1
|
Cohen NT, Xie H, Gholipour T, Gaillard WD. A scoping review of the functional magnetic resonance imaging-based functional connectivity of focal cortical dysplasia-related epilepsy. Epilepsia 2023; 64:3130-3142. [PMID: 37731142 DOI: 10.1111/epi.17775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 09/17/2023] [Accepted: 09/18/2023] [Indexed: 09/22/2023]
Abstract
Focal cortical dysplasia (FCD) is the most frequent etiology of operable pharmacoresistant epilepsy in children. There is burgeoning evidence that FCD-related epilepsy is a disorder that involves distributed brain networks. Functional magnetic resonance imaging (fMRI) is a tool that allows one to infer neuronal activity and to noninvasively map whole-brain functional networks. Despite its relatively widespread availability at most epilepsy centers, the clinical application of fMRI remains mostly task-based in epilepsy. Another approach is to map and characterize cortical functional networks of individuals using resting state fMRI (rsfMRI). The focus of this scoping review is to summarize the evidence to date of investigations of the network basis of FCD-related epilepsy, and to highlight numerous potential future applications of rsfMRI in the exploration of diagnostic and therapeutic strategies for FCD-related epilepsy. There are numerous studies demonstrating a global disruption of cortical functional networks in FCD-related epilepsy. The underlying pathological subtypes of FCD influence overall functional network patterns. There is evidence that cortical functional network mapping may help to predict postsurgical seizure outcomes, highlighting the translational potential of these findings. Additionally, several studies emphasize the important effect of FCD interaction with cortical networks and the expression of epilepsy and its comorbidities.
Collapse
Affiliation(s)
- Nathan T Cohen
- Center for Neuroscience Research, Children's National Hospital, George Washington University School of Medicine, Washington, District of Columbia, USA
- Department of Neurology, Children's National Hospital, George Washington University School of Medicine, Washington, District of Columbia, USA
| | - Hua Xie
- Center for Neuroscience Research, Children's National Hospital, George Washington University School of Medicine, Washington, District of Columbia, USA
- Department of Neurology, Children's National Hospital, George Washington University School of Medicine, Washington, District of Columbia, USA
| | - Taha Gholipour
- Center for Neuroscience Research, Children's National Hospital, George Washington University School of Medicine, Washington, District of Columbia, USA
- Department of Neurology, George Washington University Epilepsy Center, Washington, District of Columbia, USA
| | - William D Gaillard
- Center for Neuroscience Research, Children's National Hospital, George Washington University School of Medicine, Washington, District of Columbia, USA
- Department of Neurology, Children's National Hospital, George Washington University School of Medicine, Washington, District of Columbia, USA
| |
Collapse
|
2
|
Ebrahimzadeh E, Shams M, Seraji M, Sadjadi SM, Rajabion L, Soltanian-Zadeh H. Localizing Epileptic Foci Using Simultaneous EEG-fMRI Recording: Template Component Cross-Correlation. Front Neurol 2021; 12:695997. [PMID: 34867704 PMCID: PMC8634837 DOI: 10.3389/fneur.2021.695997] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 09/29/2021] [Indexed: 02/01/2023] Open
Abstract
Conventional EEG-fMRI methods have been proven to be of limited use in the sense that they cannot reveal the information existing in between the spikes. To resolve this issue, the current study obtains the epileptic components time series detected on EEG and uses them to fit the Generalized Linear Model (GLM), as a substitution for classical regressors. This approach allows for a more precise localization, and equally importantly, the prediction of the future behavior of the epileptic generators. The proposed method approaches the localization process in the component domain, rather than the electrode domain (EEG), and localizes the generators through investigating the spatial correlation between the candidate components and the spike template, as well as the medical records of the patient. To evaluate the contribution of EEG-fMRI and concordance between fMRI and EEG, this method was applied on the data of 30 patients with refractory epilepsy. The results demonstrated the significant numbers of 29 and 24 for concordance and contribution, respectively, which mark improvement as compared to the existing literature. This study also shows that while conventional methods often fail to properly localize the epileptogenic zones in deep brain structures, the proposed method can be of particular use. For further evaluation, the concordance level between IED-related BOLD clusters and Seizure Onset Zone (SOZ) has been quantitatively investigated by measuring the distance between IED/SOZ locations and the BOLD clusters in all patients. The results showed the superiority of the proposed method in delineating the spike-generating network compared to conventional EEG-fMRI approaches. In all, the proposed method goes beyond the conventional methods by breaking the dependency on spikes and using the outside-the-scanner spike templates and the selected components, achieving an accuracy of 97%. Doing so, this method contributes to improving the yield of EEG-fMRI and creates a more realistic perception of the neural behavior of epileptic generators which is almost without precedent in the literature.
Collapse
Affiliation(s)
- Elias Ebrahimzadeh
- CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.,School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Mohammad Shams
- Neural Engineering Laboratory, Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, United States
| | - Masoud Seraji
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, United States.,Behavioral and Neural Sciences Graduate Program, Rutgers University, Newark, NJ, United States
| | - Seyyed Mostafa Sadjadi
- CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Lila Rajabion
- School of Graduate Studies, SUNY Empire State College, Manhattan, NY, United States
| | - Hamid Soltanian-Zadeh
- CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.,School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.,Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, MI, United States
| |
Collapse
|
3
|
Sadjadi SM, Ebrahimzadeh E, Shams M, Seraji M, Soltanian-Zadeh H. Localization of Epileptic Foci Based on Simultaneous EEG-fMRI Data. Front Neurol 2021; 12:645594. [PMID: 33986718 PMCID: PMC8110922 DOI: 10.3389/fneur.2021.645594] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 03/11/2021] [Indexed: 02/01/2023] Open
Abstract
Combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) enables a non-invasive investigation of the human brain function and evaluation of the correlation of these two important modalities of brain activity. This paper explores recent reports on using advanced simultaneous EEG–fMRI methods proposed to map the regions and networks involved in focal epileptic seizure generation. One of the applications of EEG and fMRI combination as a valuable clinical approach is the pre-surgical evaluation of patients with epilepsy to map and localize the precise brain regions associated with epileptiform activity. In the process of conventional analysis using EEG–fMRI data, the interictal epileptiform discharges (IEDs) are visually extracted from the EEG data to be convolved as binary events with a predefined hemodynamic response function (HRF) to provide a model of epileptiform BOLD activity and use as a regressor for general linear model (GLM) analysis of the fMRI data. This review examines the methodologies involved in performing such studies, including techniques used for the recording of EEG inside the scanner, artifact removal, and statistical analysis of the fMRI signal. It then discusses the results reported for patients with primary generalized epilepsy and patients with different types of focal epileptic disorders. An important matter that these results have brought to light is that the brain regions affected by interictal epileptic discharges might not be limited to the ones where they have been generated. The developed methods can help reveal the regions involved in or affected by a seizure onset zone (SOZ). As confirmed by the reviewed literature, EEG–fMRI provides information that comes particularly useful when evaluating patients with refractory epilepsy for surgery.
Collapse
Affiliation(s)
- Seyyed Mostafa Sadjadi
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Elias Ebrahimzadeh
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.,Neuroimage Signal and Image Analysis Group, School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Mohammad Shams
- Neural Engineering Laboratory, Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, United States
| | - Masoud Seraji
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, United States.,Behavioral and Neural Sciences Graduate Program, Rutgers University, Newark, NJ, United States
| | - Hamid Soltanian-Zadeh
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.,Neuroimage Signal and Image Analysis Group, School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.,Medical Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, MI, United States
| |
Collapse
|
4
|
Abstract
AbstractEpilepsy remains one of the most common chronic neurological disorders; hence, there is a need to further investigate various models for automatic detection of seizure activity. An effective detection model can be achieved by minimizing the complexity of the model in terms of trainable parameters while still maintaining high accuracy. One way to achieve this is to select the minimum possible number of features. In this paper, we propose a long short-term memory (LSTM) network for the classification of epileptic EEG signals. Discrete wavelet transform (DWT) is employed to remove noise and extract 20 eigenvalue features. The optimal features were then identified using correlation and P value analysis. The proposed method significantly reduces the number of trainable LSTM parameters required to attain high accuracy. Finally, our model outperforms other proposed frameworks, including popular classifiers such as logistic regression (LR), support vector machine (SVM), K-nearest neighbor (K-NN) and decision tree (DT).
Collapse
|
5
|
Chen X, Tao X, Wang FL, Xie H. Global research on artificial intelligence-enhanced human electroencephalogram analysis. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05588-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
|
6
|
Gu P, Wu T, Zou M, Pan Y, Guo J, Xiahou J, Peng X, Li H, Ma J, Zhang L. Multi-Head Self-Attention Model for Classification of Temporal Lobe Epilepsy Subtypes. Front Physiol 2020; 11:604764. [PMID: 33329057 PMCID: PMC7728994 DOI: 10.3389/fphys.2020.604764] [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: 09/10/2020] [Accepted: 10/22/2020] [Indexed: 11/30/2022] Open
Abstract
As a long-standing chronic disease, Temporal Lobe Epilepsy (TLE), resulting from abnormal discharges of neurons and characterized by recurrent episodic central nervous system dysfunctions, has affected more than 70% of drug-resistant epilepsy patients across the world. As the etiology and clinical symptoms are complicated, differential diagnosis of TLE mainly relies on experienced clinicians, and specific diagnostic biomarkers remain unclear. Though great effort has been made regarding the genetics, pathology, and neuroimaging of TLE, an accurate and effective diagnosis of TLE, especially the TLE subtypes, remains an open problem. It is of a great importance to explore the brain network of TLE, since it can provide the basis for diagnoses and treatments of TLE. To this end, in this paper, we proposed a multi-head self-attention model (MSAM). By integrating the self-attention mechanism and multilayer perceptron method, the MSAM offers a promising tool to enhance the classification of TLE subtypes. In comparison with other approaches, including convolutional neural network (CNN), support vector machine (SVM), and random forest (RF), experimental results on our collected MEG dataset show that the MSAM achieves a supreme performance of 83.6% on accuracy, 90.9% on recall, 90.7% on precision, and 83.4% on F1-score, which outperforms its counterparts. Furthermore, effectiveness of varying head numbers of multi-head self-attention is assessed, which helps select the optimal number of multi-head. The self-attention aspect learns the weights of different signal locations which can effectively improve classification accuracy. In addition, the robustness of MSAM is extensively assessed with various ablation tests, which demonstrates the effectiveness and generalizability of the proposed approach.
Collapse
Affiliation(s)
- Peipei Gu
- Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Ting Wu
- Department of Magnetoencephalography, Nanjing Brain Hospital Affiliated, Nanjing Medical University, Nanjing, China
| | - Mingyang Zou
- School of Biomedical Engineering, Hubei University of Science and Technology, Xianning, China
| | - Yijie Pan
- Department of Computer Science and Technology, Tsinghua University, Beijing, China.,Ningbo Institute of Information Technology Application, Chinese Academy of Sciences, Ningbo, China
| | - Jiayang Guo
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, United States
| | | | - Xueping Peng
- Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia
| | - Hailong Li
- The Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Junxia Ma
- Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Ling Zhang
- School of Biomedical Engineering, Hubei University of Science and Technology, Xianning, China
| |
Collapse
|
7
|
Localizing confined epileptic foci in patients with an unclear focus or presumed multifocality using a component-based EEG-fMRI method. Cogn Neurodyn 2020; 15:207-222. [PMID: 33854640 DOI: 10.1007/s11571-020-09614-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 06/07/2020] [Accepted: 06/23/2020] [Indexed: 02/07/2023] Open
Abstract
Precise localization of epileptic foci is an unavoidable prerequisite in epilepsy surgery. Simultaneous EEG-fMRI recording has recently created new horizons to locate foci in patients with epilepsy and, in comparison with single-modality methods, has yielded more promising results although it is still subject to limitations such as lack of access to information between interictal events. This study assesses its potential added value in the presurgical evaluation of patients with complex source localization. Adult candidates considered ineligible for surgery on account of an unclear focus and/or presumed multifocality on the basis of EEG underwent EEG-fMRI. Adopting a component-based approach, this study attempts to identify the neural behavior of the epileptic generators and detect the components-of-interest which will later be used as input in the GLM model, substituting the classical linear regressor. Twenty-eight sets interictal epileptiform discharges (IED) from nine patients were analyzed. In eight patients, at least one BOLD response was significant, positive and topographically related to the IEDs. These patients were rejected for surgery because of an unclear focus in four, presumed multifocality in three, and a combination of the two conditions in two. Component-based EEG-fMRI improved localization in five out of six patients with unclear foci. In patients with presumed multifocality, component-based EEG-fMRI advocated one of the foci in five patients and confirmed multifocality in one of the patients. In seven patients, component-based EEG-fMRI opened new prospects for surgery and in two of these patients, intracranial EEG supported the EEG-fMRI results. In these complex cases, component-based EEG-fMRI either improved source localization or corroborated a negative decision regarding surgical candidacy. As supported by the statistical findings, the developed EEG-fMRI method leads to a more realistic estimation of localization compared to the conventional EEG-fMRI approach, making it a tool of high value in pre-surgical evaluation of patients with refractory epilepsy. To ensure proper implementation, we have included guidelines for the application of component-based EEG-fMRI in clinical practice.
Collapse
|
8
|
Ebrahimzadeh E, Soltanian-Zadeh H, Araabi BN, Fesharaki SSH, Habibabadi JM. Component-related BOLD response to localize epileptic focus using simultaneous EEG-fMRI recordings at 3T. J Neurosci Methods 2019; 322:34-49. [PMID: 31026487 DOI: 10.1016/j.jneumeth.2019.04.010] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 04/18/2019] [Accepted: 04/21/2019] [Indexed: 02/01/2023]
Abstract
BACKGROUND Simultaneous EEG-fMRI experiments record spatiotemporal dynamics of epileptic activity. A shortcoming of spike-based EEG-fMRI studies is their inability to provide information about behavior of epileptic generators when no spikes are visible. NEW METHOD We extract time series of epileptic components identified on EEG and fit them with Generalized Linear Model (GLM) model. This allows a precise and reliable localization of epileptic foci in addition to predicting generator's behavior. The proposed method works in the source domain and delineates generators considering spatial correlation between spike template and candidate components in addition to patient's medical records. RESULTS The proposed method was applied on 20 patients with refractory epilepsy and 20 age- and gender-matched healthy controls. The identified components were examined statistically and threshold of localization accuracy was determined as 86% based on Receiver Operating Characteristic (ROC) curve analysis. Accuracy, sensitivity, and specificity were found to be 88%, 85%, and 95%, respectively. Contribution of EEG-fMRI and concordance between EEG and fMRI were also evaluated. Concordance was found in 19 patients and contribution in 17. COMPARISON WITH EXISTING METHODS We compared the proposed method with conventional methods. Our comparisons showed superiority of the proposed method. In particular, when epileptogenic zone was located deep in the brain, the method outperformed existing methods. CONCLUSIONS This study contributes substantially to increasing the yield of EEG-fMRI and presents a realistic estimate of the neural behavior of epileptic generators, to the best of our knowledge, for the first time in the literature.
Collapse
Affiliation(s)
- Elias Ebrahimzadeh
- CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; Department of Clinical Neurosciences, Hotchkiss Brain Institute, Cumming School of Medicine, and Seaman Family MR Research Centre, University of Calgary, Calgary, Alberta, Canada
| | - Hamid Soltanian-Zadeh
- CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran; Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, MI, USA.
| | - Babak Nadjar Araabi
- CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | | | - Jafar Mehvari Habibabadi
- Isfahan Neurosciences Research Center, Department of Neurology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| |
Collapse
|
9
|
Sharma NK, Pedreira C, Chaudhary UJ, Centeno M, Carmichael DW, Yadee T, Murta T, Diehl B, Lemieux L. BOLD mapping of human epileptic spikes recorded during simultaneous intracranial EEG-fMRI: The impact of automated spike classification. Neuroimage 2019; 184:981-992. [PMID: 30315907 PMCID: PMC6264381 DOI: 10.1016/j.neuroimage.2018.09.065] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Revised: 08/28/2018] [Accepted: 09/24/2018] [Indexed: 12/02/2022] Open
Abstract
OBJECTIVES Simultaneous intracranial EEG and functional MRI (icEEG-fMRI) can be used to map the haemodynamic (BOLD) changes associated with the generation of IEDs. Unlike scalp EEG-fMRI, in most patients who undergo icEEG-fMRI, IEDs recorded intracranially are numerous and show variability in terms of field amplitude and morphology. Therefore, visual marking can be highly subjective and time consuming. In this study, we applied an automated spike classification algorithm, Wave_clus (WC), to IEDs marked visually on icEEG data acquired during simultaneous fMRI acquisition. The motivation of this work is to determine whether using a potentially more consistent and unbiased automated approach can produce more biologically meaningful BOLD patterns compared to the BOLD patterns obtained based on the conventional, visual classification. METHODS We analysed simultaneous icEEG-fMRI data from eight patients with severe drug resistant epilepsy, and who subsequently underwent resective surgery that resulted in a good outcome: confirmed epileptogenic zone (EZ). For each patient two fMRI analyses were performed: one based on the conventional visual IED classification and the other based on the automated classification. We used the concordance of the IED-related BOLD maps with the confirmed EZ as an indication of their biological meaning, which we compared for the automated and visual classifications for all IED originating in the EZ. RESULTS Across the group, the visual and automated classifications resulted in 32 and 24 EZ IED classes respectively, for which 75% vs 83% of the corresponding BOLD maps were concordant. At the single-subject level, the BOLD maps for the automated approach had greater concordance in four patients, and less concordance in one patient, compared to those obtained using the conventional visual classification, and equal concordance for three remaining patients. These differences did not reach statistical significance. CONCLUSION We found automated IED classification on icEEG data recorded during fMRI to be feasible and to result in IED-related BOLD maps that may contain similar or greater biological meaning compared to the conventional approach in the majority of the cases studied. We anticipate that this approach will help to gain significant new insights into the brain networks associated with IEDs and in relation to postsurgical outcome.
Collapse
Affiliation(s)
- Niraj K Sharma
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London, United Kingdom; MRI Unit, Epilepsy Society, Chalfont St Peter, Buckinghamshire, United Kingdom
| | - Carlos Pedreira
- Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom; Sensium Healthcare, Milton Park, Abingdon, Oxfordshire, United Kingdom
| | - Umair J Chaudhary
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London, United Kingdom; MRI Unit, Epilepsy Society, Chalfont St Peter, Buckinghamshire, United Kingdom; Neurology Department, Queen Elizabeth Hospital, University Hospital Birmingham, NHS Foundation Trust, United Kingdom
| | - Maria Centeno
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London, United Kingdom; MRI Unit, Epilepsy Society, Chalfont St Peter, Buckinghamshire, United Kingdom; National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, Queen Square, London, United Kingdom; Epilepsy Unit, Neurology Department, Clinica Universidad de Pamplona, Navarra, Spain
| | - David W Carmichael
- Developmental Imaging and Biophysics, UCL Institute of Child Health, London, United Kingdom; Wellcome EPSRC Centre for Medical Engineering, King's College London, St Thomas' Hospital, London, United Kingdom
| | - Tinonkorn Yadee
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London, United Kingdom; Prasat Neurological Institute, Bangkok, Thailand
| | - Teresa Murta
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London, United Kingdom; National Physical Laboratory, Teddington, Middlesex, United Kingdom
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London, United Kingdom; MRI Unit, Epilepsy Society, Chalfont St Peter, Buckinghamshire, United Kingdom; National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, Queen Square, London, United Kingdom
| | - Louis Lemieux
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London, United Kingdom; MRI Unit, Epilepsy Society, Chalfont St Peter, Buckinghamshire, United Kingdom.
| |
Collapse
|
10
|
Deshmukh A, Leichner J, Bae J, Song Y, Valdés-Hernández PA, Lin WC, Riera JJ. Histological Characterization of the Irritative Zones in Focal Cortical Dysplasia Using a Preclinical Rat Model. Front Cell Neurosci 2018; 12:52. [PMID: 29867355 PMCID: PMC5968101 DOI: 10.3389/fncel.2018.00052] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 02/15/2018] [Indexed: 12/19/2022] Open
Abstract
Current clinical practice in focal epilepsy involves brain source imaging (BSI) to localize brain areas where from interictal epileptiform discharges (IEDs) emerge. These areas, named irritative zones, have been useful to define candidate seizures-onset zones during pre-surgical workup. Since human histological data are mostly available from final resected zones, systematic studies characterizing pathophysiological mechanisms and abnormal molecular/cellular substrates in irritative zones—independent of them being epileptogenic—are challenging. Combining BSI and histological analysis from all types of irritative zones is only possible through the use of preclinical animal models. Here, we recorded 32-channel spontaneous electroencephalographic data from rats that have focal cortical dysplasia (FCD) and chronic seizures. BSI for different IED subtypes was performed using the methodology presented in Bae et al. (2015). Post-mortem brain sections containing irritative zones were stained to quantify anatomical, functional, and inflammatory biomarkers specific for epileptogenesis, and the results were compared with those obtained using the contralateral healthy brain tissue. We found abnormal anatomical structures in all irritative zones (i.e., larger neuronal processes, glioreactivity, and vascular cuffing) and larger expressions for neurotransmission (i.e., NR2B) and inflammation (i.e., ILβ1, TNFα and HMGB1). We conclude that irritative zones in this rat preclinical model of FCD comprise abnormal tissues disregarding whether they are actually involved in icto-genesis or not. We hypothesize that seizure perpetuation happens gradually; hence, our results could support the use of IED-based BSI for the early diagnosis and preventive treatment of potential epileptic foci. Further verifications in humans are yet needed.
Collapse
Affiliation(s)
- Abhay Deshmukh
- Neuronal Mass Dynamics Laboratory, Department of Biomedical Engineering, Florida International University, Miami, FL, United States
| | - Jared Leichner
- Neuronal Mass Dynamics Laboratory, Department of Biomedical Engineering, Florida International University, Miami, FL, United States
| | - Jihye Bae
- Neuronal Mass Dynamics Laboratory, Department of Biomedical Engineering, Florida International University, Miami, FL, United States
| | - Yinchen Song
- Neuronal Mass Dynamics Laboratory, Department of Biomedical Engineering, Florida International University, Miami, FL, United States
| | - Pedro A Valdés-Hernández
- Neuronal Mass Dynamics Laboratory, Department of Biomedical Engineering, Florida International University, Miami, FL, United States
| | - Wei-Chiang Lin
- Neuronal Mass Dynamics Laboratory, Department of Biomedical Engineering, Florida International University, Miami, FL, United States
| | - Jorge J Riera
- Neuronal Mass Dynamics Laboratory, Department of Biomedical Engineering, Florida International University, Miami, FL, United States
| |
Collapse
|
11
|
Sharma NK, Pedreira C, Centeno M, Chaudhary UJ, Wehner T, França LGS, Yadee T, Murta T, Leite M, Vos SB, Ourselin S, Diehl B, Lemieux L. A novel scheme for the validation of an automated classification method for epileptic spikes by comparison with multiple observers. Clin Neurophysiol 2017; 128:1246-1254. [PMID: 28531810 PMCID: PMC5476904 DOI: 10.1016/j.clinph.2017.04.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Revised: 04/07/2017] [Accepted: 04/19/2017] [Indexed: 11/07/2022]
Abstract
We created a validation method for the evaluation of automated classification of interictal spikes. We used a modified version of Wave_clus (WC) to automatically classify the data of 5 patients. WC classification was similar to EEG reviewers providing an unbiased evaluation of the clinical data.
Objective To validate the application of an automated neuronal spike classification algorithm, Wave_clus (WC), on interictal epileptiform discharges (IED) obtained from human intracranial EEG (icEEG) data. Method Five 10-min segments of icEEG recorded in 5 patients were used. WC and three expert EEG reviewers independently classified one hundred IED events into IED classes or non-IEDs. First, we determined whether WC-human agreement variability falls within inter-reviewer agreement variability by calculating the variation of information for each classifier pair and quantifying the overlap between all WC-reviewer and all reviewer-reviewer pairs. Second, we compared WC and EEG reviewers’ spike identification and individual spike class labels visually and quantitatively. Results The overlap between all WC-human pairs and all human pairs was >80% for 3/5 patients and >58% for the other 2 patients demonstrating WC falling within inter-human variation. The average sensitivity of spike marking for WC was 91% and >87% for all three EEG reviewers. Finally, there was a strong visual and quantitative similarity between WC and EEG reviewers. Conclusions WC performance is indistinguishable to that of EEG reviewers’ suggesting it could be a valid clinical tool for the assessment of IEDs. Significance WC can be used to provide quantitative analysis of epileptic spikes.
Collapse
Affiliation(s)
- Niraj K Sharma
- Dept. of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, United Kingdom.
| | - Carlos Pedreira
- Dept. of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Maria Centeno
- Dept. of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, United Kingdom
| | - Umair J Chaudhary
- Dept. of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, United Kingdom
| | - Tim Wehner
- Dept. of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, United Kingdom
| | - Lucas G S França
- Dept. of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, United Kingdom
| | - Tinonkorn Yadee
- Dept. of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, United Kingdom
| | - Teresa Murta
- Dept. of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, United Kingdom
| | - Marco Leite
- Dept. of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, United Kingdom
| | - Sjoerd B Vos
- Dept. of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, United Kingdom; Translational Imaging Group, Centre for Medical Image Computing, UCL, London, United Kingdom
| | - Sebastien Ourselin
- Dept. of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, United Kingdom; Translational Imaging Group, Centre for Medical Image Computing, UCL, London, United Kingdom; Dementia Research Centre, UCL Institute of Neurology, London, United Kingdom
| | - Beate Diehl
- Dept. of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, United Kingdom
| | - Louis Lemieux
- Dept. of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, United Kingdom
| |
Collapse
|
12
|
Jalili M. Graph theoretical analysis of Alzheimer's disease: Discrimination of AD patients from healthy subjects. Inf Sci (N Y) 2017. [DOI: 10.1016/j.ins.2016.08.047] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
13
|
Perescis MFJ, de Bruin N, Heijink L, Kruse C, Vinogradova L, Lüttjohann A, van Luijtelaar G, van Rijn CM. Cannabinoid antagonist SLV326 induces convulsive seizures and changes in the interictal EEG in rats. PLoS One 2017; 12:e0165363. [PMID: 28151935 PMCID: PMC5289424 DOI: 10.1371/journal.pone.0165363] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Accepted: 10/11/2016] [Indexed: 11/18/2022] Open
Abstract
Cannabinoid CB1 antagonists have been investigated for possible treatment of e.g. obesity-related disorders. However, clinical application was halted due to their symptoms of anxiety and depression. In addition to these adverse effects, we have shown earlier that chronic treatment with the CB1 antagonist rimonabant may induce EEG-confirmed convulsive seizures. In a regulatory repeat-dose toxicity study violent episodes of “muscle spasms” were observed in Wistar rats, daily dosed with the CB1 receptor antagonist SLV326 during 5 months. The aim of the present follow-up study was to investigate whether these violent movements were of an epileptic origin. In selected SLV326-treated and control animals, EEG and behavior were monitored for 24 hours. 25% of SLV326 treated animals showed 1 to 21 EEG-confirmed generalized convulsive seizures, whereas controls were seizure-free. The behavioral seizures were typical for a limbic origin. Moreover, interictal spikes were found in 38% of treated animals. The frequency spectrum of the interictal EEG of the treated rats showed a lower theta peak frequency, as well as lower gamma power compared to the controls. These frequency changes were state-dependent: they were only found during high locomotor activity. It is concluded that long term blockade of the endogenous cannabinoid system can provoke limbic seizures in otherwise healthy rats. Additionally, SLV326 alters the frequency spectrum of the EEG when rats are highly active, suggesting effects on complex behavior and cognition.
Collapse
Affiliation(s)
- Martin F. J. Perescis
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- HAS University of Applied Sciences, ‘s-Hertogenbosch, The Netherlands
- * E-mail:
| | - Natasja de Bruin
- Abbott Healthcare Products BV (formerly Solvay Pharmaceuticals), Weesp, The Netherlands
| | - Liesbeth Heijink
- Abbott Healthcare Products BV (formerly Solvay Pharmaceuticals), Weesp, The Netherlands
| | - Chris Kruse
- Abbott Healthcare Products BV (formerly Solvay Pharmaceuticals), Weesp, The Netherlands
| | - Lyudmila Vinogradova
- Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, Moscow, Russia
| | - Annika Lüttjohann
- Institut für Physiologie I, Westfälische Wilhelms Universität Münster, Münster, Germany
| | - Gilles van Luijtelaar
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Clementina M. van Rijn
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| |
Collapse
|
14
|
Körbl K, Jacobs J, Herbst M, Zaitsev M, Schulze-Bonhage A, Hennig J, LeVan P. Marker-based ballistocardiographic artifact correction improves spike identification in EEG-fMRI of focal epilepsy patients. Clin Neurophysiol 2016; 127:2802-2811. [PMID: 27417056 DOI: 10.1016/j.clinph.2016.05.361] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Revised: 04/20/2016] [Accepted: 05/22/2016] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Ballistocardiographic (BCG) artifacts resemble interictal epileptic discharges (IEDs) and can lead to incorrect IED identification in EEG-fMRI. This study investigates IEDs marked in EEGs corrected using information from a moiré phase tracking (MPT) marker. METHODS EEG-fMRI from 18 patients was processed with conventional methods for BCG removal, while 9 patients used a MPT marker. IEDs were marked first without ECG information. In a second review, suspicious IEDs synchronous with the BCG were discarded. After each review, an event-related fMRI analysis was performed on the marked IEDs. RESULTS No difference was found in the proportion of suspicious IEDs in the 2 patient groups. However, the distribution of IED timings was significantly related to the cardiac cycle in 11 of 18 patients recorded without MPT marker, but only 2 of 9 patients with marker. In patients recorded without marker, failing to discard suspicious IEDs led to more inaccurate fMRI maps and more distant activations. CONCLUSIONS BCG artifact correction based on MPT recordings allowed a more straightforward identification of IEDs that did not require ECG information in the large majority of patients. SIGNIFICANCE Marker-based ballistocardiographic artifact correction greatly facilitates the study of the generators of interictal discharges with EEG-fMRI.
Collapse
Affiliation(s)
- Katharina Körbl
- Dept. Neuropediatrics and Muscular Diseases, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany; Dept. Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany.
| | - Julia Jacobs
- Dept. Neuropediatrics and Muscular Diseases, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany; Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Michael Herbst
- Dept. Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany; Dept. Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, USA
| | - Maxim Zaitsev
- Dept. Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Jürgen Hennig
- Dept. Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Pierre LeVan
- Dept. Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| |
Collapse
|
15
|
Coan AC, Chaudhary UJ, Grouiller F, Campos BM, Perani S, De Ciantis A, Vulliemoz S, Diehl B, Beltramini GC, Carmichael DW, Thornton RC, Covolan RJ, Cendes F, Lemieux L. EEG-fMRI in the presurgical evaluation of temporal lobe epilepsy. J Neurol Neurosurg Psychiatry 2016. [PMID: 26216941 DOI: 10.1136/jnnp-2015-310401] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
OBJECTIVE Drug-resistant temporal lobe epilepsy (TLE) often requires thorough investigation to define the epileptogenic zone for surgical treatment. We used simultaneous interictal scalp EEG-fMRI to evaluate its value for predicting long-term postsurgical outcome. METHODS 30 patients undergoing presurgical evaluation and proceeding to temporal lobe (TL) resection were studied. Interictal epileptiform discharges (IEDs) were identified on intra-MRI EEG and used to build a model of haemodynamic changes. In addition, topographic electroencephalographic correlation maps were calculated between the average IED during video-EEG and intra-MRI EEG, and used as a condition. This allowed the analysis of all data irrespective of the presence of IED on intra-MRI EEG. Mean follow-up after surgery was 46 months. International League Against Epilepsy (ILAE) outcomes 1 and 2 were considered good, and 3-6 poor, surgical outcome. Haemodynamic maps were classified according to the presence (Concordant) or absence (Discordant) of Blood Oxygen Level-Dependent (BOLD) change in the TL overlapping with the surgical resection. RESULTS The proportion of patients with good surgical outcome was significantly higher (13/16; 81%) in the Concordant than in the Discordant group (3/14; 21%) (χ(2) test, Yates correction, p=0.003) and multivariate analysis showed that Concordant BOLD maps were independently related to good surgical outcome (p=0.007). Sensitivity and specificity of EEG-fMRI results to identify patients with good surgical outcome were 81% and 79%, respectively, and positive and negative predictive values were 81% and 79%, respectively. INTERPRETATION The presence of significant BOLD changes in the area of resection on interictal EEG-fMRI in patients with TLE retrospectively confirmed the epileptogenic zone. Surgical resection including regions of haemodynamic changes in the TL may lead to better postoperative outcome.
Collapse
Affiliation(s)
- Ana C Coan
- Neuroimaging Laboratory, Department of Neurology, University of Campinas, Campinas, Brazil
| | - Umair J Chaudhary
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, London, UK MRI Unit, Epilepsy Society, Chalfont St Peter, Buckinghamshire, UK
| | - Frédéric Grouiller
- Department of Radiology and Medical Informatics, Geneva University Hospitals, Geneva, Switzerland
| | - Brunno M Campos
- Neuroimaging Laboratory, Department of Neurology, University of Campinas, Campinas, Brazil
| | - Suejen Perani
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, London, UK MRI Unit, Epilepsy Society, Chalfont St Peter, Buckinghamshire, UK
| | - Alessio De Ciantis
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, London, UK MRI Unit, Epilepsy Society, Chalfont St Peter, Buckinghamshire, UK
| | - Serge Vulliemoz
- EEG and Epilepsy Unit and Functional Brain Mapping Laboratory, Neurology Department, University Hospitals and Faculty of Medicine of University of Geneva, Geneva, Switzerland
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, London, UK MRI Unit, Epilepsy Society, Chalfont St Peter, Buckinghamshire, UK
| | - Guilherme C Beltramini
- Neurophysics Group, Gleb Wataghin Physics Institute, University of Campinas, Campinas, Brazil
| | - David W Carmichael
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, London, UK MRI Unit, Epilepsy Society, Chalfont St Peter, Buckinghamshire, UK
| | - Rachel C Thornton
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, London, UK MRI Unit, Epilepsy Society, Chalfont St Peter, Buckinghamshire, UK
| | - Roberto J Covolan
- Neurophysics Group, Gleb Wataghin Physics Institute, University of Campinas, Campinas, Brazil
| | - Fernando Cendes
- Neuroimaging Laboratory, Department of Neurology, University of Campinas, Campinas, Brazil
| | - Louis Lemieux
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, London, UK MRI Unit, Epilepsy Society, Chalfont St Peter, Buckinghamshire, UK
| |
Collapse
|
16
|
Song Y, Torres RA, Garcia S, Frometa Y, Bae J, Deshmukh A, Lin WC, Zheng Y, Riera JJ. Dysfunction of Neurovascular/Metabolic Coupling in Chronic Focal Epilepsy. IEEE Trans Biomed Eng 2016; 63:97-110. [DOI: 10.1109/tbme.2015.2461496] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
17
|
Jäger V, Dümpelmann M, LeVan P, Ramantani G, Mader I, Schulze-Bonhage A, Jacobs J. Concordance of Epileptic Networks Associated with Epileptic Spikes Measured by High-Density EEG and Fast fMRI. PLoS One 2015; 10:e0140537. [PMID: 26496480 PMCID: PMC4619722 DOI: 10.1371/journal.pone.0140537] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2015] [Accepted: 09/28/2015] [Indexed: 11/18/2022] Open
Abstract
Objective The present study aims to investigate whether a newly developed fast fMRI called MREG (magnetic resonance encephalography) measures metabolic changes related to interictal epileptic discharges (IED). For this purpose BOLD changes are correlated with the IED distribution and variability. Methods Patients with focal epilepsy underwent EEG-MREG using a 64 channel cap. IED voltage maps were generated using 32 and 64 channels and compared regarding their correspondence to the BOLD response. The extents of IEDs (defined as number of channels with >50% of maximum IED negativity) were correlated with the extents of positive and negative BOLD responses. Differences in inter-spike variability were investigated between interictal epileptic discharges (IED) sets with and without concordant positive or negative BOLD responses. Results 17 patients showed 32 separate IED types. In 50% of IED types the BOLD changes could be confirmed by another independent imaging method. The IED extent significantly correlated with the positive BOLD extent (p = 0.04). In 6 patients the 64-channel EEG voltage maps better reflected the positive or negative BOLD response than the 32-channel EEG; in all others no difference was seen. Inter-spike variability was significantly lower in IED sets with than without concordant positive or negative BOLD responses (with p = 0.04). Significance Higher density EEG and fast fMRI seem to improve the value of EEG-fMRI in epilepsy. The correlation of positive BOLD and IED extent could suggest that widespread BOLD responses reflect the IED network. Inter-spike variability influences the likelihood to find IED concordant positive or negative BOLD responses, which is why single IED analysis may be promising.
Collapse
Affiliation(s)
- Vera Jäger
- Department of Neuropediatrics and Muscular Diseases, University Medical Center Freiburg, Freiburg, Germany
| | - Matthias Dümpelmann
- Section for Epileptology, University Medical Center Freiburg, Freiburg, Germany
| | - Pierre LeVan
- Medical Physics, University Medical Center Freiburg, Freiburg, Germany
| | - Georgia Ramantani
- Section for Epileptology, University Medical Center Freiburg, Freiburg, Germany
| | - Irina Mader
- Department for Neuroradiology, University Medical Center Freiburg, Freiburg, Germany
| | | | - Julia Jacobs
- Department of Neuropediatrics and Muscular Diseases, University Medical Center Freiburg, Freiburg, Germany
- * E-mail:
| |
Collapse
|
18
|
Hu Y, Mi X, Xu X, Fang W, Zeng K, Yang M, Li C, Wang S, Li M, Wang X. The Brain Activity in Brodmann Area 17: A Potential Bio-Marker to Predict Patient Responses to Antiepileptic Drugs. PLoS One 2015; 10:e0139819. [PMID: 26439500 PMCID: PMC4595505 DOI: 10.1371/journal.pone.0139819] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2014] [Accepted: 07/28/2015] [Indexed: 12/29/2022] Open
Abstract
In this study, we aimed to predict newly diagnosed patient responses to antiepileptic drugs (AEDs) using resting-state functional magnetic resonance imaging tools to explore changes in spontaneous brain activity. We recruited 21 newly diagnosed epileptic patients, 8 drug-resistant (DR) patients, 11 well-healed (WH) patients, and 13 healthy controls. After a 12-month follow-up, 11 newly diagnosed epileptic patients who showed a poor response to AEDs were placed into the seizures uncontrolled (SUC) group, while 10 patients were enrolled in the seizure-controlled (SC) group. By calculating the amplitude of fractional low-frequency fluctuations (fALFF) of blood oxygen level-dependent signals to measure brain activity during rest, we found that the SUC patients showed increased activity in the bilateral occipital lobe, particularly in the cuneus and lingual gyrus compared with the SC group and healthy controls. Interestingly, DR patients also showed increased activity in the identical cuneus and lingual gyrus regions, which comprise Brodmann's area 17 (BA17), compared with the SUC patients; however, these abnormalities were not observed in SC and WH patients. The receiver operating characteristic (ROC) curves indicated that the fALFF value of BA17 could differentiate SUC patients from SC patients and healthy controls with sufficient sensitivity and specificity prior to the administration of medication. Functional connectivity analysis was subsequently performed to evaluate the difference in connectivity between BA17 and other brain regions in the SUC, SC and control groups. Regions nearby the cuneus and lingual gyrus were found positive connectivity increased changes or positive connectivity changes with BA17 in the SUC patients, while remarkably negative connectivity increased changes or positive connectivity decreased changes were found in the SC patients. Additionally, default mode network (DMN) regions showed negative connectivity increased changes or negative changes with BA17 in the SUC patients. The abnormal increased in BA17 activity may be a key point that plays a substantial role in facilitating seizure onset.
Collapse
Affiliation(s)
- Yida Hu
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Xiujuan Mi
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Xin Xu
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Weidong Fang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Kebin Zeng
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Mingming Yang
- Department of Pediatrics, Chongqing City Hospital of Traditional Chinese Medicine, Chongqing, People’s Republic of China
| | - Chenyu Li
- Department of Neurology, Chongqing City Hospital of Traditional Chinese Medicine, Chongqing, People’s Republic of China
| | - Shasha Wang
- The Nursing Department, Chongqing Three Gorges Central Hospital, Chongqing, People’s Republic of China
| | - Minghui Li
- The Nursing Department, First Hospital of Shanxi Medical University, Taiyuan, People’s Republic of China
| | - Xuefeng Wang
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
- * E-mail:
| |
Collapse
|
19
|
Distributions of Irritative Zones Are Related to Individual Alterations of Resting-State Networks in Focal Epilepsy. PLoS One 2015; 10:e0134352. [PMID: 26226628 PMCID: PMC4520590 DOI: 10.1371/journal.pone.0134352] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2015] [Accepted: 07/09/2015] [Indexed: 01/04/2023] Open
Abstract
Alterations in the connectivity patterns of the fMRI-based resting-state networks (RSNs) have been reported in several types of epilepsies. Evidence pointed out these alterations might be associated with the genesis and propagation of interictal epileptiform discharges (IEDs). IEDs also evoke blood-oxygen-level dependent (BOLD) responses, which have been used to delineate irritative zones during preoperative work-up. Therefore, one may expect a relationship between the topology of the IED-evoked BOLD response network and the altered spatial patterns of the RSNs. In this study, we used EEG recordings and fMRI data obtained simultaneously from a chronic model of focal epilepsy in Wistar rats to verify our hypothesis. We found that IED-evoked BOLD response networks comprise both cortical and subcortical structures with a rat-dependent topology. In all rats, IEDs evoke both activation and deactivation types of BOLD responses. Using a Granger causality method, we found that in many cases areas with BOLD deactivation have directed influences on areas with activation (p<0.05). We were able to predict topological properties (i.e., focal/diffused, unilateral/bilateral) of the IED-evoked BOLD response network by performing hierarchical clustering analysis on major spatial features of the RSNs. All these results suggest that IEDs and disruptions in the RSNs found previously in humans may be different manifestations of the same transient events, probably reflecting altered consciousness. In our opinion, the shutdown of specific nodes of the default mode network may cause uncontrollable excitability in other functionally connected brain areas. We conclude that IED-evoked BOLD responses (i.e., activation and deactivation) and alterations of RSNs are intrinsically related, and speculate that an understanding of their interplay is necessary to discriminate focal epileptogenesis and network propagation phenomena across different brain modules via hub-based connectivity.
Collapse
|
20
|
Ruggieri A, Vaudano AE, Benuzzi F, Serafini M, Gessaroli G, Farinelli V, Nichelli PF, Meletti S. Mapping (and modeling) physiological movements during EEG-fMRI recordings: the added value of the video acquired simultaneously. J Neurosci Methods 2014; 239:223-37. [PMID: 25455344 DOI: 10.1016/j.jneumeth.2014.10.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2014] [Revised: 10/06/2014] [Accepted: 10/09/2014] [Indexed: 11/27/2022]
Abstract
BACKGROUND During resting-state EEG-fMRI studies in epilepsy, patients' spontaneous head-face movements occur frequently. We tested the usefulness of synchronous video recording to identify and model the fMRI changes associated with non-epileptic movements to improve sensitivity and specificity of fMRI maps related to interictal epileptiform discharges (IED). NEW METHODS Categorization of different facial/cranial movements during EEG-fMRI was obtained for 38 patients [with benign epilepsy with centro-temporal spikes (BECTS, n=16); with idiopathic generalized epilepsy (IGE, n=17); focal symptomatic/cryptogenic epilepsy (n=5)]. We compared at single subject- and at group-level the IED-related fMRI maps obtained with and without additional regressors related to spontaneous movements. As secondary aim, we considered facial movements as events of interest to test the usefulness of video information to obtain fMRI maps of the following face movements: swallowing, mouth-tongue movements, and blinking. RESULTS Video information substantially improved the identification and classification of the artifacts with respect to the EEG observation alone (mean gain of 28 events per exam). COMPARISON WITH EXISTING METHOD Inclusion of physiological activities as additional regressors in the GLM model demonstrated an increased Z-score and number of voxels of the global maxima and/or new BOLD clusters in around three quarters of the patients. Video-related fMRI maps for swallowing, mouth-tongue movements, and blinking were comparable to the ones obtained in previous task-based fMRI studies. CONCLUSIONS Video acquisition during EEG-fMRI is a useful source of information. Modeling physiological movements in EEG-fMRI studies for epilepsy will lead to more informative IED-related fMRI maps in different epileptic conditions.
Collapse
Affiliation(s)
- Andrea Ruggieri
- Department of Biomedical, Metabolic, and Neural Science, University of Modena and Reggio Emilia, NOCSAE Hospital, ASL Modena, Italy
| | - Anna Elisabetta Vaudano
- Department of Biomedical, Metabolic, and Neural Science, University of Modena and Reggio Emilia, NOCSAE Hospital, ASL Modena, Italy
| | - Francesca Benuzzi
- Department of Biomedical, Metabolic, and Neural Science, University of Modena and Reggio Emilia, NOCSAE Hospital, ASL Modena, Italy
| | | | - Giuliana Gessaroli
- Department of Biomedical, Metabolic, and Neural Science, University of Modena and Reggio Emilia, NOCSAE Hospital, ASL Modena, Italy
| | - Valentina Farinelli
- Department of Biomedical, Metabolic, and Neural Science, University of Modena and Reggio Emilia, NOCSAE Hospital, ASL Modena, Italy
| | - Paolo Frigio Nichelli
- Department of Biomedical, Metabolic, and Neural Science, University of Modena and Reggio Emilia, NOCSAE Hospital, ASL Modena, Italy
| | - Stefano Meletti
- Department of Biomedical, Metabolic, and Neural Science, University of Modena and Reggio Emilia, NOCSAE Hospital, ASL Modena, Italy.
| |
Collapse
|