- Email: [email protected]
Altered effective connectivity network in patients with insular epilepsy: A high-frequency oscillations magnetoencephalography study
Clinical Neurophysiology 131 (2020) 377–384
Contents lists available at ScienceDirect
Clinical Neurophysiology journal homepage: www.elsevier.com/locate/clinph
Altered effective connectivity network in patients with insular epilepsy: A high-frequency oscillations magnetoencephalography study Chunli Yin a,b,c, Xiating Zhang a,d,e, Jing Xiang f, Zheng Chen a, Xin Li a, Siqi Wu a, Peiyuan Lv b,g,⇑, Yuping Wang a,d,e,⇑ a
Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China Department of Neurology, Hebei Medical University, Shijiazhuang 050017, China Department of Neurology, Tangshan Gongren Hospital, Tangshan 063000, China d Beijing Key Laboratory of Neuromodulation, Beijing 100053, China e Center of Epilepsy, Beijing Institute for Brain Disorders, Beijing 100053, China f MEG Center, Division of Neurology, Cincinnati Children’s Hospital, Medical Center, Cincinnati, OH 45220, USA g Department of Neurology, Hebei General Hospital, Shijiazhuang 050051, China b c
a r t i c l e
i n f o
Article history: Accepted 8 November 2019 Available online 6 December 2019 Keywords: Insular epilepsy Magnetoencephalography Ripples Effective connectivity Graph theory
h i g h l i g h t s Insular epilepsy featured alterations in both whole-brain and insula-based effective connectivity. Insular epilepsy featured over-connectivity and small-world configuration in global connectivity. Left and right insular epilepsy exhibited different connectivity properties to bilateral hemispheres.
a b s t r a c t Objective: The project aimed to determine the alterations in the effective connectivity (EC) neural network in patients with insular epilepsy based on interictal high-frequency oscillations (HFOs) from magnetoencephalography (MEG) data. Methods: We studied MEG data from 22 insular epilepsy patients and 20 normal subjects. Alterations in spatial pattern and connection properties of the patients with insular epilepsy were investigated in the entire brain network and insula-based network. Results: Analyses of the parameters of graph theory revealed the over-connectivity and small-world configuration of the global connectivity patterns observed in the patients. In the insula-based network, the insular cortex ipsilateral to the seizure onset displayed increased efferent and afferent EC. Left insular epilepsy featured strong connectivity with the bilateral hemispheres, whereas right insular epilepsy featured increased connectivity with only the ipsilateral hemisphere. Conclusions: Patients with insular epilepsy display alterations in the EC network in terms of both wholebrain connectivity and the insula-based network during interictal HFOs. Significance: Alterations of interictal HFO-based networks provide evidence that epilepsy networks, instead of epileptic foci, play a key role in the complex pathophysiological mechanisms of insular epilepsy. The dysfunction of HFO networks may prove to be a novel promising biomarker and the cause of interictal brain dysfunctions in insular epilepsy. Ó 2019 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
1. Introduction
⇑ Corresponding authors at: Department of Neurology, Xuanwu Hospital, No. 45, Changchun Street, Xicheng District, Beijing 100053, China. E-mail address: [email protected] (Y. Wang).
Recent studies on epilepsy investigating structural and functional networks have exhibited the definite disruptions in network topology and connectivity, leading to a conceptual shift from ‘‘foci” to ‘‘networks”. Network analysis is gaining more interest, and connectivity analysis is progressively applied to study functional
https://doi.org/10.1016/j.clinph.2019.11.021 1388-2457/Ó 2019 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
378
C. Yin et al. / Clinical Neurophysiology 131 (2020) 377–384
neural networks. The concept of epileptogenic networks has been used to better understand the dynamic complexity of chronic epilepsy, providing essential information on the preictal state, seizure onset, propagation, and termination (Kramer et al., 2008; Schindler et al., 2008), and even the information on interictal functional connectivity (Chavez et al., 2010; Horstmann et al., 2010; Liao et al., 2010; van Dellen et al., 2012). The insula has recently been discovered to be linked to various brain regions (Almashaikhi et al., 2014; Isnard et al., 2004; Ryvlin and Kahane, 2005). Based on accumulating evidence, the insular network may play a key role in insular seizures. Insular epilepsy clinical manifestations may resemble the conditions of temporal lobe epilepsy, parietal lobe epilepsy and frontal lobe epilepsy (Nguyen et al., 2009; Harroud et al., 2012). Failure to recognize primal epileptogenic foci located in insula and operculum cortices has been identified as the cause of a significant proportion of epilepsy surgical failures because of the clinical complexity and variability in insular seizures (Isnard et al., 2004; Aghakhani et al., 2004; Harroud et al., 2012). Clinical manifestations of insular epilepsy depend on propagation pathways. Notably, a clearer understanding of insular network connectivity will provide important insights into human brain functions and is important for the surgical treatment of insular-related epilepsy. The insular network connection between the insula and other cortices has been identified by cortico-cortical evoked potentials (CCEPs) examination (Almashaikhi et al., 2014; Enatsu et al., 2016). The limitation of high-resolution intracranial electroencephalography (iEEG) recordings is its poor spatial coverage of the whole brain and the risks of neurological deficits, infection and intracranial hemorrhage (Wong et al., 2009). Scalp EEG studies have revealed lateralized topographical patterns, but these patterns are difficult to characterize due to the substantial variation among patients (Levy et al., 2017) and muscle artefacts (Dionisio et al., 2011). As a more recent clinical imaging modality, Magnetoencephalography (MEG) can localize epileptic discharges from the insular cortex (Taniguchi et al., 1998) even in patients with inconclusive results from conventional scalp EEG investigations (Park et al., 2012). The development of the MEG methodology has also enabled clinicians to analyze the functional connectivity signatures in insular epilepsy patients (Zerouali et al., 2016). However, those connectivity signatures are based on conventional epileptic spikes. Intracranial high-frequency oscillations (HFOs, higher than 80 Hz) are well localized compared with the traditional epileptic spikes discharges (Wood, 2011; Engel and Da, 2012), which are considered to be diffuse. HFOs have proven helpful in investigating the intrinsic brain abnormalities of epilepsy patients. MEG has been used to detect HFOs and reveal network abnormalities in epilepsy patients (Wu et al., 2017a, b). MEG analyses of the HFO network may provide valuable guidance for preoperative invasive intracranial recordings and surgical resection. At present, there are only a few reports on MEG studies of refractory insular epilepsy (Heers et al., 2012; Mohamed et al., 2013), which are mainly dependent on low-frequency epileptic spikes discharges. Unfortunately, little is known about the presence of high-frequency neuromagnetic signals in drug-resistant insular epilepsy patients. We recently explored the potential of MEG interictal HFOs source imaging localization in patients with insular epilepsy and found that interictal HFOs are valuable for estimating the locations of epileptogenic zones in the deep insular cortex (Yin et al., 2019). Thus, we think that interictal MEG HFOs data may be a promising avenue for exploring the interictal network connectivity of insular epilepsy. Because, unfortunately, it remains unclear if there is any alteration of interictal HFO network of insular epilepsy. Therefore, we consider it would be very interesting and valuable to study interictal HFO network and we consider the data in our study
may help us to better understand the interictal brain function information on neural circuits and pathophysiological mechanisms of insular epilepsy. The purpose of our study was to explore the interictal brain networks in patients with insular epilepsy based on HFOs MEG data. We focused on interictal HFOs because MEG is predominantly used to detect epileptic activity between seizures (interictal) (Xiang et al., 2010; Hari et al., 2018). HFOs in the epileptogenic regions of insular epilepsy are detectable interictally (Yin et al., 2019), thus, the study of interictal HFO network may provide novel insight into the connectivity between epileptogenic zones and other brain areas. The interictal brain network abnormalities may reveal the complex pathophysiological mechanisms of dysfunctions in insular epilepsy. In our research, we analyzed MEG data from patients and normal subjects to investigate the connection properties of HFOs, entire brain networks, insula-based networks, and particularly the interactions between the insula and the ipsilateral and contralateral hemispheres. According to recent papers (Wu et al., 2017a,b; Xiang et al., 2014, 2015), we analyzed the predominant effective connectivity (EC) network to reflect the interictal neuronal networks in insular epilepsy patients.
2. Materials and methods 2.1. Subjects Twenty-two patients with refractory insular epilepsy were consecutively recruited from the Department of Neurology at Xuanwu Hospital between January 2010 and July 2016. Twenty healthy controls matched to the patients in terms of age and gender were recruited from the general population. Our project was approved by the medical ethics committees of Xuanwu Hospital. All participants signed written consent. Inclusion criteria for patients included: (1) surgical removal areas including part or all of the insula and insular operculum cortices; (2) favourable surgical outcome, with significant reduction of seizure frequency or seizurefree; (3) MEG for pre-surgical investigations showing stable and frequent interictal activity; and (4) at least 12 months after surgery. Exclusion criteria included: (1) patients with an implant (pacemaker, braces or vagus nerve stimulation device, etc.); (2) presence of psychiatric or other major neurological diseases, or function failure of a vital organ; and (3) inability to cooperate with or complete MEG or magnetic resonance imaging investigations.
2.2. MEG recording We used a whole-head, 306-channel MEG system from Elekta Neuromag to obtain and digitize MEG signals in a magnetically shielded room in the MEG Center at Xuanwu Hospital. The sampling rate of MEG data was 1000 Hz and a 0.1–330 Hz bandpass filter was applied. Each participant lay in a supine position and was instructed to remain still and keep both eyes closed but not to fall asleep during approximately 60 minutes of MEG data collection. The positions of head relative to the coordinate system of MEG sensors were measured by three coils previously placed in the left and right pre-auricular and nasion points before MEG data obtainment. The accuracy of head localization was 1 millimeter (mm). The MEG data with head movement more than 5 mm were discarded. A background MEG dataset was obtained to identify outside interferences such as environmental and system noise before the MEG data acquisition. Then the software Elekta Max-filter was applied to remove these interferences and complete the pre-processing of MEG data.
C. Yin et al. / Clinical Neurophysiology 131 (2020) 377–384
2.3. MRI scan Standard MRI was performed on a 3-T Philips Achieva scanner with conventional sequences, and anatomical image data were recorded for all participants. Three small points were put in the same locations the aforementioned coils placed for MEG head localization to facilitate co-registration of MRI and MEG data. 2.4. MEG data analysis MEG Processor software (Xiang et al., 2010) was used to analyze the interictal MEG data from patients and resting MEG data from healthy controls. We visually identified the conventional spike discharges after application of a 3–70 Hz bandpass filter and removal of power-line noise at 50 Hz. Then, the MEG data without noise or artefacts were performed by a bandpass filter of 80–250 Hz. Ripples (80–250 Hz) are defined by the presence of at least four continuous high-frequency oscillations that are significantly higher than the baseline background. We selected 200-ms time windows of ripples with marked spikes and periods without spikes for analysis in patients. In controls, a 200-ms time window of a marked ripple segment from resting MEG data without evident artefacts and noise was used for analysis. We analyzed the epileptic networks at the source level ground on recent reports (Wu et al., 2017a, b; Xiang et al., 2014, 2015). Correlation and Granger causality (GC) analyses were used to estimate the EC. We computed all volumetric sources of activity (or virtual sensor waveforms) in time windows in individual MRI slices using a beamformer method (Xiang et al., 2015). We analyzed the correlations of virtual sensor waveforms to estimate the source neural networks. Specifically, we statistically analyzed the correlation between two sources by calculating the correlation coefficient or factors using the following mathematical algorithms: a ;xb Þ Rðxa ; xb Þ ¼ Cðx Sxa Sx b
In the equation, Xa and Xb represent the MEG signals of two paired sources, used to compute connections. Rðxa ; xb Þ represents the correlation between the sources in pairs located in positions ‘‘a” and ‘‘b”. C(Xa, Xb) and SxaSxb, respectively, indicate the mean and the standard deviation of the MEG signals of the aforementioned two sources in the formulas. A 6-mm resolution was used to scan the entire head to generate virtual sensor waveforms for every possible source. In other words, each subject might produce about 17,160 sources/voxels. To avoid local connections, we identified any two sources less than 10 mm apart as one virtual sensor waveform for network analysis. In source-level analysis, all possible connections of each source pair were calculated to avoid possible sources of bias, as suggested in recently published papers (Xiang et al., 2015). We overlapped magnetic source networks data onto MRIs from individual participants and calculated the excitatory and inhibitory connections, which are shown in red and blue, respectively (Xiang et al., 2014). We determined the directivity in the connections using multivariate GC analysis, which can be interpreted as follows: if the activity of one source could predict a time delay of 10 ms in the activity of another source, we defined the first source as driving the second source. Otherwise, we considered the two sources unconnected. In addition to the whole-brain analysis, we selected the left insular cortex and right insular cortex as the regions of interest (ROIs) and computed the connections between the insula and the other cortices, specifically, the ipsilateral and contralateral hemispheres. We also performed an analysis based on graph theory to quantify the source level network connectivity characteristics with individual MRI from each participant for a better forward and inverse
379
performance than a general head model (Xiang et al., 2015). We calculated a set of parameters including the degree (D), strength (S), characteristic path length (L), and clustering coefficient (C) of both global connection and local connection topological characteristics for every pair of sources. A source was mathematically considered as a node and the edges were defined as the effective connections of all node pairs above the threshold. A hub node is defined as a node that is located in the center of a network and has many connections; from network point of view, such a node is essential for the effective information communication because of its extensive connections. The D parameter of a given node is determined by the number of connections between that node and the others; According to previous report (Wang et al., 2016), D could be further subdivided and the number of incoming links and outgoing links could be respectively calculated to distinguish between the in-degrees and the out-degrees. The S parameter represents the measure of all possible links to all sources. The average shortest distance between each paired of sources, determined by the number of edges, is called the characteristic path length L (Reijneveld et al., 2007). The probability that nodes adjacent to a given node will also be connected is called clustering coefficient C. MEG Processor software was used to measure the EC network parameter values with a corresponding p < 0.01 as the threshold value. Patients and controls used the same threshold value for comparison, all EC values above the threshold value could be shown. The aforementioned formulas used in MEG Processor software have been described in detail in recently published articles (Xiang et al., 2015, 2014). 3. Statistical analysis We used the SPSS version 19.0 software package (SPSS Inc., Chicago, IL, USA) for statistical testing. The chi-square test was performed to compare the EC network patterns of patients and control group. Comparisons of the properties of the EC networks were performed on a subset of data after testing the homogeneity of variance and the normality of the distribution. We used a oneway analysis of variance (ANOVA) or Kruskal-Wallis one-way ANOVA (k samples) for multiple groups statistical comparisons, two-tailed Student’s t test for unpaired comparisons and paired t-tests for paired comparisons. P < 0.05 was considered to be a significant difference, and the Bonferroni correction was used to correct for multiple comparisons. 4. Results Twenty-two patients with refractory insular epilepsy (age range: 12–47 years; mean age: 20.64 ± 8.27 years; 12 males and 10 females) and 20 normal subjects (age range: 19–32 years; mean age: 23.80 ± 4.74 years; 11 males and 9 females) were recruited. All patients had been clinically diagnosed with seizures from the insular cortex, which were confirmed using ictal iEEG recordings and further confirmed by surgical resection and clinical outcomes. Six patients had left insular epilepsy, and 16 patients had right insular epilepsy, depending on the epileptogenic zone. Table 1 shows the demographic and clinical characteristics of all patients. 4.1. Network pattern The patterns of the network were independently visually examined in both 2D and 3D views by two researchers who were experienced epileptologists. We found that the insular cortex showed high percentage of involving in EC network in group of HFOs with spikes (14 of 22 patients), but not in the HFOs without spikes (6 of 22 patients) or healthy controls (3 of 20 subjects). There were sig-
380
C. Yin et al. / Clinical Neurophysiology 131 (2020) 377–384
Table 1 The clinical characteristics of all the patients. Case
Gender/Age (years)
Epilepsy onset (years)
Epilepsy Duration (years)
Past history
Seizure frequency (times/day/month)
Side of seizure onset
Histopathology
Follow-up (months)
Engelclass
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
M/25 F/47 F/19 F/32 M/14 M/22 M/35 M/19 M/18 F/16 F/12 M/15 M/17 F/15 F/18 M/24 F/22 F/13 F/18 M/15 M/15 M/23
9 10 3 24 13 3 12 9 10 1 7 7 9 3 11 0 11 7 6 13 9 18
16 37 16 8 1 19 23 10 8 15 5 8 8 12 7 24 11 6 12 2 6 5
Head No Head No Head No Head No No Head No No No No No No No No No No No Head
2–4/d 2–3/d 5–6/d 1–2/d 10/d 5–6/d 7–8/d 5–6/d 2–4/d 70/d 3–5/d 0.5/d 5–20/d 3–4/d 4/m 2–3/d 2–3/d 2/d 10/d 3–4/d 3–4/d 1/10d
R R R R R R R R R R L L R R R L R R L L R L
FCDIb FCDII FCDIIb FCDI FCDIb FCDIb FCDI FCDI FCDIIId FCDIIa FCDI FCDI FCDI FCDIIa FCDI TSC FCDII FCDIIa MCD FCDIIa FCDIb FCDI
78 94 81 73 72 80 32 47 50 55 56 33 31 34 33 33 37 41 36 90 84 23
Ia Ia Ia Ia Ia Ia IIa Ia Ia IIIa IIb IIIa Ia IIIa IIIa Ia Ia Ia Ia Ia Ia IIIa
injury injury injury injury
injury
injury
FCD, focal cortical dysplasia; TSC, tuberous sclerosis complex; MCD, malformation of cortical development; F, female; M, male; R, right; L, left.
nificant differences between the proportion of HFOs with and without spikes (p = 0.015) and between HFOs with spikes and healthy controls (p = 0.001). In the patients with predominantly strong connectivity involving the insular cortex, 9 of 14 patients displayed insular connections located in the insula ipsilateral to the epileptogenic zone in data from HFOs with spikes compared with 5 of 6 patients in data from HFOs without spikes, and no significant difference was observed between the two datasets. After excluding the insular areas, the patients and controls also exhibited alterations in brain regions involved in excitatory connections, the temporal, occipital, frontotemporal, centroparietal, centrotemporal, parietal, parietooccipital, and frontal cortices. Although there were various patterns of topology in these regions among individuals, the differences were not statistically significant (Fig. 1). 4.2. Graph theory analysis 4.2.1. Global connectivity We volumetrically analyzed the connections between every pair of voxels with HFOs to quantify whole-brain connectivity. The quantitative analysis of neural networks revealed significant differences in S, L and C between insular epilepsy patients and normal subjects. Compared with these values in controls, S was significantly increased and L was significantly decreased in the data of patients with and without spikes in ripple frequency bands. C was significantly higher in patients with spikes than in controls. Although the patients showed a tendency towards increased hubs compared with controls, significant differences were not observed. No significant differences in S, D, L and C were observed between HFOs datasets with and without spikes in patients. Details are shown in Fig. 2. 4.2.2. Connectivity between the insula and other brain regions By limiting the insula as the ‘‘seed” (the driving source) and ‘‘result” (the source to be driven) regions, significantly higher D and C were found when the insula was identified as a ‘‘seed” compared to when the insula was identified as a ‘‘result” in patients as well as in controls. Compared with HFOs connectivity of the insula in controls, HFOs connectivity of the insula with the seizure onset ipsilateral
cortex in patients (either data with or without spikes) showed a significantly elevated S both with the insula defined as a ‘‘seed” and as a ‘‘result”. However, in the patients presenting HFOs in the contralateral insula with or without spikes, the tendency of increased strength was not significant. Details are shown in Fig. 3. 4.2.3. Connectivity based on the seizure-side insula The left insula showed predominantly strong connections with the ipsilateral hemisphere. Nevertheless, the right insula showed tight connections to the contralateral hemisphere (left hemisphere) in the healthy controls (Fig. 4). The left insular epilepsy patients revealed significantly increased D between the left insula and other brain regions, either ipsilateral or contralateral hemispheres (Fig. 4) in HFOs data with spikes compared with those in controls. The right insular epilepsy patients revealed significantly elevated S in the right insula and increased D in the ipsilateral hemisphere compared with those in controls (Fig. 4). 5. Discussion This study investigated the MEG-baesd HFOs data from all the participants. Compared with the normal resting-state EC network of the healthy controls, the patients with insular epilepsy revealed abnormal neuromagnetic signal characteristics during the interictal period, including network patterns and brain topology measurements. 5.1. Network pattern The HFO network patterns of controls displayed excitatory connections in all major regions of the brain. Furthermore, the proportion of dominant anatomical locations was consistent with data obtained from the HFOs without spikes from patients, although their topographic patterns varied between individuals. The results were consistent with the occurrence of physiological HFOs outside the epileptic network in iEEGs (Alkawadri et al., 2014), and the HFOs without spikes might correspond to physiological oscillations. However, a rich connectivity of the EC network that predominantly involved the insular cortex was observed in HFOs with
C. Yin et al. / Clinical Neurophysiology 131 (2020) 377–384
381
Fig. 1. Typical EC networks in the HFOs observed in the patients with insular epilepsy and in controls, shown in the axial (left column) and lateral (right column) views. The patients with insular epilepsy presenting HFOs data with spikes showed a significantly altered pattern of the EC network compared with the patients presenting HFOs data without spikes and the controls; more connections were observed in the insular cortex. A and C show the predominant strong connectivity involving the insular cortex in the patients presenting HFOs data with spikes, and B and D present the same patients shown in A and C, respectively, which exhibited HFOs data without spikes. D illustrates the rich connectivity in the insular cortex, whereas B does not. E-H show the various topographic patterns observed in the four healthy controls.
spikes from patients; thus, HFOs with spikes may represent pathological HFOs or potential biomarkers in clinical epileptology. The EC network of patients with insular epilepsy displayed overconnectivity in the insular cortex during the interictal HFOs with spike period. In beta frequency band, a report aimed at studying insular cortex functional connectivity-based signatures with MEG showed that specific connectivity patterns of insular anterior and posterior subregions were qualitatively similar during rest and during spikes (Zerouali et al., 2016). The over-connectivity was mainly observed in the insula ipsilateral to seizure onset and only a few in the contralateral insula. Thus, the EC network of the patients with insular epilepsy is more complex, and the bilateral insula are connected in these patients. CCEPs has also confirmed that bilateral insular lobes in refractory insular epilepsy patients have functional interconnections featuring bi-directional homotopy and heterotopy (Lacuey et al., 2016), which is thought to explain the pathological propagation phenomenon of epileptic discharges spreading between bilateral insula, depending on the mono- or oligo-synaptic connections by the myelinated callosal fibers (Lacuey et al., 2016).
5.2. Graph theory
Fig. 2. Global connectivity of the patients and healthy controls in terms of four parameters (D, S, L, and C). *p < 0.05 compared with the controls, and the result was still significant after correction for multiple comparisons (corrected for 3 4 tests).
5.2.1. Global connectivity We defined and quantified the parameters of network characteristics in order to extract the information of connection topology and potential behaviors. The graph theory analysis of global connectivity showed altered brain network organization in insular epilepsy patients, who had a significantly increased S and C and a
382
C. Yin et al. / Clinical Neurophysiology 131 (2020) 377–384
Fig. 3. Connectivity between the insula and other brain regions as analyzed using graph theory. The EC from the insular cortex (A) and into the insular cortex (B). *p < 0.05 for the EC from the insula to other brain regions compared with the EC from other cortices to the insula within groups, # p < 0.05 compared with the controls (from the insula to other brain areas and from other brain regions to the insula, respectively). The results were still significant after correction for multiple comparisons using the FDR.
Fig. 4. Connectivity between the seizure-side insula and global, ipsilateral and contralateral hemispheres in the patients with left insular epilepsy and right insular epilepsy in terms of four parameters. Representative examples of connectivity to the ipsilateral hemisphere (A) and contralateral hemisphere (B) in right insular epilepsy. Interhemispheric connectivity in patients with left insular epilepsy (C) and in patients with right insular epilepsy (D). *p < 0.05 compared with the controls; # p < 0.05 comparison of HFOs data with and without spikes; 4 p < 0.05 comparison of the connectivity from the insula to the contralateral hemisphere with the connectivity to the ipsilateral hemisphere within groups. The results were still significant after correction for multiple comparisons using the FDR.
significantly decreased L. A tendency towards an increased D was observed, although the differences were not significant. A certain amount of connections are essential for effective information transfer within the brain. The observed increase in S of global connectivity of epilepsy patients suggests increased brain synchronization and excessive connectivity of brain network. Overconnectivity is associated with hyperexcitability of brain network, which may facilitate epileptic seizures. In present study, insular
epilepsy patients revealed an epileptogenic EC network during interictal HFOs. The characteristic path length L is derived from all pairs of vertices in the network and indicates integration or interconnection of the brain network. A shorter L represents better integration of a network. The clustering coefficients C, which defines the likelihood that neighbouring nodes will be connected, indicates a tendency toward segregation and is used to determine the local connectivity.
C. Yin et al. / Clinical Neurophysiology 131 (2020) 377–384
According to a previous research report (Watts and Strogatz, 1998), we comprehensively considered aforementioned two parameters, the characteristic path length L and the clustering coefficient C, to distinguish the basic network characteristics of small-world network, regular network, and random network. As introduced by Watts and Strogatz, regular network has a limited distance connections and many local connections characterized by a high L and a high C, and is related to the nearest neighbors. Inversely, the random network is characterized by many distant connections and limited local connections, and the nodes in a random network are randomly connected. Between regular and random networks, small-world network is considered an efficient network architecture, combining the advantages and balancing the integration and segregation processes of both aforementioned networks. The small-world network characteristics were revealed in patients with insular epilepsy characterized by decreased L and increased C, which provided evidence that the interictal EC network displayed greater small-worldness than the network of the controls. These results suggest the presence of optimized topological architecture in the pathological EC network in insular epilepsy patients based on the interictal HFOs in MEG data. However, other studies have reported inconsistent or conflicting results regarding changes in network efficiency. A study of temporal lobe epilepsy (TLE) reported the presence of decreased L and C (Liao et al., 2010), suggesting that the network characteristics tended to be closer to those of random network. Another study of partial epilepsy reported increased L and C (Horstmann et al., 2010). These inconsistent or contradictory results may be explained by differences in aetiology, methodology, epilepsy duration (van Diessen et al., 2013), or the location of the epileptogenic zone. 5.2.2. Insula-based connectivity In our study, all the participants exhibited rich connections between the insula and other brain regions. In addition, studies of human brain connectivity using CCEPs have demonstrated that insula has abundant and complex connectivity, which varies according to the function of five insular gyri. (Almashaikhi et al., 2014). The insula sends rich nerve efferent projections to other brain cortices and receives corresponding abundant afferent projections and vice versa. The insula is involved in complex and rich brain network connectivity that reflects the integration of the afferent and efferent information connections with other brain regions. Based on the study of CCEPs, eighty-seven percent of insular connections were shown to be characterized by reciprocity of more than two other brain structures (Almashaikhi et al., 2014). These findings are in agreement with the rich insular reciprocal connectivity of rhesus monkeys previously reported (Aggleton et al., 1980; Mesulam and Mufson, 1982). In the current study, the insula was either a driver or was driven by other sources. The efferent connectivity was higher than afferent connectivity in all participants. This discrepancy was also reported in rhesus monkeys (Mufson and Mesulam, 1982). Insular efference also exhibited a high degree of divergence. As previously reported, insular cortex network has rich reciprocal connectivity and highlevel divergent connectivity (Kurth et al., 2010), which is believed to be in line with the complex and diverse processes by which the insula integrates sensory, emotion and cognitive information. Compared with controls, patients exhibited greater overall efferent and afferent connectivity in EC networks in the ipsilateral insula of seizure onset. Based on this result, an epileptic EC network was present in the ipsilateral insula in the patients with insular epilepsy during interictal HFOs. Although the connectivity of contralateral insula of seizure onset was not significantly different, the tendency towards an increase might be used to interpret the partially dominant EC pattern involving the contralateral insula. The relationship between the side of epileptic seizure and domi-
383
nant hemisphere has been shown to affect connectivity and structural abnormalities in TLE (Bonilha et al., 2007; Kemmotsu et al., 2011), and left and right TLE patients also exhibit a dissociation of connectivity (Coito et al., 2015). Therefore, the reasonable interpretation of the results of this study should consider the factor of different hemispheric seizure origin. Based on this, we further examined the EC network of patients stratified according to the side where epileptic seizures originate. The patients with insular epilepsy were analyzed with left insular onset and right insular onset. Both the left insular epilepsy and right insular epilepsy exhibited significantly increased efferent and afferent connectivity compared with the homotopic insula of controls. However, the aberrant EC network was only observed in HFOs with spikes. This observation might have important clinical implications for patients presenting HFOs with spikes during the interictal period, which are more likely to be epileptogenic. We detected the connectivity from the seizure-onset insula to the ipsilateral and contralateral hemispheres to clarify the pathophysiological mechanism. In the left insula of healthy individuals, strong connections were predominantly observed in the ipsilateral hemisphere, whereas in the right insula of controls, excessive connectivity was observed in the contralateral hemisphere. The bilateral insula of healthy controls displayed physiologically increased connectivity to the left hemisphere in the resting-state HFOs which might be interpreted as showing the predominance of the left hemisphere in the controls. In contrast to the controls, the patients with left insular epilepsy displayed strong connectivity to the bilateral hemispheres; however, the increased connectivity was only observed in the ipsilateral hemisphere in the right insular epilepsy patients. An aberrant EC network was also only revealed in HFOs with spikes during the interictal period. Thus, HFOs with spikes may represent a biomarker of epilepsy. The interhemispheric connections may play a key role in the integration of insular sensorimotor function, which may explain the phenomenon of epileptic activity spreading to contralateral hemisphere. The difference in the properties of connectivity to bilateral hemispheres between left and right insular epilepsy patients revealed the greater diversity and complexity of the EC network in patients. Additional investigations are required to advance our knowledge of the propagation of insular epileptic seizures and the complex mechanism of fundamental network dysfunction in patients with insular epilepsy. 6. Conclusions In conclusion, this study is the first to provide neuromagnetic MEG data showing that patients with insular epilepsy exhibit an altered EC network that differs from healthy subjects during interictal HFOs, particularly HFOs with spikes. The aberrant EC network was not only observed in whole-brain connectivity but also the insula-based network. The increased connectivity predominantly involved the insula from which the seizure originated, which indicates that this site plays a crucial role in the interictal pathophysiological processes of insular epilepsy. In addition, the connection properties differed between the left and right insular epilepsy patients. Based on our results, an abnormal interictal EC network provides distinct insights into the complex pathophysiological mechanisms of insular epilepsy, and alterations of interictal HFObased networks may be the cause of interictal brain dysfunctions in insular epilepsy and represent new biomarkers of this type of epilepsy. Acknowledgements This study was supported by Natural Science Foundation of China (81771398) and Beijing Municipal Science & Technology Commission (Z161100002616001).
384
C. Yin et al. / Clinical Neurophysiology 131 (2020) 377–384
References Aggleton JP, Burton MJ, Passingham RE. Cortical and subcortical afferents to the amygdala of the rhesus monkey (Macaca mulatta). Brain Res 1980;190:347–68. Aghakhani Y, Rosati A, Dubeau F, Olivier A, Andermann F. Patients with temporoparietal ictal symptoms and inferomesial EEG do not benefit from anterior temporal resection. Epilepsia 2004;45:230–6. Alkawadri R, Gaspard N, Goncharova II, Spencer DD, Gerrard JL, Zaveri H, et al. The spatial and signal characteristics of physiologic high frequency oscillations. Epilepsia 2014;55:1986–95. Almashaikhi T, Rheims S, Jung J, Ostrowsky-Coste K, Montavont A, De Bellescize J, et al. Functional connectivity of insular efferences. Hum Brain Mapp 2014;35:5279–94. Bonilha L, Rorden C, Halford JJ, Eckert M, Appenzeller S, Cendes F, et al. Asymmetrical extra-hippocampal grey matter loss related to hippocampal atrophy in patients with medial temporal lobe epilepsy. J Neurol Neurosurg Psychiatry 2007;78:286–94. Chavez M, Valencia M, Navarro V, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett 2010;104:118701. Coito A, Plomp G, Genetti M, Abela E, Wiest R, Seeck M, et al. Dynamic directed interictal connectivity in left and right temporal lobe epilepsy. Epilepsia 2015;56:207–17. Dionisio S, Koenig A, Murray J, Somerville E. A gut feeling about insular seizures. BMJ Case Rep 2011;2011. Enatsu R, Gonzalez-Martinez J, Bulacio J, Mosher JC, Burgess RC, Najm I, et al. Connectivity of the frontal and anterior insular network: a cortico-cortical evoked potential study. J Neurosurg 2016;125:90–101. Engel JJ, Da SF. High-frequency oscillations - where we are and where we need to go. Prog Neurobiol 2012;98:316–8. Hari R, Baillet S, Barnes G, Burgess R, Forss N, Gross J, et al. IFCN-endorsed practical guidelines for clinical magnetoencephalography (MEG). Clin Neurophysiol 2018;129:1720–47. Harroud A, Bouthillier A, Weil AG, Nguyen DK. Temporal lobe epilepsy surgery failures: a review. Epilepsy Res Treat 2012;2012:201651. Heers M, Rampp S, Stefan H, Urbach H, Elger CE, von Lehe M, et al. MEG-based identification of the epileptogenic zone in occult peri-insular epilepsy. Seizure 2012;21:128–33. Horstmann MT, Bialonski S, Noennig N, Mai H, Prusseit J, Wellmer J, et al. State dependent properties of epileptic brain networks: comparative graphtheoretical analyses of simultaneously recorded EEG and MEG. Clin Neurophysiol 2010;121:172–85. Isnard J, Guenot M, Sindou M, Mauguiere F. Clinical manifestations of insular lobe seizures: a stereo-electroencephalographic study. Epilepsia 2004;45:1079–90. Kemmotsu N, Girard HM, Bernhardt BC, Bonilha L, Lin JJ, Tecoma ES, et al. MRI analysis in temporal lobe epilepsy: cortical thinning and white matter disruptions are related to side of seizure onset. Epilepsia 2011;52:2257–66. Kramer MA, Kolaczyk ED, Kirsch HE. Emergent network topology at seizure onset in humans. Epilepsy Res 2008;79:173–86. Kurth F, Zilles K, Fox PT, Laird AR, Eickhoff SB. A link between the systems: functional differentiation and integration within the human insula revealed by meta-analysis. Brain Struct Funct 2010;214:519–34. Lacuey N, Zonjy B, Kahriman ES, Marashly A, Miller J, Lhatoo SD, et al. Homotopic reciprocal functional connectivity between anterior human insulae. Brain Struct Funct 2016;221:2695–701. Levy A, Yen TT, Boucher O, Bouthillier A, Nguyen DK. Operculo-insular epilepsy: scalp and intracranial electroencephalographic findings. J Clin Neurophysiol 2017;34:438–47. Liao W, Zhang Z, Pan Z, Mantini D, Ding J, Duan X, et al. Altered functional connectivity and small-world in mesial temporal lobe epilepsy. PLoS ONE 2010;5:e8525.
Mesulam MM, Mufson EJ. Insula of the old world monkey. III: Efferent cortical output and comments on function. J Comp Neurol 1982;212:38–52. Mohamed IS, Gibbs SA, Robert M, Bouthillier A, Leroux JM, Khoa ND. The utility of magnetoencephalography in the presurgical evaluation of refractory insular epilepsy. Epilepsia 2013;54:1950–9. Mufson EJ, Mesulam MM. Insula of the old world monkey. II: Afferent cortical input and comments on the claustrum. J Comp Neurol 1982;212:23–37. Nguyen DK, Nguyen DB, Malak R, Leroux JM, Carmant L, Saint-Hilaire JM, et al. Revisiting the role of the insula in refractory partial epilepsy. Epilepsia 2009;50:510–20. Park HM, Nakasato N, Tominaga T. Localization of abnormal discharges causing insular epilepsy by magnetoencephalography. Tohoku J Exp Med 2012;226:207–11. Reijneveld JC, Ponten SC, Berendse HW, Stam CJ. The application of graph theoretical analysis to complex networks in the brain. Clin Neurophysiol 2007;118:2317–31. Ryvlin P, Kahane P. The hidden causes of surgery-resistant temporal lobe epilepsy: extratemporal or temporal plus?. Curr Opin Neurol 2005;18:125–7. Schindler KA, Bialonski S, Horstmann MT, Elger CE, Lehnertz K. Evolving functional network properties and synchronizability during human epileptic seizures. Chaos 2008;18:033119. Taniguchi M, Yoshimine T, Kato A, Maruno M, Hirabuki N, Nakamura H, et al. Dysembryoplastic neuroepithelial tumor in the insular cortex. Three dimensional magnetoencephalographic localization of epileptic discharges. Neurol Res 1998;20:433–8. van Dellen E, Douw L, Hillebrand A, Ris-Hilgersom IH, Schoonheim MM, Baayen JC, et al. MEG network differences between low- and high-grade glioma related to epilepsy and cognition. PLoS ONE 2012;7:e50122. van Diessen E, Diederen SJ, Braun KP, Jansen FE, Stam CJ. Functional and structural brain networks in epilepsy: what have we learned?. Epilepsia 2013;54:1855–65. Wang C, Xu J, Zhao S, Lou W. Graph theoretical analysis of EEG effective connectivity in vascular dementia patients during a visual oddball task. Clin Neurophysiol 2016;127:324–34. Watts DJ, Strogatz SH. Collective dynamics of ’small-world’ networks. Nature 1998;393:440–2. Wong CH, Birkett J, Byth K, Dexter M, Somerville E, Gill D, et al. Risk factors for complications during intracranial electrode recording in presurgical evaluation of drug resistant partial epilepsy. Acta Neurochir (Wien) 2009;151:37–50. Wood H. High-frequency oscillations pinpoint the seizure-onset zone. Nat Rev Neurol 2011;7:475. Wu C, Xiang J, Jiang W, Huang S, Gao Y, Tang L, et al. Altered effective connectivity network in childhood absence epilepsy: a multi-frequency MEG study. Brain Topogr 2017a;30:673–84. Wu C, Xiang J, Sun J, Huang S, Tang L, Miao A, et al. Quantify neuromagnetic network changes from pre-ictal to ictal activities in absence seizures. Neuroscience 2017b;357:134–44. Xiang J, Korman A, Samarasinghe KM, Wang X, Zhang F, Qiao H, et al. Volumetric imaging of brain activity with spatial-frequency decoding of neuromagnetic signals. J Neurosci Methods 2015;239:114–28. Xiang J, Luo Q, Kotecha R, Korman A, Zhang F, Luo H, et al. Accumulated source imaging of brain activity with both low and high-frequency neuromagnetic signals. Front Neuroinform 2014;8:57. Xiang J, Wang Y, Chen Y, Liu Y, Kotecha R, Huo X, et al. Noninvasive localization of epileptogenic zones with ictal high-frequency neuromagnetic signals. J Neurosurg Pediatr 2010;5:113–22. Yin C, Zhang X, Chen Z, Li X, Wu S, Lv P, et al. Detection and localization of interictal ripples with magnetoencephalography in the presurgical evaluation of drugresistant insular epilepsy. Brain Res 2019;1706:147–56. Zerouali Y, Pouliot P, Robert M, Mohamed I, Bouthillier A, Lesage F, et al. Magnetoencephalographic signatures of insular epileptic spikes based on functional connectivity. Hum Brain Mapp 2016;37:3250–61.
Contents lists available at ScienceDirect
Clinical Neurophysiology journal homepage: www.elsevier.com/locate/clinph
Altered effective connectivity network in patients with insular epilepsy: A high-frequency oscillations magnetoencephalography study Chunli Yin a,b,c, Xiating Zhang a,d,e, Jing Xiang f, Zheng Chen a, Xin Li a, Siqi Wu a, Peiyuan Lv b,g,⇑, Yuping Wang a,d,e,⇑ a
Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China Department of Neurology, Hebei Medical University, Shijiazhuang 050017, China Department of Neurology, Tangshan Gongren Hospital, Tangshan 063000, China d Beijing Key Laboratory of Neuromodulation, Beijing 100053, China e Center of Epilepsy, Beijing Institute for Brain Disorders, Beijing 100053, China f MEG Center, Division of Neurology, Cincinnati Children’s Hospital, Medical Center, Cincinnati, OH 45220, USA g Department of Neurology, Hebei General Hospital, Shijiazhuang 050051, China b c
a r t i c l e
i n f o
Article history: Accepted 8 November 2019 Available online 6 December 2019 Keywords: Insular epilepsy Magnetoencephalography Ripples Effective connectivity Graph theory
h i g h l i g h t s Insular epilepsy featured alterations in both whole-brain and insula-based effective connectivity. Insular epilepsy featured over-connectivity and small-world configuration in global connectivity. Left and right insular epilepsy exhibited different connectivity properties to bilateral hemispheres.
a b s t r a c t Objective: The project aimed to determine the alterations in the effective connectivity (EC) neural network in patients with insular epilepsy based on interictal high-frequency oscillations (HFOs) from magnetoencephalography (MEG) data. Methods: We studied MEG data from 22 insular epilepsy patients and 20 normal subjects. Alterations in spatial pattern and connection properties of the patients with insular epilepsy were investigated in the entire brain network and insula-based network. Results: Analyses of the parameters of graph theory revealed the over-connectivity and small-world configuration of the global connectivity patterns observed in the patients. In the insula-based network, the insular cortex ipsilateral to the seizure onset displayed increased efferent and afferent EC. Left insular epilepsy featured strong connectivity with the bilateral hemispheres, whereas right insular epilepsy featured increased connectivity with only the ipsilateral hemisphere. Conclusions: Patients with insular epilepsy display alterations in the EC network in terms of both wholebrain connectivity and the insula-based network during interictal HFOs. Significance: Alterations of interictal HFO-based networks provide evidence that epilepsy networks, instead of epileptic foci, play a key role in the complex pathophysiological mechanisms of insular epilepsy. The dysfunction of HFO networks may prove to be a novel promising biomarker and the cause of interictal brain dysfunctions in insular epilepsy. Ó 2019 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
1. Introduction
⇑ Corresponding authors at: Department of Neurology, Xuanwu Hospital, No. 45, Changchun Street, Xicheng District, Beijing 100053, China. E-mail address: [email protected] (Y. Wang).
Recent studies on epilepsy investigating structural and functional networks have exhibited the definite disruptions in network topology and connectivity, leading to a conceptual shift from ‘‘foci” to ‘‘networks”. Network analysis is gaining more interest, and connectivity analysis is progressively applied to study functional
https://doi.org/10.1016/j.clinph.2019.11.021 1388-2457/Ó 2019 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
378
C. Yin et al. / Clinical Neurophysiology 131 (2020) 377–384
neural networks. The concept of epileptogenic networks has been used to better understand the dynamic complexity of chronic epilepsy, providing essential information on the preictal state, seizure onset, propagation, and termination (Kramer et al., 2008; Schindler et al., 2008), and even the information on interictal functional connectivity (Chavez et al., 2010; Horstmann et al., 2010; Liao et al., 2010; van Dellen et al., 2012). The insula has recently been discovered to be linked to various brain regions (Almashaikhi et al., 2014; Isnard et al., 2004; Ryvlin and Kahane, 2005). Based on accumulating evidence, the insular network may play a key role in insular seizures. Insular epilepsy clinical manifestations may resemble the conditions of temporal lobe epilepsy, parietal lobe epilepsy and frontal lobe epilepsy (Nguyen et al., 2009; Harroud et al., 2012). Failure to recognize primal epileptogenic foci located in insula and operculum cortices has been identified as the cause of a significant proportion of epilepsy surgical failures because of the clinical complexity and variability in insular seizures (Isnard et al., 2004; Aghakhani et al., 2004; Harroud et al., 2012). Clinical manifestations of insular epilepsy depend on propagation pathways. Notably, a clearer understanding of insular network connectivity will provide important insights into human brain functions and is important for the surgical treatment of insular-related epilepsy. The insular network connection between the insula and other cortices has been identified by cortico-cortical evoked potentials (CCEPs) examination (Almashaikhi et al., 2014; Enatsu et al., 2016). The limitation of high-resolution intracranial electroencephalography (iEEG) recordings is its poor spatial coverage of the whole brain and the risks of neurological deficits, infection and intracranial hemorrhage (Wong et al., 2009). Scalp EEG studies have revealed lateralized topographical patterns, but these patterns are difficult to characterize due to the substantial variation among patients (Levy et al., 2017) and muscle artefacts (Dionisio et al., 2011). As a more recent clinical imaging modality, Magnetoencephalography (MEG) can localize epileptic discharges from the insular cortex (Taniguchi et al., 1998) even in patients with inconclusive results from conventional scalp EEG investigations (Park et al., 2012). The development of the MEG methodology has also enabled clinicians to analyze the functional connectivity signatures in insular epilepsy patients (Zerouali et al., 2016). However, those connectivity signatures are based on conventional epileptic spikes. Intracranial high-frequency oscillations (HFOs, higher than 80 Hz) are well localized compared with the traditional epileptic spikes discharges (Wood, 2011; Engel and Da, 2012), which are considered to be diffuse. HFOs have proven helpful in investigating the intrinsic brain abnormalities of epilepsy patients. MEG has been used to detect HFOs and reveal network abnormalities in epilepsy patients (Wu et al., 2017a, b). MEG analyses of the HFO network may provide valuable guidance for preoperative invasive intracranial recordings and surgical resection. At present, there are only a few reports on MEG studies of refractory insular epilepsy (Heers et al., 2012; Mohamed et al., 2013), which are mainly dependent on low-frequency epileptic spikes discharges. Unfortunately, little is known about the presence of high-frequency neuromagnetic signals in drug-resistant insular epilepsy patients. We recently explored the potential of MEG interictal HFOs source imaging localization in patients with insular epilepsy and found that interictal HFOs are valuable for estimating the locations of epileptogenic zones in the deep insular cortex (Yin et al., 2019). Thus, we think that interictal MEG HFOs data may be a promising avenue for exploring the interictal network connectivity of insular epilepsy. Because, unfortunately, it remains unclear if there is any alteration of interictal HFO network of insular epilepsy. Therefore, we consider it would be very interesting and valuable to study interictal HFO network and we consider the data in our study
may help us to better understand the interictal brain function information on neural circuits and pathophysiological mechanisms of insular epilepsy. The purpose of our study was to explore the interictal brain networks in patients with insular epilepsy based on HFOs MEG data. We focused on interictal HFOs because MEG is predominantly used to detect epileptic activity between seizures (interictal) (Xiang et al., 2010; Hari et al., 2018). HFOs in the epileptogenic regions of insular epilepsy are detectable interictally (Yin et al., 2019), thus, the study of interictal HFO network may provide novel insight into the connectivity between epileptogenic zones and other brain areas. The interictal brain network abnormalities may reveal the complex pathophysiological mechanisms of dysfunctions in insular epilepsy. In our research, we analyzed MEG data from patients and normal subjects to investigate the connection properties of HFOs, entire brain networks, insula-based networks, and particularly the interactions between the insula and the ipsilateral and contralateral hemispheres. According to recent papers (Wu et al., 2017a,b; Xiang et al., 2014, 2015), we analyzed the predominant effective connectivity (EC) network to reflect the interictal neuronal networks in insular epilepsy patients.
2. Materials and methods 2.1. Subjects Twenty-two patients with refractory insular epilepsy were consecutively recruited from the Department of Neurology at Xuanwu Hospital between January 2010 and July 2016. Twenty healthy controls matched to the patients in terms of age and gender were recruited from the general population. Our project was approved by the medical ethics committees of Xuanwu Hospital. All participants signed written consent. Inclusion criteria for patients included: (1) surgical removal areas including part or all of the insula and insular operculum cortices; (2) favourable surgical outcome, with significant reduction of seizure frequency or seizurefree; (3) MEG for pre-surgical investigations showing stable and frequent interictal activity; and (4) at least 12 months after surgery. Exclusion criteria included: (1) patients with an implant (pacemaker, braces or vagus nerve stimulation device, etc.); (2) presence of psychiatric or other major neurological diseases, or function failure of a vital organ; and (3) inability to cooperate with or complete MEG or magnetic resonance imaging investigations.
2.2. MEG recording We used a whole-head, 306-channel MEG system from Elekta Neuromag to obtain and digitize MEG signals in a magnetically shielded room in the MEG Center at Xuanwu Hospital. The sampling rate of MEG data was 1000 Hz and a 0.1–330 Hz bandpass filter was applied. Each participant lay in a supine position and was instructed to remain still and keep both eyes closed but not to fall asleep during approximately 60 minutes of MEG data collection. The positions of head relative to the coordinate system of MEG sensors were measured by three coils previously placed in the left and right pre-auricular and nasion points before MEG data obtainment. The accuracy of head localization was 1 millimeter (mm). The MEG data with head movement more than 5 mm were discarded. A background MEG dataset was obtained to identify outside interferences such as environmental and system noise before the MEG data acquisition. Then the software Elekta Max-filter was applied to remove these interferences and complete the pre-processing of MEG data.
C. Yin et al. / Clinical Neurophysiology 131 (2020) 377–384
2.3. MRI scan Standard MRI was performed on a 3-T Philips Achieva scanner with conventional sequences, and anatomical image data were recorded for all participants. Three small points were put in the same locations the aforementioned coils placed for MEG head localization to facilitate co-registration of MRI and MEG data. 2.4. MEG data analysis MEG Processor software (Xiang et al., 2010) was used to analyze the interictal MEG data from patients and resting MEG data from healthy controls. We visually identified the conventional spike discharges after application of a 3–70 Hz bandpass filter and removal of power-line noise at 50 Hz. Then, the MEG data without noise or artefacts were performed by a bandpass filter of 80–250 Hz. Ripples (80–250 Hz) are defined by the presence of at least four continuous high-frequency oscillations that are significantly higher than the baseline background. We selected 200-ms time windows of ripples with marked spikes and periods without spikes for analysis in patients. In controls, a 200-ms time window of a marked ripple segment from resting MEG data without evident artefacts and noise was used for analysis. We analyzed the epileptic networks at the source level ground on recent reports (Wu et al., 2017a, b; Xiang et al., 2014, 2015). Correlation and Granger causality (GC) analyses were used to estimate the EC. We computed all volumetric sources of activity (or virtual sensor waveforms) in time windows in individual MRI slices using a beamformer method (Xiang et al., 2015). We analyzed the correlations of virtual sensor waveforms to estimate the source neural networks. Specifically, we statistically analyzed the correlation between two sources by calculating the correlation coefficient or factors using the following mathematical algorithms: a ;xb Þ Rðxa ; xb Þ ¼ Cðx Sxa Sx b
In the equation, Xa and Xb represent the MEG signals of two paired sources, used to compute connections. Rðxa ; xb Þ represents the correlation between the sources in pairs located in positions ‘‘a” and ‘‘b”. C(Xa, Xb) and SxaSxb, respectively, indicate the mean and the standard deviation of the MEG signals of the aforementioned two sources in the formulas. A 6-mm resolution was used to scan the entire head to generate virtual sensor waveforms for every possible source. In other words, each subject might produce about 17,160 sources/voxels. To avoid local connections, we identified any two sources less than 10 mm apart as one virtual sensor waveform for network analysis. In source-level analysis, all possible connections of each source pair were calculated to avoid possible sources of bias, as suggested in recently published papers (Xiang et al., 2015). We overlapped magnetic source networks data onto MRIs from individual participants and calculated the excitatory and inhibitory connections, which are shown in red and blue, respectively (Xiang et al., 2014). We determined the directivity in the connections using multivariate GC analysis, which can be interpreted as follows: if the activity of one source could predict a time delay of 10 ms in the activity of another source, we defined the first source as driving the second source. Otherwise, we considered the two sources unconnected. In addition to the whole-brain analysis, we selected the left insular cortex and right insular cortex as the regions of interest (ROIs) and computed the connections between the insula and the other cortices, specifically, the ipsilateral and contralateral hemispheres. We also performed an analysis based on graph theory to quantify the source level network connectivity characteristics with individual MRI from each participant for a better forward and inverse
379
performance than a general head model (Xiang et al., 2015). We calculated a set of parameters including the degree (D), strength (S), characteristic path length (L), and clustering coefficient (C) of both global connection and local connection topological characteristics for every pair of sources. A source was mathematically considered as a node and the edges were defined as the effective connections of all node pairs above the threshold. A hub node is defined as a node that is located in the center of a network and has many connections; from network point of view, such a node is essential for the effective information communication because of its extensive connections. The D parameter of a given node is determined by the number of connections between that node and the others; According to previous report (Wang et al., 2016), D could be further subdivided and the number of incoming links and outgoing links could be respectively calculated to distinguish between the in-degrees and the out-degrees. The S parameter represents the measure of all possible links to all sources. The average shortest distance between each paired of sources, determined by the number of edges, is called the characteristic path length L (Reijneveld et al., 2007). The probability that nodes adjacent to a given node will also be connected is called clustering coefficient C. MEG Processor software was used to measure the EC network parameter values with a corresponding p < 0.01 as the threshold value. Patients and controls used the same threshold value for comparison, all EC values above the threshold value could be shown. The aforementioned formulas used in MEG Processor software have been described in detail in recently published articles (Xiang et al., 2015, 2014). 3. Statistical analysis We used the SPSS version 19.0 software package (SPSS Inc., Chicago, IL, USA) for statistical testing. The chi-square test was performed to compare the EC network patterns of patients and control group. Comparisons of the properties of the EC networks were performed on a subset of data after testing the homogeneity of variance and the normality of the distribution. We used a oneway analysis of variance (ANOVA) or Kruskal-Wallis one-way ANOVA (k samples) for multiple groups statistical comparisons, two-tailed Student’s t test for unpaired comparisons and paired t-tests for paired comparisons. P < 0.05 was considered to be a significant difference, and the Bonferroni correction was used to correct for multiple comparisons. 4. Results Twenty-two patients with refractory insular epilepsy (age range: 12–47 years; mean age: 20.64 ± 8.27 years; 12 males and 10 females) and 20 normal subjects (age range: 19–32 years; mean age: 23.80 ± 4.74 years; 11 males and 9 females) were recruited. All patients had been clinically diagnosed with seizures from the insular cortex, which were confirmed using ictal iEEG recordings and further confirmed by surgical resection and clinical outcomes. Six patients had left insular epilepsy, and 16 patients had right insular epilepsy, depending on the epileptogenic zone. Table 1 shows the demographic and clinical characteristics of all patients. 4.1. Network pattern The patterns of the network were independently visually examined in both 2D and 3D views by two researchers who were experienced epileptologists. We found that the insular cortex showed high percentage of involving in EC network in group of HFOs with spikes (14 of 22 patients), but not in the HFOs without spikes (6 of 22 patients) or healthy controls (3 of 20 subjects). There were sig-
380
C. Yin et al. / Clinical Neurophysiology 131 (2020) 377–384
Table 1 The clinical characteristics of all the patients. Case
Gender/Age (years)
Epilepsy onset (years)
Epilepsy Duration (years)
Past history
Seizure frequency (times/day/month)
Side of seizure onset
Histopathology
Follow-up (months)
Engelclass
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
M/25 F/47 F/19 F/32 M/14 M/22 M/35 M/19 M/18 F/16 F/12 M/15 M/17 F/15 F/18 M/24 F/22 F/13 F/18 M/15 M/15 M/23
9 10 3 24 13 3 12 9 10 1 7 7 9 3 11 0 11 7 6 13 9 18
16 37 16 8 1 19 23 10 8 15 5 8 8 12 7 24 11 6 12 2 6 5
Head No Head No Head No Head No No Head No No No No No No No No No No No Head
2–4/d 2–3/d 5–6/d 1–2/d 10/d 5–6/d 7–8/d 5–6/d 2–4/d 70/d 3–5/d 0.5/d 5–20/d 3–4/d 4/m 2–3/d 2–3/d 2/d 10/d 3–4/d 3–4/d 1/10d
R R R R R R R R R R L L R R R L R R L L R L
FCDIb FCDII FCDIIb FCDI FCDIb FCDIb FCDI FCDI FCDIIId FCDIIa FCDI FCDI FCDI FCDIIa FCDI TSC FCDII FCDIIa MCD FCDIIa FCDIb FCDI
78 94 81 73 72 80 32 47 50 55 56 33 31 34 33 33 37 41 36 90 84 23
Ia Ia Ia Ia Ia Ia IIa Ia Ia IIIa IIb IIIa Ia IIIa IIIa Ia Ia Ia Ia Ia Ia IIIa
injury injury injury injury
injury
injury
FCD, focal cortical dysplasia; TSC, tuberous sclerosis complex; MCD, malformation of cortical development; F, female; M, male; R, right; L, left.
nificant differences between the proportion of HFOs with and without spikes (p = 0.015) and between HFOs with spikes and healthy controls (p = 0.001). In the patients with predominantly strong connectivity involving the insular cortex, 9 of 14 patients displayed insular connections located in the insula ipsilateral to the epileptogenic zone in data from HFOs with spikes compared with 5 of 6 patients in data from HFOs without spikes, and no significant difference was observed between the two datasets. After excluding the insular areas, the patients and controls also exhibited alterations in brain regions involved in excitatory connections, the temporal, occipital, frontotemporal, centroparietal, centrotemporal, parietal, parietooccipital, and frontal cortices. Although there were various patterns of topology in these regions among individuals, the differences were not statistically significant (Fig. 1). 4.2. Graph theory analysis 4.2.1. Global connectivity We volumetrically analyzed the connections between every pair of voxels with HFOs to quantify whole-brain connectivity. The quantitative analysis of neural networks revealed significant differences in S, L and C between insular epilepsy patients and normal subjects. Compared with these values in controls, S was significantly increased and L was significantly decreased in the data of patients with and without spikes in ripple frequency bands. C was significantly higher in patients with spikes than in controls. Although the patients showed a tendency towards increased hubs compared with controls, significant differences were not observed. No significant differences in S, D, L and C were observed between HFOs datasets with and without spikes in patients. Details are shown in Fig. 2. 4.2.2. Connectivity between the insula and other brain regions By limiting the insula as the ‘‘seed” (the driving source) and ‘‘result” (the source to be driven) regions, significantly higher D and C were found when the insula was identified as a ‘‘seed” compared to when the insula was identified as a ‘‘result” in patients as well as in controls. Compared with HFOs connectivity of the insula in controls, HFOs connectivity of the insula with the seizure onset ipsilateral
cortex in patients (either data with or without spikes) showed a significantly elevated S both with the insula defined as a ‘‘seed” and as a ‘‘result”. However, in the patients presenting HFOs in the contralateral insula with or without spikes, the tendency of increased strength was not significant. Details are shown in Fig. 3. 4.2.3. Connectivity based on the seizure-side insula The left insula showed predominantly strong connections with the ipsilateral hemisphere. Nevertheless, the right insula showed tight connections to the contralateral hemisphere (left hemisphere) in the healthy controls (Fig. 4). The left insular epilepsy patients revealed significantly increased D between the left insula and other brain regions, either ipsilateral or contralateral hemispheres (Fig. 4) in HFOs data with spikes compared with those in controls. The right insular epilepsy patients revealed significantly elevated S in the right insula and increased D in the ipsilateral hemisphere compared with those in controls (Fig. 4). 5. Discussion This study investigated the MEG-baesd HFOs data from all the participants. Compared with the normal resting-state EC network of the healthy controls, the patients with insular epilepsy revealed abnormal neuromagnetic signal characteristics during the interictal period, including network patterns and brain topology measurements. 5.1. Network pattern The HFO network patterns of controls displayed excitatory connections in all major regions of the brain. Furthermore, the proportion of dominant anatomical locations was consistent with data obtained from the HFOs without spikes from patients, although their topographic patterns varied between individuals. The results were consistent with the occurrence of physiological HFOs outside the epileptic network in iEEGs (Alkawadri et al., 2014), and the HFOs without spikes might correspond to physiological oscillations. However, a rich connectivity of the EC network that predominantly involved the insular cortex was observed in HFOs with
C. Yin et al. / Clinical Neurophysiology 131 (2020) 377–384
381
Fig. 1. Typical EC networks in the HFOs observed in the patients with insular epilepsy and in controls, shown in the axial (left column) and lateral (right column) views. The patients with insular epilepsy presenting HFOs data with spikes showed a significantly altered pattern of the EC network compared with the patients presenting HFOs data without spikes and the controls; more connections were observed in the insular cortex. A and C show the predominant strong connectivity involving the insular cortex in the patients presenting HFOs data with spikes, and B and D present the same patients shown in A and C, respectively, which exhibited HFOs data without spikes. D illustrates the rich connectivity in the insular cortex, whereas B does not. E-H show the various topographic patterns observed in the four healthy controls.
spikes from patients; thus, HFOs with spikes may represent pathological HFOs or potential biomarkers in clinical epileptology. The EC network of patients with insular epilepsy displayed overconnectivity in the insular cortex during the interictal HFOs with spike period. In beta frequency band, a report aimed at studying insular cortex functional connectivity-based signatures with MEG showed that specific connectivity patterns of insular anterior and posterior subregions were qualitatively similar during rest and during spikes (Zerouali et al., 2016). The over-connectivity was mainly observed in the insula ipsilateral to seizure onset and only a few in the contralateral insula. Thus, the EC network of the patients with insular epilepsy is more complex, and the bilateral insula are connected in these patients. CCEPs has also confirmed that bilateral insular lobes in refractory insular epilepsy patients have functional interconnections featuring bi-directional homotopy and heterotopy (Lacuey et al., 2016), which is thought to explain the pathological propagation phenomenon of epileptic discharges spreading between bilateral insula, depending on the mono- or oligo-synaptic connections by the myelinated callosal fibers (Lacuey et al., 2016).
5.2. Graph theory
Fig. 2. Global connectivity of the patients and healthy controls in terms of four parameters (D, S, L, and C). *p < 0.05 compared with the controls, and the result was still significant after correction for multiple comparisons (corrected for 3 4 tests).
5.2.1. Global connectivity We defined and quantified the parameters of network characteristics in order to extract the information of connection topology and potential behaviors. The graph theory analysis of global connectivity showed altered brain network organization in insular epilepsy patients, who had a significantly increased S and C and a
382
C. Yin et al. / Clinical Neurophysiology 131 (2020) 377–384
Fig. 3. Connectivity between the insula and other brain regions as analyzed using graph theory. The EC from the insular cortex (A) and into the insular cortex (B). *p < 0.05 for the EC from the insula to other brain regions compared with the EC from other cortices to the insula within groups, # p < 0.05 compared with the controls (from the insula to other brain areas and from other brain regions to the insula, respectively). The results were still significant after correction for multiple comparisons using the FDR.
Fig. 4. Connectivity between the seizure-side insula and global, ipsilateral and contralateral hemispheres in the patients with left insular epilepsy and right insular epilepsy in terms of four parameters. Representative examples of connectivity to the ipsilateral hemisphere (A) and contralateral hemisphere (B) in right insular epilepsy. Interhemispheric connectivity in patients with left insular epilepsy (C) and in patients with right insular epilepsy (D). *p < 0.05 compared with the controls; # p < 0.05 comparison of HFOs data with and without spikes; 4 p < 0.05 comparison of the connectivity from the insula to the contralateral hemisphere with the connectivity to the ipsilateral hemisphere within groups. The results were still significant after correction for multiple comparisons using the FDR.
significantly decreased L. A tendency towards an increased D was observed, although the differences were not significant. A certain amount of connections are essential for effective information transfer within the brain. The observed increase in S of global connectivity of epilepsy patients suggests increased brain synchronization and excessive connectivity of brain network. Overconnectivity is associated with hyperexcitability of brain network, which may facilitate epileptic seizures. In present study, insular
epilepsy patients revealed an epileptogenic EC network during interictal HFOs. The characteristic path length L is derived from all pairs of vertices in the network and indicates integration or interconnection of the brain network. A shorter L represents better integration of a network. The clustering coefficients C, which defines the likelihood that neighbouring nodes will be connected, indicates a tendency toward segregation and is used to determine the local connectivity.
C. Yin et al. / Clinical Neurophysiology 131 (2020) 377–384
According to a previous research report (Watts and Strogatz, 1998), we comprehensively considered aforementioned two parameters, the characteristic path length L and the clustering coefficient C, to distinguish the basic network characteristics of small-world network, regular network, and random network. As introduced by Watts and Strogatz, regular network has a limited distance connections and many local connections characterized by a high L and a high C, and is related to the nearest neighbors. Inversely, the random network is characterized by many distant connections and limited local connections, and the nodes in a random network are randomly connected. Between regular and random networks, small-world network is considered an efficient network architecture, combining the advantages and balancing the integration and segregation processes of both aforementioned networks. The small-world network characteristics were revealed in patients with insular epilepsy characterized by decreased L and increased C, which provided evidence that the interictal EC network displayed greater small-worldness than the network of the controls. These results suggest the presence of optimized topological architecture in the pathological EC network in insular epilepsy patients based on the interictal HFOs in MEG data. However, other studies have reported inconsistent or conflicting results regarding changes in network efficiency. A study of temporal lobe epilepsy (TLE) reported the presence of decreased L and C (Liao et al., 2010), suggesting that the network characteristics tended to be closer to those of random network. Another study of partial epilepsy reported increased L and C (Horstmann et al., 2010). These inconsistent or contradictory results may be explained by differences in aetiology, methodology, epilepsy duration (van Diessen et al., 2013), or the location of the epileptogenic zone. 5.2.2. Insula-based connectivity In our study, all the participants exhibited rich connections between the insula and other brain regions. In addition, studies of human brain connectivity using CCEPs have demonstrated that insula has abundant and complex connectivity, which varies according to the function of five insular gyri. (Almashaikhi et al., 2014). The insula sends rich nerve efferent projections to other brain cortices and receives corresponding abundant afferent projections and vice versa. The insula is involved in complex and rich brain network connectivity that reflects the integration of the afferent and efferent information connections with other brain regions. Based on the study of CCEPs, eighty-seven percent of insular connections were shown to be characterized by reciprocity of more than two other brain structures (Almashaikhi et al., 2014). These findings are in agreement with the rich insular reciprocal connectivity of rhesus monkeys previously reported (Aggleton et al., 1980; Mesulam and Mufson, 1982). In the current study, the insula was either a driver or was driven by other sources. The efferent connectivity was higher than afferent connectivity in all participants. This discrepancy was also reported in rhesus monkeys (Mufson and Mesulam, 1982). Insular efference also exhibited a high degree of divergence. As previously reported, insular cortex network has rich reciprocal connectivity and highlevel divergent connectivity (Kurth et al., 2010), which is believed to be in line with the complex and diverse processes by which the insula integrates sensory, emotion and cognitive information. Compared with controls, patients exhibited greater overall efferent and afferent connectivity in EC networks in the ipsilateral insula of seizure onset. Based on this result, an epileptic EC network was present in the ipsilateral insula in the patients with insular epilepsy during interictal HFOs. Although the connectivity of contralateral insula of seizure onset was not significantly different, the tendency towards an increase might be used to interpret the partially dominant EC pattern involving the contralateral insula. The relationship between the side of epileptic seizure and domi-
383
nant hemisphere has been shown to affect connectivity and structural abnormalities in TLE (Bonilha et al., 2007; Kemmotsu et al., 2011), and left and right TLE patients also exhibit a dissociation of connectivity (Coito et al., 2015). Therefore, the reasonable interpretation of the results of this study should consider the factor of different hemispheric seizure origin. Based on this, we further examined the EC network of patients stratified according to the side where epileptic seizures originate. The patients with insular epilepsy were analyzed with left insular onset and right insular onset. Both the left insular epilepsy and right insular epilepsy exhibited significantly increased efferent and afferent connectivity compared with the homotopic insula of controls. However, the aberrant EC network was only observed in HFOs with spikes. This observation might have important clinical implications for patients presenting HFOs with spikes during the interictal period, which are more likely to be epileptogenic. We detected the connectivity from the seizure-onset insula to the ipsilateral and contralateral hemispheres to clarify the pathophysiological mechanism. In the left insula of healthy individuals, strong connections were predominantly observed in the ipsilateral hemisphere, whereas in the right insula of controls, excessive connectivity was observed in the contralateral hemisphere. The bilateral insula of healthy controls displayed physiologically increased connectivity to the left hemisphere in the resting-state HFOs which might be interpreted as showing the predominance of the left hemisphere in the controls. In contrast to the controls, the patients with left insular epilepsy displayed strong connectivity to the bilateral hemispheres; however, the increased connectivity was only observed in the ipsilateral hemisphere in the right insular epilepsy patients. An aberrant EC network was also only revealed in HFOs with spikes during the interictal period. Thus, HFOs with spikes may represent a biomarker of epilepsy. The interhemispheric connections may play a key role in the integration of insular sensorimotor function, which may explain the phenomenon of epileptic activity spreading to contralateral hemisphere. The difference in the properties of connectivity to bilateral hemispheres between left and right insular epilepsy patients revealed the greater diversity and complexity of the EC network in patients. Additional investigations are required to advance our knowledge of the propagation of insular epileptic seizures and the complex mechanism of fundamental network dysfunction in patients with insular epilepsy. 6. Conclusions In conclusion, this study is the first to provide neuromagnetic MEG data showing that patients with insular epilepsy exhibit an altered EC network that differs from healthy subjects during interictal HFOs, particularly HFOs with spikes. The aberrant EC network was not only observed in whole-brain connectivity but also the insula-based network. The increased connectivity predominantly involved the insula from which the seizure originated, which indicates that this site plays a crucial role in the interictal pathophysiological processes of insular epilepsy. In addition, the connection properties differed between the left and right insular epilepsy patients. Based on our results, an abnormal interictal EC network provides distinct insights into the complex pathophysiological mechanisms of insular epilepsy, and alterations of interictal HFObased networks may be the cause of interictal brain dysfunctions in insular epilepsy and represent new biomarkers of this type of epilepsy. Acknowledgements This study was supported by Natural Science Foundation of China (81771398) and Beijing Municipal Science & Technology Commission (Z161100002616001).
384
C. Yin et al. / Clinical Neurophysiology 131 (2020) 377–384
References Aggleton JP, Burton MJ, Passingham RE. Cortical and subcortical afferents to the amygdala of the rhesus monkey (Macaca mulatta). Brain Res 1980;190:347–68. Aghakhani Y, Rosati A, Dubeau F, Olivier A, Andermann F. Patients with temporoparietal ictal symptoms and inferomesial EEG do not benefit from anterior temporal resection. Epilepsia 2004;45:230–6. Alkawadri R, Gaspard N, Goncharova II, Spencer DD, Gerrard JL, Zaveri H, et al. The spatial and signal characteristics of physiologic high frequency oscillations. Epilepsia 2014;55:1986–95. Almashaikhi T, Rheims S, Jung J, Ostrowsky-Coste K, Montavont A, De Bellescize J, et al. Functional connectivity of insular efferences. Hum Brain Mapp 2014;35:5279–94. Bonilha L, Rorden C, Halford JJ, Eckert M, Appenzeller S, Cendes F, et al. Asymmetrical extra-hippocampal grey matter loss related to hippocampal atrophy in patients with medial temporal lobe epilepsy. J Neurol Neurosurg Psychiatry 2007;78:286–94. Chavez M, Valencia M, Navarro V, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett 2010;104:118701. Coito A, Plomp G, Genetti M, Abela E, Wiest R, Seeck M, et al. Dynamic directed interictal connectivity in left and right temporal lobe epilepsy. Epilepsia 2015;56:207–17. Dionisio S, Koenig A, Murray J, Somerville E. A gut feeling about insular seizures. BMJ Case Rep 2011;2011. Enatsu R, Gonzalez-Martinez J, Bulacio J, Mosher JC, Burgess RC, Najm I, et al. Connectivity of the frontal and anterior insular network: a cortico-cortical evoked potential study. J Neurosurg 2016;125:90–101. Engel JJ, Da SF. High-frequency oscillations - where we are and where we need to go. Prog Neurobiol 2012;98:316–8. Hari R, Baillet S, Barnes G, Burgess R, Forss N, Gross J, et al. IFCN-endorsed practical guidelines for clinical magnetoencephalography (MEG). Clin Neurophysiol 2018;129:1720–47. Harroud A, Bouthillier A, Weil AG, Nguyen DK. Temporal lobe epilepsy surgery failures: a review. Epilepsy Res Treat 2012;2012:201651. Heers M, Rampp S, Stefan H, Urbach H, Elger CE, von Lehe M, et al. MEG-based identification of the epileptogenic zone in occult peri-insular epilepsy. Seizure 2012;21:128–33. Horstmann MT, Bialonski S, Noennig N, Mai H, Prusseit J, Wellmer J, et al. State dependent properties of epileptic brain networks: comparative graphtheoretical analyses of simultaneously recorded EEG and MEG. Clin Neurophysiol 2010;121:172–85. Isnard J, Guenot M, Sindou M, Mauguiere F. Clinical manifestations of insular lobe seizures: a stereo-electroencephalographic study. Epilepsia 2004;45:1079–90. Kemmotsu N, Girard HM, Bernhardt BC, Bonilha L, Lin JJ, Tecoma ES, et al. MRI analysis in temporal lobe epilepsy: cortical thinning and white matter disruptions are related to side of seizure onset. Epilepsia 2011;52:2257–66. Kramer MA, Kolaczyk ED, Kirsch HE. Emergent network topology at seizure onset in humans. Epilepsy Res 2008;79:173–86. Kurth F, Zilles K, Fox PT, Laird AR, Eickhoff SB. A link between the systems: functional differentiation and integration within the human insula revealed by meta-analysis. Brain Struct Funct 2010;214:519–34. Lacuey N, Zonjy B, Kahriman ES, Marashly A, Miller J, Lhatoo SD, et al. Homotopic reciprocal functional connectivity between anterior human insulae. Brain Struct Funct 2016;221:2695–701. Levy A, Yen TT, Boucher O, Bouthillier A, Nguyen DK. Operculo-insular epilepsy: scalp and intracranial electroencephalographic findings. J Clin Neurophysiol 2017;34:438–47. Liao W, Zhang Z, Pan Z, Mantini D, Ding J, Duan X, et al. Altered functional connectivity and small-world in mesial temporal lobe epilepsy. PLoS ONE 2010;5:e8525.
Mesulam MM, Mufson EJ. Insula of the old world monkey. III: Efferent cortical output and comments on function. J Comp Neurol 1982;212:38–52. Mohamed IS, Gibbs SA, Robert M, Bouthillier A, Leroux JM, Khoa ND. The utility of magnetoencephalography in the presurgical evaluation of refractory insular epilepsy. Epilepsia 2013;54:1950–9. Mufson EJ, Mesulam MM. Insula of the old world monkey. II: Afferent cortical input and comments on the claustrum. J Comp Neurol 1982;212:23–37. Nguyen DK, Nguyen DB, Malak R, Leroux JM, Carmant L, Saint-Hilaire JM, et al. Revisiting the role of the insula in refractory partial epilepsy. Epilepsia 2009;50:510–20. Park HM, Nakasato N, Tominaga T. Localization of abnormal discharges causing insular epilepsy by magnetoencephalography. Tohoku J Exp Med 2012;226:207–11. Reijneveld JC, Ponten SC, Berendse HW, Stam CJ. The application of graph theoretical analysis to complex networks in the brain. Clin Neurophysiol 2007;118:2317–31. Ryvlin P, Kahane P. The hidden causes of surgery-resistant temporal lobe epilepsy: extratemporal or temporal plus?. Curr Opin Neurol 2005;18:125–7. Schindler KA, Bialonski S, Horstmann MT, Elger CE, Lehnertz K. Evolving functional network properties and synchronizability during human epileptic seizures. Chaos 2008;18:033119. Taniguchi M, Yoshimine T, Kato A, Maruno M, Hirabuki N, Nakamura H, et al. Dysembryoplastic neuroepithelial tumor in the insular cortex. Three dimensional magnetoencephalographic localization of epileptic discharges. Neurol Res 1998;20:433–8. van Dellen E, Douw L, Hillebrand A, Ris-Hilgersom IH, Schoonheim MM, Baayen JC, et al. MEG network differences between low- and high-grade glioma related to epilepsy and cognition. PLoS ONE 2012;7:e50122. van Diessen E, Diederen SJ, Braun KP, Jansen FE, Stam CJ. Functional and structural brain networks in epilepsy: what have we learned?. Epilepsia 2013;54:1855–65. Wang C, Xu J, Zhao S, Lou W. Graph theoretical analysis of EEG effective connectivity in vascular dementia patients during a visual oddball task. Clin Neurophysiol 2016;127:324–34. Watts DJ, Strogatz SH. Collective dynamics of ’small-world’ networks. Nature 1998;393:440–2. Wong CH, Birkett J, Byth K, Dexter M, Somerville E, Gill D, et al. Risk factors for complications during intracranial electrode recording in presurgical evaluation of drug resistant partial epilepsy. Acta Neurochir (Wien) 2009;151:37–50. Wood H. High-frequency oscillations pinpoint the seizure-onset zone. Nat Rev Neurol 2011;7:475. Wu C, Xiang J, Jiang W, Huang S, Gao Y, Tang L, et al. Altered effective connectivity network in childhood absence epilepsy: a multi-frequency MEG study. Brain Topogr 2017a;30:673–84. Wu C, Xiang J, Sun J, Huang S, Tang L, Miao A, et al. Quantify neuromagnetic network changes from pre-ictal to ictal activities in absence seizures. Neuroscience 2017b;357:134–44. Xiang J, Korman A, Samarasinghe KM, Wang X, Zhang F, Qiao H, et al. Volumetric imaging of brain activity with spatial-frequency decoding of neuromagnetic signals. J Neurosci Methods 2015;239:114–28. Xiang J, Luo Q, Kotecha R, Korman A, Zhang F, Luo H, et al. Accumulated source imaging of brain activity with both low and high-frequency neuromagnetic signals. Front Neuroinform 2014;8:57. Xiang J, Wang Y, Chen Y, Liu Y, Kotecha R, Huo X, et al. Noninvasive localization of epileptogenic zones with ictal high-frequency neuromagnetic signals. J Neurosurg Pediatr 2010;5:113–22. Yin C, Zhang X, Chen Z, Li X, Wu S, Lv P, et al. Detection and localization of interictal ripples with magnetoencephalography in the presurgical evaluation of drugresistant insular epilepsy. Brain Res 2019;1706:147–56. Zerouali Y, Pouliot P, Robert M, Mohamed I, Bouthillier A, Lesage F, et al. Magnetoencephalographic signatures of insular epileptic spikes based on functional connectivity. Hum Brain Mapp 2016;37:3250–61.