Examining the resting-state networks (RSNs) may help us to understand the neural mechanism of the frontal lobe epilepsy (FLE). Resting-state functional MRI (fMRI) data were acquired from 46 patients with FLE (study group) and 46 age- and gender-matched healthy subjects (control group). The independent component analysis (ICA) method was used to identify RSNs from each group. Compared with the healthy subjects, decreased functional connectivity was observed in all the networks; however, in some areas of RSNs, functional connectivity was increased in patients with FLE. The duration of epilepsy and the seizure frequency were used to analyze correlation with the regions of interest (ROIs) in the nine RSNs to determine their influence on FLE. The functional network connectivity (FNC) was used to study the impact on the disturbance and reorganization of FLE. The results of this study may offer new insight into the neuropathophysiological mechanisms of FLE.
As the second highest type of localization-related epilepsies, frontal lobe epilepsy (FLE) accounts for 20 to 30 percent among all partial epilepsies [
Except for the obvious abnormalities, such as previous brain trauma, neoplasms, vascular malformations, and developmental lesions, no clear cause has been found in most patients with FLE. Furthermore, regular structure MRI imaging shows nothing valuable. However, the mechanism of how the epilepsy focus influences the functional network and further leads to the deficits of the brain’s function and cognitive behavior is still not clear. Contemporary fMRI has provided effective approaches for measuring the functional alteration caused by epilepsy in brain, which is a four-dimensional medical imaging modality that captures changes in blood oxygenation over time, an indirect measure of neuronal activation.
Although EEG-fMRI and resting-state fMRI are commonly used in the study of epilepsy [
Most studies have focused on the RSNs used with resting-state fMRI in epilepsy. Widjaja et al. showed impaired default mode network (DMN) in children with medically refractory epilepsy [
Amann et al. extracted ROIs, regions of altered functional connectivity in brain, from RSNs and used
However, these studies were all focused on single resting-state functional network. Little attention was paid to the interactions between the RSNs in patients with epilepsy. Luo et al. implicated that investigating the interactions between the RSNs might be helpful to understand the neuropathophysiological mechanism of epilepsy globally [
FLE may involve multiple interacting networks, and few studies have focused on analyzing the functional connectivity of RSNs in patients with FLE and FNC relationships. Investigations on RSNs in patients with FLE could provide valuable data to validate the hypothesis that the abnormal neuroactivity generated from epilepsy focus leads to functional network impairment. The primary aim of the present work was to determine whether the intranetwork of RSNs might be aberrant in patients with FLE on resting-state fMRI data. The study also was concerned about the significant correlation between altered ROIs of the RSNs and clinical variables, including seizure frequency and the duration of epilepsy. Examination of the differences in FNC between the patients with FLE and HC may be helpful to understand the impact on the disturbance and reorganization of FLE. The results of this study may provide new insight into the neuropathophysiological mechanisms of FLE.
Patients with FLE were recruited from a patient population who had received clinical treatments in Jinling Hospital. Forty-six healthy subjects were selected as the control group that matches the age and gender of the FLE group. Two-sample
Clinical information and demographic of patients with FLE and HC.
Groups | Men | Age |
Duration |
Seizure frequency |
Treatment |
---|---|---|---|---|---|
FLE ( |
21 | 26 ± 4.59 |
9.2 ± 7.82 |
59.66 ± 166.67 |
Carbamazepine: 11, |
|
|||||
HC ( |
22 | 25.3 ± 5.33 |
/ | / | / |
Patients with FLE were diagnosed based on the International League against Epilepsy (ILAE) classification [
Functional MRI data were acquired on a Magnetom Trio 3T MR Scanner (Siemens AG, Erlangen, Germany). During the resting-state fMRI session, the participants were instructed to relax with their eyes closed and keep their heads still during the scans. Functional images were subsequently acquired in the same slice orientation with a GRE-EPI (gradient recalled echo, echo-planar imaging) sequence (TR/TE = 2,000 ms/30 ms, FOV 24.0 × 24.0 cm2, FA 90°, matrix 64 × 64, slice thickness 4.0 mm, slice gap 0.4 mm, 30 slices, acquisition voxel size = 3.0 × 3.0 × 3.0 mm) for eight minutes and twenty seconds (250 measurements). 3D T1-weighted images were also acquired by using a 3DMPRAGE sequence, matrix 256 × 256, slice thickness 1.0 mm.
A SPM- (statistical parametric mapping-) based fMRI data processing pipeline DPARSF (
Group spatial ICA was conducted by using the infomax algorithm with the GIFT software (
Eight templates of RSNs for estimating maximally independent spatial components among ninety-two subjects, including CN, DMN, CEN, SMN, SRN, DAN, AN, and VN, shown by BrainNet Viewer. The
Group comparisons were restricted to the voxels within each corresponding RSN. Two-sample
The effects of clinical variables including seizure frequency and the duration of epilepsy were analyzed in correlation with the alterations in RSNs in epilepsy. From each corresponding RSN, the voxels showing significant differences (positive or negative) between the patient and the control groups were extracted as a mask consisting of several regions of interest (ROIs). The mean
The ICA algorithm assumed that the time courses of cortical areas within one component were synchronous. Though the components were spatially independent, significant temporal correlations could exist between them. As an extension of ICA analysis, the FNC toolbox (
Since the DMN was split into aDMN and pDMN, nine resting-state networks (RSNs) were identified by using eight templates in all 29 ICA components for each of the 92 subjects. Correlation coefficients between the spatial templates and ICs of ICA analysis were as follows: CN (IC03), 0.297; aDMN (IC06), 0.488; CEN (IC07), 0.341; SMN (IC11), 0.755; SRN (IC18), 0.367; DAN (IC23), 0.511; AN (IC25), 0.783; VN (IC27), 0.771; pDMN (IC28), 0.383. The results of the one-sample
Results of one-sample
The two-sample
Group comparisons of the nine RSNs between the patients with FLE and HC.
Brain regions | Coordinates |
|
Voxels |
---|---|---|---|
|
|||
CN (cluster size > 33) | |||
Frontal_Inf_Tri_L | −51 24 18 | −3.55 | 96 |
Frontal_Sup_Medial_L | −3 57 21 | −3.27 | 35 |
Precentral_L | −39 3 48 | −3.29 | 34 |
aDMN (cluster size > 36) | |||
Frontal_Sup_Medial_L | 9 57 15 | −4.54 | 172 |
Superior frontal gyrus | 3 18 57 | −3.71 | 71 |
Precuneus_L | −3 −63 33 | −3.90 | 67 |
Angular_L | −39 −57 30 | −5.10 | 48 |
Cingulum_Mid_L | −6 −24 42 | 4.19 | 38 |
Precuneus_R | 21 −63 42 | 3.61 | 37 |
CEN (cluster size > 43) | |||
Cingulate gyrus | 6 −12 39 | −3.87 | 118 |
SMN (cluster size > 34) | |||
Precentral_L | −60 9 24 | −3.19 | 56 |
Precentral_R | 60 9 33 | −2.79 | 56 |
Postcentral_L | −42 −24 66 | −3.66 | 52 |
SRN (cluster size > 26) | |||
Medial frontal gyrus | −3 51 0 | −3.66 | 69 |
DAN (cluster size > 35) | |||
Temporal_Inf_R | 48 −60 −6 | −4.62 | 37 |
Frontal_Sup_L | −18 −3 63 | −4.04 | 44 |
Occipital_Mid_L | −24 −72 30 | −3.34 | 42 |
Precuneus_R | 0 −78 51 | −3.82 | 49 |
AN (cluster size > 36) | |||
Frontal_Inf_Tri_L | −39 39 12 | −4.97 | 69 |
Frontal_Mid_R | 36 57 0 | −4.32 | 41 |
Temporal_Mid_R | 42 −60 12 | −3.63 | 84 |
Temporal_Mid_L | −42 −63 9 | −3.60 | 50 |
Postcentral_L | −63 −21 27 | −3.87 | 104 |
VN (cluster size > 34) | |||
Lingual gyrus | 15 −69 0 | 4.29 | 186 |
Calcarine_R | 12 −63 15 | −3.64 | 35 |
pDMN (cluster size > 36) | |||
Postcentral_R | 27 −30 42 | 4.07 | 68 |
Precuneus_R | 18 −48 45 | 3.71 | 70 |
Precuneus_L | −15 −66 33 | −4.41 | 52 |
Precuneus_R | 21 −63 30 | −3.55 | 41 |
Note:
Results of the two-sample
Significant negative correlations were shown between the mean
Significant negative correlations between the mean
Significant correlation combinations were extracted for both groups separately and the results of FNC maps for each group are shown in Figure
FNC results of the FLE group (a) and the HC group (b); the color bars represent time lag (0–3 s). Arrows represented a significant correlation between RSNs (
The FNC results of the FLE group showed alterations in inter-RSNs connectivity; the combinations and the time lags were altered in the pairs of RSNs. Compared to the controls, combinations were found to be lost between aDMN/SMN, CN/SRN, CN/pDMN, and SMN/pDMN, while combinations were found to be newly added between CN/aDMN, CEN/aDMN, CEN/SRN, CEN/VN, and AN/pDMN in the FLE group. Time lags were found lower between CN/SMN but higher between DAN/SMN than in the HC group. Also observed were the directional differences in the time lags among components (i.e., from VN to CN in patients, while from CN to VN in controls).
This study focused on the intranetwork alterations of the RSNs in the FLE. Nine RSNs were selected to be analyzed. Compared with the HC group, decreased functional connectivity was observed in all the nine RSNs; however, in some areas of RSNs, functional connectivity was increased, including aDMN (Cingulum_Mid_L, Precuneus_R), VN (lingual gyrus), and pDMN (Postcentral_R, Precuneus_R). Correlation analysis found significant negative correlation between seizure frequency and CEN (ROI in cingulate gyrus), AN (ROI in Frontal_Mid_R), and VN (ROI in Calcarine_R) and significant negative correlation between duration of epilepsy and CN (ROI in Frontal_Sup_Medial_L), SMN (ROI in Precentral_R). Compared with the HC group, alterations of combinations in the intersystem of the RSNs were determined by FNC in the FLE group, but no significant difference was found between the FLE group and the HC group.
DMN, hypothesized to be involved in cognitive functions associated with intrinsic processing and external inputs [
In the perceptual network, SMN includes pre- and postcentral gyrus, the primary sensory-motor cortices, and the supplementary motor area; AN primarily encompasses the bilateral middle and superior temporal gyrus, Heschl gyrus, and temporal pole; VN includes the inferior, middle, and superior occipital gyrus and the temporal-occipital regions along with superior parietal gyrus [
In this present study, functional connectivity was found to be decreased in the CN, CEN, and DAN. It might present impairments in the three networks. CN (insula-cingulate cortices) had an important impact on cognitive control, including the anterior cingulate, the bilateral insular, and dorsolateral prefrontal cortices [
The functional connectivity was found decreased in the SRN in present study. SRN comprises the ventromedial prefrontal cortex (vMPFC), medial orbital prefrontal cortex (MOPFC), gyrus rectus, and pregenual anterior cingulate gyrus (PACC) [
Decreased functional connectivity is considered to result from the disruption of neuronal connection within a functional network and is commonly used to reflect cognitive impairments in brain disorders [
A higher frequency of seizures and a longer duration of epilepsy were associated with progression of gray and white matter atrophy in patients with mTLE [
In this study, combinations were found to be lost and newly added in inter-RSNs connectivity compared with controls. Moreover, alterations of the time lags were found in the pairs of RSNs. Otti et al. found lost connectivities or combinations in the patients [
Based on the detection of the RSNs by using ICA, 29 components were extracted by ICA, and nine RSNs were selected for study. This study explored the alterations of all the nine networks in patients with FLE. The findings of decreased and increased functional connectivity may be helpful to understanding the neuropath-physiological mechanism in patients with FLE. The decreased functional connectivity of the RSNs may implicate the impairment in patients with FLE. Increased functional connectivity of the RSNs may indicate compensatory mechanism in FLE. Five significant correlations results showed that seizure frequency and duration of epilepsy have an impact on the subregions of the RSNs in patients with FLE. Moreover, this study’s work is the first study that demonstrates results of resting FNC among RSNs in patients with FLE. The results may encourage further research about the effect of FLE on the human brain.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The authors would like to thank Dr. Zhang for the provision of data of patients with FLE and HC, Dr. Xu for his helpful suggestion to the paper, and Morgan for his revising of the paper. This research was supported by the Natural Science Foundation of China (Grant nos. 81422022, 81271553, 81201155, 81171328, 61131003, 61378092, and 81401402), Grants for Young Scholar of Jinling Hospital (Grant no. 2011061), and 12.5 Key Grant (BWS11J063 and 10z026).