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Hammer J, Kajsova M, Kalina A, Krysl D, Fabera P, Kudr M, Jezdik P, Janca R, Krsek P, Marusic P. Antagonistic behavior of brain networks mediated by low-frequency oscillations: electrophysiological dynamics during internal-external attention switching. Commun Biol 2024; 7:1105. [PMID: 39251869 PMCID: PMC11385230 DOI: 10.1038/s42003-024-06732-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 08/13/2024] [Indexed: 09/11/2024] Open
Abstract
Antagonistic activity of brain networks likely plays a fundamental role in how the brain optimizes its performance by efficient allocation of computational resources. A prominent example involves externally/internally oriented attention tasks, implicating two anticorrelated, intrinsic brain networks: the default mode network (DMN) and the dorsal attention network (DAN). To elucidate electrophysiological underpinnings and causal interplay during attention switching, we recorded intracranial EEG (iEEG) from 25 epilepsy patients with electrode contacts localized in the DMN and DAN. We show antagonistic network dynamics of activation-related changes in high-frequency (> 50 Hz) and low-frequency (< 30 Hz) power. The temporal profile of information flow between the networks estimated by functional connectivity suggests that the activated network inhibits the other one, gating its activity by increasing the amplitude of the low-frequency oscillations. Insights about inter-network communication may have profound implications for various brain disorders in which these dynamics are compromised.
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Affiliation(s)
- Jiri Hammer
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic.
| | - Michaela Kajsova
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
| | - Adam Kalina
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
| | - David Krysl
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
| | - Petr Fabera
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
| | - Martin Kudr
- Department of Pediatric Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
| | - Petr Jezdik
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Radek Janca
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Pavel Krsek
- Department of Pediatric Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
| | - Petr Marusic
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic.
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Cai T, Lin Y, Wang G, Luo J. Predicting radiofrequency thermocoagulation surgical outcomes in refractory focal epilepsy patients using functional coupled neural mass model. Front Neurol 2024; 15:1402004. [PMID: 39246608 PMCID: PMC11377261 DOI: 10.3389/fneur.2024.1402004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Accepted: 08/12/2024] [Indexed: 09/10/2024] Open
Abstract
Objective The success rate of achieving seizure freedom after radiofrequency thermocoagulation surgery for patients with refractory focal epilepsy is about 20-40%. This study aims to enhance the prediction of surgical outcomes based on preoperative decisions through network model simulation, providing a reference for clinicians to validate and optimize surgical plans. Methods Twelve patients with epilepsy who underwent radiofrequency thermocoagulation were retrospectively reviewed in this study. A coupled model based on model subsets of the neural mass model was constructed by calculating partial directed coherence as the coupling matrix from stereoelectroencephalography (SEEG) signals. Multi-channel time-varying model parameters of excitation and inhibitions were identified by fitting the real SEEG signals with the coupled model. Further incorporating these model parameters, the coupled model virtually removed contacts destroyed in radiofrequency thermocoagulation or selected randomly. Subsequently, the coupled model after virtual surgery was simulated. Results The identified excitatory and inhibitory parameters showed significant difference before and after seizure onset (p < 0.05), and the trends of parameter changes aligned with the seizure process. Additionally, excitatory parameters of epileptogenic contacts were higher than that of non-epileptogenic contacts, and opposite findings were noticed for inhibitory parameters. The simulated signals of postoperative models to predict surgical outcomes yielded an area under the curve (AUC) of 83.33% and an accuracy of 91.67%. Conclusion The multi-channel coupled model proposed in this study with physiological characteristics showed a desirable performance for preoperatively predicting patients' prognoses.
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Affiliation(s)
- Tianxin Cai
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, Guangdong Provincial Engineering and Technology Center of Advanced and Portable Medical Devices, Sun Yat-sen University, Guangzhou, China
| | - Yaoxin Lin
- Department of Functional Neurosurgery, First People's Hospital of Foshan, Foshan, China
| | - Guofu Wang
- Department of Functional Neurosurgery, First People's Hospital of Foshan, Foshan, China
| | - Jie Luo
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, Guangdong Provincial Engineering and Technology Center of Advanced and Portable Medical Devices, Sun Yat-sen University, Guangzhou, China
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Lee HJ, Chien LY, Yu HY, Lee CC, Chou CC, Kuo WJ, Lin FH. Distributed source modeling of stereoencephalographic measurements of ictal activity. Clin Neurophysiol 2024; 161:112-121. [PMID: 38461595 DOI: 10.1016/j.clinph.2024.02.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/07/2024] [Accepted: 02/17/2024] [Indexed: 03/12/2024]
Abstract
OBJECTIVES Stereoelectroencephalography (SEEG) can define the epileptogenic zone (EZ). However, SEEG is susceptible to the sampling bias, where no SEEG recording is taken within a circumscribed EZ. METHODS Nine patients with medically refractory epilepsy underwent SEEG recording, and brain resection got positive outcomes. Ictal neuronal currents were estimated by distributed source modeling using the SEEG data and individual's anatomical magnetic resonance imaging. Using a retrospective leave-one-out data sub-sampling, we evaluated the sensitivity and specificity of the current estimates using MRI after surgical resection or radio-frequency ablation. RESULTS The sensitivity and specificity in detecting the EZ were indistinguishable from either the data from all electrodes or the sub-sampled data (rank sum test: rank sum = 23719, p = 0.13) when at least one remaining electrode contact was no more than 20 mm away. CONCLUSIONS The distributed neuronal current estimates of ictal SEEG data can mitigate the challenge of delineating the boundary of the EZ in cases of missing an electrode implanted within the EZ and a required second SEEG exploration. SIGNIFICANCE Distributed source modeling can be a tool for clinicians to infer the EZ by allowing for more flexible planning of the electrode implantation route and minimizing the number of electrodes.
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Affiliation(s)
- Hsin-Ju Lee
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Lin-Yao Chien
- Institute of Neuroscience, National Yang Ming University, Taipei, Taiwan
| | - Hsiang-Yu Yu
- Department of Epilepsy, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; School of Medicine, National Yang Ming University, Taipei, Taiwan
| | - Cheng-Chia Lee
- School of Medicine, National Yang Ming University, Taipei, Taiwan; Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chien-Chen Chou
- Department of Epilepsy, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; School of Medicine, National Yang Ming University, Taipei, Taiwan
| | - Wen-Jui Kuo
- Institute of Neuroscience, National Yang Ming University, Taipei, Taiwan.
| | - Fa-Hsuan Lin
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada
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Medina Villalon S, Makhalova J, López-Madrona VJ, Garnier E, Badier JM, Bartolomei F, Bénar CG. Combining independent component analysis and source localization for improving spatial sampling of stereoelectroencephalography in epilepsy. Sci Rep 2024; 14:4071. [PMID: 38374380 PMCID: PMC10876572 DOI: 10.1038/s41598-024-54359-4] [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: 09/14/2023] [Accepted: 02/12/2024] [Indexed: 02/21/2024] Open
Abstract
Stereoelectroencephalography is a powerful intracerebral EEG recording method for the presurgical evaluation of epilepsy. It consists in implanting depth electrodes in the patient's brain to record electrical activity and map the epileptogenic zone, which should be resected to render the patient seizure-free. Stereoelectroencephalography has high spatial accuracy and signal-to-noise ratio but remains limited in the coverage of the explored brain regions. Thus, the implantation might provide a suboptimal sampling of epileptogenic regions. We investigate the potential of improving a suboptimal stereoelectroencephalography recording by performing source localization on stereoelectroencephalography signals. We propose combining independent component analysis, connectivity measures to identify components of interest, and distributed source modelling. This approach was tested on two patients with two implantations each, the first failing to characterize the epileptogenic zone and the second giving a better diagnosis. We demonstrate that ictal and interictal source localization performed on the first stereoelectroencephalography recordings matches the findings of the second stereo-EEG exploration. Our findings suggest that independent component analysis followed by source localization on the topographies of interest is a promising method for retrieving the epileptogenic zone in case of suboptimal implantation.
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Affiliation(s)
- Samuel Medina Villalon
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Julia Makhalova
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | | | - Elodie Garnier
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | | | - Fabrice Bartolomei
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Christian G Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.
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Ahveninen J, Lee HJ, Yu HY, Lee CC, Chou CC, Ahlfors SP, Kuo WJ, Jääskeläinen IP, Lin FH. Visual Stimuli Modulate Local Field Potentials But Drive No High-Frequency Activity in Human Auditory Cortex. J Neurosci 2024; 44:e0890232023. [PMID: 38129133 PMCID: PMC10869150 DOI: 10.1523/jneurosci.0890-23.2023] [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: 05/15/2023] [Revised: 11/06/2023] [Accepted: 11/07/2023] [Indexed: 12/23/2023] Open
Abstract
Neuroimaging studies suggest cross-sensory visual influences in human auditory cortices (ACs). Whether these influences reflect active visual processing in human ACs, which drives neuronal firing and concurrent broadband high-frequency activity (BHFA; >70 Hz), or whether they merely modulate sound processing is still debatable. Here, we presented auditory, visual, and audiovisual stimuli to 16 participants (7 women, 9 men) with stereo-EEG depth electrodes implanted near ACs for presurgical monitoring. Anatomically normalized group analyses were facilitated by inverse modeling of intracranial source currents. Analyses of intracranial event-related potentials (iERPs) suggested cross-sensory responses to visual stimuli in ACs, which lagged the earliest auditory responses by several tens of milliseconds. Visual stimuli also modulated the phase of intrinsic low-frequency oscillations and triggered 15-30 Hz event-related desynchronization in ACs. However, BHFA, a putative correlate of neuronal firing, was not significantly increased in ACs after visual stimuli, not even when they coincided with auditory stimuli. Intracranial recordings demonstrate cross-sensory modulations, but no indication of active visual processing in human ACs.
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Affiliation(s)
- Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts 02129
- Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
| | - Hsin-Ju Lee
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario M4N 3M5, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada
| | - Hsiang-Yu Yu
- Department of Epilepsy, Neurological Institute, Taipei Veterans General Hospital, Taipei 11217, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Cheng-Chia Lee
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
- Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei 11217, Taiwan
| | - Chien-Chen Chou
- Department of Epilepsy, Neurological Institute, Taipei Veterans General Hospital, Taipei 11217, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Seppo P Ahlfors
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts 02129
- Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
| | - Wen-Jui Kuo
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Iiro P Jääskeläinen
- Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, FI-00076 AALTO, Finland
- International Laboratory of Social Neurobiology, Institute of Cognitive Neuroscience, Higher School of Economics, Moscow 101000, Russia
| | - Fa-Hsuan Lin
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario M4N 3M5, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada
- Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, FI-00076 AALTO, Finland
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Dessert GE, Thio BJ, Grill WM. Optimization of patient-specific stereo-EEG recording sensitivity. Brain Commun 2023; 5:fcad304. [PMID: 38025277 PMCID: PMC10655844 DOI: 10.1093/braincomms/fcad304] [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: 02/20/2023] [Revised: 08/11/2023] [Accepted: 11/01/2023] [Indexed: 12/01/2023] Open
Abstract
Stereo-EEG is a minimally invasive technique used to localize the origin of epileptic activity (the epileptogenic zone) in patients with drug-resistant epilepsy. However, current stereo-EEG trajectory planning methods are agnostic to the spatial recording sensitivity of implanted electrodes. In this study, we used image-based patient-specific computational models to design optimized stereo-EEG electrode configurations. Patient-specific optimized electrode configurations exhibited substantially higher recording sensitivity than clinically implanted configurations, and this may lead to a more accurate delineation of the epileptogenic zone. The optimized configurations also achieved equally good or better recording sensitivity with fewer electrodes compared with clinically implanted configurations, and this may reduce the risk for complications, including intracranial haemorrhage. This approach improves localization of the epileptogenic zone by transforming the clinical use of stereo-EEG from a discrete ad hoc sampling to an intelligent mapping of the regions of interest.
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Affiliation(s)
- Grace E Dessert
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Brandon J Thio
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
- Department of Neurobiology, Duke University, Durham, NC 27710, USA
- Department of Neurosurgery, Duke University, Durham, NC 27710, USA
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Kalina A, Jezdik P, Fabera P, Marusic P, Hammer J. Electrical Source Imaging of Somatosensory Evoked Potentials from Intracranial EEG Signals. Brain Topogr 2023; 36:835-853. [PMID: 37642729 DOI: 10.1007/s10548-023-00994-5] [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: 09/06/2022] [Accepted: 07/24/2023] [Indexed: 08/31/2023]
Abstract
Stereoelectroencephalography (SEEG) records electrical brain activity with intracerebral electrodes. However, it has an inherently limited spatial coverage. Electrical source imaging (ESI) infers the position of the neural generators from the recorded electric potentials, and thus, could overcome this spatial undersampling problem. Here, we aimed to quantify the accuracy of SEEG ESI under clinical conditions. We measured the somatosensory evoked potential (SEP) in SEEG and in high-density EEG (HD-EEG) in 20 epilepsy surgery patients. To localize the source of the SEP, we employed standardized low resolution brain electromagnetic tomography (sLORETA) and equivalent current dipole (ECD) algorithms. Both sLORETA and ECD converged to similar solutions. Reflecting the large differences in the SEEG implantations, the localization error also varied in a wide range from 0.4 to 10 cm. The SEEG ESI localization error was linearly correlated with the distance from the putative neural source to the most activated contact. We show that it is possible to obtain reliable source reconstructions from SEEG under realistic clinical conditions, provided that the high signal fidelity recording contacts are sufficiently close to the source of the brain activity.
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Affiliation(s)
- Adam Kalina
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital (Full Member of the ERN EpiCARE), V Uvalu 84, 150 06, Prague 5, Czechia.
| | - Petr Jezdik
- Department of Measurement, Faculty of Electrical Engineering, Czech Technical University in Prague, Technicka 2, 166 27, Prague 6, Czechia
| | - Petr Fabera
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital (Full Member of the ERN EpiCARE), V Uvalu 84, 150 06, Prague 5, Czechia
| | - Petr Marusic
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital (Full Member of the ERN EpiCARE), V Uvalu 84, 150 06, Prague 5, Czechia
| | - Jiri Hammer
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital (Full Member of the ERN EpiCARE), V Uvalu 84, 150 06, Prague 5, Czechia.
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Satzer D, Wu S, Henry J, Doll E, Issa NP, Warnke PC. Ambulatory Local Field Potential Recordings from the Thalamus in Epilepsy: A Feasibility Study. Stereotact Funct Neurosurg 2023; 101:195-206. [PMID: 37232010 PMCID: PMC11227660 DOI: 10.1159/000529961] [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: 11/25/2022] [Accepted: 02/24/2023] [Indexed: 05/27/2023]
Abstract
INTRODUCTION Stimulation of the thalamus is gaining favor in the treatment of medically refractory multifocal and generalized epilepsy. Implanted brain stimulators capable of recording ambulatory local field potentials (LFPs) have recently been introduced, but there is little information to guide their use in thalamic stimulation for epilepsy. This study sought to assess the feasibility of chronically recording ambulatory interictal LFP from the thalamus in patients with epilepsy. METHODS In this pilot study, ambulatory LFP was recorded from patients who underwent sensing-enabled deep brain stimulation (DBS, 2 participants) or responsive neurostimulation (RNS, 3 participants) targeting the anterior nucleus of the thalamus (ANT, 2 electrodes), centromedian nucleus (CM, 7 electrodes), or medial pulvinar (PuM, 1 electrode) for multifocal or generalized epilepsy. Time-domain and frequency-domain LFP was investigated for epileptiform discharges, spectral peaks, circadian variation, and peri-ictal patterns. RESULTS Thalamic interictal discharges were visible on ambulatory recordings from both DBS and RNS. At-home interictal frequency-domain data could be extracted from both devices. Spectral peaks were noted at 10-15 Hz in CM, 6-11 Hz in ANT, and 19-24 Hz in PuM but varied in prominence and were not visible in all electrodes. In CM, 10-15 Hz power exhibited circadian variation and was attenuated by eye opening. CONCLUSION Chronic ambulatory recording of thalamic LFP is feasible. Common spectral peaks can be observed but vary between electrodes and across neural states. DBS and RNS devices provide a wealth of complementary data that have the potential to better inform thalamic stimulation for epilepsy.
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Affiliation(s)
- David Satzer
- Department of Neurological Surgery, University of Chicago, Chicago, IL, USA
| | - Shasha Wu
- Department of Neurology, University of Chicago, Chicago, IL, USA
| | - Julia Henry
- Department of Pediatrics, Child Neurology Section, University of Chicago, Chicago, IL, USA
| | - Emily Doll
- Department of Pediatrics, Child Neurology Section, University of Chicago, Chicago, IL, USA
| | - Naoum P. Issa
- Department of Neurology, University of Chicago, Chicago, IL, USA
| | - Peter C. Warnke
- Department of Neurological Surgery, University of Chicago, Chicago, IL, USA
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López-Madrona VJ, Villalon SM, Velmurugan J, Semeux-Bernier A, Garnier E, Badier JM, Schön D, Bénar CG. Reconstruction and localization of auditory sources from intracerebral SEEG using independent component analysis. Neuroimage 2023; 269:119905. [PMID: 36720438 DOI: 10.1016/j.neuroimage.2023.119905] [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: 10/07/2022] [Revised: 01/11/2023] [Accepted: 01/26/2023] [Indexed: 01/30/2023] Open
Abstract
Stereo-electroencephalography (SEEG) is the surgical implantation of electrodes in the brain to better localize the epileptic network in pharmaco-resistant epileptic patients. This technique has exquisite spatial and temporal resolution. Still, the number and the position of the electrodes in the brain is limited and determined by the semiology and/or preliminary non-invasive examinations, leading to a large number of unexplored brain structures in each patient. Here, we propose a new approach to reconstruct the activity of non-sampled structures in SEEG, based on independent component analysis (ICA) and dipole source localization. We have tested this approach with an auditory stimulation dataset in ten patients. The activity directly recorded from the auditory cortex served as ground truth and was compared to the ICA applied on all non-auditory electrodes. Our results show that the activity from the auditory cortex can be reconstructed at the single trial level from contacts as far as ∼40 mm from the source. Importantly, this reconstructed activity is localized via dipole fitting in the proximity of the original source. In addition, we show that the size of the confidence interval of the dipole fitting is a good indicator of the reliability of the result, which depends on the geometry of the SEEG implantation. Overall, our approach allows reconstructing the activity of structures far from the electrode locations, partially overcoming the spatial sampling limitation of intracerebral recordings.
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Affiliation(s)
| | - Samuel Medina Villalon
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille 13005, France; APHM, Timone Hospital, Epileptology and cerebral rhythmology, Marseille 13005, France
| | - Jayabal Velmurugan
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille 13005, France
| | | | - Elodie Garnier
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille 13005, France
| | - Jean-Michel Badier
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille 13005, France
| | - Daniele Schön
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille 13005, France
| | - Christian-G Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille 13005, France.
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Thio BJ, Aberra AS, Dessert GE, Grill WM. Ideal current dipoles are appropriate source representations for simulating neurons for intracranial recordings. Clin Neurophysiol 2023; 145:26-35. [PMID: 36403433 PMCID: PMC9772254 DOI: 10.1016/j.clinph.2022.11.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 09/20/2022] [Accepted: 11/01/2022] [Indexed: 11/10/2022]
Abstract
OBJECTIVE To determine whether dipoles are an appropriate simplified representation of neural sources for stereo-EEG (sEEG). METHODS We compared the distributions of voltages generated by a dipole, biophysically realistic cortical neuron models, and extended regions of cortex to determine how well a dipole represented neural sources at different spatial scales and at electrode to neuron distances relevant for sEEG. We also quantified errors introduced by the dipole approximation of neural sources in sEEG source localization using standardized low-resolution electrotomography (sLORETA). RESULTS For pyramidal neurons, the coefficient of correlation between voltages generated by a dipole and neuron model were > 0.9 for distances > 1 mm. For small regions of cortex (∼0.1 cm2), the error in voltages between a dipole and region was < 100 µV for all distances. However, larger regions of active cortex (>5 cm2) yielded > 50 µV errors within 1.5 cm of an electrode when compared to single dipoles. Finally, source localization errors were < 5 mm when using dipoles to represent realistic neural sources. CONCLUSIONS Single dipoles are an appropriate source model to represent both single neurons and small regions of active cortex, while multiple dipoles are required to represent large regions of cortex. SIGNIFICANCE Dipoles are computationally tractable and valid source models for sEEG.
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Affiliation(s)
- Brandon J Thio
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Aman S Aberra
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Grace E Dessert
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Durham, NC, United States; Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States; Department of Neurobiology, Duke University, Durham, NC, United States; Department of Neurosurgery, Duke University, Durham, NC, United States.
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11
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Satzer D, Esengul YT, Warnke PC, Issa NP, Nordli DR. Source localization of ictal SEEG to predict postoperative seizure outcome. Clin Neurophysiol 2022; 144:142-150. [PMID: 36088217 DOI: 10.1016/j.clinph.2022.08.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/02/2022] [Accepted: 08/17/2022] [Indexed: 11/28/2022]
Abstract
OBJECTIVE Stereo-electroencephalography (SEEG) is inherently-three-dimensional and can be modeled using source localization. This study aimed to assess the validity of ictal SEEG source localization. METHODS The dominant frequency at ictal onset was used for source localization in the time and frequency domains using rotating dipoles and current density maps. Validity was assessed by concordance with the epileptologist-defined seizure onset zone (conventional SOZ) and the surgical treatment volume (TV) of seizure-free versus non-seizure-free patients. RESULTS Source localization was performed on 68 seizures from 27 patients. Median distance to nearest contact in the conventional SOZ was 7 (IQR 6-12) mm for time-domain dipoles. Current density predicted ictal activity with up to 86 % (60-87 %) accuracy. Distance from time-domain dipoles to the TV was smaller (P = 0.045) in seizure-free (2 [0-4] mm) versus non-seizure-free (12 [2-17] mm) patients, and predicted surgical outcome with 91 % sensitivity and 63 % specificity. Removing near-field data from contacts within the TV negated outcome prediction (P = 0.51). CONCLUSIONS Source localization of SEEG accurately mapped ictal onset compared with conventional interpretation. Proximity of dipoles to the TV predicted seizure outcome when near-field recordings were analyzed. SIGNIFICANCE Ictal SEEG source localization is useful in corroborating the epileptogenic zone, assuming near-field recordings are obtained.
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Affiliation(s)
- David Satzer
- Department of Neurological Surgery, University of Chicago, Chicago, IL, USA.
| | - Yasar T Esengul
- Department of Neurology, University of Chicago, Chicago, IL, USA
| | - Peter C Warnke
- Department of Neurological Surgery, University of Chicago, Chicago, IL, USA
| | - Naoum P Issa
- Department of Neurology, University of Chicago, Chicago, IL, USA
| | - Douglas R Nordli
- Section of Child Neurology, Department of Pediatrics, University of Chicago, Chicago, IL, USA
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Liégeois‐Chauvel C, Dubarry A, Wang I, Chauvel P, Gonzalez‐Martinez JA, Alario F. Inter-individual variability in dorsal stream dynamics during word production. Eur J Neurosci 2022; 56:5070-5089. [PMID: 35997580 PMCID: PMC9804493 DOI: 10.1111/ejn.15807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 06/10/2022] [Accepted: 08/14/2022] [Indexed: 01/05/2023]
Abstract
The current standard model of language production involves a sensorimotor dorsal stream connecting areas in the temporo-parietal junction with those in the inferior frontal gyrus and lateral premotor cortex. These regions have been linked to various aspects of word production such as phonological processing or articulatory programming, primarily through neuropsychological and functional imaging group studies. Most if not all the theoretical descriptions of this model imply that the same network should be identifiable across individual speakers. We tested this hypothesis by quantifying the variability of activation observed across individuals within each dorsal stream anatomical region. This estimate was based on electrical activity recorded directly from the cerebral cortex with millisecond accuracy in awake epileptic patients clinically implanted with intracerebral depth electrodes for pre-surgical diagnosis. Each region's activity was quantified using two different metrics-intra-cerebral evoked related potentials and high gamma activity-at the level of the group, the individual and the recording contact. The two metrics show simultaneous activation of parietal and frontal regions during a picture naming task, in line with models that posit interactive processing during word retrieval. They also reveal different levels of between-patient variability across brain regions, except in core auditory and motor regions. The independence and non-uniformity of cortical activity estimated through the two metrics push the current model towards sub-second and sub-region explorations focused on individualized language speech production. Several hypotheses are considered for this within-region heterogeneity.
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Affiliation(s)
- Catherine Liégeois‐Chauvel
- Epilepsy Center, Neurological InstituteCleveland Clinic FoundationClevelandOhioUSA,Aix Marseille Univ, INSERM, INS, Inst Neurosci SystMarseilleFrance,Present address:
Department of Neurological Surgery, School of MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
| | | | - Irene Wang
- Epilepsy Center, Neurological InstituteCleveland Clinic FoundationClevelandOhioUSA
| | | | - Jorge A. Gonzalez‐Martinez
- Present address:
Department of Neurological Surgery, School of MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - F.‐Xavier Alario
- Present address:
Department of Neurological Surgery, School of MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA,Aix Marseille Univ, CNRS, LPCMarseilleFrance
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13
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Del Rocio Hernandez-Castanon V, Le Cam S, Ranta R. On the localization of oscillatory sources from (S)EEG recordings. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3119-3122. [PMID: 36086393 DOI: 10.1109/embc48229.2022.9871931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Electrophysiological brain source localization consists in estimating the source positions and activities responsible for the (S)EEG measurements. The localization procedure is usually carried out in the time domain, however in specific situations the activities of interest can be located at well defined frequencies, e.g. in response to a rhythmic stimulation. This paper addresses the problem of sparse localization of multiple sources oscillating at the same frequency. In particular the non-unicity of the solution is emphasized, as alternative source maps involving equivalent or less number of sources can be found, challenging source localization methods based on sparsity. These limitations are illustrated under a realistic SEEG simulation framework, and the usefulness to perform localization for this modality is strengthen out.
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14
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Shu S, Luo S, Cao M, Xu K, Qin L, Zheng L, Xu J, Wang X, Gao JH. Informed MEG/EEG source imaging reveals the locations of interictal spikes missed by SEEG. Neuroimage 2022; 254:119132. [PMID: 35337964 DOI: 10.1016/j.neuroimage.2022.119132] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 02/18/2022] [Accepted: 03/21/2022] [Indexed: 11/19/2022] Open
Abstract
Determining the accurate locations of interictal spikes has been fundamental in the presurgical evaluation of epilepsy surgery. Stereo-electroencephalography (SEEG) is able to directly record cortical activity and localize interictal spikes. However, the main caveat of SEEG techniques is that they have limited spatial sampling (covering <5% of the whole brain), which may lead to missed spikes originating from brain regions that were not covered by SEEG. To address this problem, we propose a SEEG-informed minimum-norm estimates (SIMNE) method by combining SEEG with magnetoencephalography (MEG) or EEG. Specifically, the spike locations determined by SEEG offer as a priori information to guide MEG source reconstruction. Both computer simulations and experiments using data from five epilepsy patients were conducted to evaluate the performance of SIMNE. Our results demonstrate that SIMNE generates more accurate source estimation than a traditional minimum-norm estimates method and reveals the locations of spikes missed by SEEG, which would improve presurgical evaluation of the epileptogenic zone.
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Affiliation(s)
- Su Shu
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Shen Luo
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Miao Cao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Ke Xu
- Department of Neurosurgery, Sanbo Brain Hospital of Capital Medical University, Beijing 100093, China
| | - Lang Qin
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Li Zheng
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Jing Xu
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Laboratory of Applied Brain and Cognitive Sciences, College of International Business, Shanghai International Studies University, Shanghai 201620, China
| | - Xiongfei Wang
- Department of Neurosurgery, Sanbo Brain Hospital of Capital Medical University, Beijing 100093, China
| | - Jia-Hong Gao
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; McGovern Institute for Brain Research, Peking University, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China.
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15
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Satzer D, Esengul YT, Warnke PC, Issa NP, Nordli DR. SEEG in 3D: Interictal Source Localization From Intracerebral Recordings. Front Neurol 2022; 13:782880. [PMID: 35211078 PMCID: PMC8861202 DOI: 10.3389/fneur.2022.782880] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 01/14/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Stereo-electroencephalography (SEEG) uses a three-dimensional configuration of depth electrodes to localize epileptiform activity, but traditional analysis of SEEG is spatially restricted to the point locations of the electrode contacts. Interpolation of brain activity between contacts might allow for three-dimensional representation of epileptiform activity and avoid pitfalls of SEEG interpretation. OBJECTIVE The goal of this study was to validate SEEG-based interictal source localization and assess the ability of this technique to monitor far-field activity in non-implanted brain regions. METHODS Interictal epileptiform discharges were identified on SEEG in 26 patients who underwent resection, ablation, or disconnection of the suspected epileptogenic zone. Dipoles without (free) and with (scan) gray matter restriction, and current density (sLORETA and SWARM methods), were calculated using a finite element head model. Source localization results were compared to the conventional irritative zone (IZ) and the surgical treatment volumes (TV) of seizure-free vs. non-seizure-free patients. RESULTS The median distance from dipole solutions to the nearest contact in the conventional IZ was 7 mm (interquartile range 4-15 mm for free dipoles and 4-14 mm for scan dipoles). The IZ modeled with SWARM predicted contacts within the conventional IZ with 83% (75-100%) sensitivity and 94% (88-100%) specificity. The proportion of current within the TV was greater in seizure-free patients (P = 0.04) and predicted surgical outcome with 45% sensitivity and 93% specificity. Dipole solutions and sLORETA results did not correlate with seizure outcome. Addition of scalp EEG led to more superficial modeled sources (P = 0.03) and negated the ability to predict seizure outcome (P = 0.23). Removal of near-field data from contacts within the TV resulted in smearing of the current distribution (P = 0.007) and precluded prediction of seizure freedom (P = 0.20). CONCLUSIONS Source localization accurately represented interictal discharges from SEEG. The proportion of current within the TV distinguished between seizure-free and non-seizure-free patients when near-field recordings were obtained from the surgical target. The high prevalence of deep sources in this cohort likely obscured any benefit of concurrent scalp EEG. SEEG-based interictal source localization is useful in illustrating and corroborating the epileptogenic zone. Additional techniques are needed to localize far-field epileptiform activity from non-implanted brain regions.
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Affiliation(s)
- David Satzer
- Department of Neurosurgery, University of Chicago, Chicago, IL, United States
| | - Yasar T Esengul
- Department of Neurology, University of Chicago, Chicago, IL, United States
| | - Peter C Warnke
- Department of Neurosurgery, University of Chicago, Chicago, IL, United States
| | - Naoum P Issa
- Department of Neurology, University of Chicago, Chicago, IL, United States
| | - Douglas R Nordli
- Section of Child Neurology, Department of Pediatrics, University of Chicago, Chicago, IL, United States
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16
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Speech-related auditory salience detection in the posterior superior temporal region. Neuroimage 2021; 248:118840. [PMID: 34958951 DOI: 10.1016/j.neuroimage.2021.118840] [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: 06/02/2021] [Revised: 11/13/2021] [Accepted: 12/19/2021] [Indexed: 11/22/2022] Open
Abstract
Processing auditory human speech requires both detection (early and transient) and analysis (sustained). We analyzed high gamma (70-110 Hz) activity of intracranial electroencephalography waveforms acquired during an auditory task that paired forward speech, reverse speech, and signal correlated noise. We identified widespread superior temporal sites with sustained activity responding only to forward and reverse speech regardless of paired order. More localized superior temporal auditory onset sites responded to all stimulus types when presented first in a pair and responded in recurrent fashion to the second paired stimulus in select conditions even in the absence of interstimulus silence; a novel finding. Auditory onset activity to a second paired sound recurred according to relative salience, with evidence of partial suppression during linguistic processing. We propose that temporal lobe auditory onset sites facilitate a salience detector function with hysteresis of 200 ms and are influenced by cortico-cortical feedback loops involving linguistic processing and articulation.
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17
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Bénar CG, Velmurugan J, López-Madrona VJ, Pizzo F, Badier JM. Detection and localization of deep sources in magnetoencephalography: A review. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2021. [DOI: 10.1016/j.cobme.2021.100285] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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18
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Sip V, Hashemi M, Vattikonda AN, Woodman MM, Wang H, Scholly J, Medina Villalon S, Guye M, Bartolomei F, Jirsa VK. Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography. PLoS Comput Biol 2021; 17:e1008689. [PMID: 33596194 PMCID: PMC7920393 DOI: 10.1371/journal.pcbi.1008689] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 03/01/2021] [Accepted: 01/10/2021] [Indexed: 02/07/2023] Open
Abstract
Surgical interventions in epileptic patients aimed at the removal of the epileptogenic zone have success rates at only 60-70%. This failure can be partly attributed to the insufficient spatial sampling by the implanted intracranial electrodes during the clinical evaluation, leading to an incomplete picture of spatio-temporal seizure organization in the regions that are not directly observed. Utilizing the partial observations of the seizure spreading through the brain network, complemented by the assumption that the epileptic seizures spread along the structural connections, we infer if and when are the unobserved regions recruited in the seizure. To this end we introduce a data-driven model of seizure recruitment and propagation across a weighted network, which we invert using the Bayesian inference framework. Using a leave-one-out cross-validation scheme on a cohort of 45 patients we demonstrate that the method can improve the predictions of the states of the unobserved regions compared to an empirical estimate that does not use the structural information, yet it is on the same level as the estimate that takes the structure into account. Furthermore, a comparison with the performed surgical resection and the surgery outcome indicates a link between the inferred excitable regions and the actual epileptogenic zone. The results emphasize the importance of the structural connectome in the large-scale spatio-temporal organization of epileptic seizures and introduce a novel way to integrate the patient-specific connectome and intracranial seizure recordings in a whole-brain computational model of seizure spread.
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Affiliation(s)
- Viktor Sip
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Meysam Hashemi
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | | | | | - Huifang Wang
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Julia Scholly
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, CEMEREM, Pôle d’Imagerie Médicale, CHU, Marseille, France
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, CHU, Marseille, France
| | - Samuel Medina Villalon
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, CHU, Marseille, France
| | - Maxime Guye
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, CEMEREM, Pôle d’Imagerie Médicale, CHU, Marseille, France
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
| | - Fabrice Bartolomei
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, CHU, Marseille, France
| | - Viktor K. Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
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19
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Lin FH, Lee HJ, Ahveninen J, Jääskeläinen IP, Yu HY, Lee CC, Chou CC, Kuo WJ. Distributed source modeling of intracranial stereoelectro-encephalographic measurements. Neuroimage 2021; 230:117746. [PMID: 33454414 DOI: 10.1016/j.neuroimage.2021.117746] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 12/11/2020] [Accepted: 01/06/2021] [Indexed: 11/17/2022] Open
Abstract
Intracranial stereoelectroencephalography (sEEG) provides unsurpassed sensitivity and specificity for human neurophysiology. However, functional mapping of brain functions has been limited because the implantations have sparse coverage and differ greatly across individuals. Here, we developed a distributed, anatomically realistic sEEG source-modeling approach for within- and between-subject analyses. In addition to intracranial event-related potentials (iERP), we estimated the sources of high broadband gamma activity (HBBG), a putative correlate of local neural firing. Our novel approach accounted for a significant portion of the variance of the sEEG measurements in leave-one-out cross-validation. After logarithmic transformations, the sensitivity and signal-to-noise ratio were linearly inversely related to the minimal distance between the brain location and electrode contacts (slope≈-3.6). The signa-to-noise ratio and sensitivity in the thalamus and brain stem were comparable to those locations at the vicinity of electrode contact implantation. The HGGB source estimates were remarkably consistent with analyses of intracranial-contact data. In conclusion, distributed sEEG source modeling provides a powerful neuroimaging tool, which facilitates anatomically-normalized functional mapping of human brain using both iERP and HBBG data.
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Affiliation(s)
- Fa-Hsuan Lin
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada; Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Hsin-Ju Lee
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Iiro P Jääskeläinen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; International Laboratory of Social Neurobiology, Institute of Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russian Federation
| | - Hsiang-Yu Yu
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Brain Science, Brain Research Center, National Yang-Ming University, Taipei, Taiwan
| | - Cheng-Chia Lee
- Institute of Brain Science, Brain Research Center, National Yang-Ming University, Taipei, Taiwan; Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chien-Chen Chou
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Brain Science, Brain Research Center, National Yang-Ming University, Taipei, Taiwan
| | - Wen-Jui Kuo
- Institute of Neuroscience, National Yang Ming University, Taipei, Taiwan; Brain Research Center, National Yang Ming University, Taipei, Taiwan.
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20
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Huang W, Zhang P, Yu T, Gu Z, Guo Q, Li Y. A P300-Based BCI System Using Stereoelectroencephalography and Its Application in a Brain Mechanistic Study. IEEE Trans Biomed Eng 2020; 68:2509-2519. [PMID: 33373294 DOI: 10.1109/tbme.2020.3047812] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Stereoelectroencephalography (SEEG) signals can be obtained by implanting deep intracranial electrodes. SEEG depth electrodes can record brain activity from the shallow cortical layer and deep brain structures, which is not achievable through other recording techniques. Moreover, SEEG has the advantage of a high signal-to-noise ratio (SNR). Therefore, it provides a potential way to establish a highly efficient brain-computer interface (BCI) and aid in understanding human brain activity. In this study, we implemented a P300-based BCI using SEEG signals. A single-character oddball paradigm was applied to elicit P300. To predict target characters, we fed the feature vectors extracted from the signals collected by five SEEG contacts into a Bayesian linear discriminant analysis (BLDA) classifier. Thirteen epileptic patients implanted with SEEG electrodes participated in the experiment and achieved an average online spelling accuracy of 93.85%. Moreover, through single-contact decoding analysis and simulated online analysis, we found that the SEEG-based BCI system achieved a high performance even when using a single signal channel. Furthermore, contacts with high decoding accuracies were mainly distributed in the visual ventral pathway, especially the fusiform gyrus (FG) and lingual gyrus (LG), which played an important role in building P300-based SEEG BCIs. These results might provide new insights into P300 mechanistic studies and the corresponding BCIs.
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21
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Coelli S, Nobili L, Boly M, Riedner B, Bianchi AM. Optimization of the Cortical Traveling Wave Analysis framework for feasibility in Stereo-Electroencephalography. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:3854-3857. [PMID: 31946714 DOI: 10.1109/embc.2019.8857664] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The study of brain waves propagation is of interest to understand the neural involvement in both physiological and pathological events, such as interictal epileptic spikes (IES). The possibility to track the trajectory of IESs could be useful to better characterize the role of the involved structures in the epileptic network, adding valuable information to the epileptic focus localization. Methods for the cortical traveling wave analysis (CTWA) have been proposed to trace the preferred propagation path of sleep slow waves, using scalp high-density EEG and reconstructing the trajectories both in the sensors and in the sources space. In this work, we propose a feasibility study of the application of these concepts to Stereo-EEG (SEEG) data for the analysis of IES. Through simulations, we selected the best performing Electrical Source Imaging inverse solution for our purpose and illustrate the CTWA procedure. We further show an exemplary application on real data and discuss advantages and pitfalls of the application of CTWA in SEEG.
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22
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Huang W, Yu T, Xiao J, Guo Q, Li Y. A P300-based Brain Computer Interface Using Stereo-electroencephalography Signals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:3062-3066. [PMID: 31946534 DOI: 10.1109/embc.2019.8857724] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Stereo-electroencephalography (SEEG) signals can be obtained by implanting deep intracranial electrodes, which are currently used for epileptic diagnosis. In this study, we implemented a P300-based Brain Computer Interface (BCI) using SEEG signals. 40 buttons corresponding to 40 numbers displayed in a graphical user interface (GUI) were intensified in a random order. To select a number, the user could focus on the corresponding button when it was flashing. Five epileptic patients implanted with SEEG electrodes attended the experiment and achieved an average online accuracy of 97.33%. Moreover, through single contact decoding and simulated online analysis, we found that these subjects achieved an average accuracy of 82.00% using a single channel of signal. These results indicated that our SEEG-based BCI had a high performance, which was mainly because of the high quality of SEEG signals.
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23
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Prime D, Woolfe M, Rowlands D, O'Keefe S, Dionisio S. Comparing connectivity metrics in cortico-cortical evoked potentials using synthetic cortical response patterns. J Neurosci Methods 2020; 334:108559. [PMID: 31927000 DOI: 10.1016/j.jneumeth.2019.108559] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 12/06/2019] [Accepted: 12/16/2019] [Indexed: 02/06/2023]
Abstract
BACKGROUND Cortico-Cortical Evoked Potentials (CCEPs) are a novel low frequency stimulation method used for brain mapping during intracranial epilepsy investigations. Only a handful of metrics have been applied to CCEP data to infer connectivity, and no comparison as to which is best has been performed. NEW METHOD We implement a novel method which involved superimposing synthetic cortical responses onto stereoelectroencephalographic (SEEG) data, and use this to compare several metric's ability to detect the simulated patterns. In this we compare two commonly employed metrics currently used in CCEP analysis against eight time series similarity metrics (TSSMs), which have been widely used in machine learning and pattern matching applications. RESULTS Root Mean Square (RMS), a metric commonly employed in CCEP analysis, was sensitive to a wide variety of response patterns, but insensitive to simulated epileptiform patterns. Autoregressive (AR) coefficients calculated by Burg's method were also sensitive to a wide range of patterns, but were extremely sensitive to epileptiform patterns. Other metrics which employed elastic warping techniques were less sensitive to the simulated response patterns. COMPARISON WITH EXISTING METHODS Our study is the first to compare CCEP connectivity metrics against one-another. Our results found that RMS, which has been used in many CCEP studies previously, was the most sensitive metric across a wide range of patterns. CONCLUSIONS Our novel method showed that RMS is a robust and sensitive measure, validating much of the findings of the SEEG-CCEP literature to date. Autoregressive coefficients may also be a useful metric to investigate epileptic networks.
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Affiliation(s)
- David Prime
- Griffith University School of Engineering and Built Environment, Nathan, QLD, Australia; Mater Advanced Epilepsy Unit, Brisbane, QLD, Australia.
| | - Matthew Woolfe
- Griffith University School of Engineering and Built Environment, Nathan, QLD, Australia; Mater Advanced Epilepsy Unit, Brisbane, QLD, Australia
| | - David Rowlands
- Griffith University School of Engineering and Built Environment, Nathan, QLD, Australia
| | - Steven O'Keefe
- Griffith University School of Engineering and Built Environment, Nathan, QLD, Australia
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Woolfe M, Prime D, Tjoa L, O'Keefe S, Rowlands D, Dionisio S. Nocturnal motor events in epilepsy: Is there a defined physiological network? Clin Neurophysiol 2019; 130:1531-1538. [DOI: 10.1016/j.clinph.2019.05.033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 04/22/2019] [Accepted: 05/27/2019] [Indexed: 11/15/2022]
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25
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Gillinder L, Dionisio S. Intracranial EEG recording in autoimmune epilepsy dataset. Data Brief 2019; 25:104191. [PMID: 31453277 PMCID: PMC6700534 DOI: 10.1016/j.dib.2019.104191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 06/11/2019] [Accepted: 06/18/2019] [Indexed: 11/28/2022] Open
Abstract
The appearance of seizures in autoimmune epilepsy on intracranial recordings has not been previously demonstrated. The following data shows a multifocal epilepsy in a patient with seronegative autoimmune epilepsy (reported here; “Electroclinical Insights into Autoimmune Epilepsy”, Gillinder, 2019). Independent seizures were seen to arise from 5 separate foci. These all began with slow repetitive spiking in a highly restricted area. Only after many minutes would this activity spread to other regions. Despite arising from different locations, all foci affected the posterior insula resulting in clinical symptoms.
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26
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Woolfe M, Prime D, Gillinder L, Rowlands D, O'keefe S, Dionisio S. Automatic detection of the epileptogenic zone: An application of the fingerprint of epilepsy. J Neurosci Methods 2019; 325:108347. [PMID: 31330159 DOI: 10.1016/j.jneumeth.2019.108347] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 07/04/2019] [Accepted: 07/04/2019] [Indexed: 11/16/2022]
Abstract
BACKGROUND The successful delineation of the epileptogenic zone in epilepsy monitoring is crucial for achieving seizure freedom after epilepsy surgery. NEW METHOD We aim to improve epileptogenic zone localization by utilizing a computer-assisted tool for the automated grading of the seizure activity recorded in various locations for 20 patients undergoing stereo electroencephalography. Their epileptic seizures were processed to extract two potential biomarkers. The concentration of these biomarkers from within each patient's implantation were then graded to identify their epileptogenic zone and were compared to the clinical assessment. RESULTS Our technique was capable of ranking the clinically defined epileptogenic zone with high accuracy, above 95%, with a true to false positive ratio of 1:1.52, and was effective with both temporal and extra-temporal onset epilepsies. COMPARISON WITH EXISTING METHOD We compared our method to two other groups performing localization using similar biomarkers. Our classification metrics, sensitivity and precision together were comparable to both groups and our overall accuracy from a larger population was also higher then both. CONCLUSIONS Our method is highly accurate, automated and non-parametric providing clinicians another tool that can be used to help identify the epileptogenic zone in patients undergoing the stereo electroencephalography procedure for epilepsy monitoring.
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Affiliation(s)
- Matthew Woolfe
- Advanced Epilepsy Unit, Mater Adult Hospital Brisbane, Queensland, 4101, Australia; School of Engineering and Built Environment, Griffith University, Queensland, 4111, Australia.
| | - David Prime
- Advanced Epilepsy Unit, Mater Adult Hospital Brisbane, Queensland, 4101, Australia; School of Engineering and Built Environment, Griffith University, Queensland, 4111, Australia
| | - Lisa Gillinder
- Advanced Epilepsy Unit, Mater Adult Hospital Brisbane, Queensland, 4101, Australia
| | - David Rowlands
- School of Engineering and Built Environment, Griffith University, Queensland, 4111, Australia
| | - Steven O'keefe
- School of Engineering and Built Environment, Griffith University, Queensland, 4111, Australia
| | - Sasha Dionisio
- Advanced Epilepsy Unit, Mater Adult Hospital Brisbane, Queensland, 4101, Australia
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Liu W, Tian S, Zhang J, Huang P, Wang T, Deng Y, Liu X, Miao F, Sun B, Zhan S. Utility of stereo-electroencephalography recording guided by magnetoencephalography in the surgical treatment of epilepsy patients with negative magnetic resonance imaging results. Int J Neurosci 2019; 129:1045-1052. [PMID: 31215295 DOI: 10.1080/00207454.2019.1634066] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Objective: It is challenging for neurosurgeons to perform surgeries on patients without detectable structural lesions. Therefore, this retrospective study aimed to explore the outcome of stereo-electroencephalography (SEEG) in suspicious areas guided by magnetoencephalography (MEG)-magnetic resonance imaging (MRI) reconstruction in MRI-negative epilepsy patients. Methods: This study included 47 patients with negative-MRI epilepsy. Seizure outcome at 24 months was assessed using a modified Engel's classification. Accordingly, class I and II were considered favorable outcomes, whereas classes III and IV were unfavorable. Furthermore, patients were classified into a consistent group if the results of MEG and SEEG indicated the same area of the brain. The relationship between surgical outcome and the concordance of MEG and SEEG was analyzed. Results: A complete seizure-free condition was achieved in 22 (47%) patients. Sex, handedness, age and duration of illness were not significantly associated with seizure-free outcome (p = .187 [Pearson chi-squared test]). The number of patients with favorable outcome (Engle I and II) was as high as 68% at the time of follow-up. Furthermore, more seizure-free patients were found in the SEEG and MEG consistent group. Conclusions: SEEG is a valuable tool in the pre-evaluation for resective epilepsy surgery, particularly in negative-MRI epilepsy patients; MEG greatly facilitates localization for SEEG electrode implantation. However, none of these tools are absolutely sensitive and reliable; therefore, collecting as much information as possible is necessary to achieve satisfactory results in epilepsy surgery.
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Affiliation(s)
- Wei Liu
- Department of Stereotactic and Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai , China
| | - Shuaiwei Tian
- Department of Neurosurgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai , China
| | - Jing Zhang
- Department of Stereotactic and Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai , China
| | - Peng Huang
- Department of Stereotactic and Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai , China
| | - Tao Wang
- Department of Stereotactic and Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai , China
| | - Yulei Deng
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai , China
| | - Xiaoying Liu
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai , China
| | - Fei Miao
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai , China
| | - Bomin Sun
- Department of Stereotactic and Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai , China
| | - Shikun Zhan
- Department of Stereotactic and Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai , China
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Shah P, Ashourvan A, Mikhail F, Pines A, Kini L, Oechsel K, Das SR, Stein JM, Shinohara RT, Bassett DS, Litt B, Davis KA. Characterizing the role of the structural connectome in seizure dynamics. Brain 2019; 142:1955-1972. [PMID: 31099821 PMCID: PMC6598625 DOI: 10.1093/brain/awz125] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Revised: 02/11/2019] [Accepted: 03/07/2019] [Indexed: 12/23/2022] Open
Abstract
How does the human brain's structural scaffold give rise to its intricate functional dynamics? This is a central question in translational neuroscience that is particularly relevant to epilepsy, a disorder affecting over 50 million subjects worldwide. Treatment for medication-resistant focal epilepsy is often structural-through surgery or laser ablation-but structural targets, particularly in patients without clear lesions, are largely based on functional mapping via intracranial EEG. Unfortunately, the relationship between structural and functional connectivity in the seizing brain is poorly understood. In this study, we quantify structure-function coupling, specifically between white matter connections and intracranial EEG, across pre-ictal and ictal periods in 45 seizures from nine patients with unilateral drug-resistant focal epilepsy. We use high angular resolution diffusion imaging (HARDI) tractography to construct structural connectivity networks and correlate these networks with time-varying broadband and frequency-specific functional networks derived from coregistered intracranial EEG. Across all frequency bands, we find significant increases in structure-function coupling from pre-ictal to ictal periods. We demonstrate that short-range structural connections are primarily responsible for this increase in coupling. Finally, we find that spatiotemporal patterns of structure-function coupling are highly stereotyped for each patient. These results suggest that seizures harness the underlying structural connectome as they propagate. Mapping the relationship between structural and functional connectivity in epilepsy may inform new therapies to halt seizure spread, and pave the way for targeted patient-specific interventions.
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Affiliation(s)
- Preya Shah
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Arian Ashourvan
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Fadi Mikhail
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Adam Pines
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lohith Kini
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Kelly Oechsel
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sandhitsu R Das
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Joel M Stein
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Brian Litt
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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He B, Astolfi L, Valdés-Sosa PA, Marinazzo D, Palva SO, Bénar CG, Michel CM, Koenig T. Electrophysiological Brain Connectivity: Theory and Implementation. IEEE Trans Biomed Eng 2019; 66:10.1109/TBME.2019.2913928. [PMID: 31071012 PMCID: PMC6834897 DOI: 10.1109/tbme.2019.2913928] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
We review the theory and algorithms of electrophysiological brain connectivity analysis. This tutorial is aimed at providing an introduction to brain functional connectivity from electrophysiological signals, including electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), stereoelectroencephalography (SEEG). Various connectivity estimators are discussed, and algorithms introduced. Important issues for estimating and mapping brain functional connectivity with electrophysiology are discussed.
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Affiliation(s)
- Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, USA
| | - Laura Astolfi
- Department of Computer, Control and Management Engineering, University of Rome Sapienza, and with IRCCS Fondazione Santa Lucia, Rome, Italy
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Bhattacharyya A, Ranta R, Le Cam S, Louis-Dorr V, Tyvaert L, Colnat-Coulbois S, Maillard L, Pachori RB. A multi-channel approach for cortical stimulation artefact suppression in depth EEG signals using time-frequency and spatial filtering. IEEE Trans Biomed Eng 2018; 66:1915-1926. [PMID: 30418880 DOI: 10.1109/tbme.2018.2881051] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE The stereo electroencephalogram (SEEG) recordings are the sate of the art tool used in pre-surgical evaluation of drug-unresponsive epileptic patients. Coupled with SEEG, electrical cortical stimulation (CS) offer a complementary tool to investigate the lesioned/healthy brain regions and to identify the epileptic zones with precision. However, the propagation of this stimulation inside the brain masks the cerebral activity recorded by nearby multi-contact SEEG electrodes. The objective of this paper is to propose a novel filtering approach for suppressing the CS artifact in SEEG signals using time, frequency as well as spatial information. METHODS The method combines spatial filtering with tunable-Q wavelet transform (TQWT). SEEG signals are spatially filtered to isolate the CS artifacts within a few number of sources/components. The artifacted components are then decomposed into oscillatory background and sharp varying transient signals using tunable-Q wavelet transform (TQWT). The CS artifact is assumed to lie in the transient part of the signal. Using prior known time-frequency information of the CS artifacts, we selectively mask the wavelet coefficients of the transient signal and extract out any remaining significant electrophysiological activity. RESULTS We have applied our proposed method of CS artifact suppression on simulated and real SEEG signals with convincing performance. The experimental results indicate the effectiveness of the proposed approach. CONCLUSION The proposed method suppresses CS artifacts without affecting the background SEEG signal. SIGNIFICANCE The proposed method can be applied for suppressing both low and high frequency CS artifacts and outperforms current methods from the literature.
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31
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Hosseini SAH, Sohrabpour A, He B. Electromagnetic source imaging using simultaneous scalp EEG and intracranial EEG: An emerging tool for interacting with pathological brain networks. Clin Neurophysiol 2018; 129:168-187. [PMID: 29190523 PMCID: PMC5743592 DOI: 10.1016/j.clinph.2017.10.027] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Revised: 09/05/2017] [Accepted: 10/11/2017] [Indexed: 10/18/2022]
Abstract
OBJECTIVE The goal of this study is to investigate the performance, merits and limitations of source imaging using intracranial EEG (iEEG) recordings and to compare its accuracy to the results of EEG source imaging. Accuracy in this study, is measured both by determining the location and inter-nodal connectivity of underlying brain networks. METHODS Systematic computer simulation studies are conducted to evaluate iEEG-based source imaging vs. EEG-based source imaging, and source imaging using both EEG and iEEG. To test the source imaging models, networks of inter-connected nodes (in terms of activity) are simulated. The location of the network nodes is randomly selected within a realistic geometry head model and a connectivity link is created among these nodes based on a multi-variate auto-regressive (MVAR) model. Then the forward problem is solved to calculate the potentials at the electrodes and noise (white and correlated) is added to these simulated potentials to simulate realistic measurements. Subsequently, the inverse problem is solved and an algorithm based on principle component analysis is performed on the estimated source activities to determine the location of the simulated network nodes. The activity of these nodes (over time), is then extracted, and used to estimate the connectivity links among the mentioned nodes using Granger causality analysis. RESULTS Source imaging based on iEEG recordings may or may not improve the accuracy in localization, depending on the number and location of active nodes relative to iEEG electrodes and to other nodes within the network. However, our simulation results suggest that combining EEG and iEEG modalities (simultaneous scalp and intracranial recordings) can improve the imaging accuracy significantly. CONCLUSIONS While iEEG source imaging is useful in estimating the exact location of sources near the iEEG electrodes, combining EEG and iEEG recordings can achieve a more accurate imaging due to the high spatial coverage of the scalp electrodes and the added near field information provided by the iEEG electrodes. SIGNIFICANCE The present results suggest the feasibility of localizing brain electrical sources from iEEG recordings and improving EEG source localization using simultaneous EEG and iEEG recordings to cover the whole brain. The hybrid EEG and iEEG source imaging can assist the clinicians when unequivocal decisions about determining the epileptogenic zone cannot be reached using a single modality.
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Affiliation(s)
| | - Abbas Sohrabpour
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Bin He
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA; Institute for Engineering in Medicine, University of Minnesota, Minneapolis, MN, USA.
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Cam SL, Ranta R, Caune V, Korats G, Koessler L, Maillard L, Louis-Dorr V. SEEG dipole source localization based on an empirical Bayesian approach taking into account forward model uncertainties. Neuroimage 2017; 153:1-15. [PMID: 28323161 DOI: 10.1016/j.neuroimage.2017.03.030] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Revised: 01/18/2017] [Accepted: 03/14/2017] [Indexed: 11/24/2022] Open
Abstract
Electromagnetic brain source localization consists in the inversion of a forward model based on a limited number of potential measurements. A wide range of methods has been developed to regularize this severely ill-posed problem and to reduce the solution space, imposing spatial smoothness, anatomical constraint or sparsity of the activated source map. This last criteria, based on physiological assumptions stating that in some particular events (e.g., epileptic spikes, evoked potential) few focal area of the brain are simultaneously actives, has gained more and more interest. Bayesian approaches have the ability to provide sparse solutions under adequate parametrization, and bring a convenient framework for the introduction of priors in the form of probabilistic density functions. However the quality of the forward model is rarely questioned while this parameter has undoubtedly a great influence on the solution. Its construction suffers from numerous approximation and uncertainties, even when using realistic numerical models. In addition, it often encodes a coarse sampling of the continuous solution space due to the computational burden its inversion implies. In this work we propose an empirical Bayesian approach to take into account the uncertainties of the forward model by allowing constrained variations around a prior physical model, in the particular context of SEEG measurements. We demonstrate on simulations that the method enhance the accuracy of the source time-course estimation as well as the sparsity of the resulting source map. Results on real signals prove the applicability of the method in real contexts.
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Affiliation(s)
- S Le Cam
- Université de Lorraine, CRAN, UMR 7039, 54500 Vandœuvre-lès-Nancy, France; CNRS, CRAN, UMR, 7039, France.
| | - R Ranta
- Université de Lorraine, CRAN, UMR 7039, 54500 Vandœuvre-lès-Nancy, France; CNRS, CRAN, UMR, 7039, France
| | - V Caune
- Université de Lorraine, CRAN, UMR 7039, 54500 Vandœuvre-lès-Nancy, France; CNRS, CRAN, UMR, 7039, France
| | - G Korats
- Université de Lorraine, CRAN, UMR 7039, 54500 Vandœuvre-lès-Nancy, France; CNRS, CRAN, UMR, 7039, France; Ventspils University College, 101 Inzenieruiela, LV-3601 Ventspils, Latvia
| | - L Koessler
- Université de Lorraine, CRAN, UMR 7039, 54500 Vandœuvre-lès-Nancy, France; CNRS, CRAN, UMR, 7039, France
| | - L Maillard
- Université de Lorraine, CRAN, UMR 7039, 54500 Vandœuvre-lès-Nancy, France; CNRS, CRAN, UMR, 7039, France; CHU Nancy, Neurology Department, 54000 Nancy, France
| | - V Louis-Dorr
- Université de Lorraine, CRAN, UMR 7039, 54500 Vandœuvre-lès-Nancy, France; CNRS, CRAN, UMR, 7039, France
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Ranta R, Le Cam S, Tyvaert L, Louis-Dorr V. Assessing human brain impedance using simultaneous surface and intracerebral recordings. Neuroscience 2016; 343:411-422. [PMID: 28012868 DOI: 10.1016/j.neuroscience.2016.12.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 12/06/2016] [Accepted: 12/07/2016] [Indexed: 11/15/2022]
Abstract
Most of the literature on the brain impedance proposes a frequency-independent resistive model. Recently, this conclusion was tackled by a series of papers (Bédard et al., 2006; Bédard and Destexhe, 2009; Gomes et al., 2016), based on microscopic sale modeling and measurements. Our paper aims to investigate the impedance issue using simultaneous in vivo depth and surface signals recorded during intracerebral electrical stimulation of epileptic patients, involving a priori different tissues with different impedances. Our results confirm the conclusions from Logothethis et al. (2007): there is no evidence of frequency dependence of the brain tissue impedance (more precisely, there is no difference, in terms of frequency filtering, between the brain and the skull bone), at least at a macroscopic scale. In order to conciliate findings from both microscopic and macroscopic scales, we recall different neural/synaptic current generators' models from the literature and we propose an original computational model, based on fractional dynamics.
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Affiliation(s)
- Radu Ranta
- Université de Lorraine, CRAN, UMR 7039, 2 av. de la Forêt de Haye, 54500 Vandoeuvre-lés-Nancy, France; CNRS, CRAN, UMR 7039, France.
| | - Steven Le Cam
- Université de Lorraine, CRAN, UMR 7039, 2 av. de la Forêt de Haye, 54500 Vandoeuvre-lés-Nancy, France; CNRS, CRAN, UMR 7039, France
| | - Louise Tyvaert
- Université de Lorraine, CRAN, UMR 7039, 2 av. de la Forêt de Haye, 54500 Vandoeuvre-lés-Nancy, France; CNRS, CRAN, UMR 7039, France; CHU Nancy, Neurology Department, 54000 Nancy, France
| | - Valérie Louis-Dorr
- Université de Lorraine, CRAN, UMR 7039, 2 av. de la Forêt de Haye, 54500 Vandoeuvre-lés-Nancy, France; CNRS, CRAN, UMR 7039, France
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Résolution de problèmes direct et inverse à partir de mesures SEEG et de la stimulation électrique intracérébrale. Neurophysiol Clin 2016. [DOI: 10.1016/j.neucli.2016.05.057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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35
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Le Cam S, Caune V, Ranta R, Korats G, Louis-Dorr V. Combining bayesian source imaging with equivalent dipole approach to solve the intracranial EEG source localization problem. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2015:642-645. [PMID: 26736344 DOI: 10.1109/embc.2015.7318444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The brain source localization problem has been extensively studied in the past years, yielding a large panel of methodologies, each bringing their own strengths and weaknesses. Combining several of these approaches might help in enhancing their respective performance. Our study is carried out in the particular context of intracranial recordings, with the objective to explain the measurements based on a reduced number of dipolar activities. We take benefit of the sparse nature of the Bayesian approaches to separate the noise from the source space, and to distinguish between several source contributions on the electrodes. This first step provides accurate estimates of the dipole projections, which can be used as an entry to an equivalent current dipole fitting procedure. We demonstrate on simulations that the localization results are significantly enhanced by this post-processing step when up to five dipoles are activated simultaneously.
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Kini LG, Davis KA, Wagenaar JB. Data integration: Combined imaging and electrophysiology data in the cloud. Neuroimage 2015; 124:1175-1181. [PMID: 26044858 DOI: 10.1016/j.neuroimage.2015.05.075] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2015] [Revised: 04/29/2015] [Accepted: 05/26/2015] [Indexed: 11/30/2022] Open
Abstract
There has been an increasing effort to correlate electrophysiology data with imaging in patients with refractory epilepsy over recent years. IEEG.org provides a free-access, rapidly growing archive of imaging data combined with electrophysiology data and patient metadata. It currently contains over 1200 human and animal datasets, with multiple data modalities associated with each dataset (neuroimaging, EEG, EKG, de-identified clinical and experimental data, etc.). The platform is developed around the concept that scientific data sharing requires a flexible platform that allows sharing of data from multiple file formats. IEEG.org provides high- and low-level access to the data in addition to providing an environment in which domain experts can find, visualize, and analyze data in an intuitive manner. Here, we present a summary of the current infrastructure of the platform, available datasets and goals for the near future.
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Affiliation(s)
- Lohith G Kini
- Department of Bioengineering, University of Pennsylvania, 240 Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA 19104-6321, USA.
| | - Kathryn A Davis
- Department of Neurology, Hospital of the University of Pennsylvania, 3400 Spruce Street, 3 West Gates Bldg, Philadelphia PA 19104, USA.
| | - Joost B Wagenaar
- Department of Neurology, Hospital of the University of Pennsylvania, 3400 Spruce Street, 3 West Gates Bldg, Philadelphia PA 19104, USA.
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Le Cam S, Caune V, Ranta R, Maillard L, Koessler L, Louis-Dorr V. Influence of the stereo-EEG sensors setup and of the averaging on the dipole localization problem. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2014; 2014:1147-1150. [PMID: 25570166 DOI: 10.1109/embc.2014.6943798] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
While scalp EEG/MEG source imaging have been extensively studied in the last two decades, the case of source localization from invasive measurements has resulted in few works to date. Yet there is a lot to gain from stereo-electroencephalographic (SEEG) recordings, providing high signal to noise ratio measurements of the explored brain structures. The SEEG setup consists in multi-contact electrodes inserted in the brain volume, each containing a dozen of collinear measuring contacts. This particular setup raises the question of the conditioning of the inverse problem. In recent works, we have evaluated the feasibility to localize a single dominant equivalent dipole facing different sensors and noise configurations. We deepen here the analysis by evaluating the influence of the chosen subset of sensors and of the number of averaged time samples on the accuracy of the localization. We conduct experiments on simulated data as well as on real epileptic spikes, illustrating the trade off to be made between these two factors.
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