain SA, Fallah A, Sankar R, Staba RJ, Speier W, Roychowdhury V, Nariai H. Optimizing detection and deep learning-based classification of pathological high-frequency oscillations in epilepsy. Clin Neurophysiol 2023; 154:129-140. [PMID: 37603979 PMCID: PMC10861270 DOI: 10.1016/j.clinph.2023.07.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 06/30/2023] [Accepted: 07/26/2023] [Indexed: 08/23/2023]
Abstract
OBJECTIVE This study aimed to explore sensitive detection methods for pathological high-frequency oscillations (HFOs) to improve seizure outcomes in epilepsy surgery. METHODS We analyzed interictal HFOs (80-500 Hz) in 15 children with medication-resistant focal epilepsy who underwent chronic intracranial electroencephalogram via subdural grids. The HFOs were assessed using the short-term energy (STE) and Montreal Neurological Institute (MNI) detectors and examined for spike association and time-frequency plot characteristics. A deep learning (DL)-based classification was applied to purify pathological HFOs. Postoperative seizure outcomes were correlated with HFO-resection ratios to determine the optimal HFO detection method. RESULTS The MNI detector identified a higher percentage of pathological HFOs than the STE detector, but some pathological HFOs were detected only by the STE detector. HFOs detected by both detectors had the highest spike association rate. The Union detector, which detects HFOs identified by either the MNI or STE detector, outperformed other detectors in predicting postoperative seizure outcomes using HFO-resection ratios before and after DL-based purification. CONCLUSIONS HFOs detected by standard automated detectors displayed different signal and morphological characteristics. DL-based classification effectively purified pathological HFOs. SIGNIFICANCE Enhancing the detection and classification methods of HFOs will improve their utility in predicting postoperative seizure outcomes.
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Affiliation(s)
- Tonmoy Monsoor
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Yipeng Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Atsuro Daida
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Shingo Oana
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Qiujing Lu
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Shaun A Hussain
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Aria Fallah
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Raman Sankar
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA; The UCLA Children's Discovery and Innovation Institute, Los Angeles, CA, USA
| | - Richard J Staba
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - William Speier
- Department of Bioengineering, University of California, Los Angeles, CA, USA; Department of Radiological Sciences, University of California, Los Angeles, CA, USA
| | - Vwani Roychowdhury
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Hiroki Nariai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA; The UCLA Children's Discovery and Innovation Institute, Los Angeles, CA, USA.
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22
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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] [Abstract] [Key Words] [MESH Headings] [Grants] [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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Affiliation(s)
- Lorenzo Ricci
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di Roma, Rome, Italy; Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Via Alvaro del Portillo, 200, 00128 Rome, Italy
| | - Eleonora Tamilia
- Fetal-Neonatal Neuroimaging Developmental Science Center, Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Mattia Mercier
- Rare and Complex Epilepsies, Department of Neurological Science, Bambino Gesù Children's Hospital, IRCCS, Member of European Reference Network EpiCARE, 00165 Rome, Italy
| | - Chiara Pepi
- Rare and Complex Epilepsies, Department of Neurological Science, Bambino Gesù Children's Hospital, IRCCS, Member of European Reference Network EpiCARE, 00165 Rome, Italy
| | - Giusy Carfì-Pavia
- Rare and Complex Epilepsies, Department of Neurological Science, Bambino Gesù Children's Hospital, IRCCS, Member of European Reference Network EpiCARE, 00165 Rome, Italy
| | - Alessandro De Benedictis
- Neurosurgery Unit, Department of Neurosciences, Bambino Gesù Children's Hospital, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
| | - Giovanni Assenza
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di Roma, Rome, Italy; Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Via Alvaro del Portillo, 200, 00128 Rome, Italy
| | - Vincenzo Di Lazzaro
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di Roma, Rome, Italy; Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Via Alvaro del Portillo, 200, 00128 Rome, Italy
| | - Federico Vigevano
- Department of Neurological Science, Bambino Gesù Children's Hospital, IRCCS, Member of European Reference Network EpiCARE, Rome, Italy
| | - Nicola Specchio
- Rare and Complex Epilepsies, Department of Neurological Science, Bambino Gesù Children's Hospital, IRCCS, Member of European Reference Network EpiCARE, 00165 Rome, Italy.
| | - Luca de Palma
- Rare and Complex Epilepsies, Department of Neurological Science, Bambino Gesù Children's Hospital, IRCCS, Member of European Reference Network EpiCARE, 00165 Rome, Italy
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23
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Monsoor T, Zhang Y, Daida A, Oana S, Lu Q, Hussain SA, Fallah A, Sankar R, Staba RJ, Speier W, Roychowdhury V, Nariai H. Optimizing Detection and Deep Learning-based Classification of Pathological High-Frequency Oscillations in Epilepsy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.13.23288435. [PMID: 37131743 PMCID: PMC10153337 DOI: 10.1101/2023.04.13.23288435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Objective This study aimed to explore sensitive detection methods and deep learning (DL)-based classification for pathological high-frequency oscillations (HFOs). Methods We analyzed interictal HFOs (80-500 Hz) in 15 children with medication-resistant focal epilepsy who underwent resection after chronic intracranial electroencephalogram via subdural grids. The HFOs were assessed using the short-term energy (STE) and Montreal Neurological Institute (MNI) detectors and examined for pathological features based on spike association and time-frequency plot characteristics. A DL-based classification was applied to purify pathological HFOs. Postoperative seizure outcomes were correlated with HFO-resection ratios to determine the optimal HFO detection method. Results The MNI detector identified a higher percentage of pathological HFOs than the STE detector, but some pathological HFOs were detected only by the STE detector. HFOs detected by both detectors exhibited the most pathological features. The Union detector, which detects HFOs identified by either the MNI or STE detector, outperformed other detectors in predicting postoperative seizure outcomes using HFO-resection ratios before and after DL-based purification. Conclusions HFOs detected by standard automated detectors displayed different signal and morphological characteristics. DL-based classification effectively purified pathological HFOs. Significance Enhancing the detection and classification methods of HFOs will improve their utility in predicting postoperative seizure outcomes. HIGHLIGHTS HFOs detected by the MNI detector showed different traits and higher pathological bias than those detected by the STE detectorHFOs detected by both MNI and STE detectors (the Intersection HFOs) were deemed the most pathologicalA deep learning-based classification was able to distill pathological HFOs, regard-less of the initial HFO detection methods.
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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] [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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Thomas J, Kahane P, Abdallah C, Avigdor T, Zweiphenning WJEM, Chabardes S, Jaber K, Latreille V, Minotti L, Hall J, Dubeau F, Gotman J, Frauscher B. A Subpopulation of Spikes Predicts Successful Epilepsy Surgery Outcome. Ann Neurol 2023; 93:522-535. [PMID: 36373178 DOI: 10.1002/ana.26548] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 11/09/2022] [Accepted: 11/11/2022] [Indexed: 11/15/2022]
Abstract
OBJECTIVE Epileptic spikes are the traditional interictal electroencephalographic (EEG) biomarker for epilepsy. Given their low specificity for identifying the epileptogenic zone (EZ), they are given only moderate attention in presurgical evaluation. This study aims to demonstrate that it is possible to identify specific spike features in intracranial EEG that optimally define the EZ and predict surgical outcome. METHODS We analyzed spike features on stereo-EEG segments from 83 operated patients from 2 epilepsy centers (37 Engel IA) in wakefulness, non-rapid eye movement sleep, and rapid eye movement sleep. After automated spike detection, we investigated 135 spike features based on rate, morphology, propagation, and energy to determine the best feature or feature combination to discriminate the EZ in seizure-free and non-seizure-free patients by applying 4-fold cross-validation. RESULTS The rate of spikes with preceding gamma activity in wakefulness performed better for surgical outcome classification (4-fold area under receiver operating characteristics curve [AUC] = 0.755 ± 0.07) than the seizure onset zone, the current gold standard (AUC = 0.563 ± 0.05, p = 0.015) and the ripple rate, an emerging seizure-independent biomarker (AUC = 0.537 ± 0.07, p = 0.006). Channels with a spike-gamma rate exceeding 1.9/min had an 80% probability of being in the EZ. Combining features did not improve the results. INTERPRETATION Resection of brain regions with high spike-gamma rates in wakefulness is associated with a high probability of achieving seizure freedom. This rate could be applied to determine the minimal number of spiking channels requiring resection. In addition to quantitative analysis, this feature is easily accessible to visual analysis, which could aid clinicians during presurgical evaluation. ANN NEUROL 2023;93:522-535.
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Affiliation(s)
- John Thomas
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Philippe Kahane
- Grenoble Alpes University Hospital Center, Grenoble Alpes University, Inserm, U1216, Grenoble Institute Neurosciences, Grenoble, France
| | - Chifaou Abdallah
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Tamir Avigdor
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Willemiek J E M Zweiphenning
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.,University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Stephan Chabardes
- Grenoble Alpes University Hospital Center, Grenoble Alpes University, Inserm, U1216, Grenoble Institute Neurosciences, Grenoble, France
| | - Kassem Jaber
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Véronique Latreille
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Lorella Minotti
- Grenoble Alpes University Hospital Center, Grenoble Alpes University, Inserm, U1216, Grenoble Institute Neurosciences, Grenoble, France
| | - Jeff Hall
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - François Dubeau
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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26
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Shen M, Niu H, Xia Q, Zou B, Zheng Y, Zhao Y, Li L, Liu X, Zhang L. Automatic Epileptic Tissue Localization Through Spatial Pattern Clustering of High Frequency Activity. IEEE Trans Neural Syst Rehabil Eng 2023; 31:981-990. [PMID: 37021907 DOI: 10.1109/tnsre.2023.3237226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
High-frequency activity (HFA) in intracranial electroencephalography recordings are diagnostic biomarkers for refractory epilepsy. Clinical utilities based on HFA have been extensively examined. HFA often exhibits different spatial patterns corresponding to specific states of neural activation, which will potentially improve epileptic tissue localization. However, research on quantitative measurement and separation of such patterns is still lacking. In this paper, spatial pattern clustering of HFA (SPC-HFA) is developed. The process is composed of three steps: (1) feature extraction: skewness which quantifies the intensity of HFA is extracted; (2) clustering: k-means clustering is applied to separate column vectors within the feature matrix into intrinsic spatial patterns; (3) localization: the determination of epileptic tissue is performed based on the cluster centroid with HFA expanding to the largest spatial extent. Experiments were conducted on a public iEEG dataset with 20 patients. Compared with existing localization methods, SPC-HFA demonstrates improvement (Cohen's d $>0.2$ ) and ranks top in 10 out of 20 patients in terms of the area under the curve. In addition, after extending SPC-HFA to high-frequency oscillation detection algorithms, corresponding localization results also improve with effect size Cohen's d $\geq 0.48$ . Therefore, SPC-HFA can be utilized to guide clinical and surgical treatment of refractory epilepsy.
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27
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Zhang Y, Chung H, Ngo JP, Monsoor T, Hussain SA, Matsumoto JH, Walshaw PD, Fallah A, Sim MS, Asano E, Sankar R, Staba RJ, Engel J, Speier W, Roychowdhury V, Nariai H. Characterizing physiological high-frequency oscillations using deep learning. J Neural Eng 2022; 19:10.1088/1741-2552/aca4fa. [PMID: 36541546 PMCID: PMC10364130 DOI: 10.1088/1741-2552/aca4fa] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 11/22/2022] [Indexed: 11/23/2022]
Abstract
Objective.Intracranially-recorded interictal high-frequency oscillations (HFOs) have been proposed as a promising spatial biomarker of the epileptogenic zone. However, HFOs can also be recorded in the healthy brain regions, which complicates the interpretation of HFOs. The present study aimed to characterize salient features of physiological HFOs using deep learning (DL).Approach.We studied children with neocortical epilepsy who underwent intracranial strip/grid evaluation. Time-series EEG data were transformed into DL training inputs. The eloquent cortex (EC) was defined by functional cortical mapping and used as a DL label. Morphological characteristics of HFOs obtained from EC (ecHFOs) were distilled and interpreted through a novel weakly supervised DL model.Main results.A total of 63 379 interictal intracranially-recorded HFOs from 18 children were analyzed. The ecHFOs had lower amplitude throughout the 80-500 Hz frequency band around the HFO onset and also had a lower signal amplitude in the low frequency band throughout a one-second time window than non-ecHFOs, resembling a bell-shaped template in the time-frequency map. A minority of ecHFOs were HFOs with spikes (22.9%). Such morphological characteristics were confirmed to influence DL model prediction via perturbation analyses. Using the resection ratio (removed HFOs/detected HFOs) of non-ecHFOs, the prediction of postoperative seizure outcomes improved compared to using uncorrected HFOs (area under the ROC curve of 0.82, increased from 0.76).Significance.We characterized salient features of physiological HFOs using a DL algorithm. Our results suggested that this DL-based HFO classification, once trained, might help separate physiological from pathological HFOs, and efficiently guide surgical resection using HFOs.
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Affiliation(s)
- Yipeng Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Hoyoung Chung
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Jacquline P. Ngo
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Tonmoy Monsoor
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Shaun A. Hussain
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Joyce H. Matsumoto
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Patricia D. Walshaw
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Aria Fallah
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Myung Shin Sim
- Department of Medicine, Statistics Core, University of California, Los Angeles, CA, USA
| | - Eishi Asano
- Department of Pediatrics and Neurology, Children’s Hospital of Michigan, Wayne State University School of Medicine, Detroit, MI, USA
| | - Raman Sankar
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
- The UCLA Children’s Discovery and Innovation Institute, Los Angeles, CA, USA
| | - Richard J. Staba
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Jerome Engel
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
- Department of Neurobiology, University of California, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
- The Brain Research Institute, University of California, Los Angeles, CA, USA
| | - William Speier
- Department of Radiological Sciences, University of California, Los Angeles, CA, USA
- Department of Bioengineering, University of California, Los Angeles, CA, USA
| | - Vwani Roychowdhury
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Hiroki Nariai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
- The UCLA Children’s Discovery and Innovation Institute, Los Angeles, CA, USA
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28
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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: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The present study investigates the effectiveness of a deep learning neural network for non-invasively localizing the seizure onset zone (SOZ) using multi-modal MRI data that are clinically acquired from children with drug-resistant epilepsy. A cortical parcellation was applied to localize the SOZ in cortical nodes of the epileptogenic hemisphere. At each node, the laminar surface analysis was followed to sample 1) the relative intensity of gray matter and white matter in multi-modal MRI and 2) the neighboring white matter connectivity using diffusion tractography edge strengths. A cross-validation was employed to train and test all layers of a multi-scale residual neural network (msResNet) that can classify SOZ node in an end-to-end fashion. A prediction probability of a given node belonging to the SOZ class was proposed as a non-invasive MRI marker of seizure onset likelihood. In an independent validation cohort, the proposed MRI marker provided a very large effect size of Cohen's d = 1.21 between SOZ and non-SOZ, and classified SOZ with a balanced accuracy of 0.75 in lesional and 0.67 in non-lesional MRI groups. The subsequent multi-variate logistic regression found the incorporation of the proposed MRI marker into interictal intracranial EEG (iEEG) markers further improves the differentiation between the epileptogenic focus (defined as SOZ resected during surgery) and non-epileptogenic sites (i.e., non-SOZ sites preserved during surgery) up to 15 % in non-lesional MRI group, suggesting that the proposed MRI marker could improve the localization of epileptogenic foci for successful pediatric epilepsy surgery.
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Petito GT, Housekeeper J, Buroker J, Scholle C, Ervin B, Frink C, Greiner HM, Skoch J, Mangano FT, Dye TJ, Hogenesch JB, Glauser TA, Holland KD, Arya R. Diurnal rhythm of spontaneous intracranial high-frequency oscillations. Seizure 2022; 102:105-112. [DOI: 10.1016/j.seizure.2022.09.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 09/05/2022] [Accepted: 09/28/2022] [Indexed: 11/27/2022] Open
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30
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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: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [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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31
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Bernabei JM, Sinha N, Arnold TC, Conrad E, Ong I, Pattnaik AR, Stein JM, Shinohara RT, Lucas TH, Bassett DS, Davis KA, Litt B. Normative intracranial EEG maps epileptogenic tissues in focal epilepsy. Brain 2022; 145:1949-1961. [PMID: 35640886 PMCID: PMC9630716 DOI: 10.1093/brain/awab480] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 11/14/2021] [Accepted: 11/26/2021] [Indexed: 07/25/2023] Open
Abstract
Planning surgery for patients with medically refractory epilepsy often requires recording seizures using intracranial EEG. Quantitative measures derived from interictal intracranial EEG yield potentially appealing biomarkers to guide these surgical procedures; however, their utility is limited by the sparsity of electrode implantation as well as the normal confounds of spatiotemporally varying neural activity and connectivity. We propose that comparing intracranial EEG recordings to a normative atlas of intracranial EEG activity and connectivity can reliably map abnormal regions, identify targets for invasive treatment and increase our understanding of human epilepsy. Merging data from the Penn Epilepsy Center and a public database from the Montreal Neurological Institute, we aggregated interictal intracranial EEG retrospectively across 166 subjects comprising >5000 channels. For each channel, we calculated the normalized spectral power and coherence in each canonical frequency band. We constructed an intracranial EEG atlas by mapping the distribution of each feature across the brain and tested the atlas against data from novel patients by generating a z-score for each channel. We demonstrate that for seizure onset zones within the mesial temporal lobe, measures of connectivity abnormality provide greater distinguishing value than univariate measures of abnormal neural activity. We also find that patients with a longer diagnosis of epilepsy have greater abnormalities in connectivity. By integrating measures of both single-channel activity and inter-regional functional connectivity, we find a better accuracy in predicting the seizure onset zones versus normal brain (area under the curve = 0.77) compared with either group of features alone. We propose that aggregating normative intracranial EEG data across epilepsy centres into a normative atlas provides a rigorous, quantitative method to map epileptic networks and guide invasive therapy. We publicly share our data, infrastructure and methods, and propose an international framework for leveraging big data in surgical planning for refractory epilepsy.
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Affiliation(s)
- John M Bernabei
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Nishant Sinha
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 USA
| | - T Campbell Arnold
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Erin Conrad
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Ian Ong
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Akash R Pattnaik
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Joel M Stein
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Timothy H Lucas
- Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- The Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Kathryn A Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Brian Litt
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
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Sakakura K, Sonoda M, Mitsuhashi T, Kuroda N, Firestone E, O'Hara N, Iwaki H, Lee MH, Jeong JW, Rothermel R, Luat AF, Asano E. Developmental organization of neural dynamics supporting auditory perception. Neuroimage 2022; 258:119342. [PMID: 35654375 PMCID: PMC9354710 DOI: 10.1016/j.neuroimage.2022.119342] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 05/09/2022] [Accepted: 05/29/2022] [Indexed: 11/28/2022] Open
Abstract
Purpose: A prominent view of language acquisition involves learning to ignore irrelevant auditory signals through functional reorganization, enabling more efficient processing of relevant information. Yet, few studies have characterized the neural spatiotemporal dynamics supporting rapid detection and subsequent disregard of irrelevant auditory information, in the developing brain. To address this unknown, the present study modeled the developmental acquisition of cost-efficient neural dynamics for auditory processing, using intracranial electrocorticographic responses measured in individuals receiving standard-of-care treatment for drug-resistant, focal epilepsy. We also provided evidence demonstrating the maturation of an anterior-to-posterior functional division within the superior-temporal gyrus (STG), which is known to exist in the adult STG. Methods: We studied 32 patients undergoing extraoperative electrocorticography (age range: eight months to 28 years) and analyzed 2,039 intracranial electrode sites outside the seizure onset zone, interictal spike-generating areas, and MRI lesions. Patients were given forward (normal) speech sounds, backward-played speech sounds, and signal-correlated noises during a task-free condition. We then quantified sound processing-related neural costs at given time windows using high-gamma amplitude at 70–110 Hz and animated the group-level high-gamma dynamics on a spatially normalized three-dimensional brain surface. Finally, we determined if age independently contributed to high-gamma dynamics across brain regions and time windows. Results: Group-level analysis of noise-related neural costs in the STG revealed developmental enhancement of early high-gamma augmentation and diminution of delayed augmentation. Analysis of speech-related high-gamma activity demonstrated an anterior-to-posterior functional parcellation in the STG. The left anterior STG showed sustained augmentation throughout stimulus presentation, whereas the left posterior STG showed transient augmentation after stimulus onset. We found a double dissociation between the locations and developmental changes in speech sound-related high-gamma dynamics. Early left anterior STG high-gamma augmentation (i.e., within 200 ms post-stimulus onset) showed developmental enhancement, whereas delayed left posterior STG high-gamma augmentation declined with development. Conclusions: Our observations support the model that, with age, the human STG refines neural dynamics to rapidly detect and subsequently disregard uninformative acoustic noises. Our study also supports the notion that the anterior-to-posterior functional division within the left STG is gradually strengthened for efficient speech sound perception after birth.
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Affiliation(s)
- Kazuki Sakakura
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan, 48201, USA.; Department of Neurosurgery, University of Tsukuba, Tsukuba, 3058575, Japan
| | - Masaki Sonoda
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan, 48201, USA.; Department of Neurosurgery, Yokohama City University, Yokohama, Kanagawa, 2360004, Japan
| | - Takumi Mitsuhashi
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan, 48201, USA.; Department of Neurosurgery, Juntendo University, School of Medicine, Tokyo, 1138421, Japan
| | - Naoto Kuroda
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan, 48201, USA.; Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, 9808575, Japan
| | - Ethan Firestone
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan, 48201, USA.; Department of Physiology, Wayne State University, Detroit, MI 48201, USA
| | - Nolan O'Hara
- Translational Neuroscience Program, Wayne State University, Detroit, Michigan, 48201, USA
| | - Hirotaka Iwaki
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan, 48201, USA.; Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, 9808575, Japan
| | - Min-Hee Lee
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan, 48201, USA
| | - Jeong-Won Jeong
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan, 48201, USA.; Department of Neurology, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan, 48201, USA.; Translational Neuroscience Program, Wayne State University, Detroit, Michigan, 48201, USA
| | - Robert Rothermel
- Department of Psychiatry, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan, 48201, USA
| | - Aimee F Luat
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan, 48201, USA.; Department of Neurology, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan, 48201, USA.; Department of Pediatrics, Central Michigan University, Mt. Pleasant, MI 48858, USA
| | - Eishi Asano
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan, 48201, USA.; Department of Neurology, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, Michigan, 48201, USA.; Translational Neuroscience Program, Wayne State University, Detroit, Michigan, 48201, USA..
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Sonoda M, Rothermel R, Carlson A, Jeong JW, Lee MH, Hayashi T, Luat AF, Sood S, Asano E. Naming-related spectral responses predict neuropsychological outcome after epilepsy surgery. Brain 2022; 145:517-530. [PMID: 35313351 PMCID: PMC9014727 DOI: 10.1093/brain/awab318] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 07/14/2021] [Accepted: 07/28/2021] [Indexed: 11/12/2022] Open
Abstract
This prospective study determined the use of intracranially recorded spectral responses during naming tasks in predicting neuropsychological performance following epilepsy surgery. We recruited 65 patients with drug-resistant focal epilepsy who underwent preoperative neuropsychological assessment and intracranial EEG recording. The Clinical Evaluation of Language Fundamentals evaluated the baseline and postoperative language function. During extra-operative intracranial EEG recording, we assigned patients to undergo auditory and picture naming tasks. Time-frequency analysis determined the spatiotemporal characteristics of naming-related amplitude modulations, including high gamma augmentation at 70-110 Hz. We surgically removed the presumed epileptogenic zone based on the intracranial EEG and MRI abnormalities while maximally preserving the eloquent areas defined by electrical stimulation mapping. The multivariate regression model incorporating auditory naming-related high gamma augmentation predicted the postoperative changes in Core Language Score with r2 of 0.37 and in Expressive Language Index with r2 of 0.32. Independently of the effects of epilepsy and neuroimaging profiles, higher high gamma augmentation at the resected language-dominant hemispheric area predicted a more severe postoperative decline in Core Language Score and Expressive Language Index. Conversely, the model incorporating picture naming-related high gamma augmentation predicted the change in Receptive Language Index with an r2 of 0.50. Higher high gamma augmentation independently predicted a more severe postoperative decline in Receptive Language Index. Ancillary regression analysis indicated that naming-related low gamma augmentation and alpha/beta attenuation likewise independently predicted a more severe Core Language Score decline. The machine learning-based prediction model suggested that naming-related high gamma augmentation, among all spectral responses used as predictors, most strongly contributed to the improved prediction of patients showing a >5-point Core Language Score decline (reflecting the lower 25th percentile among patients). We generated the model-based atlas visualizing sites, which, if resected, would lead to such a language decline. With a 5-fold cross-validation procedure, the auditory naming-based model predicted patients who had such a postoperative language decline with an accuracy of 0.80. The model indicated that virtual resection of an electrical stimulation mapping-defined language site would have increased the relative risk of the Core Language Score decline by 5.28 (95% confidence interval: 3.47-8.02). Especially, that of an electrical stimulation mapping-defined receptive language site would have maximized it to 15.90 (95% confidence interval: 9.59-26.33). In summary, naming-related spectral responses predict neuropsychological outcomes after epilepsy surgery. We have provided our prediction model as an open-source material, which will indicate the postoperative language function of future patients and facilitate external validation at tertiary epilepsy centres.
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Affiliation(s)
- Masaki Sonoda
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Department of Neurosurgery, Yokohama City University, Yokohama, Kanagawa 2360004, Japan
| | - Robert Rothermel
- Department of Psychiatry, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Alanna Carlson
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Department of Psychiatry, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Jeong-Won Jeong
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Department of Neurology, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Min-Hee Lee
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Takahiro Hayashi
- Department of Neurosurgery, Yokohama City University, Yokohama, Kanagawa 2360004, Japan
| | - Aimee F Luat
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Department of Neurology, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Department of Pediatrics, Central Michigan University, Mount Pleasant, MI 48858, USA
| | - Sandeep Sood
- Department of Neurosurgery, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Eishi Asano
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Department of Neurology, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Correspondence to: Eishi Asano, MD, PhD, MS (CRDSA) Division of Pediatric Neurology, Children’s Hospital of Michigan Wayne State University. 3901 Beaubien St., Detroit, MI 48201, USA E-mail:
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Ohki T. Measuring Phase-Amplitude Coupling between Neural Oscillations of Different Frequencies via the Wasserstein Distance. J Neurosci Methods 2022; 374:109578. [PMID: 35339506 DOI: 10.1016/j.jneumeth.2022.109578] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 03/03/2022] [Accepted: 03/20/2022] [Indexed: 12/01/2022]
Abstract
BACKGROUND Phase-amplitude coupling (PAC) is a key neuronal mechanism. Here, a novel method for quantifying PAC via the Wasserstein distance is presented. NEW METHOD The Wasserstein distance is an optimization algorithm for minimizing transportation cost and distance. For the first time, the author has applied this distance function to quantify PAC and named the Wasserstein Modulation Index (wMI). As the wMI accommodates the product of the amplitude value in each phase position and the coupling phase position, it allows for extraction of more detailed PAC features from the data. RESULTS The validity of the wMI calculations was examined using various simulation data, including sinusoidal and non-sinusoidal waves and empirical data sets. The current findings showed that the wMI is a more robust and stable index for quantifying PAC under various measuring conditions. Specifically, it can better reflect the timing of coupling and distinguish the shape of the coupling distribution than other measurements, both of which are the most significant parameters related to the functionality of PAC. Furthermore, the wMI is also suitable for many applications, such as more data-driven approaches and direct comparisons. COMPARISON WITH EXISTING METHOD(S) Compared with Euler-based PAC methods and the MI, the wMI is not easily affected by the non-sinusoidal nature of neural oscillation and the short data length and enables better reflection of the natures of PAC, such as the timing of coupling and the amplitude distribution in the phase plane, than the MI. CONCLUSION The wMI is expected to extract more detailed PAC characteristics, which could considerably contribute to the neuroscience field.
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Affiliation(s)
- Takefumi Ohki
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo 113-0033, Japan.
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Mitsuhashi T, Sonoda M, Firestone E, Sakakura K, Jeong JW, Luat AF, Sood S, Asano E. Temporally and functionally distinct large-scale brain network dynamics supporting task switching. Neuroimage 2022; 254:119126. [PMID: 35331870 PMCID: PMC9173207 DOI: 10.1016/j.neuroimage.2022.119126] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 02/25/2022] [Accepted: 03/20/2022] [Indexed: 11/04/2022] Open
Abstract
Objective: Our daily activities require frequent switches among competing responses at the millisecond time scale. We determined the spatiotemporal characteristics and functional significance of rapid, large-scale brain network dynamics during task switching. Methods: This cross-sectional study investigated patients with drug-resistant focal epilepsy who played a Lumosity cognitive flexibility training game during intracranial electroencephalography (iEEG) recording. According to a given task rule, unpredictably switching across trials, participants had to swipe the screen in the direction the stimulus was pointing or moving. Using this data, we described the spatiotemporal characteristics of iEEG high-gamma augmentation occurring more intensely during switch than repeat trials, unattributable to the effect of task rule (pointing or moving), within-stimulus congruence (the direction of stimulus pointing and moving was same or different in a given trial), or accuracy of an immediately preceding response. Diffusion-weighted imaging (DWI) tractography determined whether distant cortical regions showing enhanced activation during task switch trials were directly connected by white matter tracts. Trial-by-trial iEEG analysis deduced whether the intensity of task switch-related high-gamma augmentation was altered through practice and whether high-gamma amplitude predicted the accuracy of an upcoming response among switch trials. Results: The average number of completed trials during five-minute gameplay was 221.4 per patient (range: 171–285). Task switch trials increased the response times, whereas later trials reduced them. Analysis of iEEG signals sampled from 860 brain sites effectively elucidated the distinct spatiotemporal characteristics of task switch, task rule, and post-error-specific high-gamma modulations. Post-cue, task switch-related high-gamma augmentation was initiated in the right calcarine cortex after 260 ms, right precuneus after 330 ms, right entorhinal after 420 ms, and bilateral anterior middle-frontal gyri after 450 ms. DWI tractography successfully showed the presence of direct white matter tracts connecting the right visual areas to the precuneus and anterior middle-frontal regions but not between the right precuneus and anterior middle-frontal regions. Task-related high-gamma amplitudes in later trials were reduced in the calcarine, entorhinal and anterior middle-frontal regions, but increased in the precuneus. Functionally, enhanced post-cue precuneus high-gamma augmentation improved the accuracy of subsequent responses among switch trials. Conclusions: Our multimodal analysis uncovered two temporally and functionally distinct network dynamics supporting task switching. High-gamma augmentation in the visual-precuneus pathway may reflect the neural process facilitating an attentional shift to a given updated task rule. High-gamma activity in the visual-dorsolateral prefrontal pathway, rapidly reduced through practice, may reflect the cost of executing appropriate stimulus-response translation.
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Affiliation(s)
- 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
| | - 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, 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
| | - 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
| | - 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.
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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: 0.3] [Reference Citation Analysis] [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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Yao S, Zhu J, Li S, Zhang R, Zhao J, Yang X, Wang Y. Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021. Front Psychiatry 2022; 13:830819. [PMID: 35677873 PMCID: PMC9167960 DOI: 10.3389/fpsyt.2022.830819] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 04/05/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND With the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis. METHODS QEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform (bibliometric.com) were employed to perform bibliographic and visualized analysis. RESULTS A total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends. CONCLUSION The present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.
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Affiliation(s)
- Shun Yao
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Jieying Zhu
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Shuiyan Li
- Department of Rehabilitation Medicine, School of Rehabilitation Medicine, Southern Medical University, Guangzhou, China
| | - Ruibin Zhang
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Jiubo Zhao
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Xueling Yang
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - You Wang
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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High-frequency oscillations in scalp EEG: A systematic review of methodological choices and clinical findings. Clin Neurophysiol 2022; 137:46-58. [PMID: 35272185 DOI: 10.1016/j.clinph.2021.12.017] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/13/2021] [Accepted: 12/21/2021] [Indexed: 02/08/2023]
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Klimes P, Peter-Derex L, Hall J, Dubeau F, Frauscher B. Spatio-temporal spike dynamics predict surgical outcome in adult focal epilepsy. Clin Neurophysiol 2021; 134:88-99. [PMID: 34991017 DOI: 10.1016/j.clinph.2021.10.023] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 10/27/2021] [Accepted: 10/29/2021] [Indexed: 01/05/2023]
Abstract
OBJECTIVE We hypothesized that spatio-temporal dynamics of interictal spikes reflect the extent and stability of epileptic sources and determine surgical outcome. METHODS We studied 30 consecutive patients (14 good outcome). Spikes were detected in prolonged stereo-electroencephalography recordings. We quantified the spatio-temporal dynamics of spikes using the variance of the spike rate, line length and skewness of the spike distribution, and related these features to outcome. We built a logistic regression model, and compared its performance to traditional markers. RESULTS Good outcome patients had more dominant and stable sources than poor outcome patients as expressed by a higher variance of spike rates, a lower variance of line length, and a lower variance of positive skewness (ps < 0.05). The outcome was correctly predicted in 80% of patients. This was better or non-inferior to predictions based on a focal lesion (p = 0.016), focal seizure-onset zone, or complete resection (ps > 0.05). In the five patients where traditional markers failed, spike distribution predicted the outcome correctly. The best results were achieved by 18-h periods or longer. CONCLUSIONS Analysis of spike dynamics shows that surgery outcome depends on strong, single and stable sources. SIGNIFICANCE Our quantitative method has the potential to be a reliable predictor of surgical outcome.
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Affiliation(s)
- Petr Klimes
- Analytical Neurophysiology Lab, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czech Republic.
| | - Laure Peter-Derex
- Analytical Neurophysiology Lab, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Center for Sleep Medicine and Respiratory Diseases, Lyon University Hospital, Lyon 1 University, Lyon, France; Lyon Neuroscience Research Center, Lyon, France
| | - Jeff Hall
- Montreal Neurological Hospital, McGill University, Montreal, Quebec, Canada
| | - François Dubeau
- Montreal Neurological Hospital, McGill University, Montreal, Quebec, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
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Zweiphenning WJEM, von Ellenrieder N, Dubeau F, Martineau L, Minotti L, Hall JA, Chabardes S, Dudley R, Kahane P, Gotman J, Frauscher B. Correcting for physiological ripples improves epileptic focus identification and outcome prediction. Epilepsia 2021; 63:483-496. [PMID: 34919741 PMCID: PMC9300035 DOI: 10.1111/epi.17145] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 11/30/2021] [Accepted: 11/30/2021] [Indexed: 11/30/2022]
Abstract
Objective The integration of high‐frequency oscillations (HFOs; ripples [80–250 Hz], fast ripples [250–500 Hz]) in epilepsy evaluation is hampered by physiological HFOs, which cannot be reliably differentiated from pathological HFOs. We evaluated whether defining abnormal HFO rates by statistical comparison to region‐specific physiological HFO rates observed in the healthy brain improves identification of the epileptic focus and surgical outcome prediction. Methods We detected HFOs in 151 consecutive patients who underwent stereo‐electroencephalography and subsequent resective epilepsy surgery at two tertiary epilepsy centers. We compared how HFOs identified the resection cavity and predicted seizure‐free outcome using two thresholds from the literature (HFO rate > 1/min; 50% of the total number of a patient's HFOs) and three thresholds based on normative rates from the Montreal Neurological Institute Open iEEG Atlas (https://mni‐open‐ieegatlas.research.mcgill.ca/): global Atlas threshold, regional Atlas threshold, and regional + 10% threshold after regional Atlas correction. Results Using ripples, the regional + 10% threshold performed best for focus identification (77.3% accuracy, 27% sensitivity, 97.1% specificity, 80.6% positive predictive value [PPV], 78.2% negative predictive value [NPV]) and outcome prediction (69.5% accuracy, 58.6% sensitivity, 76.3% specificity, 60.7% PPV, 74.7% NPV). This was an improvement for focus identification (+1.1% accuracy, +17.0% PPV; p < .001) and outcome prediction (+12.0% sensitivity, +1.0% PPV; p = .05) compared to the 50% threshold. The improvement was particularly marked for foci in cortex, where physiological ripples are frequent (outcome: +35.3% sensitivity, +5.3% PPV; p = .014). In these cases, the regional + 10% threshold outperformed fast ripple rate > 1/min (+3.6% accuracy, +26.5% sensitivity, +21.6% PPV; p < .001) and seizure onset zone (+13.5% accuracy, +29.4% sensitivity, +17.0% PPV; p < .05–.01) for outcome prediction. Normalization did not improve the performance of fast ripples. Significance Defining abnormal HFO rates by statistical comparison to rates in healthy tissue overcomes an important weakness in the clinical use of ripples. It improves focus identification and outcome prediction compared to standard HFO measures, increasing their clinical applicability.
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Affiliation(s)
- Willemiek J E M Zweiphenning
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.,University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | | | - François Dubeau
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Laurence Martineau
- Department of Neurology, Grenoble-Alpes University Hospital and Grenoble-Alpes University, Grenoble, France
| | - Lorella Minotti
- Department of Neurology, Grenoble-Alpes University Hospital and Grenoble-Alpes University, Grenoble, France
| | - Jeffery A Hall
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Stephan Chabardes
- Department of Neurosurgery, Grenoble-Alpes University Hospital and Grenoble-Alpes University, Grenoble, France
| | - Roy Dudley
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Philippe Kahane
- Department of Neurology, Grenoble-Alpes University Hospital and Grenoble-Alpes University, Grenoble, France
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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Miyakoshi M, Nariai H, Rajaraman RR, Bernardo D, Shrey DW, Lopour BA, Sim MS, Staba RJ, Hussain SA. Automated preprocessing and phase-amplitude coupling analysis of scalp EEG discriminates infantile spasms from controls during wakefulness. Epilepsy Res 2021; 178:106809. [PMID: 34823159 DOI: 10.1016/j.eplepsyres.2021.106809] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 10/26/2021] [Accepted: 11/02/2021] [Indexed: 10/19/2022]
Abstract
OBJECTIVE Delta-gamma phase-amplitude coupling in EEG is useful for localizing epileptic sources and to evaluate severity in children with infantile spasms. We (1) develop an automated EEG preprocessing pipeline to clean data using artifact subspace reconstruction (ASR) and independent component (IC) analysis (ICA) and (2) evaluate delta-gamma modulation index (MI) as a method to distinguish children with epileptic spasms (cases) from normal controls during sleep and awake. METHODS Using 400 scalp EEG datasets (200 sleep, 200 awake) from 100 subjects, we calculated MI after applying high-pass and line-noise filters (Clean 0), and after ASR followed by either conservative (Clean 1) or stringent (Clean 2) artifactual IC rejection. Classification of cases and controls using MI was evaluated with Receiver Operating Characteristics (ROC) to obtain area under curve (AUC). RESULTS The artifact rejection algorithm reduced raw signal variance by 29-45% and 38-60% for Clean 1 and Clean 2, respectively. MI derived from sleep data, with or without preprocessing, robustly classified the groups (all AUC > 0.98). In contrast, group classification using MI derived from awake data was successful only after Clean 2 (AUC = 0.85). CONCLUSIONS We have developed an automated EEG preprocessing pipeline to perform artifact rejection and quantify delta-gamma modulation index.
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Affiliation(s)
- Makoto Miyakoshi
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, United States
| | - Hiroki Nariai
- David Geffen School of Medicine, Department of Pediatrics, University of California Los Angeles, United States.
| | - Rajsekar R Rajaraman
- David Geffen School of Medicine, Department of Pediatrics, University of California Los Angeles, United States
| | | | - Daniel W Shrey
- Children's Hospital of Orange County, Neurology, University of California, Irvine, Pediatrics, United States
| | - Beth A Lopour
- Henry Samueli School of Engineering, University of California Irvine, United States
| | - Myung Shin Sim
- Division of General Internal Medicine and Health Services Research, Department of Medicine Statistics Core, University of California Los Angeles, United States
| | - Richard J Staba
- David Geffen School of Medicine, Department of Neurology, University of California Los Angeles, United States
| | - Shaun A Hussain
- David Geffen School of Medicine, Department of Pediatrics, University of California Los Angeles, United States
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Numata-Uematsu Y, Uematsu M, Sakuraba R, Iwasaki M, Osawa S, Jin K, Nakasato N, Kure S. The Onset of Interictal Spike-Related Ripples Facilitates Detection of the Epileptogenic Zone. Front Neurol 2021; 12:724417. [PMID: 34803874 PMCID: PMC8599368 DOI: 10.3389/fneur.2021.724417] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 10/14/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: Accurate estimation of the epileptogenic zone (EZ) is essential for favorable outcomes in epilepsy surgery. Conventional ictal electrocorticography (ECoG) onset is generally used to detect the EZ but is insufficient in achieving seizure-free outcomes. By contrast, high-frequency oscillations (HFOs) could be useful markers of the EZ. Hence, we aimed to detect the EZ using interictal spikes and investigated whether the onset area of interictal spike-related HFOs was within the EZ. Methods: The EZ is considered to be included in the resection area among patients with seizure-free outcomes after surgery. Using a complex demodulation technique, we developed a method to determine the onset channels of interictal spike-related ripples (HFOs of 80-200 Hz) and investigated whether they are within the resection area. Results: We retrospectively examined 12 serial patients who achieved seizure-free status after focal resection surgery. Using the method that we developed, we determined the onset channels of interictal spike-related ripples and found that for all 12 patients, they were among the resection channels. The onset frequencies of ripples were in the range of 80-150 Hz. However, the ictal onset channels (evaluated based on ictal ECoG patterns) and ripple onset channels coincided in only 3 of 12 patients. Conclusions: Determining the onset area of interictal spike-related ripples could facilitate EZ estimation. This simple method that utilizes interictal ECoG may aid in preoperative evaluation and improve epilepsy surgery outcomes.
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Affiliation(s)
| | - Mitsugu Uematsu
- Department of Pediatrics, Tohoku University School of Medicine, Sendai, Japan
| | - Rie Sakuraba
- Department of Epileptology, Tohoku University School of Medicine, Sendai, Japan
| | - Masaki Iwasaki
- Department of Neurosurgery, Tohoku University School of Medicine, Sendai, Japan.,Department of Neurosurgery, National Center Hospital of Neurology and Psychiatry, Tokyo, Japan
| | - Shinichiro Osawa
- Department of Neurosurgery, Tohoku University School of Medicine, Sendai, Japan
| | - Kazutaka Jin
- Department of Epileptology, Tohoku University School of Medicine, Sendai, Japan
| | - Nobukazu Nakasato
- Department of Epileptology, Tohoku University School of Medicine, Sendai, Japan
| | - Shigeo Kure
- Department of Pediatrics, Tohoku University School of Medicine, Sendai, Japan
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43
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Zhang Y, Lu Q, Monsoor T, Hussain SA, Qiao JX, Salamon N, Fallah A, Sim MS, Asano E, Sankar R, Staba RJ, Engel J, Speier W, Roychowdhury V, Nariai H. Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach. Brain Commun 2021; 4:fcab267. [PMID: 35169696 PMCID: PMC8833577 DOI: 10.1093/braincomms/fcab267] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Revised: 09/29/2021] [Accepted: 10/04/2021] [Indexed: 11/12/2022] Open
Abstract
Intracranially recorded interictal high-frequency oscillations have been proposed as a promising spatial biomarker of the epileptogenic zone. However, its visual verification is time-consuming and exhibits poor inter-rater reliability. Furthermore, no method is currently available to distinguish high-frequency oscillations generated from the epileptogenic zone (epileptogenic high-frequency oscillations) from those generated from other areas (non-epileptogenic high-frequency oscillations). To address these issues, we constructed a deep learning-based algorithm using chronic intracranial EEG data via subdural grids from 19 children with medication-resistant neocortical epilepsy to: (i) replicate human expert annotation of artefacts and high-frequency oscillations with or without spikes, and (ii) discover epileptogenic high-frequency oscillations by designing a novel weakly supervised model. The ‘purification power’ of deep learning is then used to automatically relabel the high-frequency oscillations to distill epileptogenic high-frequency oscillations. Using 12 958 annotated high-frequency oscillation events from 19 patients, the model achieved 96.3% accuracy on artefact detection (F1 score = 96.8%) and 86.5% accuracy on classifying high-frequency oscillations with or without spikes (F1 score = 80.8%) using patient-wise cross-validation. Based on the algorithm trained from 84 602 high-frequency oscillation events from nine patients who achieved seizure-freedom after resection, the majority of such discovered epileptogenic high-frequency oscillations were found to be ones with spikes (78.6%, P < 0.001). While the resection ratio of detected high-frequency oscillations (number of resected events/number of detected events) did not correlate significantly with post-operative seizure freedom (the area under the curve = 0.76, P = 0.06), the resection ratio of epileptogenic high-frequency oscillations positively correlated with post-operative seizure freedom (the area under the curve = 0.87, P = 0.01). We discovered that epileptogenic high-frequency oscillations had a higher signal intensity associated with ripple (80–250 Hz) and fast ripple (250–500 Hz) bands at the high-frequency oscillation onset and with a lower frequency band throughout the event time window (the inverted T-shaped), compared to non-epileptogenic high-frequency oscillations. We then designed perturbations on the input of the trained model for non-epileptogenic high-frequency oscillations to determine the model’s decision-making logic. The model confidence significantly increased towards epileptogenic high-frequency oscillations by the artificial introduction of the inverted T-shaped signal template (mean probability increase: 0.285, P < 0.001), and by the artificial insertion of spike-like signals into the time domain (mean probability increase: 0.452, P < 0.001). With this deep learning-based framework, we reliably replicated high-frequency oscillation classification tasks by human experts. Using a reverse engineering technique, we distinguished epileptogenic high-frequency oscillations from others and identified its salient features that aligned with current knowledge.
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Affiliation(s)
- Yipeng Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA
| | - Qiujing Lu
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA
| | - Tonmoy Monsoor
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA
| | - Shaun A. Hussain
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Joe X. Qiao
- Division of Neuroradiology, Department of Radiology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Noriko Salamon
- Division of Neuroradiology, Department of Radiology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Aria Fallah
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Myung Shin Sim
- Department of Medicine, Statistics Core, University of California, Los Angeles, CA 90095, USA
| | - Eishi Asano
- Department of Pediatrics and Neurology, Children’s Hospital of Michigan, Wayne State University School of Medicine, Detroit, MI 48201, USA
| | - Raman Sankar
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA 90095, USA
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
- The UCLA Children’s Discovery and Innovation Institute, Los Angeles, CA, USA
| | - Richard J. Staba
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Jerome Engel
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
- Department of Neurobiology, University of California, Los Angeles, CA 90095, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA 90095, USA
- The Brain Research Institute, University of California, Los Angeles, CA 90095, USA
| | - William Speier
- Department of Radiological Sciences, University of California, Los Angeles, CA 90095, USA
- Department of Bioengineering, University of California, Los Angeles, CA 90095, USA
| | - Vwani Roychowdhury
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA
| | - Hiroki Nariai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA 90095, USA
- The UCLA Children’s Discovery and Innovation Institute, Los Angeles, CA, USA
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Sonoda M, Carlson A, Rothermel R, Kuroda N, Iwaki H, Luat AF, Sood S, Asano E. Long-term satisfaction after extraoperative invasive EEG recording. Epilepsy Behav 2021; 124:108363. [PMID: 34717248 PMCID: PMC9043037 DOI: 10.1016/j.yebeh.2021.108363] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 09/24/2021] [Accepted: 09/25/2021] [Indexed: 11/24/2022]
Abstract
This retrospective cohort study investigated 53 patients with drug-resistant focal epilepsy and identified factors predictive of long-term satisfaction of patients and families following extraoperative intracranial EEG (iEEG) recording. The mixed model analysis assessed the utility of intracranial EEG (iEEG) predictor variables, including the seizure-onset zone (SOZ), modulation index (MI), and naming-related high-gamma activity. Modulation index, quantifying the coupling between high-frequency activity at >80 Hz and local slow wave at 3-4 Hz, effectively functions as a surrogate marker of the burden of interictal spike-and-slow-wave discharges. The mixed model specifically incorporated 'subtraction-MI', defined as the subtraction of mean z-score normalized MI across all preserved sites from that across all resected sites. Auditory naming-related high-gamma activity at 70-110 Hz is a biomarker to characterize the underlying language and speech function. The model incorporated 'maximum resected high-gamma', defined as the high-gamma percent change largest among sites included in the resected language-dominant hemispheric region. The model also incorporated the clinical and imaging profiles of given patients. The analysis revealed that complete removal of SOZ (p = 0.003) and younger patient age (p = 0.040) were independently associated with greater satisfaction. Neither 'subtraction-MI' nor 'maximum naming-related high-gamma' showed a significant and independent association with long-term satisfaction in our patient cohort. The observed impact of complete resection of SOZ and early surgery can be considered when counseling candidates for epilepsy surgery.
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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
| | - 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
| | - Robert Rothermel
- Department of Psychiatry, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - 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
| | - Hirotaka Iwaki
- 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
| | - 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.
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45
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Mitsuhashi T, Sonoda M, Sakakura K, Jeong JW, Luat AF, Sood S, Asano E. Dynamic tractography-based localization of spike sources and animation of spike propagations. Epilepsia 2021; 62:2372-2384. [PMID: 34324194 PMCID: PMC8487933 DOI: 10.1111/epi.17025] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 07/14/2021] [Accepted: 07/15/2021] [Indexed: 12/23/2022]
Abstract
OBJECTIVE This study was undertaken to build and validate a novel dynamic tractography-based model for localizing interictal spike sources and visualizing monosynaptic spike propagations through the white matter. METHODS This cross-sectional study investigated 1900 spike events recorded in 19 patients with drug-resistant temporal lobe epilepsy (TLE) who underwent extraoperative intracranial electroencephalography (iEEG) and resective surgery. Twelve patients had mesial TLE (mTLE) without a magnetic resonance imaging-visible mass lesion. The remaining seven had a mass lesion in the temporal lobe neocortex. We identified the leading and lagging sites, defined as those initially and subsequently (but within ≤50 ms) showing spike-related augmentation of broadband iEEG activity. In each patient, we estimated the sources of 100 spike discharges using the latencies at given electrode sites and diffusion-weighted imaging-based streamline length measures. We determined whether the spatial relationship between the estimated spike sources and resection was associated with postoperative seizure outcomes. We generated videos presenting the spatiotemporal change of spike-related fiber activation sites by estimating the propagation velocity using the streamline length and spike latency measures. RESULTS The spike propagation velocity from the source was 1.03 mm/ms on average (95% confidence interval = .91-1.15) across 133 tracts noted in the 19 patients. The estimated spike sources in mTLE patients with International League Against Epilepsy Class 1 outcome were more likely to be in the resected area (83.9% vs. 72.3%, φ = .137, p < .001) and in the medial temporal lobe region (80.5% vs. 72.5%, φ = .090, p = .002) than those associated with the Class ≥2 outcomes. The resulting video successfully animated spike propagations, which were confined within the temporal lobe in mTLE but involved extratemporal lobe areas in lesional TLE. SIGNIFICANCE We have, for the first time, provided dynamic tractography visualizing the spatiotemporal profiles of rapid propagations of interictal spikes through the white matter. Dynamic tractography has the potential to serve as a unique epilepsy biomarker.
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Affiliation(s)
- 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
| | - 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, 2360004, Japan
| | - 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
| | - 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
| | - 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
| | - 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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Schönberger J, Knopf A, Klotz KA, Dümpelmann M, Schulze-Bonhage A, Jacobs J. Distinction of Physiologic and Epileptic Ripples: An Electrical Stimulation Study. Brain Sci 2021; 11:brainsci11050538. [PMID: 33923317 PMCID: PMC8146715 DOI: 10.3390/brainsci11050538] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/15/2021] [Accepted: 04/19/2021] [Indexed: 11/16/2022] Open
Abstract
Ripple oscillations (80-250 Hz) are a promising biomarker of epileptic activity, but are also involved in memory consolidation, which impairs their value as a diagnostic tool. Distinguishing physiologic from epileptic ripples has been particularly challenging because usually, invasive recordings are only performed in patients with refractory epilepsy. Here, we identified 'healthy' brain areas based on electrical stimulation and hypothesized that these regions specifically generate 'pure' ripples not coupled to spikes. Intracranial electroencephalography (EEG) recorded with subdural grid electrodes was retrospectively analyzed in 19 patients with drug-resistant focal epilepsy. Interictal spikes and ripples were automatically detected in slow-wave sleep using the publicly available Delphos software. We found that rates of spikes, ripples and ripples coupled to spikes ('spike-ripples') were higher inside the seizure-onset zone (p < 0.001). A comparison of receiver operating characteristic curves revealed that spike-ripples slightly delineated the seizure-onset zone channels, but did this significantly better than spikes (p < 0.001). Ripples were more frequent in the eloquent neocortex than in the remaining non-seizure onset zone areas (p < 0.001). This was due to the higher rates of 'pure' ripples (p < 0.001; median rates 3.3/min vs. 1.4/min), whereas spike-ripple rates were not significantly different (p = 0.87). 'Pure' ripples identified 'healthy' channels significantly better than chance (p < 0.001). Our findings suggest that, in contrast to epileptic spike-ripples, 'pure' ripples are mainly physiological. They may be considered, in addition to electrical stimulation, to delineate eloquent cortex in pre-surgical patients. Since we applied open source software for detection, our approach may be generally suited to tackle a variety of research questions in epilepsy and cognitive science.
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Affiliation(s)
- Jan Schönberger
- Epilepsy Center, Medical Center, University of Freiburg, 79106 Freiburg, Germany; (A.K.); (K.A.K.); (M.D.); (A.S.-B.)
- Department of Neuropediatrics and Muscle Disorders, Medical Center, University of Freiburg, 79106 Freiburg, Germany;
- Faculty of Medicine, University of Freiburg, 79085 Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, 79085 Freiburg, Germany
- Correspondence:
| | - Anja Knopf
- Epilepsy Center, Medical Center, University of Freiburg, 79106 Freiburg, Germany; (A.K.); (K.A.K.); (M.D.); (A.S.-B.)
- Department of Neuropediatrics and Muscle Disorders, Medical Center, University of Freiburg, 79106 Freiburg, Germany;
- Faculty of Medicine, University of Freiburg, 79085 Freiburg, Germany
| | - Kerstin Alexandra Klotz
- Epilepsy Center, Medical Center, University of Freiburg, 79106 Freiburg, Germany; (A.K.); (K.A.K.); (M.D.); (A.S.-B.)
- Department of Neuropediatrics and Muscle Disorders, Medical Center, University of Freiburg, 79106 Freiburg, Germany;
- Faculty of Medicine, University of Freiburg, 79085 Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, 79085 Freiburg, Germany
| | - Matthias Dümpelmann
- Epilepsy Center, Medical Center, University of Freiburg, 79106 Freiburg, Germany; (A.K.); (K.A.K.); (M.D.); (A.S.-B.)
- Faculty of Medicine, University of Freiburg, 79085 Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center, University of Freiburg, 79106 Freiburg, Germany; (A.K.); (K.A.K.); (M.D.); (A.S.-B.)
- Faculty of Medicine, University of Freiburg, 79085 Freiburg, Germany
| | - Julia Jacobs
- Department of Neuropediatrics and Muscle Disorders, Medical Center, University of Freiburg, 79106 Freiburg, Germany;
- Faculty of Medicine, University of Freiburg, 79085 Freiburg, Germany
- Department of Paediatrics and Department of Neuroscience, Cumming School of Medicine, University of Calgary, Calgary, AB T3B 6A8, Canada
- Hotchkiss Brain Institute and Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
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