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Li Y, Ran Y, Yao M, Chen Q. Altered static and dynamic functional connectivity of the default mode network across epilepsy subtypes in children: A resting-state fMRI study. Neurobiol Dis 2024; 192:106425. [PMID: 38296113 DOI: 10.1016/j.nbd.2024.106425] [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: 10/28/2023] [Revised: 01/08/2024] [Accepted: 01/27/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND Epilepsy is a chronic neurologic disorder characterized by abnormal functioning of brain networks, making it a complex research topic. Recent advancements in neuroimaging technology offer an effective approach to unraveling the intricacies of the human brain. Within different types of epilepsy, there is growing recognition regarding ongoing changes in the default mode network (DMN). However, little is known about the shared and distinct alterations of static functional connectivity (sFC) and dynamic functional connectivity (dFC) in DMN among epileptic subtypes, especially in children with epilepsy. METHODS Here, 110 children with epilepsy at a single center, including idiopathic generalized epilepsy (IGE), frontal lobe epilepsy (FLE), temporal lobe epilepsy (TLE), and parietal lobe epilepsy (PLE), as well as 84 healthy controls (HC) underwent resting-state functional magnetic resonance imaging (fMRI) scan. We investigated both sFC and dFC between groups of the DMN. RESULTS Decreased static and dynamic connectivity within the DMN subsystem were shared by all subtypes. In each epilepsy subtype, children with epilepsy displayed significant and distinct patterns of DMN connectivity compared to the control group: the IGE group showed reduced interhemispheric connectivity, the FLE group consistently demonstrated disturbances in frontal region connectivity, the TLE group exhibited significant disruptions in hippocampal connectivity, and the PLE group displayed a notable decrease in parietal-temporal connectivity within the DMN. Some state-specific FC disruptions (decreased dFC) were observed in each epilepsy subtype that cannot detect by sFC. To determine their uniqueness within specific subtypes, bootstrapping methods were employed and found the significant results (IGE: between PCC and bilateral precuneus, FLE: between right middle frontal gyrus and bilateral middle temporal gyrus, TLE: between left Hippocampus and right fusiform, PLE: between left angular and cingulate cortex). Furthermore, only children with IGE exhibited dynamic features associated with clinical variables. CONCLUSIONS Our findings highlight both shared and distinct FC alterations within the DMN in children with different types of epilepsy. Furthermore, our work provides a novel perspective on the functional alterations in the DMN of pediatric patients, suggesting that combined sFC and dFC analysis can provide valuable insights for deepening our understanding of the neuronal mechanism underlying epilepsy in children.
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Affiliation(s)
- Yongxin Li
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China.
| | - Yun Ran
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Maohua Yao
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Qian Chen
- Department of Pediatric Neurosurgery, Shenzhen Children's Hospital, Shenzhen, China
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2
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Lu M, Guo Z, Gao Z. Effect of intracranial electrical stimulation on dynamic functional connectivity in medically refractory epilepsy. Front Hum Neurosci 2023; 17:1295326. [PMID: 38178992 PMCID: PMC10765510 DOI: 10.3389/fnhum.2023.1295326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 11/21/2023] [Indexed: 01/06/2024] Open
Abstract
Objective The objective of this study was to explore the distributed network effects of intracranial electrical stimulation in patients with medically refractory epilepsy using dynamic functional connectivity (dFC) and graph indicators. Methods The time-varying connectivity patterns of dFC (state-based metrics) as well as topological properties of static functional connectivity (sFC) and dFC (graph indicators) were assessed before and after the intracranial electrical stimulation. The sliding window method and k-means clustering were used for the analysis of dFC states, which were characterized by connectivity strength, occupancy rate, dwell time, and transition. Graph indicators for sFC and dFC were obtained using group statistical tests. Results DFCs were clustered into two connectivity configurations: a strongly connected state (state 1) and a sparsely connected state (state 2). After electrical stimulation, the dwell time and occupancy rate of state 1 decreased, while that of state 2 increased. Connectivity strengths of both state 1 and state 2 decreased. For graph indicators, the clustering coefficient, k-core, global efficiency, and local efficiency of patients showed a significant decrease, but the brain networks of patients exhibited higher modularity after electrical stimulation. Especially, for state 1, there was a significant decrease in functional connectivity strength after stimulation within and between the frontal lobe and temporary lobe, both of which are associated with the seizure onset. Conclusion Our findings demonstrated that intracranial electrical stimulation significantly changed the time-varying connectivity patterns and graph indicators of the brain in patients with medically refractory epilepsy. Specifically, the electrical stimulation decreased functional connectivity strength in both local-level and global-level networks. This might provide a mechanism of understanding for the distributed network effects of intracranial electrical stimulation and extend the knowledge of the pathophysiological network of medically refractory epilepsy.
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Affiliation(s)
- Meili Lu
- School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China
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3
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Li Y, Ran Y, Chen Q. Abnormal static and dynamic functional network connectivity of the whole-brain in children with generalized tonic-clonic seizures. Front Neurosci 2023; 17:1236696. [PMID: 37670842 PMCID: PMC10475552 DOI: 10.3389/fnins.2023.1236696] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 08/07/2023] [Indexed: 09/07/2023] Open
Abstract
Introduction Generalized tonic-clonic seizures (GTCS) are a subtype of generalized seizures exhibiting bursts of bilaterally synchronous generalized spike-wave discharges. Numerous neuroimaging studies have reported aberrant functional activity and topological organization of brain network in epilepsy patients with GTCS, but most studies have focused on adults. However, the effect of GTCS on the spatial and temporal properties of brain function in children remains unclear. The present study aimed to explore whole-brain static (sFC) and dynamic functional connectivity (dFC) in children with GTCS. Methods Twenty-three children with GTCS and 32 matched healthy controls (HCs) were recruited for the present study. Resting-state functional magnetic resonance imaging (MRI) data were collected for each subject. The group independent component analysis method was used to obtain independent components (ICs). Then, sFC and dFC methods were applied and the differences in functional connectivity (FC) were compared between the children with GTCS and the HCs. Additionally, we investigated the correlations between the dFC indicators and epilepsy duration. Results Compared to HCs, GTCS patients exhibited a significant decrease in sFC strengths among most networks. The K-means clustering method was implemented for dFC analysis, and the optimal number of clusters was estimated: two discrete connectivity configurations, State 1 (strong connection) and State 2 (weak connection). The decreased dFC mainly occurred in State 1, especially the dFC between the visual network (VIS) and somatomotor network (SMN); but the increased dFC mainly occurred in State 2 among most networks in GTCS children. In addition, GTCS children showed significantly shorter mean dwell time and lower fractional windows in stronger connected State 1, while GTCS children showed significantly longer mean dwell time in weaker connected State 2. In addition, the dFC properties, including mean dwell time and fractional windows, were significantly correlated with epilepsy duration. Conclusion Our results indicated that GTCS epilepsy not only alters the connectivity strength but also changes the temporal properties of connectivity in networks in the whole brain. These findings also emphasized the differences in sFC and dFC in children with GTCS. Combining sFC and dFC methods may provide more comprehensive understanding of the abnormal changes in brain architecture in children with GTCS.
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Affiliation(s)
- Yongxin Li
- Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Yun Ran
- Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Qian Chen
- Department of Pediatric Neurosurgery, Shenzhen Children’s Hospital, Shenzhen, China
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4
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Fischer D, Rapalino O, Fecchio M, Edlow BL. Ictal fMRI: Mapping Seizure Topography with Rhythmic BOLD Oscillations. Brain Sci 2022; 12:1710. [PMID: 36552169 PMCID: PMC9775283 DOI: 10.3390/brainsci12121710] [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: 10/24/2022] [Revised: 12/06/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) has shown elevations in the blood-oxygen-level-dependent (BOLD) signal associated with, but insensitive for, seizure. Rather than evaluating absolute BOLD signal elevations, assessing rhythmic oscillations in the BOLD signal with fMRI may improve the accuracy of seizure mapping. We report a case of a patient with non-convulsive, right hemispheric seizures who underwent fMRI. Unbiased processing methods revealed a map of rhythmically oscillating BOLD signal over the cortical region affected by seizure, and synchronous BOLD signal in the contralateral cerebellum. High-resolution fMRI may help identify the spatial topography of seizure and provide insights into seizure physiology.
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Affiliation(s)
- David Fischer
- Division of Neurocritical Care, Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Otto Rapalino
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02129, USA
| | - Matteo Fecchio
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Brian L. Edlow
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
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5
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Lee YJ, Bae H, Byun JC, Kwon S, Oh SS, Kim S. Clinical Usefulness of Simultaneous Electroencephalography and Functional Magnetic Resonance Imaging in Children With Focal Epilepsy. J Clin Neurol 2022; 18:535-546. [PMID: 36062771 PMCID: PMC9444567 DOI: 10.3988/jcn.2022.18.5.535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 04/04/2022] [Accepted: 04/04/2022] [Indexed: 11/17/2022] Open
Abstract
Background and Purpose The current study analyzed the interictal epileptiform discharge (IED)-related hemodynamic response and aimed to determine the clinical usefulness of simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) in defining the epileptogenic zone (EZ) in children with focal epilepsy. Methods Patients with focal epilepsy showing IEDs on conventional EEG were evaluated using EEG-fMRI. Statistical analyses were performed using the times of spike as events modeled with multiple hemodynamic response functions. The area showing the most significant t-value for blood-oxygen-level-dependent (BOLD) changes was compared with the presumed EZ. Moreover, BOLD responses between -9 and +9 s around the spike times were analyzed to track the hemodynamic response patterns over time. Results Half (n=13) of 26 EEG-fMRI investigations of 19 patients were successful. Two patients showed 2 different types of spikes, resulting in 15 analyses. The maximum BOLD response was concordant with the EZ in 11 (73.3%) of the 15 analyses. In 10 (66.7%) analyses, the BOLD response localized the EZs more specifically. Focal BOLD responses in the EZs occurred before IEDs in 11 analyses and were often widespread after IEDs. Hemodynamic response patterns were consistent in the same epilepsy syndrome or when repeating the investigation in the same patients. Conclusions EEG-fMRI can provide additional information for localizing the EZ in children with focal epilepsy, and also reveal the pathogenesis of pediatric epilepsy by evaluating the patterns in the hemodynamic response across time windows of IEDs.
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Affiliation(s)
- Yun Jeong Lee
- Department of Pediatrics, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, Korea
| | - Hyunwoo Bae
- Department of Pediatrics, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, Korea
| | - Jun Chul Byun
- Department of Pediatrics, Keimyung University Dongsan Hospital, Keimyung University School of Medicine, Daegu, Korea
| | - Soonhak Kwon
- Department of Pediatrics, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, Korea
| | - Sung Suk Oh
- Medical Device Development Center, Daegu-Gyeongbuk Medical Innovation Foundation (K-MEDI hub), Daegu, Korea.
| | - Saeyoon Kim
- Department of Pediatrics, Yeungnam University Medical Center, Yeungnam University College of Medicine, Daegu, Korea.
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6
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Li Y, Wang J, Wang X, Chen Q, Qin B, Chen J. Reconfiguration of static and dynamic thalamo-cortical network functional connectivity of epileptic children with generalized tonic-clonic seizures. Front Neurosci 2022; 16:953356. [PMID: 35937891 PMCID: PMC9353948 DOI: 10.3389/fnins.2022.953356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 06/24/2022] [Indexed: 12/05/2022] Open
Abstract
Objective A number of studies in adults and children with generalized tonic-clonic seizure (GTCS) have reported the alterations in morphometry, functional activity, and functional connectivity (FC) in the thalamus. However, the neural mechanisms underlying the alterations in the thalamus of patients with GTCS are not well understood, particularly in children. The aim of the current study was to explore the temporal properties of functional pathways connecting thalamus in children with GTCS. Methods Here, we recruited 24 children with GTCS and 36 age-matched healthy controls. Static and dynamic FC approaches were used to evaluate alterations in the temporal variability of thalamo-cortical networks in children with GTCS. The dynamic effective connectivity (dEC) method was also used to evaluate the directions of the fluctuations in effective connectivity. In addition, the relationships between the dynamic properties and clinical features were assessed. Results The static FC analysis presented significantly decreased connectivity patterns between the bilateral thalamus and between the thalamus and right inferior temporal gyrus. The dynamic connectivity analysis found decreased FC variability in the thalamo-cortical network of children with epilepsy. Dynamic EC analyses identified increased connectivity variability from the frontal gyrus to the bilateral thalamus, and decreased connectivity variability from the right thalamus to the left thalamus and from the right thalamus to the right superior parietal lobe. In addition, correlation analysis revealed that both static FC and connectivity temporal variability in the thalamo-cortical network related to the clinical features (epilepsy duration and epilepsy onset time). Significance Our findings of both increased and decreased connectivity variability in the thalamo-cortical network imply a dynamic restructuring of the functional pathways connecting the thalamus in children with GTCS. These alterations in static and temporal dynamic pathways connecting the bilateral thalamus may extend our understanding of the neural mechanisms underlying the GTCS in children.
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Affiliation(s)
- Yongxin Li
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
- *Correspondence: Yongxin Li,
| | - Jianping Wang
- The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiao Wang
- Epilepsy Center and Department of Neurosurgery, The First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Qian Chen
- Department of Pediatric Neurosurgery, Shenzhen Children’s Hospital, Shenzhen, China
| | - Bing Qin
- Epilepsy Center and Department of Neurosurgery, The First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Jiaxu Chen
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
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7
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Ikemoto S, von Ellenrieder N, Gotman J. EEG-fMRI of epileptiform discharges: non-invasive investigation of the whole brain. Epilepsia 2022; 63:2725-2744. [PMID: 35822919 DOI: 10.1111/epi.17364] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 07/08/2022] [Accepted: 07/11/2022] [Indexed: 02/01/2023]
Abstract
Simultaneous EEG-fMRI is a unique and non-invasive method for investigating epileptic activity. Interictal epileptiform discharge-related EEG-fMRI provides cortical and subcortical blood oxygen level-dependent (BOLD) signal changes specific to epileptic discharges. As a result, EEG-fMRI has revealed insights into generators and networks involved in epileptic activity in different types of epilepsy, demonstrating-for instance-the implication of the thalamus in human generalized spike and wave discharges and the role of the Default Mode Network (DMN) in absences and focal epilepsy, and proposed a mechanism for the cortico-subcortical interactions in Lennox-Gastaut syndrome discharges. EEG-fMRI can find deep sources of epileptic activity not available to scalp EEG or MEG and provides critical new information to delineate the epileptic focus when considering surgical treatment or electrode implantation. In recent years, methodological advances, such as artifact removal and automatic detection of events have rendered this method easier to implement, and its clinical potential has since been established by evidence of the impact of BOLD response on clinical decision-making and of the relationship between concordance of BOLD responses with extent of resection and surgical outcome. This review presents the recent developments in EEG-fMRI methodology and EEG-fMRI studies in different types of epileptic disorders as follows: EEG-fMRI acquisition, gradient and pulse artifact removal, statistical analysis, clinical applications, pre-surgical evaluation, altered physiological state in generalized genetic epilepsy, and pediatric EEG-fMRI studies.
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Affiliation(s)
- Satoru Ikemoto
- Montreal Neurological Institute and Hospital, 3801 Rue University, Montreal, QC, Canada.,The Jikei University School of Medicine, Department of Pediatrics, 3-25-8 Nishi-Shimbashi, Minato-ku, Tokyo, Japan
| | | | - Jean Gotman
- Montreal Neurological Institute and Hospital, 3801 Rue University, Montreal, QC, Canada
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8
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Sainburg LE, Little AA, Johnson GW, Janson AP, Levine KK, González HFJ, Rogers BP, Chang C, Englot DJ, Morgan VL. Characterization of resting functional MRI activity alterations across epileptic foci and networks. Cereb Cortex 2022; 32:5555-5568. [PMID: 35149867 PMCID: PMC9753043 DOI: 10.1093/cercor/bhac035] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 01/19/2022] [Accepted: 01/24/2022] [Indexed: 01/25/2023] Open
Abstract
Brain network alterations have been studied extensively in patients with mesial temporal lobe epilepsy (mTLE) and other focal epilepsies using resting-state functional magnetic resonance imaging (fMRI). However, little has been done to characterize the basic fMRI signal alterations caused by focal epilepsy. Here, we characterize how mTLE affects the fMRI signal in epileptic foci and networks. Resting-state fMRI and diffusion MRI were collected from 47 unilateral mTLE patients and 96 healthy controls. FMRI activity, quantified by amplitude of low-frequency fluctuations, was increased in the epileptic focus and connected regions in mTLE. Evidence for spread of this epileptic fMRI activity was found through linear relationships of regional activity across subjects, the association of these relationships with functional connectivity, and increased activity along white matter tracts. These fMRI activity increases were found to be dependent on the epileptic focus, where the activity was related to disease severity, suggesting the focus to be the origin of these pathological alterations. Furthermore, we found fMRI activity decreases in the default mode network of right mTLE patients with different properties than the activity increases found in the epileptic focus. This work provides insights into basic fMRI signal alterations and their potential spread across networks in focal epilepsy.
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Affiliation(s)
- Lucas E Sainburg
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37212, USA,Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Aubrey A Little
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Graham W Johnson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37212, USA,Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Andrew P Janson
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Kaela K Levine
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Hernán F J González
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37212, USA,Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Baxter P Rogers
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37212, USA,Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Catie Chang
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37212, USA,Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37212, USA,Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37212, USA
| | - Dario J Englot
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37212, USA,Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37212, USA,Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37212, USA,Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Victoria L Morgan
- Corresponding author: Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, R0102 Medical Center North, 1161 21st Ave South, Nashville, TN 37232, USA.
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9
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Vaudano AE, Mirandola L, Talami F, Giovannini G, Monti G, Riguzzi P, Volpi L, Michelucci R, Bisulli F, Pasini E, Tinuper P, Di Vito L, Gessaroli G, Malagoli M, Pavesi G, Cardinale F, Tassi L, Lemieux L, Meletti S. fMRI-Based Effective Connectivity in Surgical Remediable Epilepsies: A Pilot Study. Brain Topogr 2021; 34:632-650. [PMID: 34152513 DOI: 10.1007/s10548-021-00857-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 06/13/2021] [Indexed: 11/24/2022]
Abstract
Simultaneous EEG-fMRI can contribute to identify the epileptogenic zone (EZ) in focal epilepsies. However, fMRI maps related to Interictal Epileptiform Discharges (IED) commonly show multiple regions of signal change rather than focal ones. Dynamic causal modeling (DCM) can estimate effective connectivity, i.e. the causal effects exerted by one brain region over another, based on fMRI data. Here, we employed DCM on fMRI data in 10 focal epilepsy patients with multiple IED-related regions of BOLD signal change, to test whether this approach can help the localization process of EZ. For each subject, a family of competing deterministic, plausible DCM models were constructed using IED as autonomous input at each node, one at time. The DCM findings were compared to the presurgical evaluation results and classified as: "Concordant" if the node identified by DCM matches the presumed focus, "Discordant" if the node is distant from the presumed focus, or "Inconclusive" (no statistically significant result). Furthermore, patients who subsequently underwent intracranial EEG recordings or surgery were considered as having an independent validation of DCM results. The effective connectivity focus identified using DCM was Concordant in 7 patients, Discordant in two cases and Inconclusive in one. In four of the 6 patients operated, the DCM findings were validated. Notably, the two Discordant and Invalidated results were found in patients with poor surgical outcome. Our findings provide preliminary evidence to support the applicability of DCM on fMRI data to investigate the epileptic networks in focal epilepsy and, particularly, to identify the EZ in complex cases.
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Affiliation(s)
- A E Vaudano
- Neurology Unit, OCB Hospital, Azienda Ospedaliero-Universitaria of Modena, Via Giardini 1355, 41100, Modena, Italy. .,Center for Neuroscience and Neurotechnology, Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.
| | - L Mirandola
- Center for Neuroscience and Neurotechnology, Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - F Talami
- Center for Neuroscience and Neurotechnology, Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - G Giovannini
- Neurology Unit, OCB Hospital, Azienda Ospedaliero-Universitaria of Modena, Via Giardini 1355, 41100, Modena, Italy.,Center for Neuroscience and Neurotechnology, Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - G Monti
- Neurology Unit, AUSL Modena, Ospedale Ramazzini, Carpi, MO, Italy
| | - P Riguzzi
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Unit of Neurology, Bellaria Hospital, Bologna, Italy
| | - L Volpi
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Unit of Neurology, Bellaria Hospital, Bologna, Italy
| | - R Michelucci
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Unit of Neurology, Bellaria Hospital, Bologna, Italy
| | - F Bisulli
- Department of Biomedical and NeuroMotor Sciences (DIBINEM), University of Bologna, Bologna, Italy.,IRCCS Istituto delle Scienze Neurologiche di Bologna, Epilepsy Center (Reference Center for Rare and Complex Epilepsies - EpiCARE), Bologna, Italy
| | - E Pasini
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Unit of Neurology, Bellaria Hospital, Bologna, Italy
| | - P Tinuper
- Department of Biomedical and NeuroMotor Sciences (DIBINEM), University of Bologna, Bologna, Italy.,IRCCS Istituto delle Scienze Neurologiche di Bologna, Epilepsy Center (Reference Center for Rare and Complex Epilepsies - EpiCARE), Bologna, Italy
| | - L Di Vito
- Department of Biomedical and NeuroMotor Sciences (DIBINEM), University of Bologna, Bologna, Italy.,IRCCS Istituto delle Scienze Neurologiche di Bologna, Epilepsy Center (Reference Center for Rare and Complex Epilepsies - EpiCARE), Bologna, Italy
| | - G Gessaroli
- Neurology Unit, OCB Hospital, Azienda Ospedaliero-Universitaria of Modena, Via Giardini 1355, 41100, Modena, Italy
| | - M Malagoli
- Neuroradiology Unit, OCB Hospital, Azienda Ospedaliero-Universitaria of Modena, Modena, Italy
| | - G Pavesi
- Center for Neuroscience and Neurotechnology, Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.,Neurosurgery Unit, OCB Hospital, Azienda Ospedaliero-Universitaria of Modena, Modena, Italy
| | - F Cardinale
- "Claudio Munari" Epilepsy Surgery Center, Niguarda Hospital, Milan, Italy
| | - L Tassi
- "Claudio Munari" Epilepsy Surgery Center, Niguarda Hospital, Milan, Italy
| | - L Lemieux
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - S Meletti
- Neurology Unit, OCB Hospital, Azienda Ospedaliero-Universitaria of Modena, Via Giardini 1355, 41100, Modena, Italy.,Center for Neuroscience and Neurotechnology, Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
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10
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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.
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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
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