1
|
Krishnan B, Tousseyn S, Taylor K, Wu G, Serletis D, Najm I, Bulacio J, Alexopoulos AV. Measurable transitions during seizures in intracranial EEG: A stereoelectroencephalography and SPECT study. Clin Neurophysiol 2024; 161:80-92. [PMID: 38452427 DOI: 10.1016/j.clinph.2024.02.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 02/05/2024] [Accepted: 02/17/2024] [Indexed: 03/09/2024]
Abstract
OBJECTIVE Ictal Single Photon Emission Computed Tomography (SPECT) and stereo-electroencephalography (SEEG) are diagnostic techniques used for the management of patients with drug-resistant focal epilepsies. While hyperperfusion patterns in ictal SPECT studies reveal seizure onset and propagation pathways, the role of ictal hypoperfusion remains poorly understood. The goal of this study was to systematically characterize the spatio-temporal information flow dynamics between differently perfused brain regions using stereo-EEG recordings. METHODS We identified seizure-free patients after resective epilepsy surgery who had prior ictal SPECT and SEEG investigations. We estimated directional connectivity between the epileptogenic-zone (EZ), non-resected areas of hyperperfusion, hypoperfusion, and baseline perfusion during the interictal, preictal, ictal, and postictal periods. RESULTS Compared to the background, we noted significant information flow (1) during the preictal period from the EZ to the baseline and hyperperfused regions, (2) during the ictal onset from the EZ to all three regions, and (3) during the period of seizure evolution from the area of hypoperfusion to all three regions. CONCLUSIONS Hypoperfused brain regions were found to indirectly interact with the EZ during the ictal period. SIGNIFICANCE Our unique study, combining intracranial electrophysiology and perfusion imaging, presents compelling evidence of dynamic changes in directional connectivity between brain regions during the transition from interictal to ictal states.
Collapse
Affiliation(s)
- Balu Krishnan
- Neurological Institute, Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA.
| | - Simon Tousseyn
- Academic Center for Epileptology, Kempenhaeghe and Maastricht UMC+, Heeze, The Netherlands; School for Mental Health and Neuroscience (MHeNs), University Maastricht (UM), Maastricht, The Netherlands
| | - Kenneth Taylor
- Neurological Institute, Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
| | - Guiyun Wu
- Neurological Institute, Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
| | - Demitre Serletis
- Neurological Institute, Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
| | - Imad Najm
- Neurological Institute, Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
| | - Juan Bulacio
- Neurological Institute, Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
| | | |
Collapse
|
2
|
Miao Y, Suzuki H, Sugano H, Ueda T, Iimura Y, Matsui R, Tanaka T. Causal Connectivity Network Analysis of Ictal Electrocorticogram With Temporal Lobe Epilepsy Based on Dynamic Phase Transfer Entropy. IEEE Trans Biomed Eng 2024; 71:531-541. [PMID: 37624716 DOI: 10.1109/tbme.2023.3308616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/27/2023]
Abstract
Temporallobe epilepsy (TLE) has been conceptualized as a brain network disease, which generates brain connectivity dynamics within and beyond the temporal lobe structures in seizures. The hippocampus is a representative epileptogenic focus in TLE. Understanding the causal connectivity in terms of brain network during seizures is crucial in revealing the triggering mechanism of epileptic seizures originating from the hippocampus (HPC) spread to the lateral temporal cortex (LTC) by ictal electrocorticogram (ECoG), particularly in high-frequency oscillations (HFOs) bands. In this study, we proposed the unified-epoch dynamic causality analysis method to investigate the causal influence dynamics between two brain regions (HPC and LTC) at interictal and ictal phases in the frequency range of 1-500 Hz by introducing the phase transfer entropy (PTE) out/in-ratio and sliding window. We also proposed PTE-based machine learning algorithms to identify epileptogenic zone (EZ). Nine patients with a total of 26 seizures were included in this study. We hypothesized that: 1) HPC is the focus with the stronger causal connectivity than that in LTC in the ictal state at gamma and HFOs bands. 2) Causal connectivity in the ictal phase shows significant changes compared to that in the interictal phase. 3) The PTE out/in-ratio in the HFOs band can identify the EZ with the best prediction performance.
Collapse
|
3
|
Matarrese MAG, Loppini A, Fabbri L, Tamilia E, Perry MS, Madsen JR, Bolton J, Stone SSD, Pearl PL, Filippi S, Papadelis C. Spike propagation mapping reveals effective connectivity and predicts surgical outcome in epilepsy. Brain 2023; 146:3898-3912. [PMID: 37018068 PMCID: PMC10473571 DOI: 10.1093/brain/awad118] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/14/2023] [Accepted: 03/23/2023] [Indexed: 04/06/2023] Open
Abstract
Neurosurgical intervention is the best available treatment for selected patients with drug resistant epilepsy. For these patients, surgical planning requires biomarkers that delineate the epileptogenic zone, the brain area that is indispensable for the generation of seizures. Interictal spikes recorded with electrophysiological techniques are considered key biomarkers of epilepsy. Yet, they lack specificity, mostly because they propagate across brain areas forming networks. Understanding the relationship between interictal spike propagation and functional connections among the involved brain areas may help develop novel biomarkers that can delineate the epileptogenic zone with high precision. Here, we reveal the relationship between spike propagation and effective connectivity among onset and areas of spread and assess the prognostic value of resecting these areas. We analysed intracranial EEG data from 43 children with drug resistant epilepsy who underwent invasive monitoring for neurosurgical planning. Using electric source imaging, we mapped spike propagation in the source domain and identified three zones: onset, early-spread and late-spread. For each zone, we calculated the overlap and distance from surgical resection. We then estimated a virtual sensor for each zone and the direction of information flow among them via Granger causality. Finally, we compared the prognostic value of resecting these zones, the clinically-defined seizure onset zone and the spike onset on intracranial EEG channels by estimating their overlap with resection. We observed a spike propagation in source space for 37 patients with a median duration of 95 ms (interquartile range: 34-206), a spatial displacement of 14 cm (7.5-22 cm) and a velocity of 0.5 m/s (0.3-0.8 m/s). In patients with good surgical outcome (25 patients, Engel I), the onset had higher overlap with resection [96% (40-100%)] than early-spread [86% (34-100%), P = 0.01] and late-spread [59% (12-100%), P = 0.002], and it was also closer to resection than late-spread [5 mm versus 9 mm, P = 0.007]. We found an information flow from onset to early-spread in 66% of patients with good outcomes, and from early-spread to onset in 50% of patients with poor outcome. Finally, resection of spike onset, but not area of spike spread or the seizure onset zone, predicted outcome with positive predictive value of 79% and negative predictive value of 56% (P = 0.04). Spatiotemporal mapping of spike propagation reveals information flow from onset to areas of spread in epilepsy brain. Surgical resection of the spike onset disrupts the epileptogenic network and may render patients with drug resistant epilepsy seizure-free without having to wait for a seizure to occur during intracranial monitoring.
Collapse
Affiliation(s)
- Margherita A G Matarrese
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
- Laboratory of Nonlinear Physics and Mathematical Modeling, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, USA
| | - Alessandro Loppini
- Laboratory of Nonlinear Physics and Mathematical Modeling, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Lorenzo Fabbri
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, USA
| | - Eleonora Tamilia
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - M Scott Perry
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Jeffrey Bolton
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Scellig S D Stone
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Simonetta Filippi
- Laboratory of Nonlinear Physics and Mathematical Modeling, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Christos Papadelis
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, USA
- School of Medicine, Texas Christian University, Fort Worth, TX, USA
| |
Collapse
|
4
|
Nahvi M, Ardeshir G, Ezoji M, Tafakhori A, Shafiee S, Babajani-Feremi A. An application of dynamical directed connectivity of ictal intracranial EEG recordings in seizure onset zone localization. J Neurosci Methods 2023; 386:109775. [PMID: 36596400 DOI: 10.1016/j.jneumeth.2022.109775] [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: 04/17/2022] [Revised: 11/26/2022] [Accepted: 12/14/2022] [Indexed: 01/01/2023]
Abstract
BACKGROUND Identification of the seizure onset zone (SOZ) is a challenging task in epilepsy surgery. Patients with epilepsy have an altered brain network, allowing connectivity-based analyses to have a great potential in SOZ identification. We investigated a dynamical directed connectivity analysis utilizing ictal intracranial electroencephalographic (iEEG) recordings and proposed an algorithm for SOZ identification based on grouping iEEG contacts. NEW METHODS Granger Causality was used for directed connectivity analysis in this study. The intracranial contacts were grouped into visually detected contacts (VDCs), which were identified as SOZ by epileptologists, and non-resected contacts (NRCs). The intragroup and intergroup directed connectivity for VDCs and NRCs were calculated around seizure onset. We then proposed an algorithm for SOZ identification based on the cross-correlation of intragroup outflow and inflow of SOZ candidate contacts. RESULTS Our results revealed that the intragroup connectivity of VDCs (VDC→VDC) was significantly larger than the intragroup connectivity of NRCs (NRC→NRC) and the intergroup connectivity between NRCs and VDCs (NRC→VDC) around seizure onset. We found that the proposed algorithm had 90.1 % accuracy for SOZ identification in the seizure-free patients. COMPARISON WITH EXISTING METHODS The existing connectivity-based methods for SOZ identification often use either outflow or inflow. In this study, SOZ contacts were identified by integrating outflow and inflow based on the cross correlation between these two measures. CONCLUSIONS The proposed group-based dynamical connectivity analysis in this study can aid our understanding of underlying seizure network and may be used to assist in identifying the SOZ contacts before epilepsy surgery.
Collapse
Affiliation(s)
| | | | - Mehdi Ezoji
- Babol Noshirvani University of Technology, Babol, Iran
| | - Abbas Tafakhori
- Iranian Center of Neurological Research, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Sajad Shafiee
- Department of Neurosurgery, Mazandaran University of Medical Sciences, Sari, Iran
| | - Abbas Babajani-Feremi
- Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, TX, USA
| |
Collapse
|
5
|
Jiang H, Kokkinos V, Ye S, Urban A, Bagić A, Richardson M, He B. Interictal SEEG Resting-State Connectivity Localizes the Seizure Onset Zone and Predicts Seizure Outcome. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2200887. [PMID: 35545899 PMCID: PMC9218648 DOI: 10.1002/advs.202200887] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Indexed: 05/23/2023]
Abstract
Localization of epileptogenic zone currently requires prolonged intracranial recordings to capture seizure, which may take days to weeks. The authors developed a novel method to identify the seizure onset zone (SOZ) and predict seizure outcome using short-time resting-state stereotacticelectroencephalography (SEEG) data. In a cohort of 27 drug-resistant epilepsy patients, the authors estimated the information flow via directional connectivity and inferred the excitation-inhibition ratio from the 1/f power slope. They hypothesized that the antagonism of information flow at multiple frequencies between SOZ and non-SOZ underlying the relatively stable epilepsy resting state could be related to the disrupted excitation-inhibition balance. They found flatter 1/f power slope in non-SOZ regions compared to the SOZ, with dominant information flow from non-SOZ to SOZ regions. Greater differences in resting-state information flow between SOZ and non-SOZ regions are associated with favorable seizure outcome. By integrating a balanced random forest model with resting-state connectivity, their method localized the SOZ with an accuracy of 88% and predicted the seizure outcome with an accuracy of 92% using clinically determined SOZ. Overall, this study suggests that brief resting-state SEEG data can significantly facilitate the identification of SOZ and may eventually predict seizure outcomes without requiring long-term ictal recordings.
Collapse
Affiliation(s)
- Haiteng Jiang
- Department of Biomedical EngineeringCarnegie Mellon UniversityPittsburghPA15213USA
- Department of NeurobiologyAffiliated Mental Health Center & Hangzhou Seventh People's HospitalZhejiang University School of MedicineHangzhou310013P. R. China
- NHC and CAMS Key Laboratory of Medical NeurobiologyMOE Frontier Science Center for Brain Science and Brain‐machine IntegrationSchool of Brain Science and Brain MedicineZhejiang UniversityHangzhou310058P. R. China
| | - Vasileios Kokkinos
- University of Pittsburgh Comprehensive Epilepsy CenterDepartment of NeurologyUniversity of Pittsburgh School of MedicinePittsburghPA15232USA
- Massachusetts General HospitalBostonMA02114USA
| | - Shuai Ye
- Department of Biomedical EngineeringCarnegie Mellon UniversityPittsburghPA15213USA
| | - Alexandra Urban
- University of Pittsburgh Comprehensive Epilepsy CenterDepartment of NeurologyUniversity of Pittsburgh School of MedicinePittsburghPA15232USA
| | - Anto Bagić
- University of Pittsburgh Comprehensive Epilepsy CenterDepartment of NeurologyUniversity of Pittsburgh School of MedicinePittsburghPA15232USA
| | - Mark Richardson
- University of Pittsburgh Comprehensive Epilepsy CenterDepartment of NeurologyUniversity of Pittsburgh School of MedicinePittsburghPA15232USA
- Massachusetts General HospitalBostonMA02114USA
| | - Bin He
- Department of Biomedical EngineeringCarnegie Mellon UniversityPittsburghPA15213USA
- Neuroscience InstituteCarnegie Mellon UniversityPittsburghPA15213USA
| |
Collapse
|
6
|
Yang JC, Paulk AC, Salami P, Lee SH, Ganji M, Soper DJ, Cleary D, Simon M, Maus D, Lee JW, Nahed BV, Jones PS, Cahill DP, Cosgrove GR, Chu CJ, Williams Z, Halgren E, Dayeh S, Cash SS. Microscale dynamics of electrophysiological markers of epilepsy. Clin Neurophysiol 2021; 132:2916-2931. [PMID: 34419344 DOI: 10.1016/j.clinph.2021.06.024] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 06/22/2021] [Accepted: 06/29/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVE Interictal discharges (IIDs) and high frequency oscillations (HFOs) are established neurophysiologic biomarkers of epilepsy, while microseizures are less well studied. We used custom poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) microelectrodes to better understand these markers' microscale spatial dynamics. METHODS Electrodes with spatial resolution down to 50 µm were used to record intraoperatively in 30 subjects. IIDs' degree of spread and spatiotemporal paths were generated by peak-tracking followed by clustering. Repeating HFO patterns were delineated by clustering similar time windows. Multi-unit activity (MUA) was analyzed in relation to IID and HFO timing. RESULTS We detected IIDs encompassing the entire array in 93% of subjects, while localized IIDs, observed across < 50% of channels, were seen in 53%. IIDs traveled along specific paths. HFOs appeared in small, repeated spatiotemporal patterns. Finally, we identified microseizure events that spanned 50-100 µm. HFOs covaried with MUA, but not with IIDs. CONCLUSIONS Overall, these data suggest that irritable cortex micro-domains may form part of an underlying pathologic architecture which could contribute to the seizure network. SIGNIFICANCE These results, supporting the possibility that epileptogenic cortex comprises a mosaic of irritable domains, suggests that microscale approaches might be an important perspective in devising novel seizure control therapies.
Collapse
Affiliation(s)
- Jimmy C Yang
- Department of Neurosurgery, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA; Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Pariya Salami
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Sang Heon Lee
- Department of Electrical and Computer Engineering, University of California, San Diego; 9500 Gilman Dr., La Jolla, CA 92093, USA
| | - Mehran Ganji
- Department of Electrical and Computer Engineering, University of California, San Diego; 9500 Gilman Dr., La Jolla, CA 92093, USA
| | - Daniel J Soper
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Daniel Cleary
- Department of Neurosurgery, University of California, San Diego; 9500 Gilman Dr., La Jolla, CA 92093, USA
| | - Mirela Simon
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Douglas Maus
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Jong Woo Lee
- Department of Neurology, Brigham and Women's Hospital, 60 Fenwood Rd., Boston, MA 02115, USA
| | - Brian V Nahed
- Department of Neurosurgery, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Pamela S Jones
- Department of Neurosurgery, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Daniel P Cahill
- Department of Neurosurgery, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Garth Rees Cosgrove
- Department of Neurosurgery, Brigham and Women's Hospital, 60 Fenwood Rd., Boston, MA 02115, USA
| | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Ziv Williams
- Department of Neurosurgery, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Eric Halgren
- Department of Radiology, University of California, San Diego; 9500 Gilman Dr.; La Jolla, CA 92093, USA
| | - Shadi Dayeh
- Department of Electrical and Computer Engineering, University of California, San Diego; 9500 Gilman Dr., La Jolla, CA 92093, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA.
| |
Collapse
|
7
|
Cohen N, Ebrahimi Y, Medvedovsky M, Gurevitch G, Aizenstein O, Hendler T, Fahoum F, Gazit T. Interictal Epileptiform Discharge Dynamics in Peri-sylvian Polymicrogyria Using EEG-fMRI. Front Neurol 2021; 12:658239. [PMID: 34149595 PMCID: PMC8212705 DOI: 10.3389/fneur.2021.658239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 05/05/2021] [Indexed: 11/13/2022] Open
Abstract
Polymicrogyria (PMG) is a common malformation of cortical development associated with a higher susceptibility to epileptic seizures. Seizures secondary to PMG are characterized by difficult-to-localize cerebral sources due to the complex and widespread lesion structure. Tracing the dynamics of interictal epileptiform discharges (IEDs) in patients with epilepsy has been shown to reveal the location of epileptic activity sources, crucial for successful treatment in cases of focal drug-resistant epilepsy. In this case series IED dynamics were evaluated with simultaneous EEG-fMRI recordings in four patients with unilateral peri-sylvian polymicrogyria (PSPMG) by tracking BOLD activations over time: before, during and following IED appearance on scalp EEG. In all cases, focal BOLD activations within the lesion itself preceded the activity associated with the time of IED appearance on EEG, which showed stronger and more widespread activations. We therefore propose that early hemodynamic activity corresponding to IEDs may hold important localizing information potentially leading to the cerebral sources of epileptic activity. IEDs are suggested to develop within a small area in the PSPMG lesion with structural properties obscuring the appearance of their electric field on the scalp and only later engage widespread structures which allow the production of large currents which are recognized as IEDs on EEG.
Collapse
Affiliation(s)
- Noa Cohen
- Sagol Brain Institute, Wohl Institute for Advanced Imaging, Sourasky Medical Center, Tel Aviv, Israel.,Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Yoram Ebrahimi
- Sagol Brain Institute, Wohl Institute for Advanced Imaging, Sourasky Medical Center, Tel Aviv, Israel
| | - Mordekhay Medvedovsky
- Department of Neurology, Agnes Ginges Center of Neurogenetics, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | - Guy Gurevitch
- Sagol Brain Institute, Wohl Institute for Advanced Imaging, Sourasky Medical Center, Tel Aviv, Israel.,Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Orna Aizenstein
- Sagol Brain Institute, Wohl Institute for Advanced Imaging, Sourasky Medical Center, Tel Aviv, Israel.,Department of Diagnostic Imaging, Sourasky Medical Center, Tel Aviv, Israel
| | - Talma Hendler
- Sagol Brain Institute, Wohl Institute for Advanced Imaging, Sourasky Medical Center, Tel Aviv, Israel.,Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.,School of Psychological Science, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Firas Fahoum
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.,Electroencephalography and Epilepsy Unit, Sourasky Medical Center, Tel Aviv, Israel
| | - Tomer Gazit
- Sagol Brain Institute, Wohl Institute for Advanced Imaging, Sourasky Medical Center, Tel Aviv, Israel.,Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| |
Collapse
|
8
|
Ictal gamma-band interactions localize ictogenic nodes of the epileptic network in focal cortical dysplasia. Clin Neurophysiol 2021; 132:1927-1936. [PMID: 34157635 DOI: 10.1016/j.clinph.2021.04.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 03/18/2021] [Accepted: 04/05/2021] [Indexed: 11/22/2022]
Abstract
OBJECTIVE Epilepsy surgery fails in > 30% of patients with focal cortical dysplasia (FCD). The seizure persistence after surgery can be attributed to the inability to precisely localize the tissue with an endogenous potential to generate seizures. In this study, we aimed to identify the critical components of the epileptic network that were actively involved in seizure genesis. METHODS The directed transfer function was applied to intracranial EEG recordings and the effective connectivity was determined with a high temporal and frequency resolution. Pre-ictal network properties were compared with ictal epochs to identify regions actively generating ictal activity and discriminate them from the areas of propagation. RESULTS Analysis of 276 seizures from 30 patients revealed the existence of a seizure-related network reconfiguration in the gamma-band (25-170 Hz; p < 0.005) - ictogenic nodes. Unlike seizure onset zone, resecting the majority of ictogenic nodes correlated with favorable outcomes (p < 0.012). CONCLUSION The prerequisite to successful epilepsy surgery is the accurate identification of brain areas from which seizures arise. We show that in FCD-related epilepsy, gamma-band network markers can reliably identify and distinguish ictogenic areas in macroelectrode recordings, improve intracranial EEG interpretation and better delineate the epileptogenic zone. SIGNIFICANCE Ictogenic nodes localize the critical parts of the epileptogenic tissue and increase the diagnostic yield of intracranial evaluation.
Collapse
|
9
|
Ashourvan A, Pequito S, Khambhati AN, Mikhail F, Baldassano SN, Davis KA, Lucas TH, Vettel JM, Litt B, Pappas GJ, Bassett DS. Model-based design for seizure control by stimulation. J Neural Eng 2020; 17:026009. [PMID: 32103826 PMCID: PMC8341467 DOI: 10.1088/1741-2552/ab7a4e] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
OBJECTIVE Current brain stimulation paradigms are largely empirical rather than theoretical. An opportunity exists to improve upon their modest effectiveness in closed-loop control strategies with the development of theoretically grounded, model-based designs. APPROACH Inspired by this need, here we couple experimental data and mathematical modeling with a control-theoretic strategy for seizure termination. We begin by exercising a dynamical systems approach to model seizures (n = 94) recorded using intracranial EEG (iEEG) from 21 patients with medication-resistant, localization-related epilepsy. MAIN RESULTS Although each patient's seizures displayed unique spatial and temporal patterns, their evolution can be parsimoniously characterized by the same model form. Idiosyncracies of the model can inform individualized intervention strategies, specifically in iEEG samples with well-localized seizure onset zones. Temporal fluctuations in the spatial profiles of the oscillatory modes show that seizure onset marks a transition into a regime in which the underlying system supports prolonged rhythmic and focal activity. Based on these observations, we propose a control-theoretic strategy that aims to stabilize ictal activity using static output feedback for linear time-invariant switching systems. Finally, we demonstrate in silico that our proposed strategy allows us to dampen the emerging focal oscillatory sources using only a small set of electrodes. SIGNIFICANCE Our integrative study informs the development of modulation and control algorithms for neurostimulation that could improve the effectiveness of implantable, closed-loop anti-epileptic devices.
Collapse
Affiliation(s)
- Arian Ashourvan
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, United States of America. U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005, United States of America
| | | | | | | | | | | | | | | | | | | | | |
Collapse
|
10
|
Ammanuel SG, Kleen JK, Leonard MK, Chang EF. Interictal Epileptiform Discharges and the Quality of Human Intracranial Neurophysiology Data. Front Hum Neurosci 2020; 14:44. [PMID: 32194384 PMCID: PMC7062638 DOI: 10.3389/fnhum.2020.00044] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 01/31/2020] [Indexed: 12/25/2022] Open
Abstract
Intracranial electroencephalography (IEEG) involves recording from electrodes placed directly onto the cortical surface or deep brain locations. It is performed on patients with medically refractory epilepsy, undergoing pre-surgical seizure localization. IEEG recordings, combined with advancements in computational capacity and analysis tools, have accelerated cognitive neuroscience. This Perspective describes a potential pitfall latent in many of these recordings by virtue of the subject population—namely interictal epileptiform discharges (IEDs), which can cause spurious results due to the contamination of normal neurophysiological signals by pathological waveforms related to epilepsy. We first discuss the nature of IED hazards, and why they deserve the attention of neurophysiology researchers. We then describe four general strategies used when handling IEDs (manual identification, automated identification, manual-automated hybrids, and ignoring by leaving them in the data), and discuss their pros, cons, and contextual factors. Finally, we describe current practices of human neurophysiology researchers worldwide based on a cross-sectional literature review and a voluntary survey. We put these results in the context of the listed strategies and make suggestions on improving awareness and clarity of reporting to enrich both data quality and communication in the field.
Collapse
Affiliation(s)
- Simon G Ammanuel
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Jonathan K Kleen
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Matthew K Leonard
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| |
Collapse
|
11
|
Kouti M, Ansari-Asl K, Namjoo E. Epileptic source connectivity analysis based on estimating of dynamic time series of regions of interest. NETWORK (BRISTOL, ENGLAND) 2019; 30:1-30. [PMID: 31240983 DOI: 10.1080/0954898x.2019.1634290] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 04/30/2019] [Accepted: 06/17/2019] [Indexed: 06/09/2023]
Abstract
We propose a new source connectivity method by focusing on estimating time courses of the regions of interest (ROIs). To this aim, it is necessary to consider the strong inherent non-stationary behavior of neural activity. We develop an iterative dynamic approach to extract a single time course for each ROI encoding the temporal non-stationary features. The proposed approach explicitly includes dynamic constraints by taking into account the evolution of the sources activities for further dynamic connectivity analysis. We simulated an epileptic network with a non-stationary structure; accordingly, EEG source reconstruction using LORETA is performed. Using the reconstructed sources, the spatially compact ROIs are selected. Then, a single time course encoding the temporal non-stationarity is extracted for each ROI. An adaptive directed transfer function (ADTF) is applied to measure the information flow of underlying brain networks. Obtained results demonstrate that the contributed approach is more efficient to estimate the ROI time series and ROI to ROI information flow in comparison with existing methods. Our work is validated in three drug-resistance epilepsy patients. The proposed ROI time series estimation directly affects the quality of connectivity analysis, leading to the best possible seizure onset zone (SOZ) localization verified by electrocorticography and post-operational results.
Collapse
Affiliation(s)
- Mayadeh Kouti
- Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
| | - Karim Ansari-Asl
- Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
| | - Ehsan Namjoo
- Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
| |
Collapse
|
12
|
|
13
|
Wang X, Yao L, Zhao Y, Xing L, Qian Z, Li W, Yang Y. Effects of disparity on visual discomfort caused by short-term stereoscopic viewing based on electroencephalograph analysis. Biomed Eng Online 2018; 17:166. [PMID: 30390658 PMCID: PMC6215628 DOI: 10.1186/s12938-018-0595-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Accepted: 10/25/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Discomfort evoked by stereoscopic depth has been widely concerned. Previous studies have proposed a comfortable disparity range and considered that disparities exceed this range would cause visual discomfort. Brain activity recordings including Electroencephalograph (EEG) monitoring enable better understanding of perceptual and cognitive processes related to stereo depth-induced visual comfort. METHODS EEG data was collected using a stereo-visual evoked potential (VEP) test system by providing visual stimulus to subjects aged from 21 to 25 with normal stereoscopic vision. For each type of visual stimulus, data were processed using directed transfer function (DTF) and adaptive directed transfer function (ADTF) in combination with subjective feedbacks (comfort or discomfort). The topographies of information flow were constructed to compare responses stimulated by different stereoscopic depth, and to determine the difference in comfort and discomfort situations upon stimulation with same stereoscopic depth. RESULTS Based on EEG analysis results, we found that the occipital P270 was moderately related to the disparity. Moreover, the ADTF of P270 showed that the information flows at frontal lobe and central-parietal lobe changed when stimulation with different stereoscopic depth applied. As to the stereo images with same stereoscopic depth, the DTF outflows at the temporal and temporal-parietal lobes in δ band, central and central-parietal lobes in α and θ bands, and the comparison of inflows in these three bands could be considered as discriminated indexes for matching the stereoscopic effect with viewers' comfort or discomfort state impacted by disparity. The subjective feedbacks indicated that the comfort judgments remained as a result of cumulative effect. CONCLUSIONS This study proposed a short-term stereo-VEP experiment that shorted the duration of each stimulus in the experimental scheme to minimize the interference from other factors except the disparity. The occipital P270 had a mid-relevance to the disparity and its ADTF showed the affected areas when viewers are receiving stimulations with different disparities. DTF could be considered as discriminated indexes for matching the stereoscopic effect with viewers' comfort or discomfort state induced by disparity. This study proposed a preferable experiment to observe the single effect of disparity and provided an intuitive and easy-to-read result in a more convenient manner.
Collapse
Affiliation(s)
- Xiao Wang
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjun Avenue, Jiangning District, Nanjing, 211106, China
| | - Liuye Yao
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjun Avenue, Jiangning District, Nanjing, 211106, China
| | - Yuemei Zhao
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjun Avenue, Jiangning District, Nanjing, 211106, China
| | - Lidong Xing
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjun Avenue, Jiangning District, Nanjing, 211106, China
| | - Zhiyu Qian
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjun Avenue, Jiangning District, Nanjing, 211106, China.
| | - Weitao Li
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjun Avenue, Jiangning District, Nanjing, 211106, China
| | - Yamin Yang
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjun Avenue, Jiangning District, Nanjing, 211106, China
| |
Collapse
|
14
|
Yang C, Luan G, Wang Q, Liu Z, Zhai F, Wang Q. Localization of Epileptogenic Zone With the Correction of Pathological Networks. Front Neurol 2018; 9:143. [PMID: 29593641 PMCID: PMC5861205 DOI: 10.3389/fneur.2018.00143] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Accepted: 02/27/2018] [Indexed: 12/11/2022] Open
Abstract
Patients with focal drug-resistant epilepsy are potential candidates for surgery. Stereo-electroencephalograph (SEEG) is often considered as the “gold standard” to identify the epileptogenic zone (EZ) that accounts for the onset and propagation of epileptiform discharges. However, visual analysis of SEEG still prevails in clinical practice. In addition, epilepsy is increasingly understood to be the result of network disorder, but the specific organization of the epileptic network is still unclear. Therefore, it is necessary to quantitatively localize the EZ and investigate the nature of epileptogenic networks. In this study, intracranial recordings from 10 patients were analyzed through adaptive directed transfer function, and the out-degree of effective network was selected as the principal indicator to localize the epileptogenic area. Furthermore, a coupled neuronal population model was used to qualitatively simulate electrical activity in the brain. By removing individual populations, virtual surgery adjusting the network organization could be performed. Results suggested that the accuracy and detection rate of the EZ localization were 82.86 and 85.29%, respectively. In addition, the same stage shared a relatively stable connectivity pattern, while the patterns changed with transition to different processes. Meanwhile, eight cases of simulations indicated that networks in the ictal stage were more likely to generate rhythmic spikes. This indicated the existence of epileptogenic networks, which could enhance local excitability and facilitate synchronization. The removal of the EZ could correct these pathological networks and reduce the amount of spikes by at least 75%. This might be one reason why accurate resection could reduce or even suppress seizures. This study provides novel insights into epilepsy and surgical treatments from the network perspective.
Collapse
Affiliation(s)
- Chuanzuo Yang
- Department of Dynamics and Control, Beihang University, Beijing, China
| | - Guoming Luan
- Beijing Key Laboratory of Epilepsy, Sanbo Brain Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Epilepsy Center, Sanbo Brain Hospital, Capital Medical University, Beijing, China.,Beijing Institute for Brain Disorders, Beijing, China
| | - Qian Wang
- Beijing Key Laboratory of Epilepsy, Sanbo Brain Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Epilepsy Center, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Zhao Liu
- Beijing Key Laboratory of Epilepsy, Sanbo Brain Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Epilepsy Center, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Feng Zhai
- Beijing Key Laboratory of Epilepsy, Sanbo Brain Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Epilepsy Center, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Qingyun Wang
- Department of Dynamics and Control, Beihang University, Beijing, China
| |
Collapse
|
15
|
Sperdin HF, Coito A, Kojovic N, Rihs TA, Jan RK, Franchini M, Plomp G, Vulliemoz S, Eliez S, Michel CM, Schaer M. Early alterations of social brain networks in young children with autism. eLife 2018; 7:31670. [PMID: 29482718 PMCID: PMC5828667 DOI: 10.7554/elife.31670] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Accepted: 01/22/2018] [Indexed: 11/30/2022] Open
Abstract
Social impairments are a hallmark of Autism Spectrum Disorders (ASD), but empirical evidence for early brain network alterations in response to social stimuli is scant in ASD. We recorded the gaze patterns and brain activity of toddlers with ASD and their typically developing peers while they explored dynamic social scenes. Directed functional connectivity analyses based on electrical source imaging revealed frequency specific network atypicalities in the theta and alpha frequency bands, manifesting as alterations in both the driving and the connections from key nodes of the social brain associated with autism. Analyses of brain-behavioural relationships within the ASD group suggested that compensatory mechanisms from dorsomedial frontal, inferior temporal and insular cortical regions were associated with less atypical gaze patterns and lower clinical impairment. Our results provide strong evidence that directed functional connectivity alterations of social brain networks is a core component of atypical brain development at early stages of ASD. Newborns are attracted to voices, faces and social gestures. Paying attention to these social cues in everyday life helps infants and young children learn how to interact with others. During this period of development, a network of connections forms between different parts of the brain that helps children to understand other people’s social behaviors. During their first year of life, infants who later develop autism spectrum disorders (ASD) pay less attention to social cues. This early indifference to these important signals leads to social deficits in children with ASD. They are less able to understand other people’s behaviors or engage in typical social interactions. It’s not yet clear why children with ASD are less attuned to social cues. But is likely that the development of brain networks essential for understanding social behavior suffers as a result. Studying how such networks develop in typical very young children and those with ASD may help scientist learn more. Now, Sperdin et al. confirm there are differences in the social brain-networks of very young children with ASD compared with their typical peers. In the experiment, 3-year-old children with ASD and without watched videos of other children playing, while Sperdin et al. recorded what they looked at and what happened in their brains. Eyemovements were measured with a tracker, and the brain activity was recorded using an electroencephalogram (EEG), which uses sensors placed on the scalp to measure electrical signals. What children with ASD looked at was different than their typical peers, and these differences corresponded with alterations in the brain networks that process social information. Children with ASD who had less severe symptoms had stronger activity in these brain networks. What they looked at also was more similar to typical children. This suggests less severely affected children with ASD may be able to compensate that way. Identifying ASD-like behaviors and brain differences early in life may help scientists to better understand what causes the condition. It may also help clinicians provide more individualized therapies early in life when the brain is most adaptable. Long-term studies of these brain-network differences in children with ASD are necessary to better understand how therapies can influence these changes.
Collapse
Affiliation(s)
- Holger Franz Sperdin
- Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva, Geneva, Switzerland
| | - Ana Coito
- Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
| | - Nada Kojovic
- Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva, Geneva, Switzerland
| | - Tonia Anahi Rihs
- Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
| | - Reem Kais Jan
- Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland.,College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
| | - Martina Franchini
- Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva, Geneva, Switzerland
| | - Gijs Plomp
- Department of Psychology, University of Fribourg, Fribourg, Switzerland
| | - Serge Vulliemoz
- EEG and Epilepsy Unit, Neurology, University Hospitals of Geneva, Geneva, Switzerland
| | - Stephan Eliez
- Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva, Geneva, Switzerland
| | - Christoph Martin Michel
- Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
| | - Marie Schaer
- Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva, Geneva, Switzerland
| |
Collapse
|
16
|
Misra A, Long X, Sperling MR, Sharan AD, Moxon KA. Increased neuronal synchrony prepares mesial temporal networks for seizures of neocortical origin. Epilepsia 2018; 59:636-649. [PMID: 29442363 DOI: 10.1111/epi.14007] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/21/2017] [Indexed: 12/11/2022]
Abstract
OBJECTIVE To gain understanding of the neuronal mechanisms underlying regional seizure spread, the impact of regional synchrony between seizure focus and downstream networks on neuronal activity during the transition to seizure in those downstream networks was assessed. METHODS Seven patients undergoing diagnostic intracranial electroencephalographic studies for surgical resection of epileptogenic regions were implanted with subdural clinical electrodes into the cortex (site of seizure initiation) and mesial temporal lobe (MTL) structures (downstream) as well as microwires into MTL. Neural activity was recorded (24/7) in parallel with the clinical intracranial electroencephalogram recordings for the duration of the patient's diagnostic stay. Changes in (1) regional synchrony (ie, coherence) between the presumptive neocortical seizure focus and MTL, (2) local synchrony between MTL neurons and their local field potential, and (3) neuronal firing rates within MTL in the time leading up to seizure were examined to study the mechanisms underlying seizure spread. RESULTS In seizures of neocortical origin, an increase in regional synchrony preceded the spread of seizures into MTL (predominantly hippocampal). Within frequencies similar to those of regional synchrony, MTL networks showed an increase in unit-field coherence and a decrease in neuronal firing rate, specifically for inhibitory interneuron populations but not pyramidal cell populations. SIGNIFICANCE These results suggest a mechanism of spreading seizures whereby the seizure focus first synchronizes local field potentials in downstream networks to the seizure activity. This change in local field coherence modifies the activity of interneuron populations in these downstream networks, which leads to the attenuation of interneuronal firing rate, effectively shutting down local interneuron populations prior to the spread of seizure. Therefore, regional synchrony may influence the failure of downstream interneurons to prevent the spread of the seizures during generalization.
Collapse
Affiliation(s)
- Amrit Misra
- School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA, USA
| | - Xianda Long
- School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA, USA
| | - Michael R Sperling
- Department of Neurology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Ashwini D Sharan
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, USA
| | - Karen A Moxon
- School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA, USA.,Department of Biomedical Engineering, University of California, Davis, Davis, CA, USA
| |
Collapse
|
17
|
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.
Collapse
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.
| |
Collapse
|
18
|
Verhoeven T, Coito A, Plomp G, Thomschewski A, Pittau F, Trinka E, Wiest R, Schaller K, Michel C, Seeck M, Dambre J, Vulliemoz S, van Mierlo P. Automated diagnosis of temporal lobe epilepsy in the absence of interictal spikes. NEUROIMAGE-CLINICAL 2017. [PMID: 29527470 PMCID: PMC5842753 DOI: 10.1016/j.nicl.2017.09.021] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Objective To diagnose and lateralise temporal lobe epilepsy (TLE) by building a classification system that uses directed functional connectivity patterns estimated during EEG periods without visible pathological activity. Methods Resting-state high-density EEG recording data from 20 left TLE patients, 20 right TLE patients and 35 healthy controls was used. Epochs without interictal spikes were selected. The cortical source activity was obtained for 82 regions of interest and whole-brain directed functional connectivity was estimated in the theta, alpha and beta frequency bands. These connectivity values were then used to build a classification system based on two two-class Random Forests classifiers: TLE vs healthy controls and left vs right TLE. Feature selection and classifier training were done in a leave-one-out procedure to compute the mean classification accuracy. Results The diagnosis and lateralization classifiers achieved a high accuracy (90.7% and 90.0% respectively), sensitivity (95.0% and 90.0% respectively) and specificity (85.7% and 90.0% respectively). The most important features for diagnosis were the outflows from left and right medial temporal lobe, and for lateralization the right anterior cingulate cortex. The interaction between features was important to achieve correct classification. Significance This is the first study to automatically diagnose and lateralise TLE based on EEG. The high accuracy achieved demonstrates the potential of directed functional connectivity estimated from EEG periods without visible pathological activity for helping in the diagnosis and lateralization of TLE. Both classifiers for TLE diagnosis and lateralization achieved high accuracy (90%). Outflow from left and right hippocampus was the most important for diagnosis. Outflow from the right ACC was the most important for lateralization. The interaction between features is important for a correct classification.
Collapse
Affiliation(s)
- Thibault Verhoeven
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium.
| | - Ana Coito
- Functional Brain Mapping Lab, Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
| | - Gijs Plomp
- Perceptual Networks Group, Department of Psychology, University of Fribourg, Fribourg, Switzerland
| | - Aljoscha Thomschewski
- Department of Neurology, Paracelsus Medical University and Center for Cognitive Neuroscience, Salzburg, Austria
| | - Francesca Pittau
- Epilepsy Unit, Neurology Clinic, University Hospital of Geneva, Geneva, Switzerland
| | - Eugen Trinka
- Department of Neurology, Paracelsus Medical University and Center for Cognitive Neuroscience, Salzburg, Austria
| | - Roland Wiest
- Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
| | - Karl Schaller
- Neurosurgery Clinic, University Hospital Geneva, Geneva, Switzerland
| | - Christoph Michel
- Functional Brain Mapping Lab, Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
| | - Margitta Seeck
- Epilepsy Unit, Neurology Clinic, University Hospital of Geneva, Geneva, Switzerland
| | - Joni Dambre
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Serge Vulliemoz
- Epilepsy Unit, Neurology Clinic, University Hospital of Geneva, Geneva, Switzerland
| | - Pieter van Mierlo
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium; Functional Brain Mapping Lab, Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
| |
Collapse
|
19
|
Exploring the Epileptic Brain Network Using Time-Variant Effective Connectivity and Graph Theory. IEEE J Biomed Health Inform 2017; 21:1411-1421. [DOI: 10.1109/jbhi.2016.2607802] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
|
20
|
Dimension reduction of frequency-based direct Granger causality measures on short time series. J Neurosci Methods 2017; 289:64-74. [DOI: 10.1016/j.jneumeth.2017.06.021] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Revised: 06/30/2017] [Accepted: 06/30/2017] [Indexed: 11/22/2022]
|
21
|
Zhang L, Liang Y, Li F, Sun H, Peng W, Du P, Si Y, Song L, Yu L, Xu P. Time-Varying Networks of Inter-Ictal Discharging Reveal Epileptogenic Zone. Front Comput Neurosci 2017; 11:77. [PMID: 28867999 PMCID: PMC5563307 DOI: 10.3389/fncom.2017.00077] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Accepted: 08/02/2017] [Indexed: 01/01/2023] Open
Abstract
The neuronal synchronous discharging may cause an epileptic seizure. Currently, most of the studies conducted to investigate the mechanism of epilepsy are based on EEGs or functional magnetic resonance imaging (fMRI) recorded during the ictal discharging or the resting-state, and few studies have probed into the dynamic patterns during the inter-ictal discharging that are much easier to record in clinical applications. Here, we propose a time-varying network analysis based on adaptive directed transfer function to uncover the dynamic brain network patterns during the inter-ictal discharging. In addition, an algorithm based on the time-varying outflow of information derived from the network analysis is developed to detect the epileptogenic zone. The analysis performed revealed the time-varying network patterns during different stages of inter-ictal discharging; the epileptogenic zone was activated prior to the discharge onset then worked as the source to propagate the activity to other brain regions. Consistence between the epileptogenic zones detected by our proposed approach and the actual epileptogenic zones proved that time-varying network analysis could not only reveal the underlying neural mechanism of epilepsy, but also function as a useful tool in detecting the epileptogenic zone based on the EEGs in the inter-ictal discharging.
Collapse
Affiliation(s)
- Luyan Zhang
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengdu, China
| | - Yi Liang
- Department of Neurology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's HospitalChengdu, China.,Department of Neurology, Affiliated Hospital of University of Electronic Science and Technology of ChinaChengdu, China
| | - Fali Li
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengdu, China
| | - Hongbin Sun
- Department of Neurology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's HospitalChengdu, China.,Department of Neurology, Affiliated Hospital of University of Electronic Science and Technology of ChinaChengdu, China
| | - Wenjing Peng
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengdu, China
| | - Peishan Du
- Department of Neurology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's HospitalChengdu, China.,Department of Neurology, Affiliated Hospital of University of Electronic Science and Technology of ChinaChengdu, China
| | - Yajing Si
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengdu, China
| | - Limeng Song
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengdu, China
| | - Liang Yu
- Department of Neurology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's HospitalChengdu, China.,Department of Neurology, Affiliated Hospital of University of Electronic Science and Technology of ChinaChengdu, China
| | - Peng Xu
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengdu, China.,Center for Information in BioMedicine, University of Electronic Science and Technology of ChinaChengdu, China
| |
Collapse
|
22
|
Abstract
Focal epileptic seizures have long been considered to arise from a small susceptible brain area and spread through uninvolved regions. In the past decade, the idea that focal seizures instead arise from coordinated activity across large-scale epileptic networks has become widely accepted. Understanding the network model's applicability is critical, due to its increasing influence on clinical research and surgical treatment paradigms. In this review, we examine the origins of the concept of epileptic networks as the nidus for recurring seizures. We summarize analytical and methodological elements of epileptic network studies and discuss findings from recent detailed electrophysiological investigations. Our review highlights the strengths and limitations of the epileptic network theory as a metaphor for the complex interactions that occur during seizures. We present lines of investigation that may usefully probe these interactions and thus serve to advance our understanding of the long-range effects of epileptiform activity.
Collapse
Affiliation(s)
- Elliot H Smith
- Department of Neurological Surgery, Columbia University Medical Center, New York, NY, 10032, USA
| | - Catherine A Schevon
- Department of Neurology, Columbia University Medical Center, New York, NY, 10032, USA.
- Neurological Institute, 710 West 168th Street, New York, NY, 10032, USA.
| |
Collapse
|
23
|
Khambhati AN, Bassett DS, Oommen BS, Chen SH, Lucas TH, Davis KA, Litt B. Recurring Functional Interactions Predict Network Architecture of Interictal and Ictal States in Neocortical Epilepsy. eNeuro 2017; 4:ENEURO.0091-16.2017. [PMID: 28303256 PMCID: PMC5343278 DOI: 10.1523/eneuro.0091-16.2017] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2016] [Revised: 01/09/2017] [Accepted: 01/10/2017] [Indexed: 01/10/2023] Open
Abstract
Human epilepsy patients suffer from spontaneous seizures, which originate in brain regions that also subserve normal function. Prior studies demonstrate focal, neocortical epilepsy is associated with dysfunction, several hours before seizures. How does the epileptic network perpetuate dysfunction during baseline periods? To address this question, we developed an unsupervised machine learning technique to disentangle patterns of functional interactions between brain regions, or subgraphs, from dynamic functional networks constructed from approximately 100 h of intracranial recordings in each of 22 neocortical epilepsy patients. Using this approach, we found: (1) subgraphs from ictal (seizure) and interictal (baseline) epochs are topologically similar, (2) interictal subgraph topology and dynamics can predict brain regions that generate seizures, and (3) subgraphs undergo slower and more coordinated fluctuations during ictal epochs compared to interictal epochs. Our observations suggest that seizures mark a critical shift away from interictal states that is driven by changes in the dynamical expression of strongly interacting components of the epileptic network.
Collapse
Affiliation(s)
- Ankit N. Khambhati
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
| | - Danielle S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104
| | - Brian S. Oommen
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104
| | - Stephanie H. Chen
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104
| | - Timothy H. Lucas
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA 19104
| | - Kathryn A. Davis
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104
| | - Brian Litt
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104
| |
Collapse
|
24
|
Tomlinson SB, Bermudez C, Conley C, Brown MW, Porter BE, Marsh ED. Spatiotemporal Mapping of Interictal Spike Propagation: A Novel Methodology Applied to Pediatric Intracranial EEG Recordings. Front Neurol 2016; 7:229. [PMID: 28066315 PMCID: PMC5165024 DOI: 10.3389/fneur.2016.00229] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 11/30/2016] [Indexed: 12/19/2022] Open
Abstract
Synchronized cortical activity is implicated in both normative cognitive functioning and many neurologic disorders. For epilepsy patients with intractable seizures, irregular synchronization within the epileptogenic zone (EZ) is believed to provide the network substrate through which seizures initiate and propagate. Mapping the EZ prior to epilepsy surgery is critical for detecting seizure networks in order to achieve postsurgical seizure control. However, automated techniques for characterizing epileptic networks have yet to gain traction in the clinical setting. Recent advances in signal processing and spike detection have made it possible to examine the spatiotemporal propagation of interictal spike discharges across the epileptic cortex. In this study, we present a novel methodology for detecting, extracting, and visualizing spike propagation and demonstrate its potential utility as a biomarker for the EZ. Eighteen presurgical intracranial EEG recordings were obtained from pediatric patients ultimately experiencing favorable (i.e., seizure-free, n = 9) or unfavorable (i.e., seizure-persistent, n = 9) surgical outcomes. Novel algorithms were applied to extract multichannel spike discharges and visualize their spatiotemporal propagation. Quantitative analysis of spike propagation was performed using trajectory clustering and spatial autocorrelation techniques. Comparison of interictal propagation patterns revealed an increase in trajectory organization (i.e., spatial autocorrelation) among Sz-Free patients compared with Sz-Persist patients. The pathophysiological basis and clinical implications of these findings are considered.
Collapse
Affiliation(s)
- Samuel B Tomlinson
- Department of Pediatrics, Division of Child Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, NY, USA
| | - Camilo Bermudez
- Department of Pediatrics, Division of Child Neurology, Children's Hospital of Philadelphia , Philadelphia, PA , USA
| | - Chiara Conley
- Department of Pediatrics, Division of Child Neurology, Children's Hospital of Philadelphia , Philadelphia, PA , USA
| | - Merritt W Brown
- Department of Pediatrics, Division of Child Neurology, Children's Hospital of Philadelphia , Philadelphia, PA , USA
| | - Brenda E Porter
- Department of Neurology and Neurological Science, Stanford School of Medicine , Palo Alto, CA , USA
| | - Eric D Marsh
- Department of Pediatrics, Division of Child Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| |
Collapse
|
25
|
Sohrabpour A, Ye S, Worrell GA, Zhang W, He B. Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach. IEEE Trans Biomed Eng 2016; 63:2474-2487. [PMID: 27740473 PMCID: PMC5152676 DOI: 10.1109/tbme.2016.2616474] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
OBJECTIVE Combined source-imaging techniques and directional connectivity analysis can provide useful information about the underlying brain networks in a noninvasive fashion. Source-imaging techniques have been used successfully to either determine the source of activity or to extract source time-courses for Granger causality analysis, previously. In this work, we utilize source-imaging algorithms to both find the network nodes [regions of interest (ROI)] and then extract the activation time series for further Granger causality analysis. The aim of this work is to find network nodes objectively from noninvasive electromagnetic signals, extract activation time-courses, and apply Granger analysis on the extracted series to study brain networks under realistic conditions. METHODS Source-imaging methods are used to identify network nodes and extract time-courses and then Granger causality analysis is applied to delineate the directional functional connectivity of underlying brain networks. Computer simulations studies where the underlying network (nodes and connectivity pattern) is known were performed; additionally, this approach has been evaluated in partial epilepsy patients to study epilepsy networks from interictal and ictal signals recorded by EEG and/or Magnetoencephalography (MEG). RESULTS Localization errors of network nodes are less than 5 mm and normalized connectivity errors of ∼20% in estimating underlying brain networks in simulation studies. Additionally, two focal epilepsy patients were studied and the identified nodes driving the epileptic network were concordant with clinical findings from intracranial recordings or surgical resection. CONCLUSION Our study indicates that combined source-imaging algorithms with Granger causality analysis can identify underlying networks precisely (both in terms of network nodes location and internodal connectivity). SIGNIFICANCE The combined source imaging and Granger analysis technique is an effective tool for studying normal or pathological brain conditions.
Collapse
Affiliation(s)
- Abbas Sohrabpour
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455 USA
| | - Shuai Ye
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455 USA
| | | | - Wenbo Zhang
- Minnesota Epilepsy Group, United Hospital, MN 55102 USA and also with the Department of Neurology, University of Minnesota, Minneapolis, 55455 USA
| | - Bin He
- Department of Biomedical Engineering, and the Institute for Engineering in Medicine, University of Minnesota, Minneapolis, MN 55455 USA
| |
Collapse
|
26
|
Mao JW, Ye XL, Li YH, Liang PJ, Xu JW, Zhang PM. Dynamic Network Connectivity Analysis to Identify Epileptogenic Zones Based on Stereo-Electroencephalography. Front Comput Neurosci 2016; 10:113. [PMID: 27833545 PMCID: PMC5081385 DOI: 10.3389/fncom.2016.00113] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Accepted: 10/12/2016] [Indexed: 01/04/2023] Open
Abstract
Objectives: Accurate localization of epileptogenic zones (EZs) is essential for successful surgical treatment of refractory focal epilepsy. The aim of the present study is to investigate whether a dynamic network connectivity analysis based on stereo-electroencephalography (SEEG) signals is effective in localizing EZs. Methods: SEEG data were recorded from seven patients who underwent presurgical evaluation for the treatment of refractory focal epilepsy and for whom the subsequent resective surgery gave a good outcome. A time-variant multivariate autoregressive model was constructed using a Kalman filter, and the time-variant partial directed coherence was computed. This was then used to construct a dynamic directed network model of the epileptic brain. Three graph measures (in-degree, out-degree, and betweenness centrality) were used to analyze the characteristics of the dynamic network and to find the important nodes in it. Results: In all seven patients, the indicative EZs localized by the in-degree and the betweenness centrality were highly consistent with the clinically diagnosed EZs. However, the out-degree did not indicate any significant differences between nodes in the network. Conclusions: In this work, a method based on ictal SEEG signals and effective connectivity analysis localized EZs accurately. The results suggest that the in-degree and betweenness centrality may be better network characteristics to localize EZs than the out-degree.
Collapse
Affiliation(s)
- Jun-Wei Mao
- School of Biomedical Engineering, Shanghai Jiao Tong University Shanghai, China
| | - Xiao-Lai Ye
- Department of Functional Neurosurgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University Shanghai, China
| | - Yong-Hua Li
- School of Biomedical Engineering, Shanghai Jiao Tong University Shanghai, China
| | - Pei-Ji Liang
- School of Biomedical Engineering, Shanghai Jiao Tong University Shanghai, China
| | - Ji-Wen Xu
- Department of Functional Neurosurgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University Shanghai, China
| | - Pu-Ming Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University Shanghai, China
| |
Collapse
|
27
|
Coito A, Michel CM, van Mierlo P, Vulliemoz S, Plomp G. Directed Functional Brain Connectivity Based on EEG Source Imaging: Methodology and Application to Temporal Lobe Epilepsy. IEEE Trans Biomed Eng 2016; 63:2619-2628. [PMID: 27775899 DOI: 10.1109/tbme.2016.2619665] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE The importance of functional brain connectivity to study physiological and pathological brain activity has been widely recognized. Here, we aimed to 1) review a methodological pipeline to investigate directed functional connectivity between brain regions using source signals derived from high-density EEG; 2) elaborate on some methodological challenges; and 3) apply this pipeline to temporal lobe epilepsy (TLE) patients and healthy controls to investigate directed functional connectivity differences in the theta and beta frequency bands during EEG epochs without visible pathological activity. METHODS The methodological pipeline includes: EEG acquisition and preprocessing, electrical-source imaging (ESI) using individual head models and distributed inverse solutions, parcellation of the gray matter in regions of interest, fixation of the dipole orientation for each region, computation of the spectral power in the source space, and directed functional connectivity estimation using Granger-causal modeling. We specifically analyzed how the signal-to-noise ratio (SNR) changes using different approaches for the dipole orientation fixation. We applied this pipeline to 20 left TLE patients, 20 right TLE patients, and 20 healthy controls. RESULTS Projecting each dipole to the predominant dipole orientation leads to a threefold SNR increase as compared to the norm of the dipoles. By comparing connectivity in TLE versus controls, we found significant frequency-specific outflow differences in physiologically plausible regions. CONCLUSION The results suggest that directed functional connectivity derived from ESI can help better understand frequency-specific resting-state network alterations underlying focal epilepsy. SIGNIFICANCE EEG-based directed functional connectivity could contribute to the search of new biomarkers of this disorder.
Collapse
|
28
|
Lie OV, van Mierlo P. Seizure-Onset Mapping Based on Time-Variant Multivariate Functional Connectivity Analysis of High-Dimensional Intracranial EEG: A Kalman Filter Approach. Brain Topogr 2016; 30:46-59. [PMID: 27722839 DOI: 10.1007/s10548-016-0527-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2016] [Accepted: 09/29/2016] [Indexed: 11/29/2022]
Abstract
The visual interpretation of intracranial EEG (iEEG) is the standard method used in complex epilepsy surgery cases to map the regions of seizure onset targeted for resection. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signals based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (<60). The aim of this study was to test two Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used on simulated seizures together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared for their ability to reconstitute the designed seizure signal connections from noisy data. Next, focal seizures from iEEG recordings (73-113 channels) in three patients rendered seizure-free after surgery were mapped with the outdegree, a graph-theory index of outward directed connectivity. Simulation results indicated high levels of mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach.
Collapse
Affiliation(s)
- Octavian V Lie
- Department of Neurology, University of Texas Health Science Center at San Antonio, 8300 Floyd Curl Drive MSC: 7883, San Antonio, TX, 78229-3900, USA.
| | - Pieter van Mierlo
- Functional Brain Mapping Laboratory, EEG and Epilepsy Unit, University of Geneva, Geneva, Switzerland.,iMinds Medical IT Department, Medical Image and Signal Processing Group, Ghent University, Ghent, Belgium
| |
Collapse
|
29
|
Hur YJ, Kim HD. Predictive role of brain connectivity for resective surgery in Lennox–Gastaut syndrome. Clin Neurophysiol 2016; 127:2862-2868. [DOI: 10.1016/j.clinph.2016.05.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2016] [Revised: 04/14/2016] [Accepted: 05/09/2016] [Indexed: 01/05/2023]
|
30
|
Identification of Source Signals by Estimating Directional Index of Phase Coupling in Multivariate Neural Systems. J Med Biol Eng 2016. [DOI: 10.1007/s40846-016-0131-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
31
|
Hur YJ, Kim HD. The causal epileptic network identifies the primary epileptogenic zone in Lennox–Gastaut syndrome. Seizure 2015; 33:1-7. [DOI: 10.1016/j.seizure.2015.10.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2014] [Revised: 10/06/2015] [Accepted: 10/07/2015] [Indexed: 11/15/2022] Open
|
32
|
Lehnertz K, Dickten H. Assessing directionality and strength of coupling through symbolic analysis: an application to epilepsy patients. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2015; 373:rsta.2014.0094. [PMID: 25548267 PMCID: PMC4281866 DOI: 10.1098/rsta.2014.0094] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Inferring strength and direction of interactions from electroencephalographic (EEG) recordings is of crucial importance to improve our understanding of dynamical interdependencies underlying various physiological and pathophysiological conditions in the human epileptic brain. We here use approaches from symbolic analysis to investigate--in a time-resolved manner--weighted and directed, short- to long-ranged interactions between various brain regions constituting the epileptic network. Our observations point to complex spatial-temporal interdependencies underlying the epileptic process and their role in the generation of epileptic seizures, despite the massive reduction of the complex information content of multi-day, multi-channel EEG recordings through symbolization. We discuss limitations and potential future improvements of this approach.
Collapse
Affiliation(s)
- Klaus Lehnertz
- Department of Epileptology, University of Bonn, Sigmund-Freud-Straße 25, 53105 Bonn, Germany Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn, Germany Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53175 Bonn, Germany
| | - Henning Dickten
- Department of Epileptology, University of Bonn, Sigmund-Freud-Straße 25, 53105 Bonn, Germany Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn, Germany Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53175 Bonn, Germany
| |
Collapse
|
33
|
Lateralization of Epileptic Foci Through Causal Analysis of Scalp-EEG Interictal Spike Activity. J Clin Neurophysiol 2015; 32:57-65. [DOI: 10.1097/wnp.0000000000000120] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
|
34
|
Coito A, Plomp G, Genetti M, Abela E, Wiest R, Seeck M, Michel CM, Vulliemoz S. Dynamic directed interictal connectivity in left and right temporal lobe epilepsy. Epilepsia 2015; 56:207-17. [PMID: 25599821 DOI: 10.1111/epi.12904] [Citation(s) in RCA: 98] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/25/2014] [Indexed: 11/27/2022]
Abstract
OBJECTIVE There is increasing evidence that epileptic activity involves widespread brain networks rather than single sources and that these networks contribute to interictal brain dysfunction. We investigated the fast-varying behavior of epileptic networks during interictal spikes in right and left temporal lobe epilepsy (RTLE and LTLE) at a whole-brain scale using directed connectivity. METHODS In 16 patients, 8 with LTLE and 8 with RTLE, we estimated the electrical source activity in 82 cortical regions of interest (ROIs) using high-density electroencephalography (EEG), individual head models, and a distributed linear inverse solution. A multivariate, time-varying, and frequency-resolved Granger-causal modeling (weighted Partial Directed Coherence) was applied to the source signal of all ROIs. A nonparametric statistical test assessed differences between spike and baseline epochs. Connectivity results between RTLE and LTLE were compared between RTLE and LTLE and with neuropsychological impairments. RESULTS Ipsilateral anterior temporal structures were identified as key drivers for both groups, concordant with the epileptogenic zone estimated invasively. We observed an increase in outflow from the key driver already before the spike. There were also important temporal and extratemporal ipsilateral drivers in both conditions, and contralateral only in RTLE. A different network pattern between LTLE and RTLE was found: in RTLE there was a much more prominent ipsilateral to contralateral pattern than in LTLE. Half of the RTLE patients but none of the LTLE patients had neuropsychological deficits consistent with contralateral temporal lobe dysfunction, suggesting a relationship between connectivity changes and cognitive deficits. SIGNIFICANCE The different patterns of time-varying connectivity in LTLE and RTLE suggest that they are not symmetrical entities, in line with our neuropsychological results. The highest outflow region was concordant with invasive validation of the epileptogenic zone. This enhanced characterization of dynamic connectivity patterns could better explain cognitive deficits and help the management of epilepsy surgery candidates.
Collapse
Affiliation(s)
- Ana Coito
- Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
| | | | | | | | | | | | | | | |
Collapse
|
35
|
Kim JY, Kang HC, Kim K, Kim HD, Im CH. Localization of epileptogenic zones in Lennox-Gastaut syndrome (LGS) using graph theoretical analysis of ictal intracranial EEG: a preliminary investigation. Brain Dev 2015; 37:29-36. [PMID: 24630492 DOI: 10.1016/j.braindev.2014.02.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2013] [Revised: 02/13/2014] [Accepted: 02/13/2014] [Indexed: 10/25/2022]
Abstract
INTRODUCTION Precise localization of epileptogenic zones is essential for the successful surgical treatment of refractory epilepsy including Lennox-Gastaut syndrome (LGS). The surgical resection areas are generally determined by epileptologists based on diverse neuroimaging modalities; however, exact epileptogenic zones cannot be accurately localized in many patients with LGS using the conventional methods. Therefore, new reliable algorithms are still required for enhancing the success rate of the resective epilepsy surgery. In the present study, we introduce an approach to localize epileptogenic zones in LGS based on the graph theoretical analysis of ical intracranial EEG (iEEG). METHODS Four patients with LGS who became seizure-free after the resective epilepsy surgery were selected. Before the surgery, their epileptogenic zones were delineated using EEG, iEEG, and several conventional imaging modalities. Phase locking value (PLV) analysis was applied to construct functional connectivity networks during ictal events, and then several graph theoretical indices including betweenness centrality (BC) were evaluated for each iEEG sensor to find the primary hubs of the ictal epileptic network. The graph theoretical index values were then overlaid on 3D individual cortical surface. RESULTS The iEEG channels with high BC values coincided well with the surgical resection areas. Among various graph theoretical measures such as local efficiency, participation coefficient, and eigenvector centrality, only BC showed fair correspondence with the surgical resection areas. CONCLUSIONS The primary hubs in the ictal epileptic networks coincided well with areas of surgical resection in LGS patients with successful surgical outcomes. This observation warrants further studies to determine if the graph theoretical network analysis of ictal iEEG recordings can serve as a new auxiliary tool to localize epileptogenic zones in LGS.
Collapse
Affiliation(s)
- Jeong-Youn Kim
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Hoon-Chul Kang
- Department of Pediatrics, Yonsei University College of Medicine, Seoul, South Korea
| | - Kiwoong Kim
- Korea Research Institute of Standard and Science (KRISS), Daejeon, South Korea
| | - Heung Dong Kim
- Department of Pediatrics, Yonsei University College of Medicine, Seoul, South Korea
| | - Chang-Hwan Im
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea.
| |
Collapse
|
36
|
van Mierlo P, Papadopoulou M, Carrette E, Boon P, Vandenberghe S, Vonck K, Marinazzo D. Functional brain connectivity from EEG in epilepsy: seizure prediction and epileptogenic focus localization. Prog Neurobiol 2014; 121:19-35. [PMID: 25014528 DOI: 10.1016/j.pneurobio.2014.06.004] [Citation(s) in RCA: 135] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2013] [Revised: 06/21/2014] [Accepted: 06/29/2014] [Indexed: 11/26/2022]
Abstract
Today, neuroimaging techniques are frequently used to investigate the integration of functionally specialized brain regions in a network. Functional connectivity, which quantifies the statistical dependencies among the dynamics of simultaneously recorded signals, allows to infer the dynamical interactions of segregated brain regions. In this review we discuss how the functional connectivity patterns obtained from intracranial and scalp electroencephalographic (EEG) recordings reveal information about the dynamics of the epileptic brain and can be used to predict upcoming seizures and to localize the seizure onset zone. The added value of extracting information that is not visibly identifiable in the EEG data using functional connectivity analysis is stressed. Despite the fact that many studies have showed promising results, we must conclude that functional connectivity analysis has not made its way into clinical practice yet.
Collapse
Affiliation(s)
- Pieter van Mierlo
- Medical Imaging and Signal Processing Group, Department of Electronics and Information Systems, Ghent University - iMinds Medical IT Department, Ghent, Belgium.
| | - Margarita Papadopoulou
- Department of Data Analysis, Faculty of Psychology and Pedagogical Sciences, Ghent University, Ghent, Belgium
| | - Evelien Carrette
- Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and Neuropsychology, Ghent University, Ghent, Belgium
| | - Paul Boon
- Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and Neuropsychology, Ghent University, Ghent, Belgium
| | - Stefaan Vandenberghe
- Medical Imaging and Signal Processing Group, Department of Electronics and Information Systems, Ghent University - iMinds Medical IT Department, Ghent, Belgium
| | - Kristl Vonck
- Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and Neuropsychology, Ghent University, Ghent, Belgium
| | - Daniele Marinazzo
- Department of Data Analysis, Faculty of Psychology and Pedagogical Sciences, Ghent University, Ghent, Belgium
| |
Collapse
|
37
|
Korzeniewska A, Cervenka MC, Jouny CC, Perilla JR, Harezlak J, Bergey GK, Franaszczuk PJ, Crone NE. Ictal propagation of high frequency activity is recapitulated in interictal recordings: effective connectivity of epileptogenic networks recorded with intracranial EEG. Neuroimage 2014; 101:96-113. [PMID: 25003814 DOI: 10.1016/j.neuroimage.2014.06.078] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2014] [Revised: 06/08/2014] [Accepted: 06/30/2014] [Indexed: 01/08/2023] Open
Abstract
Seizures are increasingly understood to arise from epileptogenic networks across which ictal activity is propagated and sustained. In patients undergoing invasive monitoring for epilepsy surgery, high frequency oscillations have been observed within the seizure onset zone during both ictal and interictal intervals. We hypothesized that the patterns by which high frequency activity is propagated would help elucidate epileptogenic networks and thereby identify network nodes relevant for surgical planning. Intracranial EEG recordings were analyzed with a multivariate autoregressive modeling technique (short-time direct directed transfer function--SdDTF), based on the concept of Granger causality, to estimate the directionality and intensity of propagation of high frequency activity (70-175 Hz) during ictal and interictal recordings. These analyses revealed prominent divergence and convergence of high frequency activity propagation at sites identified by epileptologists as part of the ictal onset zone. In contrast, relatively little propagation of this activity was observed among the other analyzed sites. This pattern was observed in both subdural and depth electrode recordings of patients with focal ictal onset, but not in patients with a widely distributed ictal onset. In patients with focal ictal onsets, the patterns of propagation recorded during pre-ictal (up to 5 min immediately preceding ictal onset) and interictal (more than 24h before and after seizures) intervals were very similar to those recorded during seizures. The ability to characterize epileptogenic networks from interictal recordings could have important clinical implications for epilepsy surgery planning by reducing the need for prolonged invasive monitoring to record spontaneous seizures.
Collapse
Affiliation(s)
- A Korzeniewska
- Department of Neurology, Johns Hopkins University School of Medicine, 600 N. Wolfe St., Meyer 2-147, Baltimore, MD 21287, USA.
| | - M C Cervenka
- Department of Neurology, Johns Hopkins University School of Medicine, 600 N. Wolfe St., Meyer 2-147, Baltimore, MD 21287, USA
| | - C C Jouny
- Department of Neurology, Johns Hopkins University School of Medicine, 600 N. Wolfe St., Meyer 2-147, Baltimore, MD 21287, USA
| | - J R Perilla
- Beckman Institute and Department of Physics, University of Illinois Urbana-Champaign, 405 N. Mathews Ave., Urbana, IL 61801, USA
| | - J Harezlak
- Department of Biostatistics, Richard M. Fairbanks School of Public Health and School of Medicine Indiana University, 410 W 10th St., Suite 3000, Indianapolis, IN 46202, USA
| | - G K Bergey
- Department of Neurology, Johns Hopkins University School of Medicine, 600 N. Wolfe St., Meyer 2-147, Baltimore, MD 21287, USA
| | - P J Franaszczuk
- Department of Neurology, Johns Hopkins University School of Medicine, 600 N. Wolfe St., Meyer 2-147, Baltimore, MD 21287, USA; Human Research and Engineering Directorate, US Army Research Laboratory, 459 Mulberry Point Rd, Aberdeen Proving Ground, MD 21005, USA
| | - N E Crone
- Department of Neurology, Johns Hopkins University School of Medicine, 600 N. Wolfe St., Meyer 2-147, Baltimore, MD 21287, USA
| |
Collapse
|
38
|
Xu H, Lu Y, Zhu S, He B. Assessing dynamic spectral causality by lagged adaptive directed transfer function and instantaneous effect factor. IEEE Trans Biomed Eng 2014; 61:1979-88. [PMID: 24956616 PMCID: PMC4068271 DOI: 10.1109/tbme.2014.2311034] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
It is of significance to assess the dynamic spectral causality among physiological signals. Several practical estimators adapted from spectral Granger causality have been exploited to track dynamic causality based on the framework of time-varying multivariate autoregressive (tvMVAR) models. The nonzero covariance of the model's residuals has been used to describe the instantaneous effect phenomenon in some causality estimators. However, for the situations with Gaussian residuals in some autoregressive models, it is challenging to distinguish the directed instantaneous causality if the sufficient prior information about the "causal ordering" is missing. Here, we propose a new algorithm to assess the time-varying causal ordering of tvMVAR model under the assumption that the signals follow the same acyclic causal ordering for all time lags and to estimate the instantaneous effect factor (IEF) value in order to track the dynamic directed instantaneous connectivity. The time-lagged adaptive directed transfer function (ADTF) is also estimated to assess the lagged causality after removing the instantaneous effect. In this study, we first investigated the performance of the causal-ordering estimation algorithm and the accuracy of IEF value. Then, we presented the results of IEF and time-lagged ADTF method by comparing with the conventional ADTF method through simulations of various propagation models. Statistical analysis results suggest that the new algorithm could accurately estimate the causal ordering and give a good estimation of the IEF values in the Gaussian residual conditions. Meanwhile, the time-lagged ADTF approach is also more accurate in estimating the time-lagged dynamic interactions in a complex nervous system after extracting the instantaneous effect. In addition to the simulation studies, we applied the proposed method to estimate the dynamic spectral causality on real visual evoked potential (VEP) data in a human subject. Its usefulness in time-variant spectral causality assessment was demonstrated through the mutual causality investigation of brain activity during the VEP experiments.
Collapse
Affiliation(s)
- Haojie Xu
- College of Electrical Engineering, Zhejiang University, Hangzhou, China
| | - Yunfeng Lu
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455 USA
| | - Shanan Zhu
- College of Electrical Engineering, Zhejiang University, Hangzhou, China
| | - Bin He
- Department of Biomedical Engineering and Institute for Engineering in Medicine, University of Minnesota, Minneapolis, MN 55455 USA
| |
Collapse
|
39
|
Janca R, Jezdik P, Cmejla R, Tomasek M, Worrell GA, Stead M, Wagenaar J, Jefferys JGR, Krsek P, Komarek V, Jiruska P, Marusic P. Detection of interictal epileptiform discharges using signal envelope distribution modelling: application to epileptic and non-epileptic intracranial recordings. Brain Topogr 2014; 28:172-83. [PMID: 24970691 DOI: 10.1007/s10548-014-0379-1] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2013] [Accepted: 05/27/2014] [Indexed: 10/25/2022]
Abstract
Interictal epileptiform discharges (spikes, IEDs) are electrographic markers of epileptic tissue and their quantification is utilized in planning of surgical resection. Visual analysis of long-term multi-channel intracranial recordings is extremely laborious and prone to bias. Development of new and reliable techniques of automatic spike detection represents a crucial step towards increasing the information yield of intracranial recordings and to improve surgical outcome. In this study, we designed a novel and robust detection algorithm that adaptively models statistical distributions of signal envelopes and enables discrimination of signals containing IEDs from signals with background activity. This detector demonstrates performance superior both to human readers and to an established detector. It is even capable of identifying low-amplitude IEDs which are often missed by experts and which may represent an important source of clinical information. Application of the detector to non-epileptic intracranial data from patients with intractable facial pain revealed the existence of sharp transients with waveforms reminiscent of interictal discharges that can represent biological sources of false positive detections. Identification of these transients enabled us to develop and propose secondary processing steps, which may exclude these transients, improving the detector's specificity and having important implications for future development of spike detectors in general.
Collapse
Affiliation(s)
- Radek Janca
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
40
|
Blakely T, Ojemann JG, Rao RPN. Short-time windowed covariance: a metric for identifying non-stationary, event-related covariant cortical sites. J Neurosci Methods 2013; 222:24-33. [PMID: 24211499 DOI: 10.1016/j.jneumeth.2013.10.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2013] [Revised: 09/05/2013] [Accepted: 10/08/2013] [Indexed: 11/17/2022]
Abstract
BACKGROUND Electrocorticography (ECoG) signals can provide high spatio-temporal resolution and high signal to noise ratio recordings of local neural activity from the surface of the brain. Previous studies have shown that broad-band, spatially focal, high-frequency increases in ECoG signals are highly correlated with movement and other cognitive tasks and can be volitionally modulated. However, significant additional information may be present in inter-electrode interactions, but adding additional higher order inter-electrode interactions can be impractical from a computational aspect, if not impossible. NEW METHOD In this paper we present a new method of calculating high frequency interactions between electrodes called Short-Time Windowed Covariance (STWC) that builds on mathematical techniques currently used in neural signal analysis, along with an implementation that accelerates the algorithm by orders of magnitude by leveraging commodity, off-the-shelf graphics processing unit (GPU) hardware. RESULTS Using the hardware-accelerated implementation of STWC, we identify many types of event-related inter-electrode interactions from human ECoG recordings on global and local scales that have not been identified by previous methods. Unique temporal patterns are observed for digit flexion in both low- (10mm spacing) and high-resolution (3mm spacing) electrode arrays. COMPARISON WITH EXISTING METHODS Covariance is a commonly used metric for identifying correlated signals, but the standard covariance calculations do not allow for temporally varying covariance. In contrast STWC allows and identifies event-driven changes in covariance without identifying spurious noise correlations. CONCLUSIONS STWC can be used to identify event-related neural interactions whose high computational load is well suited to GPU capabilities.
Collapse
Affiliation(s)
- Timothy Blakely
- Department of Bioengineering, University of Washington, United States.
| | | | - Rajesh P N Rao
- Department of Computer Science, University of Washington, United States
| |
Collapse
|
41
|
Simulation Study of Direct Causality Measures in Multivariate Time Series. ENTROPY 2013. [DOI: 10.3390/e15072635] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
42
|
Ashrafulla S, Haldar JP, Joshi AA, Leahy RM. Canonical Granger causality between regions of interest. Neuroimage 2013; 83:189-99. [PMID: 23811410 DOI: 10.1016/j.neuroimage.2013.06.056] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2013] [Revised: 06/14/2013] [Accepted: 06/17/2013] [Indexed: 11/25/2022] Open
Abstract
Estimating and modeling functional connectivity in the brain is a challenging problem with potential applications in the understanding of brain organization and various neurological and neuropsychological conditions. An important objective in connectivity analysis is to determine the connections between regions of interest in the brain. However, traditional functional connectivity analyses have frequently focused on modeling interactions between time series recordings at individual sensors, voxels, or vertices despite the fact that a single region of interest will often include multiple such recordings. In this paper, we present a novel measure of interaction between regions of interest rather than individual signals. The proposed measure, termed canonical Granger causality, combines ideas from canonical correlation and Granger causality analysis to yield a measure that reflects directed causality between two regions of interest. In particular, canonical Granger causality uses optimized linear combinations of signals from each region of interest to enable accurate causality measurements from substantially less data compared to alternative multivariate methods that have previously been proposed for this scenario. The optimized linear combinations are obtained using a variation of a technique developed for optimization on the Stiefel manifold. We demonstrate the advantages of canonical Granger causality in comparison to alternative causality measures for a range of different simulated datasets. We also apply the proposed measure to local field potential data recorded in a macaque brain during a visuomotor task. Results demonstrate that canonical Granger causality can be used to identify causal relationships between striate and prestriate cortexes in cases where standard Granger causality is unable to identify statistically significant interactions.
Collapse
Affiliation(s)
- Syed Ashrafulla
- Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA
| | | | | | | |
Collapse
|
43
|
van Mierlo P, Carrette E, Hallez H, Raedt R, Meurs A, Vandenberghe S, Van Roost D, Boon P, Staelens S, Vonck K. Ictal-onset localization through connectivity analysis of intracranial EEG signals in patients with refractory epilepsy. Epilepsia 2013; 54:1409-18. [DOI: 10.1111/epi.12206] [Citation(s) in RCA: 94] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/26/2013] [Indexed: 11/28/2022]
Affiliation(s)
- Pieter van Mierlo
- Medical Image and Signal Processing Group; Department of Electronics and Information Systems; Ghent University - iMinds; Ghent Belgium
| | - Evelien Carrette
- Laboratory for Clinical and Experimental Neurophysiology; Neurobiology and Neuropsychology; Department of Neurology; Ghent University Hospital; Ghent Belgium
| | - Hans Hallez
- Electronic Circuit Design and Realization; Department of Industrial Sciences and Technology; College University of Bruges-Ostend; Ostend Belgium
| | - Robrecht Raedt
- Laboratory for Clinical and Experimental Neurophysiology; Neurobiology and Neuropsychology; Department of Neurology; Ghent University Hospital; Ghent Belgium
| | - Alfred Meurs
- Laboratory for Clinical and Experimental Neurophysiology; Neurobiology and Neuropsychology; Department of Neurology; Ghent University Hospital; Ghent Belgium
| | - Stefaan Vandenberghe
- Medical Image and Signal Processing Group; Department of Electronics and Information Systems; Ghent University - iMinds; Ghent Belgium
| | - Dirk Van Roost
- Department of Neurosurgery; Ghent University Hospital; Ghent Belgium
| | - Paul Boon
- Laboratory for Clinical and Experimental Neurophysiology; Neurobiology and Neuropsychology; Department of Neurology; Ghent University Hospital; Ghent Belgium
| | - Steven Staelens
- Molecular Imaging Center Antwerp; Faculty of Medicine; Antwerp University; Antwerp Belgium
| | - Kristl Vonck
- Laboratory for Clinical and Experimental Neurophysiology; Neurobiology and Neuropsychology; Department of Neurology; Ghent University Hospital; Ghent Belgium
| |
Collapse
|
44
|
Leal AJ, Lopes R, Ferreira JC. Origin and dynamics of epileptic activity in a symptomatic case of Panayiotopoulos syndrome: Correlation with clinical manifestations. Clin Neurophysiol 2013; 124:20-6. [DOI: 10.1016/j.clinph.2012.06.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2012] [Revised: 05/16/2012] [Accepted: 06/01/2012] [Indexed: 10/28/2022]
|
45
|
Kaiboriboon K, Lüders HO, Hamaneh M, Turnbull J, Lhatoo SD. EEG source imaging in epilepsy--practicalities and pitfalls. Nat Rev Neurol 2012; 8:498-507. [PMID: 22868868 DOI: 10.1038/nrneurol.2012.150] [Citation(s) in RCA: 79] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
EEG source imaging (ESI) is a model-based imaging technique that integrates temporal and spatial components of EEG to identify the generating source of electrical potentials recorded on the scalp. Recent advances in computer technologies have made the analysis of ESI data less time-consuming, and have rekindled interest in this technique as a clinical diagnostic tool. On the basis of the available body of evidence, ESI seems to be a promising tool for epilepsy evaluation; however, the precise clinical value of ESI in presurgical evaluation of epilepsy and in localization of eloquent cortex remains to be investigated. In this Review, we describe two fundamental issues in ESI; namely, the forward and inverse problems, and their solutions. The clinical application of ESI in surgical planning for patients with medically refractory focal epilepsy, and its use in source reconstruction together with invasive recordings, is also discussed. As ESI can be used to map evoked responses, we discuss the clinical utility of this technique in cortical mapping-an essential process when planning resective surgery for brain regions that are in close proximity to eloquent cortex.
Collapse
Affiliation(s)
- Kitti Kaiboriboon
- Epilepsy Center, Neurological Institute, University Hospitals Case Medical Center, Case Western Reserve University School of Medicine, 11100 Euclid Avenue, Lakeside 3200, Cleveland, OH 44106, USA. kitti.kaiboriboon@ uhhospitals.org
| | | | | | | | | |
Collapse
|
46
|
Lu Y, Yang L, Worrell GA, Brinkmann B, Nelson C, He B. Dynamic imaging of seizure activity in pediatric epilepsy patients. Clin Neurophysiol 2012; 123:2122-9. [PMID: 22608485 DOI: 10.1016/j.clinph.2012.04.021] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2012] [Revised: 04/19/2012] [Accepted: 04/20/2012] [Indexed: 11/19/2022]
Abstract
OBJECTIVE To investigate the feasibility of using noninvasive EEG source imaging approach to image continuous seizure activity in pediatric epilepsy patients. METHODS Nine pediatric patients with medically intractable epilepsy were included in this study. Eight of the patients had extratemporal lobe epilepsy and one had temporal lobe epilepsy. All of the patients underwent resective surgery and seven of them underwent intracranial EEG (iEEG) monitoring. The ictal EEG was analyzed using a noninvasive dynamic seizure imaging (DSI) approach. The DSI approach separates scalp EEGs into independent components and extracts the spatio-temporal ictal features to achieve dynamic imaging of seizure sources. Surgical resection and intracranial recordings were used to validate the noninvasive imaging results. RESULTS The DSI determined seizure onset zones (SOZs) in these patients were localized within or in close vicinity to the surgically resected region. In the seven patients with intracranial monitoring, the estimated seizure onset sources were concordant with the seizure onset zones of iEEG. The DSI also localized the multiple foci involved in the later seizure propagation, which were confirmed by the iEEG recordings. CONCLUSIONS Dynamic seizure imaging can noninvasively image the seizure activations in pediatric patients with both temporal and extratemporal lobe epilepsy. SIGNIFICANCE EEG seizure imaging can potentially be used to noninvasively image the SOZs and aid the pre-surgical planning in pediatric epilepsy patients.
Collapse
Affiliation(s)
- Yunfeng Lu
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA
| | | | | | | | | | | |
Collapse
|
47
|
Cho JH, Kang HC, Jung YJ, Lee YH, Jung KY, Kim HD, Im CH. Localization of ictal onset zones in Lennox–Gastaut syndrome (LGS) based on information theoretical time delay analysis of intracranial electroencephalography (iEEG). Epilepsy Res 2012; 99:78-86. [DOI: 10.1016/j.eplepsyres.2011.10.024] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2011] [Revised: 10/09/2011] [Accepted: 10/15/2011] [Indexed: 11/27/2022]
|
48
|
Cho JH, Jung YJ, Kang HC, Kim HD, Im CH. Localization of ictal onset zones in Lennox-Gastaut syndrome using directed transfer function method. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:5020-3. [PMID: 22255466 DOI: 10.1109/iembs.2011.6091244] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Neuroscientists are becoming interested in the application of computational EEG analysis to the identification of ictal onset zones, however, most studies have focused on the localization of ictal onset zones in focal epilepsy. The present study aimed to estimate the ictal onset zone of Lennox-Gastaut syndrome (LGS) with bilaterally synchronous epileptiform discharges from intracranial electroencephalography (iEEG) recordings using directional connectivity analysis. We analyzed ictal iEEG data acquired from three patients with LGS. To identify the ictal onset zones, we estimated the functional directional connectivity network among the intracerebral electrodes using the directed transfer function (DTF) method. The analysis results demonstrated that areas with high average outflow values corresponded well with the surgical resection areas identified using electrophysiologic data and conventional neuroimaging modalities. Our results suggest that the DTF analysis can be a useful auxiliary tool for determining surgical resection areas prior to epilepsy surgery in LGS patients.
Collapse
Affiliation(s)
- Jae-Hyun Cho
- Department of Biomedical Engineering, Yonsei University, Wonju, Kangwon-do, South Korea.
| | | | | | | | | |
Collapse
|
49
|
Chan HL, Tsai YT, Wang YC, Ju JH, Chang BL, Wu T, Lee ST, Lin BS. Partial directed coherence analysis of intracranial neural spikes in epilepsy patients. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2012:5174-5177. [PMID: 23367094 DOI: 10.1109/embc.2012.6347159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Intracranial electroencephalograms (EEG) provide a direct observation of neural activity by placing an electrode array on the cortical surface near the suspected epileptic foci. The neural spikes appeared during inter-ictal stages are mainly produced by abnormal neural discharges from epileptic foci. The topological mapping of spikes' potentials is commonly used to identify the epileptogenic zone. However, the propagations among multi-channel spikes are also important to identify the epileptic source activity. In addition, the changes of source activities in a series of consecutive spikes reveal the time-varying neural activations during discharge process, which provide alternative information for interpreting epileptic source activity. This paper proposes a spike classification based on the similarity of phase-space features to select candidate spikes from the intracranial EEGs recorded from an 8×8 electrocorticogram grid. Then, the partial directed coherence (PDC), which can provide the flow of source activity, at each spiking time point is computed. The outflow PDCs of all electrodes are therefore displayed on the grid. Our result showed that the derived source activities in the preceding spikes had high concentrated distributions but decreased in latter spikes. This implied the epileptic discharges were initially induced by a small-area cortex neurons and then spread out.
Collapse
Affiliation(s)
- Hsiao-Lung Chan
- Department of Electrical Engineering, Chang Gung University, Taoyuan, Taiwan.
| | | | | | | | | | | | | | | |
Collapse
|
50
|
Lu Y, Yang L, Worrell GA, He B. Seizure source imaging by means of FINE spatio-temporal dipole localization and directed transfer function in partial epilepsy patients. Clin Neurophysiol 2011; 123:1275-83. [PMID: 22172768 DOI: 10.1016/j.clinph.2011.11.007] [Citation(s) in RCA: 70] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2011] [Revised: 11/15/2011] [Accepted: 11/17/2011] [Indexed: 11/25/2022]
Abstract
OBJECTIVE To investigate the usage of a high-density EEG recording system and source imaging technique for localizing seizure activity in patients with medically intractable partial epilepsy. METHODS High-density, 76-channel scalp EEG signals were recorded in 10 patients with partial epilepsy. The patients underwent routine clinical pre-surgical evaluation and all had resective surgery with seizure free outcome. After applying a FINE (first principle vectors) spatio-temporal source localization and DTF (directed transfer function) connectivity analysis approach, ictal sources were imaged. Effects of number of scalp EEG electrodes on the seizure localization were also assessed using 76, 64, 48, 32, and 21 electrodes, respectively. RESULTS Surgical resections were used to assess the source imaging results. Results from the 76-channel EEG in the 10 patients showed high correlation with the surgically resected brain regions. The localization of seizure onset zone from 76-channel EEG showed improved source detection accuracy compared to other EEG configurations with fewer electrodes. CONCLUSIONS FINE together with DTF was able to localize seizure onset zones of partial epilepsy patients. High-density EEG recording can help achieve improved seizure source imaging. SIGNIFICANCE The present results suggest the promise of high-density EEG and electrical source imaging for noninvasively localizing seizure onset zones.
Collapse
Affiliation(s)
- Yunfeng Lu
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | | | | | | |
Collapse
|