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Lin J, Smith GC, Gliske SV, Zochowski M, Shedden K, Stacey WC. High frequency oscillation network dynamics predict outcome in non-palliative epilepsy surgery. Brain Commun 2024; 6:fcae032. [PMID: 38384998 PMCID: PMC10881100 DOI: 10.1093/braincomms/fcae032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 12/28/2023] [Accepted: 02/05/2024] [Indexed: 02/23/2024] Open
Abstract
High frequency oscillations are a promising biomarker of outcome in intractable epilepsy. Prior high frequency oscillation work focused on counting high frequency oscillations on individual channels, and it is still unclear how to translate those results into clinical care. We show that high frequency oscillations arise as network discharges that have valuable properties as predictive biomarkers. Here, we develop a tool to predict patient outcome before surgical resection is performed, based on only prospective information. In addition to determining high frequency oscillation rate on every channel, we performed a correlational analysis to evaluate the functional connectivity of high frequency oscillations in 28 patients with intracranial electrodes. We found that high frequency oscillations were often not solitary events on a single channel, but part of a local network discharge. Eigenvector and outcloseness centrality were used to rank channel importance within the connectivity network, then used to compare patient outcome by comparison with the seizure onset zone or a proportion within the proposed resected channels (critical resection percentage). Combining the knowledge of each patient's seizure onset zone resection plan along with our computed high frequency oscillation network centralities and high frequency oscillation rate, we develop a Naïve Bayes model that predicts outcome (positive predictive value: 100%) better than predicting based upon fully resecting the seizure onset zone (positive predictive value: 71%). Surgical margins had a large effect on outcomes: non-palliative patients in whom most of the seizure onset zone was resected ('definitive surgery', ≥ 80% resected) had predictable outcomes, whereas palliative surgeries (<80% resected) were not predictable. These results suggest that the addition of network properties of high frequency oscillations is more accurate in predicting patient outcome than seizure onset zone alone in patients with most of the seizure onset zone removed and offer great promise for informing clinical decisions in surgery for refractory epilepsy.
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Affiliation(s)
- Jack Lin
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Garnett C Smith
- Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Stephen V Gliske
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Michal Zochowski
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Physics and Biophysics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kerby Shedden
- Department of Statistics and Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - William C Stacey
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
- Division of Neurology, Ann Arbor VA Health System, Ann Arbor, MI 48109, USA
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2
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Peng G, Nourani M, Dave H, Harvey J. SEEG-based epileptic seizure network modeling and analysis for pre-surgery evaluation. Comput Biol Med 2023; 167:107692. [PMID: 37976827 DOI: 10.1016/j.compbiomed.2023.107692] [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: 06/21/2023] [Revised: 09/27/2023] [Accepted: 11/06/2023] [Indexed: 11/19/2023]
Abstract
Stereo-electroencephalography is a minimally invasive technique for patients with refractory epilepsy pursuing surgery to reduce or control seizures. Electrodes are implanted based on pre-surgery evaluations and can collect deep brain activities for surgery decisions. This paper presents a methodology to analyze stereo-electroencephalography and assist clinicians by recommending the optimal surgical option and target areas for focal epilepsy patients. A seizure network (graph) model is proposed to characterize the spatial distribution and temporal changes of ictal events. The network nodes and edges correspond to specific epileptogenic regions and propagation/impact pathways (weighted by directed transfer function), respectively. We then employ a K-means clustering strategy to group nodes into a few clusters, from which the target surgical areas can be identified. Ten patients with different types of focal seizures were thoroughly analyzed. Promising consistency between results of our method's recommendations, clinical decisions and surgery outcomes were observed.
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Affiliation(s)
- Genchang Peng
- Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA.
| | - Mehrdad Nourani
- Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA.
| | - Hina Dave
- Department of Neurology and Neurotherapeutics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
| | - Jay Harvey
- Department of Neurology, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
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3
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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.
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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
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4
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Hermann BP, Struck AF, Busch RM, Reyes A, Kaestner E, McDonald CR. Neurobehavioural comorbidities of epilepsy: towards a network-based precision taxonomy. Nat Rev Neurol 2021; 17:731-746. [PMID: 34552218 PMCID: PMC8900353 DOI: 10.1038/s41582-021-00555-z] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/10/2021] [Indexed: 02/06/2023]
Abstract
Cognitive and behavioural comorbidities are prevalent in childhood and adult epilepsies and impose a substantial human and economic burden. Over the past century, the classic approach to understanding the aetiology and course of these comorbidities has been through the prism of the medical taxonomy of epilepsy, including its causes, course, characteristics and syndromes. Although this 'lesion model' has long served as the organizing paradigm for the field, substantial challenges to this model have accumulated from diverse sources, including neuroimaging, neuropathology, neuropsychology and network science. Advances in patient stratification and phenotyping point towards a new taxonomy for the cognitive and behavioural comorbidities of epilepsy, which reflects the heterogeneity of their clinical presentation and raises the possibility of a precision medicine approach. As we discuss in this Review, these advances are informing the development of a revised aetiological paradigm that incorporates sophisticated neurobiological measures, genomics, comorbid disease, diversity and adversity, and resilience factors. We describe modifiable risk factors that could guide early identification, treatment and, ultimately, prevention of cognitive and broader neurobehavioural comorbidities in epilepsy and propose a road map to guide future research.
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Affiliation(s)
- Bruce P. Hermann
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.,
| | - Aaron F. Struck
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.,William S. Middleton Veterans Administration Hospital, Madison, WI, USA
| | - Robyn M. Busch
- Epilepsy Center and Department of Neurology, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA.,Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Anny Reyes
- Department of Psychiatry and Center for Multimodal Imaging and Genetics, University of California, San Diego, San Diego, CA, USA
| | - Erik Kaestner
- Department of Psychiatry and Center for Multimodal Imaging and Genetics, University of California, San Diego, San Diego, CA, USA
| | - Carrie R. McDonald
- Department of Psychiatry and Center for Multimodal Imaging and Genetics, University of California, San Diego, San Diego, CA, USA
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5
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Luo WY, Liu H, Feng Y, Hao JX, Zhang YJ, Peng WF, Zhang PM, Ding J, Wang X. Efficacy of cathodal transcranial direct current stimulation on electroencephalographic functional networks in patients with focal epilepsy: Preliminary findings. Epilepsy Res 2021; 178:106791. [PMID: 34837824 DOI: 10.1016/j.eplepsyres.2021.106791] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 09/13/2021] [Accepted: 10/15/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Neuromodulation is a promising therapeutic alternative for epilepsy. We aimed to explore the efficacy and safety of cathodal transcranial current direct stimulation (ctDCS) on electroencephalographic functional networks in focal epilepsy. METHODS A sham-controlled, double-blinded, randomized study was conducted on 25 participants with focal epilepsy who underwent a 5-day, -1.0 mA, 20 min ctDCS, which targeted at the most active interictal epileptiform discharge (IED) region. We examined the electroencephalograms (EEGs) at baseline, immediately and at 4 weeks following ctDCS. The graph theory-based brain networks were established through time-variant partial directed coherence (TVPDC), and were calculated between each pair of EEG signals. The functional networks were characterized using average clustering coefficient, characteristic path length, and small-worldness index. The seizure frequencies, IEDs, graph-theory metrics and cognitive tests were compared. RESULTS Preliminary findings indicated an IED reduction of 30.2% at the end of 5-day active ctDCS compared to baseline (p < 0.10) and a significant IED reduction of 33.4% 4 weeks later (p < 0.05). In terms of the EEG functional network, the small-worldness index significantly reduced by 3.5% (p < 0.05) and the characteristic path length increased by 1.8% (p < 0.10) at the end of the session compared to the baseline. No obvious change was found in the seizure frequency during follow-up (p > 0.05). The Mini-Mental State Examination (MMSE) showed no difference between the active and sham groups (p > 0.05). No severe adverse reactions were observed. CONCLUSIONS In focal epilepsy, the 5-day consecutive ctDCS may potentially decrease the IEDs and ameliorate the EEG functional network, proposing a novel personalized therapeutic scenario for epilepsy.
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Affiliation(s)
- Wen-Yi Luo
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Hao Liu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yu Feng
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jia-Xin Hao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yi-Jun Zhang
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Wei-Feng Peng
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Pu-Ming Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Jing Ding
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China.
| | - Xin Wang
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China; Department of The State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China.
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6
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Struck AF, Boly M, Hwang G, Nair V, Mathis J, Nencka A, Conant LL, DeYoe EA, Ragahavan M, Prabhakaran V, Binder JR, Meyerand ME, Hermann BP. Regional and global resting-state functional MR connectivity in temporal lobe epilepsy: Results from the Epilepsy Connectome Project. Epilepsy Behav 2021; 117:107841. [PMID: 33611101 PMCID: PMC8035304 DOI: 10.1016/j.yebeh.2021.107841] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 01/20/2021] [Accepted: 02/01/2021] [Indexed: 12/28/2022]
Abstract
Temporal lobe epilepsy (TLE) has been conceptualized as focal disease with a discrete neurobiological focus and can respond well to targeted resection or ablation. In contrast, the neuro-cognitive deficits resulting from TLE can be widespread involving regions beyond the primary epileptic network. We hypothesize that this seemingly paradoxical findings can be explained by differences in connectivity between the primary epileptic region which is hyper-connected and its secondary influence on global connectome organization. This hypothesis is tested using regional and global graph theory metrics where we anticipate that regional mesial-temporal hyperconnectivity will be found and correlate with seizure frequency while global networks will be disorganized and be more closely associated with neuro-cognitive deficits. Resting-state fMRI was used to examine temporal lobe regional connectivity and global functional connectivity from 102 patients with TLE and 55 controls. Connectivity matrices were calculated for subcortical volumes and cortical parcellations. Graph theory metrics (global clustering coefficient (GCC), degree, closeness) were compared between groups and in relation to neuropsychological profiles and disease covariates using permutation testing and causal analysis. In TLE there was a decrease in GCC (p = 0.0345) associated with a worse neuropsychological profile (p = 0.0134). There was increased connectivity in the left hippocampus/amygdala (degree p = 0.0103, closeness p = 0.0104) and a decrease in connectivity in the right lateral temporal lobe (degree p = 0.0186, closeness p = 0.0122). A ratio between the hippocampus/amygdala and lateral temporal lobe-temporal lobe connectivity ratio (TLCR) revealed differences between TLE and controls for closeness (left p = 0.00149, right p = 0.0494) and for degree on left p = 0.00169; with trend on right p = 0.0567. Causal analysis suggested that "Epilepsy Activity" (seizure frequency, anti-seizure medications) was associated with increase in TLCR but not in GCC, while cognitive decline was associated with decreased GCC. These findings support the hypothesis that in TLE there is hyperconnectivity in the hippocampus/amygdala and hypoconnectivity in the lateral temporal lobe associated with "Epilepsy Activity." While, global connectome disorganization was associated with worse neuropsychological phenotype.
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Affiliation(s)
- Aaron F Struck
- University of Wisconsin-Madison, Department of Neurology, United States; William S. Middleton Veterans Administration Hospital, Madison, WI, United States.
| | - Melanie Boly
- University of Wisconsin-Madison, Department of Neurology
| | - Gyujoon Hwang
- University of Wisconsin-Madison, Department of Medical Physics
| | - Veena Nair
- University of Wisconsin-Madison, Department of Radiology
| | | | - Andrew Nencka
- Medical College of Wisconsin, Department of Radiology
| | - Lisa L Conant
- Medical College of Wisconsin, Department of Neurology
| | - Edgar A DeYoe
- Medical College of Wisconsin, Department of Radiology
| | | | | | | | - Mary E Meyerand
- University of Wisconsin-Madison, Department of Medical Physics
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7
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An N, Ye X, Liu Q, Xu J, Zhang P. Localization of the epileptogenic zone based on ictal stereo-electroencephalogram: Brain network and single-channel signal feature analysis. Epilepsy Res 2020; 167:106475. [PMID: 33045665 DOI: 10.1016/j.eplepsyres.2020.106475] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 06/22/2020] [Accepted: 09/17/2020] [Indexed: 01/21/2023]
Abstract
Accurate localization of the epileptogenic zone (EZ) is crucial for refractory focal epilepsy patients to achieve freedom from seizures following epilepsy surgery. In this study, ictal stereo-electroencephalography data from 35 patients with refractory focal epilepsy were analyzed. Effective networks based on partial directed coherence were analyzed, and a gray level co-occurrence matrix was applied to extract the time-varying features of the in-degree. These features, combined with the single-channel signal time-frequency features, including approximate entropy and line length, were used to localize the EZ based on a cluster algorithm. For all seizure-free patients (n = 23), the proposed method was effective in identifying the clinical-EZ-contacts and clinical-EZ-blocks, with an F1-score of 62.47 % and 72.18 %, respectively. The sensitivity was 96.00 % for the clinical-EZ-block identification, which provided the information for the decision-making of clinicians, prompting clinicians to focus on the identified EZ-blocks and their nearby contacts. The agreement between the EZ identified by the proposed method and the clinical-EZ was worse for non-seizure-free patients (n = 12) than for seizure-free patients. Furthermore, our method provided better results than using only brain network or single-channel signal features. This suggests that combining these complementary features can facilitate more accurate localization of the EZ.
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Affiliation(s)
- Nan An
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Xiaolai Ye
- Department of Functional Neurosurgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
| | - Qiangqiang Liu
- Department of Functional Neurosurgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
| | - Jiwen Xu
- Department of Functional Neurosurgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
| | - Puming Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
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8
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Narasimhan S, Kundassery KB, Gupta K, Johnson GW, Wills KE, Goodale SE, Haas K, Rolston JD, Naftel RP, Morgan VL, Dawant BM, González HFJ, Englot DJ. Seizure-onset regions demonstrate high inward directed connectivity during resting-state: An SEEG study in focal epilepsy. Epilepsia 2020; 61:2534-2544. [PMID: 32944945 DOI: 10.1111/epi.16686] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 08/15/2020] [Accepted: 08/17/2020] [Indexed: 01/06/2023]
Abstract
OBJECTIVE In patients with medically refractory focal epilepsy, stereotactic-electroencephalography (SEEG) can aid in localizing epileptogenic regions for surgical treatment. SEEG, however, requires long hospitalizations to record seizures, and ictal interpretation can be incomplete or inaccurate. Our recent work showed that non-directed resting-state analyses may identify brain regions as epileptogenic or uninvolved. Our present objective is to map epileptogenic networks in greater detail and more accurately identify seizure-onset regions using directed resting-state SEEG connectivity. METHODS In 25 patients with focal epilepsy who underwent SEEG, 2 minutes of resting-state, artifact-free, SEEG data were selected and functional connectivity was estimated. Using standard clinical interpretation, brain regions were classified into four categories: ictogenic, early propagation, irritative, or uninvolved. Three non-directed connectivity measures (mutual information [MI] strength, and imaginary coherence between and within regions) and four directed measures (partial directed coherence [PDC] and directed transfer function [DTF], inward and outward strength) were calculated. Logistic regression was used to generate a predictive model of ictogenicity. RESULTS Ictogenic regions had the highest and uninvolved regions had the lowest MI strength. Although both PDC and DTF inward strengths were highest in ictogenic regions, outward strengths did not differ among categories. A model incorporating directed and nondirected connectivity measures demonstrated an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.88 in predicting ictogenicity of individual regions. The AUC of this model was 0.93 when restricted to patients with favorable postsurgical seizure outcomes. SIGNIFICANCE Directed connectivity measures may help identify epileptogenic networks without requiring ictal recordings. Greater inward but not outward connectivity in ictogenic regions at rest may represent broad inhibitory input to prevent seizure generation.
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Affiliation(s)
- Saramati Narasimhan
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Keshav B Kundassery
- Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kanupriya Gupta
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Graham W Johnson
- Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Kristin E Wills
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Sarah E Goodale
- Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Kevin Haas
- Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - John D Rolston
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
| | - Robert P Naftel
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Victoria L Morgan
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Benoit M Dawant
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Hernán F J González
- Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Dario J Englot
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
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9
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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.
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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
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10
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Li Z, Yuan G, Huang P, Wang H, Yao M, Li C. [Isolated effective coherence analysis of epileptogenic networks in temporal lobe epilepsy using stereo-electroencephalography]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2019; 36:541-547. [PMID: 31441253 PMCID: PMC10319498 DOI: 10.7507/1001-5515.201806003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Indexed: 06/10/2023]
Abstract
Stereo-electroencephalography (SEEG) is widely used to record the electrical activity of patients' brain in clinical. The SEEG-based epileptogenic network can better describe the origin and the spreading of seizures, which makes it an important measure to localize epileptogenic zone (EZ). SEEG data from six patients with refractory epilepsy are used in this study. Five of them are with temporal lobe epilepsy, and the other is with extratemporal lobe epilepsy. The node outflow (out-degree) and inflow (in-degree) of information are calculated in each node of epileptic network, and the overlay between selected nodes and resected nodes is analyzed. In this study, SEEG data is transformed to bipolar montage, and then the epileptic network is established by using independent effective coherence (iCoh) method. The SEEG segments at onset, middle and termination of seizures in Delta, Theta, Alpha, Beta, and Gamma rhythms are used respectively. Finally, the K-means clustering algorithm is applied on the node values of out-degree and in-degree respectively. The nodes in the cluster with high value are compared with the resected regions. The final results show that the accuracy of selected nodes in resected region in the Delta, Alpha and Beta rhythm are 0.90, 0.88 and 0.89 based on out-degree values in temporal lobe epilepsy patients respectively, while the in-degree values cannot differentiate them. In contrast, the out-degree values are higher outside the temporal lobe in the patient with extratemporal lobe epilepsy. Based on the out-degree feature in low-frequency epileptic network, this study provides a potential quantitative measure for identifying patients with temporal lobe epilepsy in clinical.
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Affiliation(s)
- Zunyu Li
- Department of Biomedical Engineering, School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, P.R.China
| | - Guanqian Yuan
- Department of Neurosurgery, Northern Theater General Hospital, Shenyang 110016, P.R.China
| | - Ping Huang
- Department of Neurosurgery, Northern Theater General Hospital, Shenyang 110016, P.R.China
| | - Huijie Wang
- Department of Neurosurgery, Northern Theater General Hospital, Shenyang 110016, P.R.China
| | - Meiheng Yao
- Department of Biomedical Engineering, School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, P.R.China
| | - Chunsheng Li
- Department of Biomedical Engineering, School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870,
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11
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Precursors of seizures due to specific spatial-temporal modifications of evolving large-scale epileptic brain networks. Sci Rep 2019; 9:10623. [PMID: 31337840 PMCID: PMC6650408 DOI: 10.1038/s41598-019-47092-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 07/10/2019] [Indexed: 12/25/2022] Open
Abstract
Knowing when, where, and how seizures are initiated in large-scale epileptic brain networks remains a widely unsolved problem. Seizure precursors – changes in brain dynamics predictive of an impending seizure – can now be identified well ahead of clinical manifestations, but either the seizure onset zone or remote brain areas are reported as network nodes from which seizure precursors emerge. We aimed to shed more light on the role of constituents of evolving epileptic networks that recurrently transit into and out of seizures. We constructed such networks from more than 3200 hours of continuous intracranial electroencephalograms recorded in 38 patients with medication refractory epilepsy. We succeeded in singling out predictive edges and predictive nodes. Their particular characteristics, namely edge weight respectively node centrality (a fundamental concept of network theory), from the pre-ictal periods of 78 out of 97 seizures differed significantly from the characteristics seen during inter-ictal periods. The vast majority of predictive nodes were connected by most of the predictive edges, but these nodes never played a central role in the evolving epileptic networks. Interestingly, predictive nodes were entirely associated with brain regions deemed unaffected by the focal epileptic process. We propose a network mechanism for a transition into the pre-seizure state, which puts into perspective the role of the seizure onset zone in this transition and highlights the necessity to reassess current concepts for seizure generation and seizure prevention.
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12
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Phase Synchronization Dynamics of Neural Network during Seizures. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2018; 2018:1354915. [PMID: 30410569 PMCID: PMC6205102 DOI: 10.1155/2018/1354915] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Accepted: 09/13/2018] [Indexed: 11/19/2022]
Abstract
Epilepsy has been considered as a network-level disorder characterized by recurrent seizures, which result from network reorganization with evolution of synchronization. In this study, the brain networks were established by calculating phase synchronization based on electrocorticogram (ECoG) signals from eleven refractory epilepsy patients. Results showed that there was a significant increase of synchronization prior to seizure termination and no significant difference of the transitions of network states among the preseizure, seizure, and postseizure periods. Those results indicated that synchronization might participate in termination of seizures, and the network states transitions might not dominate the seizure evolution.
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13
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Nissen IA, Stam CJ, van Straaten ECW, Wottschel V, Reijneveld JC, Baayen JC, de Witt Hamer PC, Idema S, Velis DN, Hillebrand A. Localization of the Epileptogenic Zone Using Interictal MEG and Machine Learning in a Large Cohort of Drug-Resistant Epilepsy Patients. Front Neurol 2018; 9:647. [PMID: 30131762 PMCID: PMC6090046 DOI: 10.3389/fneur.2018.00647] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Accepted: 07/18/2018] [Indexed: 01/01/2023] Open
Abstract
Objective: Epilepsy surgery results in seizure freedom in the majority of drug-resistant patients. To improve surgery outcome we studied whether MEG metrics combined with machine learning can improve localization of the epileptogenic zone, thereby enhancing the chance of seizure freedom. Methods: Presurgical interictal MEG recordings of 94 patients (64 seizure-free >1y post-surgery) were analyzed to extract four metrics in source space: delta power, low-to-high-frequency power ratio, functional connectivity (phase lag index), and minimum spanning tree betweenness centrality. At the group level, we estimated the overlap of the resection area with the five highest values for each metric and determined whether this overlap differed between surgery outcomes. At the individual level, those metrics were used in machine learning classifiers (linear support vector machine (SVM) and random forest) to distinguish between resection and non-resection areas and between surgery outcome groups. Results: The highest values, for all metrics, overlapped with the resection area in more than half of the patients, but the overlap did not differ between surgery outcome groups. The classifiers distinguished the resection areas from non-resection areas with 59.94% accuracy (95% confidence interval: 59.67–60.22%) for SVM and 60.34% (59.98–60.71%) for random forest, but could not differentiate seizure-free from not seizure-free patients [43.77% accuracy (42.08–45.45%) for SVM and 49.03% (47.25–50.82%) for random forest]. Significance: All four metrics localized the resection area but did not distinguish between surgery outcome groups, demonstrating that metrics derived from interictal MEG correspond to expert consensus based on several presurgical evaluation modalities, but do not yet localize the epileptogenic zone. Metrics should be improved such that they correspond to the resection area in seizure-free patients but not in patients with persistent seizures. It is important to test such localization strategies at an individual level, for example by using machine learning or individualized models, since surgery is individually tailored.
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Affiliation(s)
- Ida A Nissen
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, Netherlands
| | - Elisabeth C W van Straaten
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, Netherlands
| | - Viktor Wottschel
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, Netherlands
| | - Jaap C Reijneveld
- Brain Tumor Center Amsterdam & Department of Neurology, VU University Medical Center, Amsterdam, Netherlands
| | - Johannes C Baayen
- Neurosurgical Center Amsterdam, VU University Medical Center, Amsterdam, Netherlands
| | - Philip C de Witt Hamer
- Brain Tumor Center Amsterdam & Department of Neurology, VU University Medical Center, Amsterdam, Netherlands.,Neurosurgical Center Amsterdam, VU University Medical Center, Amsterdam, Netherlands
| | - Sander Idema
- Neurosurgical Center Amsterdam, VU University Medical Center, Amsterdam, Netherlands
| | - Demetrios N Velis
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, Netherlands.,Neurosurgical Center Amsterdam, VU University Medical Center, Amsterdam, Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, Netherlands
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14
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Ghumare EG, Schrooten M, Vandenberghe R, Dupont P. A Time-Varying Connectivity Analysis from Distributed EEG Sources: A Simulation Study. Brain Topogr 2018; 31:721-737. [PMID: 29374816 PMCID: PMC6097773 DOI: 10.1007/s10548-018-0621-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Accepted: 01/15/2018] [Indexed: 11/10/2022]
Abstract
Time-varying connectivity analysis based on sources reconstructed using inverse modeling of electroencephalographic (EEG) data is important to understand the dynamic behaviour of the brain. We simulated cortical data from a visual spatial attention network with a time-varying connectivity structure, and then simulated the propagation to the scalp to obtain EEG data. Distributed EEG source modeling using sLORETA was applied. We compared different dipole (representing a source) selection strategies based on their time series in a region of interest. Next, we estimated multivariate autoregressive (MVAR) parameters using classical Kalman filter and general linear Kalman filter approaches followed by the calculation of partial directed coherence (PDC). MVAR parameters and PDC values for the selected sources were compared with the ground-truth. We found that the best strategy to extract the time series of a region of interest was to select a dipole with time series showing the highest correlation with the average time series in the region of interest. Dipole selection based on power or based on the largest singular value offer comparable alternatives. Among the different Kalman filter approaches, the use of a general linear Kalman filter was preferred to estimate PDC based connectivity except when only a small number of trials are available. In the latter case, the classical Kalman filter can be an alternative.
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Affiliation(s)
- Eshwar G Ghumare
- The Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Maarten Schrooten
- The Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium.,The Neurology Department, University Hospitals Leuven, Leuven, Belgium
| | - Rik Vandenberghe
- The Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium.,The Neurology Department, University Hospitals Leuven, Leuven, Belgium
| | - Patrick Dupont
- The Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium.
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