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Lee DA, Lee HJ, Park KM. Structural brain network analysis in occipital lobe epilepsy. BMC Neurol 2023; 23:268. [PMID: 37454057 PMCID: PMC10349483 DOI: 10.1186/s12883-023-03326-z] [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: 02/13/2023] [Accepted: 07/09/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND This study aimed to analyze the structural brain network in patients with occipital lobe epilepsy (OLE) and investigate the differences in structural brain networks between patients with OLE and healthy controls. METHODS Patients with OLE and healthy controls with normal brain MRI findings were enrolled. They underwent diffusion tensor imaging using a 3.0T MRI scanner, and we computed the network measures of global and local structural networks in patients with OLE and healthy controls using the DSI studio program. We compared network measures between the groups. RESULTS We enrolled 23 patients with OLE and 42 healthy controls. There were significant differences in the global structural network between patients with OLE and healthy controls. The assortativity coefficient (-0.0864 vs. -0.0814, p = 0.0214), mean clustering coefficient (0.0061 vs. 0.0064, p = 0.0203), global efficiency (0.0315 vs. 0.0353, p = 0.0086), and small-worldness index (0.0001 vs. 0.0001, p = 0.0175) were lower, whereas the characteristic path length (59.2724 vs. 53.4684, p = 0.0120) was higher in patients with OLE than those in the healthy controls. There were several nodes beyond the occipital lobe that showed significant differences in the local structural network between the groups. In addition, the assortativity coefficient was negatively correlated with the duration of epilepsy (r=-0.676, p = 0.001).
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Affiliation(s)
- Dong Ah Lee
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Ho-Joon Lee
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Kang Min Park
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea.
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Lucas A, Cornblath EJ, Sinha N, Caciagli L, Hadar P, Tranquille A, Stein JM, Das S, Davis KA. Improved Seizure Onset-Zone Lateralization in Temporal Lobe Epilepsy using 7T Resting-State fMRI: A Direct Comparison with 3T. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.06.23291025. [PMID: 37333141 PMCID: PMC10275004 DOI: 10.1101/2023.06.06.23291025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Objective Resting-state functional magnetic resonance imaging (rs-fMRI) at ultra high-field strengths (≥7T) is known to provide superior signal-to-noise and statistical power than comparable acquisitions at lower field strengths. In this study, we aim to provide a direct comparison of the seizure onset-zone (SOZ) lateralizing ability of 7T rs-fMRI and 3T rs-fMRI. Methods We investigated a cohort of 70 temporal lobe epilepsy (TLE) patients. A paired cohort of 19 patients had 3T and 7T rs-fMRI acquisitions for direct comparison between the two field strengths. Forty-three patients had only 3T, and 8 patients had only 7T rs-fMRI acquisitions. We quantified the functional connectivity between the hippocampus and other nodes within the default mode network (DMN) using seed-to-voxel connectivity, and measured how hippocampo-DMN connectivity could inform SOZ lateralization at 7T and 3T field strengths. Results Differences between hippocampo-DMN connectivity ipsilateral and contralateral to the SOZ were significantly higher at 7T (pFDR=0.008) than at 3T (pFDR=0.80) when measured in the same subjects. We found that our ability to lateralize the SOZ, by distinguishing subjects with left TLE from subjects with right TLE, was superior at 7T (AUC = 0.97) than 3T (AUC = 0.68). Our findings were reproduced in extended cohorts of subjects scanned at either 3T or 7T. Our rs-fMRI findings at 7T, but not 3T, are consistent and highly correlated (Spearman Rho=0.65) with clinical FDG-PET lateralizing hypometabolism. Significance We show superior SOZ lateralization in TLE patients when using 7T relative to 3T rs-fMRI, supporting the adoption of high-field strength functional imaging in the epilepsy presurgical evaluation.
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Affiliation(s)
- Alfredo Lucas
- Perelman School of Medicine, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
| | | | | | | | - Peter Hadar
- Department of Neurology, Massachussets General Hospital (work conducted while at the University of Pennsylvania)
| | | | - Joel M Stein
- Department of Radiology, University of Pennsylvania
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Sala-Padro J, Gifreu-Fraixino A, Miró J, Rodriguez-Fornells A, Rico I, Plans G, Santurino M, Falip M, Càmara E. Verbal Learning and Longitudinal Hippocampal Network Connectivity in Temporal Lobe Epilepsy Surgery. Front Neurol 2022; 13:854313. [PMID: 35800085 PMCID: PMC9253296 DOI: 10.3389/fneur.2022.854313] [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: 01/13/2022] [Accepted: 05/23/2022] [Indexed: 11/24/2022] Open
Abstract
Introduction Learning new verbal information can be impaired in 20–40% of patients after mesial temporal lobe resection. In recent years, understanding epilepsy as a brain network disease, and investigating the relationship between large-scale resting networks and cognition has led to several advances. Aligned studies suggest that it is the integrity of the hippocampal connectivity with these large-scale networks what is relevant for cognition, with evidence showing a functional and structural heterogeneity along the long axis hippocampus bilaterally. Objective Our aim is to examine whether pre-operative resting-state connectivity along the long hippocampal axis is associated with verbal learning decline after anterior temporal lobe resection. Methods Thirty-one patients with epilepsy who underwent an anterior temporal lobe resection were pre-surgically scanned at 3-tesla, and pre/post-surgery evaluated for learning deficits using the Rey Auditory Verbal Learning Task (RAVLT). Eighteen controls matched by age, gender and handedness were also scanned and evaluated with the RAVLT. We studied the functional connectivity along the (anterior/posterior) long axis hippocampal subregions and resting-state functionally-defined brain networks involved in learning [executive (EXE), dorsal attention (DAN) and default-mode (DMN) networks]. Functional connectivity differences between the two groups of patients (learning intact or with learning decline) and controls were investigated with MANOVA and discriminant analysis. Results There were significant differences in the pattern of hippocampal connectivity among the groups. Regarding the anterior connectivity hippocampal pattern, our data showed an increase of connectivity in the pathological side with the DAN (p = 0.011) and the EXE (p = 0.008) when comparing learning-decline vs. learning-intact patients. Moreover, the non-pathological side showed an increase in the anterior connectivity pattern with the DAN (p = 0.027) between learning-decline vs. learning-intact patients. In contrast, the posterior hippocampus showed a reduction of connectivity in the learning-decline patients with the DMN, both in the pathological (p = 0.004) and the non-pathological sides (p = 0.036). Finally, the discriminant analysis based on the pre-operative connectivity pattern significantly differentiated the learning-decline patients from the other groups (p = 0.019). Conclusion Our findings reveal bilateral connectivity disruptions along the longitudinal axis of the hippocampi with resting-state networks, which could be key to identify those patients at risk of verbal learning decline after epilepsy surgery.
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Affiliation(s)
- Jacint Sala-Padro
- Epilepsy Unit, Hospital de Bellvitge, Barcelona, Spain
- Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
| | | | - Júlia Miró
- Epilepsy Unit, Hospital de Bellvitge, Barcelona, Spain
- Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
| | - Antoni Rodriguez-Fornells
- Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Department of Cognition, Development and Educational Science, L'Hospitalet de Llobregat, University of Barcelona, Barcelona, Spain
- Catalan Institution for Research and Advanced Studies, ICREA, Barcelona, Spain
| | | | - Gerard Plans
- Epilepsy Unit, Hospital de Bellvitge, Barcelona, Spain
| | | | - Mercè Falip
- Epilepsy Unit, Hospital de Bellvitge, Barcelona, Spain
| | - Estela Càmara
- Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Department of Cognition, Development and Educational Science, L'Hospitalet de Llobregat, University of Barcelona, Barcelona, Spain
- *Correspondence: Estela Càmara
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Prediction of seizure outcome following temporal lobectomy: a magnetoencephalography-based graph theory approach". Seizure 2022; 97:73-81. [DOI: 10.1016/j.seizure.2022.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/12/2022] [Accepted: 03/15/2022] [Indexed: 11/22/2022] Open
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Fallahi A, Pooyan M, Habibabadi JM, Hashemi-Fesharaki SS, Tabatabaei NH, Ay M, Nazem-Zadeh MR. A novel approach for extracting functional brain networks involved in mesial temporal lobe epilepsy based on self organizing maps. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100876] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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Zhou X, Zhang Z, Yu L, Fan B, Wang M, Jiang B, Su Y, Li P, Zheng J. Disturbance of functional and effective connectivity of the salience network involved in attention deficits in right temporal lobe epilepsy. Epilepsy Behav 2021; 124:108308. [PMID: 34536737 DOI: 10.1016/j.yebeh.2021.108308] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/04/2021] [Accepted: 08/23/2021] [Indexed: 12/23/2022]
Abstract
The salience network (SN) acts as a switch that generates transient control signals to regulate the executive control network (ECN) and the default mode network (DMN) and has been implicated in cognitive processes. Temporal lobe epilepsy (TLE) is usually accompanied by different types of cognitive deficits, but whether it is associated with dysfunctional connectivity of the SN remains unknown. To address this, thirty-six patients with right TLE (rTLE) and thirty-six healthy controls (HCs) were recruited for the present study. All of the participants were subjected to attention network test (ANT) and resting-state functional resonance imaging (rs-fMRI) scanning. The patient group showed deficits in attention performance. Moreover, the functional connectivity (FC) and effective connectivity (EC) were analyzed based on key SN hubs (the anterior cingulate cortex (ACC) and the bilateral anterior insula (AI)). When compared with those in the HC group, the ACC showed increased FC with the left middle frontal gyrus and the left precentral gyrus, and the right AI showed decreased FC with the right precuneus and the right superior occipital gyrus in the patient group. The EC analysis revealed an increased inflow of information from the left middle temporal gyrus to the ACC and the right AI and an increased outflow of information from the bilateral AI to the left middle frontal gyrus. Furthermore, in the correlation analysis, the abnormal EC from the right AI to the left middle temporal gyrus was positively correlated with the executive control effect. These findings demonstrated aberrant modulation of the SN in rTLE, which was particularly characterized by dysfunctional connectivity between the SN and key brain regions in the DMN and ECN. Elucidation of this effect may further contribute to the comprehensive understanding of the neural mechanisms of the SN in regard to attention deficits in patients with TLE.
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Affiliation(s)
- Xia Zhou
- Department of Neurology, the People's Hospital of Guangxi Zhuang Autonomous Region, Nanning 530021, China
| | - Zhao Zhang
- Department of Neurology, the Fifth Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
| | - Lu Yu
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
| | - Binglin Fan
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
| | - Minli Wang
- Department of Neurology, the People's Hospital of Guangxi Zhuang Autonomous Region, Nanning 530021, China
| | - Binjian Jiang
- Department of Neurology, the People's Hospital of Guangxi Zhuang Autonomous Region, Nanning 530021, China
| | - Yuying Su
- Department of Neurology, the People's Hospital of Guangxi Zhuang Autonomous Region, Nanning 530021, China
| | - Peihu Li
- Department of Neurology, the People's Hospital of Guangxi Zhuang Autonomous Region, Nanning 530021, China
| | - Jinou Zheng
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
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Comparison of multimodal findings on epileptogenic side in temporal lobe epilepsy using self-organizing maps. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2021; 35:249-266. [PMID: 34347200 DOI: 10.1007/s10334-021-00948-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 06/20/2021] [Accepted: 07/19/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVE To develop a decision-making tool to evaluate and compare the performance of neuroimaging markers with clinical findings and the significance of attributes for presurgical lateralization of mesial temporal lobe epilepsy (mTLE). METHODS Thirty-five unilateral mTLE patients who qualified as candidates for surgical resection were studied. Seizure semiology, ictal EEG, ictal epileptogenic zone, interictal-irritative zone, and MRI findings were used as clinical markers. Hippocampal T1 volumetry and FLAIR intensity, DTI estimated; mean diffusivity (MD) in the hippocampus and fractional anisotropy (FA) in posteroinferior cingulum and crus of fornix, and the output of logistic regression method on volumetrics of the hippocampus, amygdala, and thalamus were adopted as neuroimaging markers. The self-organizing map (SOM) method was applied to markers to provide predictive methods for mTLE lateralization. RESULTS The SOM clustered all clinical attributes correctly with 100% accuracy and sensitivity for both the left and right mTLE. Among the clinical markers, seizure semiology and interictal-irrelative zone are the most sensitive attribute for the left-mTLE group lateralization. The accuracy achieved by applying the SOM method to the neuroimaging attributes was 94%, while the sensitivity was achieved 90% for left and 100% for right mTLE. SOM evidence indicated that the hippocampal volume is the most sensitive attribute for the prediction of the laterality in left-mTLE groups. CONCLUSION The proposed SOM method showed that neuroimaging markers may not replace with clinical findings. Nevertheless, multimodal neuroimaging can play an effective role in preoperative lateralization to reduce the costs and risks of surgical resection.
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Ma M, Wei X, Cheng Y, Chen Z, Zhou Y. Spatiotemporal evolution of epileptic seizure based on mutual information and dynamic brain network. BMC Med Inform Decis Mak 2021; 21:80. [PMID: 34330251 PMCID: PMC8323270 DOI: 10.1186/s12911-021-01439-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 02/15/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Epilepsy was defined as an abnormal brain network model disease in the latest definition. From a microscopic perspective, it is also particularly important to observe the Mutual Information (MI) of the whole brain network based on different lead positions. METHODS In this study, we selected EEG data from representative temporal lobe and frontal lobe epilepsy patients. Based on Phase Space Reconstruction and the calculation of MI indicator, we used Complex Network technology to construct a dynamic brain network function model of epilepsy seizure. At the same time, about the analysis of our network, we described the index changes and propagation paths of epilepsy discharge in different periods, and spatially monitors the seizure change process based on the analysis of the parameter characteristics of the complex network. RESULTS Our model portrayed the functional synergy between the various regions of the brain and the state transition during the seizure process. We also characterized the EEG synchronous propagation path and core nodes during seizures. The results shown the full node change path and the distribution of important indicators during the seizure process, which makes the state change of the seizure process more clearly. CONCLUSION In this study, we have demonstrated that synchronization-based brain networks change with time and space. The EEG synchronous propagation path and core nodes during epileptic seizures can provide a reference for finding the focus area.
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Affiliation(s)
- Mengnan Ma
- School of Biomedical Engineering, Sun Yat-sen University, No. 132 Waihuan East Road, Guangzhou, 510006, China.,Department of Medical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, No. 74 Zhongshan 2nd Road, Guangzhou, 510080, China
| | - Xiaoyan Wei
- Minister of Science, Education and Data Management Department, Guangzhou Women and Children's Medical Center, National Children's Medical Center for South Central Region, Guangzhou Medical University, No. 9 Jinsui Road, Guangzhou, 510623, China
| | - Yinlin Cheng
- School of Biomedical Engineering, Sun Yat-sen University, No. 132 Waihuan East Road, Guangzhou, 510006, China.,Department of Medical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, No. 74 Zhongshan 2nd Road, Guangzhou, 510080, China
| | - Ziyi Chen
- Department of Neurology, First Affiliated Hospital, Sun Yat-sen University, No. 58 Zhongshan 2nd Road, Guangzhou, 510080, China
| | - Yi Zhou
- Department of Medical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, No. 74 Zhongshan 2nd Road, Guangzhou, 510080, China. .,Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, No. 74 Zhongshan 2nd Road, Guangzhou, 510080, China.
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Aberrant cerebral intrinsic activity and cerebro-cerebellar functional connectivity in right temporal lobe epilepsy: a resting-state functional MRI study. Neuroreport 2021; 32:1009-1016. [PMID: 34075003 DOI: 10.1097/wnr.0000000000001681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Numerous neuroimaging studies have demonstrated that functional brain aberrations are associated with cognitive impairments in temporal lobe epilepsy (TLE). Here, we aimed to investigate the neural substrates of attention deficits by combining assessment of regional intrinsic brain activities with large-scale functional connectivity in patients with right TLE (rTLE). METHODS Thirty-five patients with rTLE and 33 matched healthy controls were recruited. All participants completed the Attention Network Test (ANT) and resting-sate functional MRI (rs-fMRI) scans. The z-standardized fractional amplitude of the low-frequency fluctuation (zfALFF) approach was applied to evaluate the brain's intrinsic activity. The cerebral regions with significant zfALFF values were selected as seeds for subsequent functional connectivity analyses. A correlation analysis was performed between functional activity and clinical variables. RESULTS Compared with the healthy control group, the patients showed decreased zfALFF in the right inferior temporal gyrus and bilateral superior parietal gyrus, and the right inferior temporal gyrus exhibited increased functional connectivity with the bilateral cerebellum-6/vermis-6 and decreased functional connectivity with right superior frontal gyrus. The ANT indicated that the rTLE group exhibited attention deficits. Furthermore, a positive correlation was found between the zfALFF value of the left superior parietal gyrus and alerting performance, while a negative correlation between the zfALFF value of the right superior parietal gyrus and disease duration. CONCLUSION This study demonstrated aberrant intrinsic cerebral activity and functional connectivity in the whole brain network, which may act as responsible and compensatory factors in attention deficits, especially further profoundly illuminated the compensatory role of cerebellum in patients with rTLE.
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Sanjari Moghaddam H, Rahmani F, Aarabi MH, Nazem-Zadeh MR, Davoodi-Bojd E, Soltanian-Zadeh H. White matter microstructural differences between right and left mesial temporal lobe epilepsy. Acta Neurol Belg 2020; 120:1323-1331. [PMID: 30635771 DOI: 10.1007/s13760-019-01074-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Accepted: 01/05/2019] [Indexed: 01/20/2023]
Abstract
PURPOSE Mesial temporal lobe epilepsy (mTLE) is a chronic focal epileptic disorder characterized by recalcitrant seizures often necessitating surgical intervention. Identifying the laterality of seizure focus is crucial for pre-surgical planning. We implemented diffusion MRI (DMRI) connectometry to identify differences in white matter connectivity in patients with left and right mTLE relative to healthy control subjects. METHOD We enrolled 12 patients with right mTLE, 12 patients with left mTLE, and 12 age/sex matched healthy controls (HCs). We used DMRI connectometry to identify local connectivity patterns of white matter tracts, based on quantitative anisotropy (QA). We compared QA of white matter to reconstruct tracts with significant difference in connectivity between patients and HCs and then between patients with left and right mTLE. RESULTS Right mTLE patients show higher anisotropy in left inferior longitudinal fasciculus (ILF) and forceps minor and lower QA in genu of corpus callosum (CC), bilateral corticospinal tracts (CSTs), and bilateral middle cerebellar peduncles (MCPs) compared to HCs. Left mTLE patients show higher anisotropy in genu of CC, bilateral CSTs, and right MCP and decreased anisotropy in forceps minor compared to HCs. Compared to patients with right mTLE, left mTLE patients showed increased and decreased connectivity in some major tracts. CONCLUSIONS Our study showed the pattern of microstructural disintegrity in mTLE patients relative to HCs. We demonstrated that left and right mTLE patients have discrepant alternations in their white matter microstructure. These results may indicate that left and right mTLE have different underlying pathologic mechanisms.
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Affiliation(s)
| | - Farzaneh Rahmani
- NeuroImaging Network (NIN), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Student's Scientific Research Center (SSRC), Tehran University of Medical Sciences, Tehran, Iran
| | | | - Mohammad-Reza Nazem-Zadeh
- Research Center for Science and Technology in Medicine (RCSTIM), Tehran University of Medical Sciences, Tehran, Iran
| | - Esmaeil Davoodi-Bojd
- Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, One Ford Place, 2F, Detroit, MI, 48202, USA
| | - Hamid Soltanian-Zadeh
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, North Kargar Ave., Tehran, Iran.
- Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, One Ford Place, 2F, Detroit, MI, 48202, USA.
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Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach. Neurol Sci 2020; 42:2379-2390. [PMID: 33052576 DOI: 10.1007/s10072-020-04759-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Accepted: 09/23/2020] [Indexed: 12/11/2022]
Abstract
PURPOSE Functional magnetic resonance imaging (fMRI) in resting state can be used to evaluate the functional organization of the human brain in the absence of any task or stimulus. The functional connectivity (FC) has non-stationary nature and consented to be varying over time. By considering the dynamic characteristics of the FC and using graph theoretical analysis and a machine learning approach, we aim to identify the laterality in cases of temporal lobe epilepsy (TLE). METHODS Six global graph measures are extracted from static and dynamic functional connectivity matrices using fMRI data of 35 unilateral TLE subjects. Alterations in the time trend of the graph measures are quantified. The random forest (RF) method is used for the determination of feature importance and selection of dynamic graph features including mean, variance, skewness, kurtosis, and Shannon entropy. The selected features are used in the support vector machine (SVM) classifier to identify the left and right epileptogenic sides in patients with TLE. RESULTS Our results for the performance of SVM demonstrate that the utility of dynamic features improves the classification outcome in terms of accuracy (88.5% for dynamic features compared with 82% for static features). Selecting the best dynamic features also elevates the accuracy to 91.5%. CONCLUSION Accounting for the non-stationary characteristics of functional connectivity, dynamic connectivity analysis of graph measures along with machine learning approach can identify the temporal trend of some specific network features. These network features may be used as potential imaging markers in determining the epileptogenic hemisphere in patients with TLE.
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Pattern analysis of glucose metabolic brain data for lateralization of MRI-negative temporal lobe epilepsy. Epilepsy Res 2020; 167:106474. [PMID: 32992074 DOI: 10.1016/j.eplepsyres.2020.106474] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 08/25/2020] [Accepted: 09/17/2020] [Indexed: 11/23/2022]
Abstract
In this paper, we assessed the reliability of glucose metabolic brain data for identifying lateralization of magnetic resonance imaging (MRI)-negative temporal lobe epilepsy (TLE) patients. We designed and developed an efficacious and automatic metabolic-wise lateralization framework. The proposed lateralization framework comprises three main systematic levels. In the first stage of our investigation, we pre-processed interictal fluorodeoxyglucose positron emission tomography images to extract glucose metabolic brain data. In the second stage, we used a voxel selection method involving a feature-ranking strategy to select the most discriminative metabolic voxels. Finally, we used a support vector machine followed by a 10-fold cross-validation strategy to assess the proposed lateralization framework in 27 patients with right MRI-negative TLE and 29 patients with left MRI-negative TLE. The proposed lateralization framework achieved an excellent accuracy of 96.43 % concordance with experienced PET interpreter. Thus, we show that pattern analysis of glucose metabolic brain data can accurately lateralize MRI-negative TLE patients in the clinical setting.
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Dynamical Mechanisms of Interictal Resting-State Functional Connectivity in Epilepsy. J Neurosci 2020; 40:5572-5588. [PMID: 32513827 PMCID: PMC7363471 DOI: 10.1523/jneurosci.0905-19.2020] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2019] [Revised: 05/31/2020] [Accepted: 06/02/2020] [Indexed: 12/18/2022] Open
Abstract
Drug-resistant focal epilepsy is a large-scale brain networks disorder characterized by altered spatiotemporal patterns of functional connectivity (FC), even during interictal resting state (RS). Although RS-FC-based metrics can detect these changes, results from RS functional magnetic resonance imaging (RS-fMRI) studies are unclear and difficult to interpret, and the underlying dynamical mechanisms are still largely unknown. To better capture the RS dynamics, we phenomenologically extended the neural mass model of partial seizures, the Epileptor, by including two neuron subpopulations of epileptogenic and nonepileptogenic type, making it capable of producing physiological oscillations in addition to the epileptiform activity. Using the neuroinformatics platform The Virtual Brain, we reconstructed 14 epileptic and 5 healthy human (of either sex) brain network models (BNMs), based on individual anatomical connectivity and clinically defined epileptogenic heatmaps. Through systematic parameter exploration and fitting to neuroimaging data, we demonstrated that epileptic brains during interictal RS are associated with lower global excitability induced by a shift in the working point of the model, indicating that epileptic brains operate closer to a stable equilibrium point than healthy brains. Moreover, we showed that functional networks are unaffected by interictal spikes, corroborating previous experimental findings; additionally, we observed higher excitability in epileptogenic regions, in agreement with the data. We shed light on new dynamical mechanisms responsible for altered RS-FC in epilepsy, involving the following two key factors: (1) a shift of excitability of the whole brain leading to increased stability; and (2) a locally increased excitability in the epileptogenic regions supporting the mixture of hyperconnectivity and hypoconnectivity in these areas. SIGNIFICANCE STATEMENT Advances in functional neuroimaging provide compelling evidence for epilepsy-related brain network alterations, even during the interictal resting state (RS). However, the dynamical mechanisms underlying these changes are still elusive. To identify local and network processes behind the RS-functional connectivity (FC) spatiotemporal patterns, we systematically manipulated the local excitability and the global coupling in the virtual human epileptic patient brain network models (BNMs), complemented by the analysis of the impact of interictal spikes and fitting to the neuroimaging data. Our results suggest that a global shift of the dynamic working point of the brain model, coupled with locally hyperexcitable node dynamics of the epileptogenic networks, provides a mechanistic explanation of the epileptic processes during the interictal RS period. These, in turn, are associated with the changes in FC.
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Dumlu SN, Ademoğlu A, Sun W. Investigation of functional variability and connectivity in temporal lobe epilepsy: A resting state fMRI study. Neurosci Lett 2020; 733:135076. [PMID: 32446775 DOI: 10.1016/j.neulet.2020.135076] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Revised: 04/12/2020] [Accepted: 05/19/2020] [Indexed: 10/24/2022]
Abstract
It is crucial to reveal the variability between patients with epilepsy and healthy subjects to elucidate the underpinnings of the disease pathology. Herein, we assessed the inter-subject variability between patients with temporal lobe epilepsy (TLE) and healthy subjects in terms of estimating the functional connectivity using resting-state functional magnetic resonance (rs-fMRI) scans. According to inter-subject variability results between healthy and TLE population, the latter showed more variability mainly in frontoparietal control, default mode, dorsal/ventral attention, visual and somatomotor networks in line with the broad seizure onset and propagation pathway. As a result of 17-Network parcellation, a significant attenuation is observed in functional connectivity, mostly in bilateral frontoparietal control, somatomotor, default mode and ventral attention networks associated with the functional impairment in attention, long/short term memory, executive functioning. The results are in favor of the argument that the functional disruption in TLE spreads throughout the cortex beyond the temporal lobe with an implication of greater diversity in the TLE population.
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Affiliation(s)
- Seda Nilgün Dumlu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Institute of Biomedical Engineering, Bogazici University, Istanbul 34684, Turkey
| | - Ahmet Ademoğlu
- Institute of Biomedical Engineering, Bogazici University, Istanbul 34684, Turkey
| | - Wei Sun
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
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15
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Roger E, Pichat C, Torlay L, David O, Renard F, Banjac S, Attyé A, Minotti L, Lamalle L, Kahane P, Baciu M. Hubs disruption in mesial temporal lobe epilepsy. A resting-state fMRI study on a language-and-memory network. Hum Brain Mapp 2019; 41:779-796. [PMID: 31721361 PMCID: PMC7268007 DOI: 10.1002/hbm.24839] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 09/23/2019] [Accepted: 10/09/2019] [Indexed: 12/13/2022] Open
Abstract
Mesial temporal lobe epilepsy (mTLE) affects the brain networks at several levels and patients suffering from mTLE experience cognitive impairment for language and memory. Considering the importance of language and memory reorganization in this condition, the present study explores changes of the embedded language‐and‐memory network (LMN) in terms of functional connectivity (FC) at rest, as measured with functional MRI. We also evaluate the cognitive efficiency of the reorganization, that is, whether or not the reorganizations support or allow the maintenance of optimal cognitive functioning despite the seizure‐related damage. Data from 37 patients presenting unifocal mTLE were analyzed and compared to 48 healthy volunteers in terms of LMN‐FC using two methods: pairwise correlations (region of interest [ROI]‐to‐ROI) and graph theory. The cognitive efficiency of the LMN‐FC reorganization was measured using correlations between FC parameters and language and memory scores. Our findings revealed a large perturbation of the LMN hubs in patients. We observed a hyperconnectivity of limbic areas near the dysfunctional hippocampus and mainly a hypoconnectivity for several cortical regions remote from the dysfunctional hippocampus. The loss of FC was more important in left mTLE (L‐mTLE) than in right (R‐mTLE) patients. The LMN‐FC reorganization may not be always compensatory and not always useful for patients as it may be associated with lower cognitive performance. We discuss the different connectivity patterns obtained and conclude that interpretation of FC changes in relation to neuropsychological scores is important to determine cognitive efficiency, suggesting the concept of “connectome” would gain to be associated with a “cognitome” concept.
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Affiliation(s)
- Elise Roger
- LPNC, CNRS, UMR 5105, University Grenoble Alpes, Grenoble, France
| | - Cedric Pichat
- LPNC, CNRS, UMR 5105, University Grenoble Alpes, Grenoble, France
| | - Laurent Torlay
- LPNC, CNRS, UMR 5105, University Grenoble Alpes, Grenoble, France
| | - Olivier David
- Grenoble Institute of Neuroscience, INSERM, Brain Stimulation and System Neuroscience, University Grenoble Alpes, Grenoble, France
| | | | - Sonja Banjac
- LPNC, CNRS, UMR 5105, University Grenoble Alpes, Grenoble, France
| | | | - Lorella Minotti
- Grenoble Institute of Neuroscience, Synchronisation et Modulation des Réseaux Neuronaux dans l'Epilepsie and Neurology Department, University Grenoble Alpes, Grenoble, France
| | | | - Philippe Kahane
- Grenoble Institute of Neuroscience, Synchronisation et Modulation des Réseaux Neuronaux dans l'Epilepsie and Neurology Department, University Grenoble Alpes, Grenoble, France
| | - Monica Baciu
- LPNC, CNRS, UMR 5105, University Grenoble Alpes, Grenoble, France
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16
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Zhao X, Wu Q, Chen Y, Song X, Ni H, Ming D. Hub Patterns-Based Detection of Dynamic Functional Network Metastates in Resting State: A Test-Retest Analysis. Front Neurosci 2019; 13:856. [PMID: 31572105 PMCID: PMC6749078 DOI: 10.3389/fnins.2019.00856] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 07/30/2019] [Indexed: 11/13/2022] Open
Abstract
The spontaneous dynamic characteristics of resting-state functional networks contain much internal brain physiological or pathological information. The metastate analysis of brain functional networks is an effective technique to quantify the essence of brain functional connectome dynamics. However, the widely used functional connectivity-based metastate analysis ignored the topological structure, which could be locally reflected by node centrality. In this study, 23 healthy young volunteers (21-26 years) were recruited and scanned twice with a 1-week interval. Based on the time sequences of node centrality, we promoted a node centrality-based clustering method to find metastates of functional connectome and conducted a test-retest experiment to assess the stability of those identified metastates using the described method. The hub regions of metastates were further compared with the structural networks' organization to depict its potential relationship with brain structure. Results of extracted metastates showed repeatable dynamic features between repeated scans and high overlapping rate of hub regions with brain intrinsic sub-networks. These identified hub patterns from metastates further highly overlapped with the structural hub regions. These findings indicated that the proposed node centrality-based metastates detection method could reveal reliable and meaningful metastates of spontaneous dynamics and indicate the underlying nature of brain dynamics as well as the potential relationship between these dynamics and the organization of the brain connectome.
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Affiliation(s)
- Xin Zhao
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Qiong Wu
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Yuanyuan Chen
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Xizi Song
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Hongyan Ni
- Department of Radiology, Tianjin First Center Hospital, Tianjin, China
| | - Dong Ming
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.,Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
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Smitha KA, Arun KM, Rajesh PG, Thomas B, Radhakrishnan A, Sarma PS, Kesavadas C. Resting fMRI as an alternative for task-based fMRI for language lateralization in temporal lobe epilepsy patients: a study using independent component analysis. Neuroradiology 2019; 61:803-810. [PMID: 31020344 DOI: 10.1007/s00234-019-02209-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Accepted: 04/02/2019] [Indexed: 02/04/2023]
Abstract
PURPOSE Our aim is to investigate whether rs-fMRI can be used as an effective technique to study language lateralization. We aim to find out the most appropriate language network among different networks identified using ICA. METHODS Fifteen healthy right-handed subjects, sixteen left, and sixteen right temporal lobe epilepsy patients prospectively underwent MR scanning in 3T MRI (GE Discovery™ MR750w), using optimized imaging protocol. We obtained task-fMRI data using a visual-verb generation paradigm. Rs-fMRI and language-fMRI analysis were conducted using FSL software. Independent component analysis (ICA) was used to estimate rs-fMRI networks. Dice coefficient was calculated to examine the similarity in activated voxels of a common language template and the rs-fMRI language networks. Laterality index (LI) was calculated from the task-based language activation and rs-fMRI language network, for a range of LI thresholds at different z scores. RESULTS Measurement of hemispheric language dominance with rs-fMRI was highly concordant with task-fMRI results. Among the evaluated z scores for a range of LI thresholds, rs-fMRI yielded a maximum accuracy of 95%, a sensitivity of 83%, and specificity of 92.8% for z = 2 at 0.05 LI threshold. CONCLUSION The present study suggests that rs-fMRI networks obtained using ICA technique can be used as an alternative for task-fMRI language laterality. The novel aspect of the work is suggestive of optimal thresholds while applying rs-fMRI, is an important endeavor given that many patients with epilepsy have co-morbid cognitive deficits. Thus, an accurate method to determine language laterality without requiring a patient to complete the language task would be advantageous.
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Affiliation(s)
- K A Smitha
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, 695011, India
| | - K M Arun
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, 695011, India
| | - P G Rajesh
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, 695011, India
| | - Bejoy Thomas
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, 695011, India
| | - Ashalatha Radhakrishnan
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, 695011, India
| | - P Sankara Sarma
- Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Science and Technology, Trivandrum, Kerala, 695011, India
| | - C Kesavadas
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, 695011, India.
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Mei T, Wei X, Chen Z, Tian X, Dong N, Li D, Zhou Y. Epileptic foci localization based on mapping the synchronization of dynamic brain network. BMC Med Inform Decis Mak 2019; 19:19. [PMID: 30700279 PMCID: PMC6354332 DOI: 10.1186/s12911-019-0737-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Characterizing the synchronous changes of epileptic seizures in different stages between different regions is profound to understand the transmission pathways of epileptic brain network and epileptogenic foci. There is currently no adequate quantitative calculation method for describing the propagation pathways of electroencephalogram (EEG) signals in the brain network from the short and long term. The goal of this study is to explore the innovative method to locate epileptic foci, mapping synchronization in the brain networks based on EEG. METHODS Mutual information was used to analyze the short-term synchronization in the full electrodes; while nonlinear dynamics quantifies the statistical independencies in the long -term among all electrodes. Then graph theory based on the complex network was employed to construct a dynamic brain network for epilepsy patients when they were awake, asleep and in seizure, analyzing the changing topology indexes. RESULTS Epileptic network achieved a high degree of nonlinear synchronization compared to awake time. and the main path of epileptiform activity was revealed by searching core nodes. The core nodes of the brain network were in connection with the onset zone. Seizures always happened with a high degree of distribution. CONCLUSIONS This study indicated the path of EEG synchronous propagation in seizures, and core nodes could locate the epileptic foci accurately in some epileptic patients.
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Affiliation(s)
- Tian Mei
- Department of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.,Department of Information, Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510655, China
| | - Xiaoyan Wei
- Department of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Ziyi Chen
- Department of Neurology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Xianghua Tian
- Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830011, China
| | - Nan Dong
- Department of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Dongmei Li
- College of Public Health, Xinjiang Medical University, Urumqi, 830011, China
| | - Yi Zhou
- Department of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
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Yang M, Li J, Li Z, Yao D, Liao W, Chen H. Whole-brain functional connectome-based multivariate classification of post-stroke aphasia. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.10.094] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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20
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Smitha KA, Akhil Raja K, Arun KM, Rajesh PG, Thomas B, Kapilamoorthy TR, Kesavadas C. Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks. Neuroradiol J 2017; 30:305-317. [PMID: 28353416 DOI: 10.1177/1971400917697342] [Citation(s) in RCA: 327] [Impact Index Per Article: 46.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The inquisitiveness about what happens in the brain has been there since the beginning of humankind. Functional magnetic resonance imaging is a prominent tool which helps in the non-invasive examination, localisation as well as lateralisation of brain functions such as language, memory, etc. In recent years, there is an apparent shift in the focus of neuroscience research to studies dealing with a brain at 'resting state'. Here the spotlight is on the intrinsic activity within the brain, in the absence of any sensory or cognitive stimulus. The analyses of functional brain connectivity in the state of rest have revealed different resting state networks, which depict specific functions and varied spatial topology. However, different statistical methods have been introduced to study resting state functional magnetic resonance imaging connectivity, yet producing consistent results. In this article, we introduce the concept of resting state functional magnetic resonance imaging in detail, then discuss three most widely used methods for analysis, describe a few of the resting state networks featuring the brain regions, associated cognitive functions and clinical applications of resting state functional magnetic resonance imaging. This review aims to highlight the utility and importance of studying resting state functional magnetic resonance imaging connectivity, underlining its complementary nature to the task-based functional magnetic resonance imaging.
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Affiliation(s)
- K A Smitha
- 1 Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Science and Technology, India
| | - K Akhil Raja
- 1 Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Science and Technology, India
| | - K M Arun
- 1 Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Science and Technology, India
| | - P G Rajesh
- 2 Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, India
| | - Bejoy Thomas
- 1 Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Science and Technology, India
| | - T R Kapilamoorthy
- 1 Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Science and Technology, India
| | - Chandrasekharan Kesavadas
- 1 Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Science and Technology, India
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Liu F, Wang Y, Li M, Wang W, Li R, Zhang Z, Lu G, Chen H. Dynamic functional network connectivity in idiopathic generalized epilepsy with generalized tonic-clonic seizure. Hum Brain Mapp 2016; 38:957-973. [PMID: 27726245 DOI: 10.1002/hbm.23430] [Citation(s) in RCA: 251] [Impact Index Per Article: 31.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2016] [Revised: 09/27/2016] [Accepted: 09/28/2016] [Indexed: 12/23/2022] Open
Abstract
Idiopathic generalized epilepsy (IGE) has been linked with disrupted intra-network connectivity of multiple resting-state networks (RSNs); however, whether impairment is present in inter-network interactions between RSNs, remains largely unclear. Here, 50 patients with IGE characterized by generalized tonic-clonic seizures (GTCS) and 50 demographically matched healthy controls underwent resting-state fMRI scans. A dynamic method was implemented to investigate functional network connectivity (FNC) in patients with IGE-GTCS. Specifically, independent component analysis was first carried out to extract RSNs, and then sliding window correlation approach was employed to obtain dynamic FNC patterns. Finally, k-mean clustering was performed to characterize six discrete functional connectivity states, and state analysis was conducted to explore the potential alterations in FNC and other dynamic metrics. Our results revealed that state-specific FNC disruptions were observed in IGE-GTCS and the majority of aberrant functional connectivity manifested itself in default mode network. In addition, temporal metrics derived from state transition vectors were altered in patients including the total number of transitions across states and the mean dwell time, the fraction of time spent and the number of subjects in specific FNC state. Furthermore, the alterations were significantly correlated with disease duration and seizure frequency. It was also found that dynamic FNC could distinguish patients with IGE-GTCS from controls with an accuracy of 77.91% (P < 0.001). Taken together, this study not only provided novel insights into the pathophysiological mechanisms of IGE-GTCS but also suggested that the dynamic FNC analysis was a promising avenue to deepen our understanding of this disease. Hum Brain Mapp 38:957-973, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Feng Liu
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, People's Republic of China.,Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, People's Republic of China
| | - Yifeng Wang
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, People's Republic of China
| | - Meiling Li
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, People's Republic of China
| | - Wenqin Wang
- School of Sciences, Tianjin Polytechnic University, Tianjin, 300130, People's Republic of China
| | - Rong Li
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, People's Republic of China
| | - Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, People's Republic of China
| | - Guangming Lu
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, People's Republic of China
| | - Huafu Chen
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, People's Republic of China
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Wirsich J, Perry A, Ridley B, Proix T, Golos M, Bénar C, Ranjeva JP, Bartolomei F, Breakspear M, Jirsa V, Guye M. Whole-brain analytic measures of network communication reveal increased structure-function correlation in right temporal lobe epilepsy. NEUROIMAGE-CLINICAL 2016; 11:707-718. [PMID: 27330970 PMCID: PMC4909094 DOI: 10.1016/j.nicl.2016.05.010] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2015] [Revised: 03/15/2016] [Accepted: 05/18/2016] [Indexed: 12/13/2022]
Abstract
The in vivo structure-function relationship is key to understanding brain network reorganization due to pathologies. This relationship is likely to be particularly complex in brain network diseases such as temporal lobe epilepsy, in which disturbed large-scale systems are involved in both transient electrical events and long-lasting functional and structural impairments. Herein, we estimated this relationship by analyzing the correlation between structural connectivity and functional connectivity in terms of analytical network communication parameters. As such, we targeted the gradual topological structure-function reorganization caused by the pathology not only at the whole brain scale but also both in core and peripheral regions of the brain. We acquired diffusion (dMRI) and resting-state fMRI (rsfMRI) data in seven right-lateralized TLE (rTLE) patients and fourteen healthy controls and analyzed the structure-function relationship by using analytical network communication metrics derived from the structural connectome. In rTLE patients, we found a widespread hypercorrelated functional network. Network communication analysis revealed greater unspecific branching of the shortest path (search information) in the structural connectome and a higher global correlation between the structural and functional connectivity for the patient group. We also found evidence for a preserved structural rich-club in the patient group. In sum, global augmentation of structure-function correlation might be linked to a smaller functional repertoire in rTLE patients, while sparing the central core of the brain which may represent a pathway that facilitates the spread of seizures.
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Key Words
- CSD, constrained spherical deconvolution
- CSF, cerebrospinal fluid
- FA, fractional anisotropy
- FCA, analytic functional connectivity
- FCD, functional connectivity dynamics
- FOD, fiber orientation distribution
- Functional connectivity
- NBS, network based statistics
- Network based statistics
- Network communication
- Rich club
- Structural connectivity
- Temporal lobe epilepsy
- dMRI, diffusion magnetic resonance imaging
- rTLE, right temporal lobe epilepsy
- rsfMRI, resting state functional magnetic resonance imaging
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Affiliation(s)
- Jonathan Wirsich
- Aix-Marseille Université, CNRS, CRMBM UMR 7339, 13385 Marseille, France; APHM, Hôpitaux de la Timone, Pôle d'imagerie Médicale, CEMEREM, 13005 Marseille, France; Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France.
| | - Alistair Perry
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia; School of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia; Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia.
| | - Ben Ridley
- Aix-Marseille Université, CNRS, CRMBM UMR 7339, 13385 Marseille, France; APHM, Hôpitaux de la Timone, Pôle d'imagerie Médicale, CEMEREM, 13005 Marseille, France.
| | - Timothée Proix
- Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France.
| | - Mathieu Golos
- Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France.
| | - Christian Bénar
- Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France.
| | - Jean-Philippe Ranjeva
- Aix-Marseille Université, CNRS, CRMBM UMR 7339, 13385 Marseille, France; APHM, Hôpitaux de la Timone, Pôle d'imagerie Médicale, CEMEREM, 13005 Marseille, France.
| | - Fabrice Bartolomei
- Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France; APHM, Hôpitaux de la Timone, Pôle de Neurosciences Cliniques, Service de Neurophysiologie Clinique, 13005 Marseille, France.
| | - Michael Breakspear
- School of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia; Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia; Metro North Mental Health Services, Brisbane, QLD 4006, Australia.
| | - Viktor Jirsa
- Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France.
| | - Maxime Guye
- Aix-Marseille Université, CNRS, CRMBM UMR 7339, 13385 Marseille, France; APHM, Hôpitaux de la Timone, Pôle d'imagerie Médicale, CEMEREM, 13005 Marseille, France.
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