Association Between Disconnected Networks and Surgical Outcome in Temporal Lobe Epilepsy

Association Between Disconnected Networks and Surgical Outcome in Temporal Lobe Epilepsy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association Between Disconnected Networks and Surgical Outcome in Temporal Lobe Epilepsy Xiaoting Huang, Chunyao Zhou, Danni Guo, Yangsa Du, Fangfang Xie, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4758395/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The purpose of the current study was to assess the change characteristics of structural network connection strength (SNCS), functional network connection strength (FNCS), and structural-functional correlation (SFC) of preoperative disconnected networks in temporal lobe epilepsy (TLE) and the capability of these measures to predict postoperative seizure outcomes. This study enrolled 39 TLE patients with 29 healthy controls (HCs). Patients were divided into seizure-free (SF) and non-seizure-free (NSF) groups. Each individual network was divided into four-tier subnets. FNCS was significantly lower in the 1st level (p = 0.007/p = 0.009), 2nd level (p = 0.002/p < 0.001), and 3rd level disconnect networks (p < 0.001/p < 0.001) in SF and NSF(27.0 ± 8.4, 16 men; 30.9 ± 10.9, 8 men). SNCS was significantly lower in SF than HCs (33.3 ± 8.9, 15 men) and NSF (p = 0.008/p = 0.037) in the 1st level disconnected network. Further, significant SFC in the 1st level (p = 0.026/R²=0.169), 2nd level (p = 0.002/R²=0.283), and 3rd level disconnected networks (p = 0.011/R²=0.213) in HCs. Meanwhile, the SF group showed a positive SFC at the 2nd level disconnected network (p = 0.039/R²=0.159). Our study demonstrated that the consistency in SNCS and FNCS of the regions indirectly affected by surgery is associated with lower risk of post-surgical seizure. Preoperative multimodal magnetic resonance imaging may serve as an important tool to guide surgical planning. Biological sciences/Neuroscience/Diseases of the nervous system Biological sciences/Neuroscience/Diseases of the nervous system/Epilepsy Biological sciences/Structural biology/Nmr spectroscopy Health sciences/Medical research/Outcomes research Health sciences/Medical research/Study design/Clinical trials Health sciences/Neurology/Neurological disorders Health sciences/Medical research Health sciences/Neurology functional magnetic resonance imaging diffusion tensor imaging temporal lobe epilepsy surgical outcomes Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Surgery is an effective treatment option for patients with drug-resistant temporal lobe epilepsy (TLE); however, 30–40% of patients continue to experience post-surgical seizures 1 .Several factors may contribute to favorable post-surgical outcomes, including shorter disease duration, lower seizure frequency, absence of generalized seizures, and the presence of unilateral mesial temporal lobe sclerosis 2–5 . Despite preoperative multidisciplinary clinical evaluations, such as electrophysiology, seizure semiology, neuropsychiatric and imaging assessment, surgeons are currently unable to guarantee long-term post-surgical outcomes. Thus, there is an urgent need to identify a novel marker that can noninvasively predict surgical outcomes in TLE. Evidence has shown that epileptogenic regions of TLE are abnormally organized in the form of epileptogenic networks (EN) 6,7 . Non-invasive neuroimaging techniques and network analyses provide substantial advantages for estimation of global EN distribution. In terms of network characteristics, previous studies have revealed disruptions in both structural and functional networks in TLE patients 8–11 , which manifest as regional or global decreases in connectivity strength 12 . Notably, surgery-induced network connectivity alternations are closely linked to surgical outcomes 13 . For instance, in patients with good surgical prognosis, the functional connectivity of the basal ganglia network gradually decreases, while the functional connections between the sensorimotor network, dorsal attention network, and visual network increases 14 . However, structural and functional network abnormalities, which manifest as totally different patterns, are usually studied independently. For instance, the change of functional connectivity in TLE is mainly characterized by an extensive decrease of the whole brain, as shown by DMN 12 and the subcortical networ 14 , while the alterations in the structural connectivity are relatively limited, and generally confined to the temporal lobe and adjacent areas 15–17 . Therefore, it is difficult to quantitively delineate any discrepancies. Thus, only a few studies have combined diffusion imaging and functional imaging to analyze the relationship between whole-brain connectomes and TLE, using network-wise structural connectome-functional connectome (SC-FC) coupling features or macroscale voxel-wise connectome analysis. Wei et al. analyzed the topological properties and connection characteristics of the two-level disconnection networks in glioma patients and explored the relationship between the injury mapping network and postoperative curative effects 18 . Other studies have validated the association between postoperative curative effects and hippocampal removal or remote structural nodal abnormalities 19,20 . No study has analyzed the relationship between network SC-FC correlation and surgical prognosis in TLE. Constructing a “disconnect” network based on the region of resection (ROR) might be the key to quantitative analysis of network prognosis of temporal lobectomy in intractable TLE. Therefore, we aimed to identify the characteristics of the disconnection network in patients with TLE and further investigate the relationship between network-based disconnect features and surgical outcomes. We hypothesized that structural and functional changes in the preoperative disconnect network are associated with surgical prognoses. Results Demographic and clinical characteristics Thirty-nine patients with drug resistance TLE who underwent epilepsy surgery at the Department of Functional Neurosurgery, Xiangya Hospital from 2018 to 2022 were retrospectively enrolled, including 27 SF patients and 12 NSF patients. No significant differences between SF and NSF patients were detected for total resection volume (p = 0.34). Twenty-nine age-sex-matched HCs were included. ANOVA showed no significant differences among the three groups in age (p = 0.05), sex (p = 0.31), educational level (p = 0.32), or age of onset (p = 0.53) (Table 1 ). Table 1 Demographic and Clinical Characteristics HCs SF NSF p value n 29 27 12 / Age (years, mean ± SD) 33.3 ± 8.9 27.0 ± 8.4 30.9 ± 10.9 P = 0.051 Years of education (years, mean ± SD) 11.2 ± 3.1 11.0 ± 3.3 9.6 ± 2.7 P = 0.326 Sex(M/F) 15/14 11/16 8/4 χ²=2.290 P = 0.317 Handedness(L/R/A) 0/29/0 0/27/0 0/12/0 / Age at onset (years, mean ± SD) / 13.7 ± 9.0 11.5 ± 12.8 P = 0.539 Duration of epilepsy (years, mean ± SD) / 14.1 ± 9.2 17.5 ± 10.7 P = 0.329 Number of medications / 1.5 ± 0.7 2.0 ± 0.7 P = 0.051 Presence of HS / 21 12 / TLE laterality L/R / 16/11 4/8 χ²=2.230 P = 0.134 Total resection volume / 32.6 ± 16.8 27.0 ± 17.7 0.348 *HCs: healthy controls; SF: seizure free; NSF: not seizure free; (L/R/A):(left/right/ambidextrous); HS: hippocampal sclerosis; TLE: temporary lope epilepsy; Spacial distribution of disconnected nodes Symmetric nodes structurally connected to the ROR (47 left nodes [47/59, 79%] and 47 right nodes [47/52, 90%]) were selected as the final disconnected nodes in subsequent analyses. According to Yeo’s functional subnets, the disconnect nodes were mostly distributed at subcortical (n = 14/14), limbic (n = 10/10), and default mode networks (DMN) (n = 6/8). Other nodes were located at visual (n = 4/5), sensorimotor (n = 4/4), ventral attention (n = 4/4), dorsal attention (n = 1/2), and frontoparietal networks (n = 1/3) (Fig. 2 ). FNCS differences in the disconnected network Total FNCS of the four subnetworks for each individual showed significant inter-group differences under the 1st level (p = 0.007), 2nd level (p < 0.001), and 3rd level disconnect networks (p < 0.001) (one-way ANCOVA) (Fig. 3 a). Area under the curve (AUC) was applied to calculate overall FNCS according to sparsity. The results were approximately the same as above. Post-hoc analysis of the functional AUC revealed that FNCS was significantly lower in the 1st level (p = 0.007/0.009), 2nd level (p = 0.002/p < 0.001), and 3rd level disconnect networks (p < 0.001/p < 0.001) in SF and NSF compared to HCs. Meanwhile, within all sparsity groups and the AUC value, NSF had lower mean values than the SF, but the difference was not significant. Compared to SF, the total FNCS was significantly lower in NSF in the 3rd level disconnect network only (post-hoc least significant difference [LSD], p = 0.049) (Fig. 3 b, Table 2 ). Table 2 FNCS and SNCS in the disconnected network HC SF NSF P-Anove P-value HCvsSF HCvsNSF NSFvsSF F-1st 93.31 ± 35.52 67.17 ± 36.47 60.73 ± 31.99 0.007 0.007 0.009 0.602 F-2nd 607.3 ± 175.5 464.70 ± 170.6 375.1 ± 132.7 <0.001 0.002 <0.001 0.127 F-3rd 4090 ± 801.8 2971 ± 846.6 2398 ± 830.6 <0.001 <0.001 <0.001 0.049 F-unaffected 13407 ± 2311 13432 ± 2733 12734 ± 2414 0.693 0.970 0.437 0.425 S-1st 1867 ± 249.3 1572 ± 459.2 1870 ± 555.4 0.017 0.008 0.983 0.037 S-2nd 5025 ± 623.9 5017 ± 896.7 4764 ± 890.3 0.595 0.969 0.340 0.360 S-3rd 5123 ± 519.9 5116 ± 808.7 4968 ± 812.2 0.793 0.968 0.521 0.545 S-unaffected 45334 ± 5069 46000 ± 6064 47858 ± 7641 0.472 0.678 0.223 0.37 *HC: healthy control; SF: seizure free; NSF: not seizure free; FNC: Functional Network Connectivity; SNC: Structural Network Connectivity; F-1st :Function-1st level disconnect network; F-2nd : Function-2nd level disconnect network; F-3rd :Function-3rd level disconnect network; F- unaffected :Function- Unaffected network; S-1st : Structure − 1st level disconnect network; S-2nd : Structure − 2nd level disconnect network; S-3rd : Structure − 3rd level disconnect network; S- unaffected : Structure - Unaffected network; SNCS differences in the disconnect network Compared to the FNCS, the SNCS showed fewer inter-group variates. ANCOVA calculations yielded significant inter-group differences among the three groups only for the 1st level disconnected network (p = 0.017). No statistical difference was found in the other three networks. Post-hoc LSD suggested that SFCS was significantly lower in SF than HCs (p = 0.008) and NSF (p = 0.037) (Fig. 3 c, Table 2 ). SFC of disconnected networks We calculated SFC at each level of the disconnected network of every participant. Significant SFC in the 1st level (p = 0.007/R = 0.504), 2nd level (p = 0.001/R = 0.624), and 3rd level disconnected networks (p = 0.003/R = 0.546) were discovered in HCs, but not in the unaffected network (p = 0.587/R = 0.109). No SFC was observed at any subnetwork in either SF or NSF (Table 3 ). Linear regression revealed a significantly positive linear relationship between structural and functional connection strength in the 1st level (p = 0.026/R 2 = 0.169), 2nd level (p = 0.002/R²=0.283), and 3rd level disconnected networks (p = 0.011/R²=0.213) in HCs. Meanwhile, the SF group showed a positive SFC at the 2nd level disconnected network (p = 0.039/R²= 0.159) (Fig. 4 ). Table 3 correlation between FNCS and SNCS HCs SF NSF 1st level discon P = 0.007 R = 0.504 P = 0.268 R = 0.057 P = 0.193 R = 0.051 2nd level discon P = 0.001 R = 0.624 P = 0.066 R = 0.382 P = 0.372 R = 0.372 3rd level discon P = 0.003 R = 0.546 P = 0.108 R = 0.336 P = 0.303 R = 0.387 Unaffected P = 0.587 R = 0.109 P = 0.790 R = 0.268 P = 0.896 R=-0.478 *HCs: healthy controls; SF: seizure-free. NSF: non-seizure-free. FNCS: functional network connection strength; SNCS: structural network connection strength. Discussion In this study, we analyzed the preoperative DTI and rs-fMRI of patients with TLE to quantify macro-scaled structural and functional disconnected networks. Our main findings included: (i) among all subnetworks classified by direct or indirect structural connection to the ROR, the structural and functional connectivity of disconnection subnets showed specific abnormalities in TLE patients with different surgical outcomes; (ii) the degree of functional abnormalities was higher in NSF than SF; and (iii) apart from the second disconnect network in SF, the SFC of disconnected networks in TLE patients generally decreased, suggesting that different SFC statuses at disconnect subnets may be a potential radiological feature leading to prognosis prediction. This study provides a theoretical basis for preoperative imaging evaluation of seizure outcomes in intractable TLE. It is the first study to establish a relationship between SFC and surgical prognosis based on disconnect networks. The disconnected nodes affected by surgical resection were located symmetrically at the DMN, limbic system, and subcortical networks. SNCs and FNCs are highly coupled at those networks in healthy populations 14,21–23 , which matched our findings on the disconnect subnets in HCs. Interestingly, these networks have also exhibited functional or structural segregation in previous epilepsy studies 14,21–23 . The structural adjacency to the epicenter and functional complexity of those hub nodes strongly indicates that disconnect subnets are highly complex and delicate systems. These disconnect networks are naturally vulnerable to impact, such as surgical resection, and show advantages in surgery outcome prediction 24 . TLE is a chronic neurological disorder with a broad spectrum of brain network abnormalities, commonly accompanied by a regional decrease in network integration and connection strength 25 , mostly at the DMN 12 and basal ganglia network 14 . Further studies found that lower whole-brain FNCS is associated with persistent seizures after surgery 26 . However, in our study, similar results were observed at the 1st, 2nd, and 3rd level disconnect subnets only and the abnormality patterns differed between SF and NSF at the 3rd subnets, demonstrating that networks indirectly affected by surgical resection might be the key to determining surgical prognosis, which has been proven in other modules 20 . We speculate that the abnormal preoperative functional connection of the 2nd level disconnected network may be the main cause of postoperative seizures because the segmentation of functional modules within the 2nd level disconnected network will not disappear after surgical resection. SNC alternations in TLE patients are relatively limited compared with the wide decrease of FNC in the whole-brain. The most common changes in SNC in TLE patients are changes in the diffusion coefficient of the hippocampus and the peripheral WM in the mesial temporal lobe, which are generally restricted to the temporal lobe and adjacent areas, 15 as well as decreases in total connection count to the epicenter 12 . In our study, similar results were observed. We found a significant decrease in the SNCS only in the 1st level of the disconnected network. Different from FNCS changes, the decrease of the 1st level in SNCS in the SF group was more significant than that in the NSF and HC 27 groups. Previous studies have demonstrated that synchronous electroencephalogram activity in TLE patients is associated with highly arranged structural connection, indicating that more structural pathways can be beneficial for seizure facilitation 28 Therefore, we speculate that patients with high SNCS may be more likely to cause synchronous discharges, leading to recurrent seizures. Our findings suggest that both the SNCS and FNCS of TLE patients undergo robust changes, and the patterns of these changes differ among modalities. Furthermore, SFC in patients with TLE decreased significantly or even vanished in the disconnected network, while a significant SFC was observed in HCs. Previous studies have reported strong SFC in brain regions with dense structural connections in healthy individuals 29 , while it is weakened in epilepsy patients 8 . For instance, individuals with TLE and hippocampal sclerosis exhibit a significant decoupling in various brain regions 8,9 , including the hippocampus, anterior and posterior DMN 10 , and auditory and language network (left frontal parietal network) 8 . In the current study, disconnected nodes were primarily situated in the limbic system, DMN, and frontoparietal network, which further elucidates the reduction in SFC of the disconnected network in TLE patients. According to Liu et al., disruption of superficial WM in individuals with TLE is a crucial factor in the decline of SFC. Therefore, we contend that the varying distribution of abnormal structural and functional connections is also a specific pathological manifestation of TLE. Due to significant differences in the spatial distribution and pattern of structural and functional network changes in patients with TLE, numerous studies have shown a widespread decrease or absence of SFC in the brain networks of epilepsy patients 8–10 . However, we observed a positive SFC in the brain regions that were indirectly affected by surgical resection in the SF group. This phenomenon suggested that the disconnectome may be a key factor influencing postoperative prognosis. Since these networks are not directly removed by surgery, any preoperative imbalance in their structural and functional consistency will remain. However, removing the epileptogenic focus from the original network can still cause indirect damage to disconnect networks and worsen its structural and functional decoupling. This can lead to the development of further structural and functional disconnection of those regions, ultimately creating a new segregation zone, which may result in the recurrence of epilepsy. Therefore, we hypothesize that the consistency of the structural and functional connections within the preoperative disconnected network could minimize the occurrence of abnormal nodes and reduce the incidence of postoperative seizures. Our study had several limitations. First, the retrospective design may limit the reliability of the results. Second, we only observed an indirect impact of the brain network on surgical outcome, and further investigations are needed to elucidate the specific mechanisms underlying postoperative brain network discharges and their association with seizures. Third, the sample size was relatively small, and no predictive model was established. Therefore, future studies with larger sample sizes are required. In summary, patients with TLE exhibit a widespread decrease in functional connectivity intensity within the disconnection network, indicating a weak SFC of the brain network. The concordance of changes of SNCS and FNCS in brain regions indirectly affected by surgery is predictive of postoperative seizure freedom. Therefore, preoperative multimodal magnetic resonance imaging may serve as an important tool to guide surgical planning. Materials and methods Participants This study was approved by the Ethics Committee of Xiangya Hospital and all methods were performed in accordance with the relevant guidelines and regulations. All participants or their legal guardians provided written informed consent according to the Declaration of Helsinki. Thirty-nine unilateral drug-resistant TLE patients diagnosed and surgically treated with anterior temporal lobectomy at Xiangya Hospital between 2017 and 2022 were retrospectively analyzed. Diagnosis of drug-resistant TLE was determined based on the International League Against Epilepsy criteria 30 . Inclusion criteria included diagnosis of TLE, diagnosis of drug-resistant epilepsy, and no contraindications for surgical resection. Exclusion criteria included pre-surgical intracranial monitoring, progressive neurological disease, focal lesion not in the temporal region, and severe mental disorder. Seizure outcomes were assessed by an epileptologist 1 year post-surgery (up to 2 years) using the Engel surgery outcome classification 31 as seizure-free (SF; Engel Class Ia) or non-seizure-free (NSF; Engel Class Ib through IV). Twenty-nine healthy controls (HCs) were also analyzed. MRI acquisition Resting-state fMRI (rs-fMRI) data were acquired on a 3.0 Tesla Siemens Prisma MRI system with a standard 32-channel head coil (Xiangya Hospital, Central South University). rs-fMRI was collected utilizing echo planar imaging (EPI) sequences with the following parameters: TR = 720 ms, TE = 37 ms, flip angle = 52°, 64 axial slices with 2.5-mm thickness and 2.5-mm gap, matrix size = 90×90, field of view (FOV) = 225 mm×255 mm, voxel size = 2.5 mm×2.5 mm×2.5 mm. Each resting-state scan lasted 9.456 min, resulting in 788 volumes. Diffusion tensor imaging (DTI) data were acquired using a multi-shell EPI sequence according to the following parameters: TR = 5400 ms, TE = 72 ms, axial slices = 75, resolution = 2.0 × 2.0 × 2.0 mm, flip angle = 90°; FOV = 215 × 215 mm; voxel size = 1.6 × 1.6 × 1.6 mm, number of directions = 96, b = 0/1000/2000/3000 s/mm 2 , and EPI factor = 154. rs-fMRI preprocessing rs-fMRI data were preprocessed using a Graph Theory Network Analysis Toolkit. Briefly, the preprocessing steps were: ( 1 ) data format conversion from DICOM to NIFTI; ( 2 ) removal of the first 18 timepoint volumes, resulting in a total of 770 volumes; ( 3 ) slice timing correction; ( 4 ) spatial realignment for motion correction; ( 5 ) normalization to Montreal Neurological Institute space via EPI template; ( 6 ) spatial smoothing; ( 7 ) temporally detrending; and ( 8 ) global signal regression. The detailed processing steps are described on the GRETNA 32 website (GRETNA. https://github.com/sandywang/GRETNA ), a pipeline toolbox based on SPM 12 ( https://www.fil.ion.ucl.ac.uk/spm/software/spm12/ ). DTI preprocessing The DTI preprocessing pipeline was applied with FSL ( https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/ ). Briefly, diffusion data were preprocessed as follows: ( 1 ) dicom to nii conversion; ( 2 ) B0 image extraction; ( 3 ) global denoising; ( 4 ) skull stripping with BET; ( 5 ) Eddy current correction; and ( 6 ) estimation of basic diffusion metrics, including fractional anisotropy (FA) and mean diffusivity (MD). After preprocessing, constrained spherical deconvulsion (CSD)-based diffusion tensor estimation and global probabilistic tractography was performed with Mrtrix3 ( https://www.mrtrix.org/ ). The brief steps were: ( 1 ) estimating response function, ( 2 ) multi-shell multi-tissue CSD, ( 3 ) estimation of fiber orientation distributions (FOD) 33 ; and ( 4 ) global tractography. Region of resection (ROR) To minimize the possible effects of short-term postoperative brain tissue edema or long-term postoperative brain atrophy and scarring on the surgical region, we used CT within 6 h after surgical resection, supplemented by MRI within 24–48 h for ROR segmentation. Postoperatively, resection masks were manually segmented by two experienced neurologists using MRICRON. Masks were nonlinearly normalized and overlapped by SPM12 ( https://www.fil.ion.ucl.ac.uk/spm/ ), and for the overlapped volumes, voxels with overlap values of 5 or less were dropped to enhance the specificity of resected regions. Left- and right-sided masks were calculated separately. Finally, masks were superimposed onto the Brainnetome Atlas (BN) 34 ( http://www.brainnetome.org/ ) to obtain the resection-related segmentation map (Fig. 1 a). To explore the distribution of disconnected nodes, we segmented all disconnected nodes into seven functional networks according to Brainnetome’s open-source data ( http://atlas.brainnetome.org/download.html ), which is based on the Yeo-7 Atlas 35 . We included the subcortical regions as the 8th module (Fig. 2 ). Structural connectivity network construction To construct the whole-brain white matter (WM) connectome matrix, CSD-based probabilistic fiber tracking was used to track WM connections for all possible node pairs including the ROR. We selected all brain regions in the BN based on the left and right atlases with ROR. Before each individual’s matrix was delivered for further analysis, a backbone method with a threshold of > 75% was applied to minimize errors caused by normalization errors or partial volume effects. For each participant, the WM connection strength was determined by the number of tractography streamlines between all brain regions. The columns and rows of each individualized connection matrix represented nodes, while the elements in the matrices (Cij) represented the strength of WM connection between the i and j nodes (Fig. 1 c). Functional connectivity network construction Correlation matrices were generated based on time series signals. Pearson’s correlation was used to evaluate the relationship among the time series by r-values, from which a 247×247 correlation matrix could be obtained for each subject. For the connectivity matrices that entailed correlation strengths (r), we transformed the correlation values into z-values using Fisher’s r to z transformation. For each connectivity matrix, network sparsity ranging from 5–12% with intervals of 1% were selected (Fig. 1 c). Building the disconnectome atlas A three-tier disconnection atlas was built based on the group-averaged WM connectome and the group ROR mask. The steps were: Definition of nodes : (i) disconnected nodes: nodes that have a direct functional connection to the ROR, and (ii) unaffected nodes: whole-brain nodes except for the remaining nodes of the disconnected nodes. To make the left and right disconnected networks comparable, we selected 47 symmetrical disconnected nodes on the basis of the original disconnected nodes. Definition of disconnected networks : (i) 1st level disconnect network: the edges between ROR and neighboring disconnected nodes, (ii) 2nd level disconnect network: the edges within the disconnected nodes, (iii) 3rd level disconnect network: the edges between the disconnected nodes and the unaffected nodes, and (iv) unaffected network: the edges composed entirely of unaffected nodes. The disconnectome atlas was set and we projected this atlas to every individual’s functional and structural connectivity matrices. Thus, each structural/functional edge or node was marked with one subtype of the disconnected network (Fig. 1 b). Statistical analysis All statistical analyses were performed using SPSS ( https://www.ibm.com/cn-zh/spss ) version 22 (IBM Corporation, Armonk, NY, United States). According to the type of information, between-group differences among the TLE subgroups (SF and NSF) and HCs were investigated using one-way ANCOVA followed by post-hoc t-tests. The level of significance was p < 0.05 (two-sided significant testing), and multiple comparison corrections were performed using the false discovery rate. Partial correlation analysis and linear regression analysis were used to identify linear trends between the strength of structural and functional connectivity of disconnect subnets.· Group comparisons of clinical characteristics were calculated by nonparametric T-test, one-way ANOVA, and chi-square test. In group analyses, baseline characteristics, including age, sex, and education level were set as covariates. Declarations Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This study has received funding by Grant No. 2022YFC2503804 from National Key Program of China, Grant Nos. 82071461, 82271503 from National Natural Science Foundation of China, and Grant No. 2021JJ31060 from Natural Science Foundation of Hunan Province. Author Contribution C.Z. and L.F. conceived the study. X.H. D.G. Y.D.and F.X. collected the data. C.Z. and X.H. analyzed the data. X.H. wrote the manuscript. L.F. and C.Z. revised the manuscript. X.H. D.L. collected the follow-up data. All authors contributed to the article and approved the submitted version. Acknowledgement We are grateful to all of our research participants, without their patience and cooperation, this work would not be completed. This work was supported by the Department of Neurology, Neurosurgery and Radiology, Xiangya Hospital, Central South University and Key Laboratory for Neuroinformation of Ministry of Education, Center for Information in BioMedicine, School of Life Sciences and Technology, University of Electronic Science and Technology of China. Data Availability The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors. References Engel, J. et al. Early Surgical Therapy for Drug-Resistant Temporal Lobe Epilepsy A Randomized Trial. Jama-J Am Med Assoc 307 , 922-930, doi:10.1001/jama.2012.220 (2012). Foldvary, N. et al. Seizure outcome after temporal lobectomy for temporal lobe epilepsy - A Kaplan-Meier survival analysis. Neurology 54 , 630-634, doi:Doi 10.1212/Wnl.54.3.630 (2000). Janszky, J. et al. Temporal lobe epilepsy with hippocampal sclerosis: predictors for long-term surgical outcome. 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Structural Brain Network Abnormalities and the Probability of Seizure Recurrence After Epilepsy Surgery. Neurology 96 , E758-E771, doi:10.1212/Wnl.0000000000011315 (2021). Focke, N. K. et al. Voxel-based diffusion tensor imaging in patients with mesial temporal lobe epilepsy and hippocampal sclerosis. Neuroimage 40 , 728-737, doi:10.1016/j.neuroimage.2007.12.031 (2008). Guo, D. N. et al. Altered Temporal Variations of Functional Connectivity Associated With Surgical Outcomes in Drug-Resistant Temporal Lobe Epilepsy. Front Neurosci-Switz 16 , doi:ARTN 840481 10.3389/fnins.2022.840481 (2022). Bernhardt, B. C., Bernasconi, N., Hong, S. J., Dery, S. & Bernasconi, A. Subregional Mesiotemporal Network Topology Is Altered in Temporal Lobe Epilepsy. Cereb Cortex 26 , 3237-3248, doi:10.1093/cercor/bhv166 (2016). Talozzi, L. et al. Latent disconnectome prediction of long-term cognitive-behavioural symptoms in stroke. Brain 146 , 1963-1978, doi:10.1093/brain/awad013 (2023). Van Diessen, E. et al. Brain Network Organization in Focal Epilepsy: A Systematic Review and Meta-Analysis. Epilepsia 55 , 234-234 (2014). DeSalvo, M. N., Tanaka, N., Douw, L., Cole, A. J. & Stufflebeam, S. M. Contralateral Preoperative Resting-State Functional MRI Network Integration Is Associated with Surgical Outcome in Temporal Lobe Epilepsy. Radiology 294 , 622-627, doi:10.1148/radiol.2020191008 (2020). Douw, L. et al. Dissociated multimodal hubs and seizures in temporal lobe epilepsy. Ann Clin Transl Neur 2 , 338-352, doi:10.1002/acn3.173 (2015). Shah, P. et al. High interictal connectivity within the resection zone is associated with favorable post-surgical outcomes in focal epilepsy patients. Neuroimage-Clin 23 , doi:ARTN 101908 10.1016/j.nicl.2019.101908 (2019). Wang, Z. J., Dai, Z. J., Gong, G. L., Zhou, C. S. & He, Y. Understanding Structural-Functional Relationships in the Human Brain: A Large-Scale Network Perspective. Neuroscientist 21 , 290-305, doi:10.1177/1073858414537560 (2015). Fisher, R. S. et al. Operational classification of seizure types by the International League Against Epilepsy: Position Paper of the ILAE Commission for Classification and Terminology. Epilepsia 58 , 522-530, doi:10.1111/epi.13670 (2017). Engel, J. et al. Practice parameter: Temporal lobe and localized neocortical resections for epilepsy - Report of the Quality Standards Subcommittee of the American Academy of Neurology, in association with the American Epilepsy Society and the American Association of Neurological Surgeons. Epilepsia 44 , 741-751, doi:DOI 10.1046/j.1528-1157.2003.48202.x (2003). Wang, J. H. et al. GRETNA: a graph theoretical network analysis toolbox for imaging connectomics (vol 9, 386, 2015). Front Hum Neurosci 9 , doi:ARTN 458 10.3389/fnhum.2015.00458 (2015). Tournier, J. D., Calamante, F. & Connelly, A. Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution. Neuroimage 35 , 1459-1472, doi:10.1016/j.neuroimage.2007.02.016 (2007). Fan, L. Z. et al. The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture. Cereb Cortex 26 , 3508-3526, doi:10.1093/cercor/bhw157 (2016). Yeo, B. T. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 106 , 1125-1165, doi:10.1152/jn.00338.2011 (2011). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4758395","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":338225465,"identity":"26d05358-c4b2-4865-875d-4f6ad0461603","order_by":0,"name":"Xiaoting Huang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoting","middleName":"","lastName":"Huang","suffix":""},{"id":338225466,"identity":"1fd6910b-7efb-4ed7-97af-1212d9f6b350","order_by":1,"name":"Chunyao Zhou","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Chunyao","middleName":"","lastName":"Zhou","suffix":""},{"id":338225467,"identity":"237ba177-986f-4fdf-8023-21a21bbda898","order_by":2,"name":"Danni Guo","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Danni","middleName":"","lastName":"Guo","suffix":""},{"id":338225468,"identity":"27c0dc36-41fb-4cf1-bf14-0e4cc22ace66","order_by":3,"name":"Yangsa Du","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Yangsa","middleName":"","lastName":"Du","suffix":""},{"id":338225470,"identity":"fee95a84-9a0e-4671-803b-c01505608a1d","order_by":4,"name":"Fangfang Xie","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Fangfang","middleName":"","lastName":"Xie","suffix":""},{"id":338225473,"identity":"dc23ba15-736c-4e8c-b518-ce94de08b52f","order_by":5,"name":"Li Feng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBAC9gYg8cDgAAM/M/PhB0Rp4TkAJBKAWiTb2dIMSNDCcIDB4DyPggRxWth7D79IKLiTuPkwD4MBQ41NNGEtPOfSLBIMnhmbHeY98IDhWFpuAyEt9hI5ZgYJBoflzA7zJRgwNhwmrIVH/g1YC49xM4+BBHFaJHiMH4BsMWAmWgtPjhkwkA8bSxwGBnICMX7hYT9j/OHDn8OJ/f2HDz/4UGNDWAsQsCGiI4EI5SDA/IFIhaNgFIyCUTBSAQAVhj5Rnhj5BwAAAABJRU5ErkJggg==","orcid":"","institution":"Central South University","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Feng","suffix":""}],"badges":[],"createdAt":"2024-07-17 18:59:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4758395/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4758395/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63156584,"identity":"02b33643-1a63-4164-9897-dc78947654f3","added_by":"auto","created_at":"2024-08-23 21:15:25","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3610643,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic illustration of the construction of a multi-stage disconnection network from functional magnetic resonance imaging (fMRI) and diffusion tensor imaging data. a) The region of resection is masked according to the computed tomography (CT) or magnetic resonance imaging (MRI) after surgery. b) The whole-brain disconnection network is divided based on individual structural network and region of resection and projected to structural network and functional network matrices. c) Visualization of the whole-brain disconnected network.\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4758395/v1/44ca7c10feac53f6215d33dc.jpg"},{"id":63156586,"identity":"fc84099c-a982-40e0-8d7e-e2e5fab03c6d","added_by":"auto","created_at":"2024-08-23 21:15:25","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1848308,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of disconnected nodes in the whole-brain. a) Symmetric nodes structurally connected to the region of resection (ROR) (left 47 nodes [59/47, 79%] and right 47 nodes [47/52, 90%]) are selected as the final disconnected nodes. b) Bar chart of the distribution of left and right disconnected nodes in each functional network. c) Distribution model diagram of left and right disconnected nodes in each functional network.\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4758395/v1/d60258b06ecdd898188d87c2.jpg"},{"id":63156587,"identity":"5e85310a-bcfd-4b40-b311-152373e5feda","added_by":"auto","created_at":"2024-08-23 21:15:26","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2213931,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of structural network connectivity strength (SNCS) and functional network connectivity strength (FNCS) among seizure-free (SF), non-seizure-free (NSF), and healthy controls (HCs) groups. a) The inter-group differences in FNCS according to sparsity in the four-tier subnets. b) The area under the curve (AUC) was applied to calculate the overall FNCS according to sparsity. c) The inter-group differences of SNCS according to sparsity in the four-tier subnets.\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4758395/v1/ceafb60039177be85756a2a7.jpg"},{"id":63156585,"identity":"bbfdde41-e433-4d33-9bbd-9423538fc4ec","added_by":"auto","created_at":"2024-08-23 21:15:25","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1944193,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between functional network connectivity strength (FNCS) and structural network connectivity strength (SNCS) at each level of the disconnected network among the three groups.\u003c/p\u003e","description":"","filename":"figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4758395/v1/29f23667c89eb27cd9b4a009.jpg"},{"id":65440242,"identity":"1ee38183-f1b2-47f5-a6ba-74ff7b859440","added_by":"auto","created_at":"2024-09-27 12:46:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10232858,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4758395/v1/6292abfc-b341-4a70-93ba-da10115c1bcc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Disconnected Networks and Surgical Outcome in Temporal Lobe Epilepsy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSurgery is an effective treatment option for patients with drug-resistant temporal lobe epilepsy (TLE); however, 30\u0026ndash;40% of patients continue to experience post-surgical seizures\u003csup\u003e1\u003c/sup\u003e.Several factors may contribute to favorable post-surgical outcomes, including shorter disease duration, lower seizure frequency, absence of generalized seizures, and the presence of unilateral mesial temporal lobe sclerosis\u003csup\u003e2\u0026ndash;5\u003c/sup\u003e. Despite preoperative multidisciplinary clinical evaluations, such as electrophysiology, seizure semiology, neuropsychiatric and imaging assessment, surgeons are currently unable to guarantee long-term post-surgical outcomes. Thus, there is an urgent need to identify a novel marker that can noninvasively predict surgical outcomes in TLE.\u003c/p\u003e \u003cp\u003eEvidence has shown that epileptogenic regions of TLE are abnormally organized in the form of epileptogenic networks (EN)\u003csup\u003e6,7\u003c/sup\u003e. Non-invasive neuroimaging techniques and network analyses provide substantial advantages for estimation of global EN distribution. In terms of network characteristics, previous studies have revealed disruptions in both structural and functional networks in TLE patients\u003csup\u003e8\u0026ndash;11\u003c/sup\u003e, which manifest as regional or global decreases in connectivity strength\u003csup\u003e12\u003c/sup\u003e. Notably, surgery-induced network connectivity alternations are closely linked to surgical outcomes\u003csup\u003e13\u003c/sup\u003e. For instance, in patients with good surgical prognosis, the functional connectivity of the basal ganglia network gradually decreases, while the functional connections between the sensorimotor network, dorsal attention network, and visual network increases\u003csup\u003e14\u003c/sup\u003e. However, structural and functional network abnormalities, which manifest as totally different patterns, are usually studied independently. For instance, the change of functional connectivity in TLE is mainly characterized by an extensive decrease of the whole brain, as shown by DMN\u003csup\u003e12\u003c/sup\u003e and the subcortical networ\u003csup\u003e14\u003c/sup\u003e, while the alterations in the structural connectivity are relatively limited, and generally confined to the temporal lobe and adjacent areas\u003csup\u003e15\u0026ndash;17\u003c/sup\u003e. Therefore, it is difficult to quantitively delineate any discrepancies. Thus, only a few studies have combined diffusion imaging and functional imaging to analyze the relationship between whole-brain connectomes and TLE, using network-wise structural connectome-functional connectome (SC-FC) coupling features or macroscale voxel-wise connectome analysis. Wei et al. analyzed the topological properties and connection characteristics of the two-level disconnection networks in glioma patients and explored the relationship between the injury mapping network and postoperative curative effects\u003csup\u003e18\u003c/sup\u003e. Other studies have validated the association between postoperative curative effects and hippocampal removal or remote structural nodal abnormalities\u003csup\u003e19,20\u003c/sup\u003e. No study has analyzed the relationship between network SC-FC correlation and surgical prognosis in TLE. Constructing a \u0026ldquo;disconnect\u0026rdquo; network based on the region of resection (ROR) might be the key to quantitative analysis of network prognosis of temporal lobectomy in intractable TLE.\u003c/p\u003e \u003cp\u003eTherefore, we aimed to identify the characteristics of the disconnection network in patients with TLE and further investigate the relationship between network-based disconnect features and surgical outcomes. We hypothesized that structural and functional changes in the preoperative disconnect network are associated with surgical prognoses.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and clinical characteristics\u003c/h2\u003e \u003cp\u003eThirty-nine patients with drug resistance TLE who underwent epilepsy surgery at the Department of Functional Neurosurgery, Xiangya Hospital from 2018 to 2022 were retrospectively enrolled, including 27 SF patients and 12 NSF patients. No significant differences between SF and NSF patients were detected for total resection volume (p\u0026thinsp;=\u0026thinsp;0.34). Twenty-nine age-sex-matched HCs were included. ANOVA showed no significant differences among the three groups in age (p\u0026thinsp;=\u0026thinsp;0.05), sex (p\u0026thinsp;=\u0026thinsp;0.31), educational level (p\u0026thinsp;=\u0026thinsp;0.32), or age of onset (p\u0026thinsp;=\u0026thinsp;0.53) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and Clinical Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNSF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of education (years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex(M/F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15/14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11/16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=2.290 P\u0026thinsp;=\u0026thinsp;0.317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHandedness(L/R/A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0/29/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0/27/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/12/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at onset (years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.539\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of epilepsy (years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of medications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence of HS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLE laterality L/R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16/11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4/8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=2.230 P\u0026thinsp;=\u0026thinsp;0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal resection volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.6\u0026thinsp;\u0026plusmn;\u0026thinsp;16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*HCs: healthy controls; SF: seizure free; NSF: not seizure free; (L/R/A):(left/right/ambidextrous); HS: hippocampal sclerosis; TLE: temporary lope epilepsy;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSpacial distribution of disconnected nodes\u003c/h2\u003e \u003cp\u003eSymmetric nodes structurally connected to the ROR (47 left nodes [47/59, 79%] and 47 right nodes [47/52, 90%]) were selected as the final disconnected nodes in subsequent analyses. According to Yeo\u0026rsquo;s functional subnets, the disconnect nodes were mostly distributed at subcortical (n\u0026thinsp;=\u0026thinsp;14/14), limbic (n\u0026thinsp;=\u0026thinsp;10/10), and default mode networks (DMN) (n\u0026thinsp;=\u0026thinsp;6/8). Other nodes were located at visual (n\u0026thinsp;=\u0026thinsp;4/5), sensorimotor (n\u0026thinsp;=\u0026thinsp;4/4), ventral attention (n\u0026thinsp;=\u0026thinsp;4/4), dorsal attention (n\u0026thinsp;=\u0026thinsp;1/2), and frontoparietal networks (n\u0026thinsp;=\u0026thinsp;1/3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFNCS differences in the disconnected network\u003c/h2\u003e \u003cp\u003eTotal FNCS of the four subnetworks for each individual showed significant inter-group differences under the 1st level (p\u0026thinsp;=\u0026thinsp;0.007), 2nd level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 3rd level disconnect networks (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (one-way ANCOVA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Area under the curve (AUC) was applied to calculate overall FNCS according to sparsity. The results were approximately the same as above. Post-hoc analysis of the functional AUC revealed that FNCS was significantly lower in the 1st level (p\u0026thinsp;=\u0026thinsp;0.007/0.009), 2nd level (p\u0026thinsp;=\u0026thinsp;0.002/p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 3rd level disconnect networks (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001/p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in SF and NSF compared to HCs. Meanwhile, within all sparsity groups and the AUC value, NSF had lower mean values than the SF, but the difference was not significant. Compared to SF, the total FNCS was significantly lower in NSF in the 3rd level disconnect network only (post-hoc least significant difference [LSD], p\u0026thinsp;=\u0026thinsp;0.049) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFNCS and SNCS in the disconnected network\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNSF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-Anove\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHCvsSF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHCvsNSF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNSFvsSF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-1st\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e93.31\u0026thinsp;\u0026plusmn;\u0026thinsp;35.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e67.17\u0026thinsp;\u0026plusmn;\u0026thinsp;36.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e60.73\u0026thinsp;\u0026plusmn;\u0026thinsp;31.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-2nd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e607.3\u0026thinsp;\u0026plusmn;\u0026thinsp;175.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e464.70\u0026thinsp;\u0026plusmn;\u0026thinsp;170.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e375.1\u0026thinsp;\u0026plusmn;\u0026thinsp;132.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-3rd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4090\u0026thinsp;\u0026plusmn;\u0026thinsp;801.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2971\u0026thinsp;\u0026plusmn;\u0026thinsp;846.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2398\u0026thinsp;\u0026plusmn;\u0026thinsp;830.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-unaffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e13407\u0026thinsp;\u0026plusmn;\u0026thinsp;2311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e13432\u0026thinsp;\u0026plusmn;\u0026thinsp;2733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e12734\u0026thinsp;\u0026plusmn;\u0026thinsp;2414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-1st\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1867\u0026thinsp;\u0026plusmn;\u0026thinsp;249.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1572\u0026thinsp;\u0026plusmn;\u0026thinsp;459.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1870\u0026thinsp;\u0026plusmn;\u0026thinsp;555.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-2nd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5025\u0026thinsp;\u0026plusmn;\u0026thinsp;623.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5017\u0026thinsp;\u0026plusmn;\u0026thinsp;896.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4764\u0026thinsp;\u0026plusmn;\u0026thinsp;890.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-3rd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5123\u0026thinsp;\u0026plusmn;\u0026thinsp;519.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5116\u0026thinsp;\u0026plusmn;\u0026thinsp;808.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4968\u0026thinsp;\u0026plusmn;\u0026thinsp;812.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-unaffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e45334\u0026thinsp;\u0026plusmn;\u0026thinsp;5069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e46000\u0026thinsp;\u0026plusmn;\u0026thinsp;6064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e47858\u0026thinsp;\u0026plusmn;\u0026thinsp;7641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e*HC: healthy control; SF: seizure free; NSF: not seizure free; FNC: Functional Network Connectivity; SNC: Structural Network Connectivity; F-1st :Function-1st level disconnect network; F-2nd : Function-2nd level disconnect network; F-3rd :Function-3rd level disconnect network; F- unaffected :Function- Unaffected network; S-1st : Structure \u0026minus;\u0026thinsp;1st level disconnect network; S-2nd : Structure \u0026minus;\u0026thinsp;2nd level disconnect network; S-3rd : Structure \u0026minus;\u0026thinsp;3rd level disconnect network; S- unaffected : Structure - Unaffected network;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSNCS differences in the disconnect network\u003c/h2\u003e \u003cp\u003eCompared to the FNCS, the SNCS showed fewer inter-group variates. ANCOVA calculations yielded significant inter-group differences among the three groups only for the 1st level disconnected network (p\u0026thinsp;=\u0026thinsp;0.017). No statistical difference was found in the other three networks. Post-hoc LSD suggested that SFCS was significantly lower in SF than HCs (p\u0026thinsp;=\u0026thinsp;0.008) and NSF (p\u0026thinsp;=\u0026thinsp;0.037) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSFC of disconnected networks\u003c/h2\u003e \u003cp\u003eWe calculated SFC at each level of the disconnected network of every participant. Significant SFC in the 1st level (p\u0026thinsp;=\u0026thinsp;0.007/R\u0026thinsp;=\u0026thinsp;0.504), 2nd level (p\u0026thinsp;=\u0026thinsp;0.001/R\u0026thinsp;=\u0026thinsp;0.624), and 3rd level disconnected networks (p\u0026thinsp;=\u0026thinsp;0.003/R\u0026thinsp;=\u0026thinsp;0.546) were discovered in HCs, but not in the unaffected network (p\u0026thinsp;=\u0026thinsp;0.587/R\u0026thinsp;=\u0026thinsp;0.109). No SFC was observed at any subnetwork in either SF or NSF (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Linear regression revealed a significantly positive linear relationship between structural and functional connection strength in the 1st level (p\u0026thinsp;=\u0026thinsp;0.026/R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.169), 2nd level (p\u0026thinsp;=\u0026thinsp;0.002/R\u0026sup2;=0.283), and 3rd level disconnected networks (p\u0026thinsp;=\u0026thinsp;0.011/R\u0026sup2;=0.213) in HCs. Meanwhile, the SF group showed a positive SFC at the 2nd level disconnected network (p\u0026thinsp;=\u0026thinsp;0.039/R\u0026sup2;= 0.159) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ecorrelation between FNCS and SNCS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNSF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st level discon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.007 R\u0026thinsp;=\u0026thinsp;0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.268 R\u0026thinsp;=\u0026thinsp;0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.193 R\u0026thinsp;=\u0026thinsp;0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd level discon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.001 R\u0026thinsp;=\u0026thinsp;0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.066 R\u0026thinsp;=\u0026thinsp;0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.372 R\u0026thinsp;=\u0026thinsp;0.372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd level discon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.003 R\u0026thinsp;=\u0026thinsp;0.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.108 R\u0026thinsp;=\u0026thinsp;0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.303 R\u0026thinsp;=\u0026thinsp;0.387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnaffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.587 R\u0026thinsp;=\u0026thinsp;0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.790 R\u0026thinsp;=\u0026thinsp;0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.896 R=-0.478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*HCs: healthy controls; SF: seizure-free. NSF: non-seizure-free. FNCS: functional network connection strength; SNCS: structural network connection strength.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we analyzed the preoperative DTI and rs-fMRI of patients with TLE to quantify macro-scaled structural and functional disconnected networks. Our main findings included: (i) among all subnetworks classified by direct or indirect structural connection to the ROR, the structural and functional connectivity of disconnection subnets showed specific abnormalities in TLE patients with different surgical outcomes; (ii) the degree of functional abnormalities was higher in NSF than SF; and (iii) apart from the second disconnect network in SF, the SFC of disconnected networks in TLE patients generally decreased, suggesting that different SFC statuses at disconnect subnets may be a potential radiological feature leading to prognosis prediction. This study provides a theoretical basis for preoperative imaging evaluation of seizure outcomes in intractable TLE. It is the first study to establish a relationship between SFC and surgical prognosis based on disconnect networks.\u003c/p\u003e \u003cp\u003eThe disconnected nodes affected by surgical resection were located symmetrically at the DMN, limbic system, and subcortical networks. SNCs and FNCs are highly coupled at those networks in healthy populations\u003csup\u003e14,21\u0026ndash;23\u003c/sup\u003e, which matched our findings on the disconnect subnets in HCs. Interestingly, these networks have also exhibited functional or structural segregation in previous epilepsy studies\u003csup\u003e14,21\u0026ndash;23\u003c/sup\u003e. The structural adjacency to the epicenter and functional complexity of those hub nodes strongly indicates that disconnect subnets are highly complex and delicate systems. These disconnect networks are naturally vulnerable to impact, such as surgical resection, and show advantages in surgery outcome prediction \u003csup\u003e24\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTLE is a chronic neurological disorder with a broad spectrum of brain network abnormalities, commonly accompanied by a regional decrease in network integration and connection strength\u003csup\u003e25\u003c/sup\u003e, mostly at the DMN\u003csup\u003e12\u003c/sup\u003e and basal ganglia network\u003csup\u003e14\u003c/sup\u003e. Further studies found that lower whole-brain FNCS is associated with persistent seizures after surgery\u003csup\u003e26\u003c/sup\u003e. However, in our study, similar results were observed at the 1st, 2nd, and 3rd level disconnect subnets only and the abnormality patterns differed between SF and NSF at the 3rd subnets, demonstrating that networks indirectly affected by surgical resection might be the key to determining surgical prognosis, which has been proven in other modules\u003csup\u003e20\u003c/sup\u003e. We speculate that the abnormal preoperative functional connection of the 2nd level disconnected network may be the main cause of postoperative seizures because the segmentation of functional modules within the 2nd level disconnected network will not disappear after surgical resection.\u003c/p\u003e \u003cp\u003eSNC alternations in TLE patients are relatively limited compared with the wide decrease of FNC in the whole-brain. The most common changes in SNC in TLE patients are changes in the diffusion coefficient of the hippocampus and the peripheral WM in the mesial temporal lobe, which are generally restricted to the temporal lobe and adjacent areas, \u003csup\u003e15\u003c/sup\u003e as well as decreases in total connection count to the epicenter\u003csup\u003e12\u003c/sup\u003e. In our study, similar results were observed. We found a significant decrease in the SNCS only in the 1st level of the disconnected network. Different from FNCS changes, the decrease of the 1st level in SNCS in the SF group was more significant than that in the NSF and HC\u003csup\u003e27\u003c/sup\u003e groups. Previous studies have demonstrated that synchronous electroencephalogram activity in TLE patients is associated with highly arranged structural connection, indicating that more structural pathways can be beneficial for seizure facilitation\u003csup\u003e28\u003c/sup\u003e Therefore, we speculate that patients with high SNCS may be more likely to cause synchronous discharges, leading to recurrent seizures.\u003c/p\u003e \u003cp\u003eOur findings suggest that both the SNCS and FNCS of TLE patients undergo robust changes, and the patterns of these changes differ among modalities. Furthermore, SFC in patients with TLE decreased significantly or even vanished in the disconnected network, while a significant SFC was observed in HCs. Previous studies have reported strong SFC in brain regions with dense structural connections in healthy individuals\u003csup\u003e29\u003c/sup\u003e, while it is weakened in epilepsy patients\u003csup\u003e8\u003c/sup\u003e. For instance, individuals with TLE and hippocampal sclerosis exhibit a significant decoupling in various brain regions\u003csup\u003e8,9\u003c/sup\u003e, including the hippocampus, anterior and posterior DMN\u003csup\u003e10\u003c/sup\u003e, and auditory and language network (left frontal parietal network)\u003csup\u003e8\u003c/sup\u003e. In the current study, disconnected nodes were primarily situated in the limbic system, DMN, and frontoparietal network, which further elucidates the reduction in SFC of the disconnected network in TLE patients. According to Liu et al., disruption of superficial WM in individuals with TLE is a crucial factor in the decline of SFC. Therefore, we contend that the varying distribution of abnormal structural and functional connections is also a specific pathological manifestation of TLE.\u003c/p\u003e \u003cp\u003eDue to significant differences in the spatial distribution and pattern of structural and functional network changes in patients with TLE, numerous studies have shown a widespread decrease or absence of SFC in the brain networks of epilepsy patients \u003csup\u003e8\u0026ndash;10\u003c/sup\u003e. However, we observed a positive SFC in the brain regions that were indirectly affected by surgical resection in the SF group. This phenomenon suggested that the disconnectome may be a key factor influencing postoperative prognosis. Since these networks are not directly removed by surgery, any preoperative imbalance in their structural and functional consistency will remain. However, removing the epileptogenic focus from the original network can still cause indirect damage to disconnect networks and worsen its structural and functional decoupling. This can lead to the development of further structural and functional disconnection of those regions, ultimately creating a new segregation zone, which may result in the recurrence of epilepsy. Therefore, we hypothesize that the consistency of the structural and functional connections within the preoperative disconnected network could minimize the occurrence of abnormal nodes and reduce the incidence of postoperative seizures.\u003c/p\u003e \u003cp\u003eOur study had several limitations. First, the retrospective design may limit the reliability of the results. Second, we only observed an indirect impact of the brain network on surgical outcome, and further investigations are needed to elucidate the specific mechanisms underlying postoperative brain network discharges and their association with seizures. Third, the sample size was relatively small, and no predictive model was established. Therefore, future studies with larger sample sizes are required.\u003c/p\u003e \u003cp\u003eIn summary, patients with TLE exhibit a widespread decrease in functional connectivity intensity within the disconnection network, indicating a weak SFC of the brain network. The concordance of changes of SNCS and FNCS in brain regions indirectly affected by surgery is predictive of postoperative seizure freedom. Therefore, preoperative multimodal magnetic resonance imaging may serve as an important tool to guide surgical planning.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003e This study was approved by the Ethics Committee of Xiangya Hospital and all methods were performed in accordance with the relevant guidelines and regulations. All participants or their legal guardians provided written informed consent according to the Declaration of Helsinki. Thirty-nine unilateral drug-resistant TLE patients diagnosed and surgically treated with anterior temporal lobectomy at Xiangya Hospital between 2017 and 2022 were retrospectively analyzed. Diagnosis of drug-resistant TLE was determined based on the International League Against Epilepsy criteria\u003csup\u003e30\u003c/sup\u003e. Inclusion criteria included diagnosis of TLE, diagnosis of drug-resistant epilepsy, and no contraindications for surgical resection. Exclusion criteria included pre-surgical intracranial monitoring, progressive neurological disease, focal lesion not in the temporal region, and severe mental disorder. Seizure outcomes were assessed by an epileptologist 1 year post-surgery (up to 2 years) using the Engel surgery outcome classification\u003csup\u003e31\u003c/sup\u003e as seizure-free (SF; Engel Class Ia) or non-seizure-free (NSF; Engel Class Ib through IV). Twenty-nine healthy controls (HCs) were also analyzed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMRI acquisition\u003c/h2\u003e \u003cp\u003eResting-state fMRI (rs-fMRI) data were acquired on a 3.0 Tesla Siemens Prisma MRI system with a standard 32-channel head coil (Xiangya Hospital, Central South University). rs-fMRI was collected utilizing echo planar imaging (EPI) sequences with the following parameters: TR\u0026thinsp;=\u0026thinsp;720 ms, TE\u0026thinsp;=\u0026thinsp;37 ms, flip angle\u0026thinsp;=\u0026thinsp;52\u0026deg;, 64 axial slices with 2.5-mm thickness and 2.5-mm gap, matrix size\u0026thinsp;=\u0026thinsp;90\u0026times;90, field of view (FOV)\u0026thinsp;=\u0026thinsp;225 mm\u0026times;255 mm, voxel size\u0026thinsp;=\u0026thinsp;2.5 mm\u0026times;2.5 mm\u0026times;2.5 mm. Each resting-state scan lasted 9.456 min, resulting in 788 volumes.\u003c/p\u003e \u003cp\u003eDiffusion tensor imaging (DTI) data were acquired using a multi-shell EPI sequence according to the following parameters: TR\u0026thinsp;=\u0026thinsp;5400 ms, TE\u0026thinsp;=\u0026thinsp;72 ms, axial slices\u0026thinsp;=\u0026thinsp;75, resolution\u0026thinsp;=\u0026thinsp;2.0\u003cem\u003e\u0026times;\u003c/em\u003e2.0\u003cem\u003e\u0026times;\u003c/em\u003e2.0 mm, flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg;; FOV\u0026thinsp;=\u0026thinsp;215\u003cem\u003e\u0026times;\u003c/em\u003e215 mm; voxel size\u0026thinsp;=\u0026thinsp;1.6\u003cem\u003e\u0026times;\u003c/em\u003e1.6\u003cem\u003e\u0026times;\u003c/em\u003e1.6 mm, number of directions\u0026thinsp;=\u0026thinsp;96, b\u0026thinsp;=\u0026thinsp;0/1000/2000/3000 s/mm\u003csup\u003e2\u003c/sup\u003e, and EPI factor\u0026thinsp;=\u0026thinsp;154.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ers-fMRI preprocessing\u003c/h2\u003e \u003cp\u003ers-fMRI data were preprocessed using a Graph Theory Network Analysis Toolkit. Briefly, the preprocessing steps were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) data format conversion from DICOM to NIFTI; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) removal of the first 18 timepoint volumes, resulting in a total of 770 volumes; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) slice timing correction; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) spatial realignment for motion correction; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) normalization to Montreal Neurological Institute space via EPI template; (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) spatial smoothing; (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) temporally detrending; and (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) global signal regression. The detailed processing steps are described on the GRETNA\u003csup\u003e32\u003c/sup\u003e website (GRETNA.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/sandywang/GRETNA\u003c/span\u003e\u003cspan address=\"https://github.com/sandywang/GRETNA\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a pipeline toolbox based on SPM 12 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fil.ion.ucl.ac.uk/spm/software/spm12/\u003c/span\u003e\u003cspan address=\"https://www.fil.ion.ucl.ac.uk/spm/software/spm12/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDTI preprocessing\u003c/h2\u003e \u003cp\u003eThe DTI preprocessing pipeline was applied with FSL (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki/\u003c/span\u003e\u003cspan address=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Briefly, diffusion data were preprocessed as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) dicom to nii conversion; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) B0 image extraction; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) global denoising; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) skull stripping with BET; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Eddy current correction; and (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) estimation of basic diffusion metrics, including fractional anisotropy (FA) and mean diffusivity (MD). After preprocessing, constrained spherical deconvulsion (CSD)-based diffusion tensor estimation and global probabilistic tractography was performed with Mrtrix3 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mrtrix.org/\u003c/span\u003e\u003cspan address=\"https://www.mrtrix.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The brief steps were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) estimating response function, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) multi-shell multi-tissue CSD, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) estimation of fiber orientation distributions (FOD)\u003csup\u003e33\u003c/sup\u003e; and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) global tractography.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRegion of resection (ROR)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo minimize the possible effects of short-term postoperative brain tissue edema or long-term postoperative brain atrophy and scarring on the surgical region, we used CT within 6 h after surgical resection, supplemented by MRI within 24\u0026ndash;48 h for ROR segmentation. Postoperatively, resection masks were manually segmented by two experienced neurologists using MRICRON. Masks were nonlinearly normalized and overlapped by SPM12 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fil.ion.ucl.ac.uk/spm/\u003c/span\u003e\u003cspan address=\"https://www.fil.ion.ucl.ac.uk/spm/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and for the overlapped volumes, voxels with overlap values of 5 or less were dropped to enhance the specificity of resected regions. Left- and right-sided masks were calculated separately. Finally, masks were superimposed onto the Brainnetome Atlas (BN)\u003csup\u003e34\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.brainnetome.org/\u003c/span\u003e\u003cspan address=\"http://www.brainnetome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to obtain the resection-related segmentation map (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). To explore the distribution of disconnected nodes, we segmented all disconnected nodes into seven functional networks according to Brainnetome\u0026rsquo;s open-source data (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://atlas.brainnetome.org/download.html\u003c/span\u003e\u003cspan address=\"http://atlas.brainnetome.org/download.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which is based on the Yeo-7 Atlas\u003csup\u003e35\u003c/sup\u003e. We included the subcortical regions as the 8th module (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStructural connectivity network construction\u003c/h2\u003e \u003cp\u003eTo construct the whole-brain white matter (WM) connectome matrix, CSD-based probabilistic fiber tracking was used to track WM connections for all possible node pairs including the ROR. We selected all brain regions in the BN based on the left and right atlases with ROR. Before each individual\u0026rsquo;s matrix was delivered for further analysis, a backbone method with a threshold of \u0026gt;\u0026thinsp;75% was applied to minimize errors caused by normalization errors or partial volume effects. For each participant, the WM connection strength was determined by the number of tractography streamlines between all brain regions. The columns and rows of each individualized connection matrix represented nodes, while the elements in the matrices (Cij) represented the strength of WM connection between the i and j nodes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFunctional connectivity network construction\u003c/h2\u003e \u003cp\u003eCorrelation matrices were generated based on time series signals. Pearson\u0026rsquo;s correlation was used to evaluate the relationship among the time series by r-values, from which a 247\u0026times;247 correlation matrix could be obtained for each subject. For the connectivity matrices that entailed correlation strengths (r), we transformed the correlation values into z-values using Fisher\u0026rsquo;s r to z transformation. For each connectivity matrix, network sparsity ranging from 5\u0026ndash;12% with intervals of 1% were selected (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBuilding the disconnectome atlas\u003c/h2\u003e \u003cp\u003eA three-tier disconnection atlas was built based on the group-averaged WM connectome and the group ROR mask. The steps were:\u003c/p\u003e \u003cp\u003e \u003cem\u003eDefinition of nodes\u003c/em\u003e: (i) disconnected nodes: nodes that have a direct functional connection to the ROR, and\u003c/p\u003e \u003cp\u003e(ii) unaffected nodes: whole-brain nodes except for the remaining nodes of the disconnected nodes.\u003c/p\u003e \u003cp\u003eTo make the left and right disconnected networks comparable, we selected 47 symmetrical disconnected nodes on the basis of the original disconnected nodes.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDefinition of disconnected networks\u003c/em\u003e: (i) 1st level disconnect network: the edges between ROR and neighboring disconnected nodes, (ii) 2nd level disconnect network: the edges within the disconnected nodes, (iii) 3rd level disconnect network: the edges between the disconnected nodes and the unaffected nodes, and (iv) unaffected network: the edges composed entirely of unaffected nodes.\u003c/p\u003e \u003cp\u003eThe disconnectome atlas was set and we projected this atlas to every individual\u0026rsquo;s functional and structural connectivity matrices. Thus, each structural/functional edge or node was marked with one subtype of the disconnected network (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using SPSS (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ibm.com/cn-zh/spss\u003c/span\u003e\u003cspan address=\"https://www.ibm.com/cn-zh/spss\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) version 22 (IBM Corporation, Armonk, NY, United States). According to the type of information, between-group differences among the TLE subgroups (SF and NSF) and HCs were investigated using one-way ANCOVA followed by post-hoc t-tests. The level of significance was p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-sided significant testing), and multiple comparison corrections were performed using the false discovery rate. Partial correlation analysis and linear regression analysis were used to identify linear trends between the strength of structural and functional connectivity of disconnect subnets.\u0026middot;\u003c/p\u003e \u003cp\u003eGroup comparisons of clinical characteristics were calculated by nonparametric T-test, one-way ANOVA, and chi-square test. In group analyses, baseline characteristics, including age, sex, and education level were set as covariates.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study has received funding by Grant No. 2022YFC2503804 from National Key Program of China, Grant Nos. 82071461, 82271503 from National Natural Science Foundation of China, and Grant No. 2021JJ31060 from Natural Science Foundation of Hunan Province.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC.Z. and L.F. conceived the study. X.H. D.G. Y.D.and F.X. collected the data. C.Z. and X.H. analyzed the data. X.H. wrote the manuscript. L.F. and C.Z. revised the manuscript. X.H. D.L. collected the follow-up data. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe are grateful to all of our research participants, without their patience and cooperation, this work would not be completed. This work was supported by the Department of Neurology, Neurosurgery and Radiology, Xiangya Hospital, Central South University and Key Laboratory for Neuroinformation of Ministry of Education, Center for Information in BioMedicine, School of Life Sciences and Technology, University of Electronic Science and Technology of China.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEngel, J.\u003cem\u003e et al.\u003c/em\u003e Early Surgical Therapy for Drug-Resistant Temporal Lobe Epilepsy A Randomized Trial. \u003cem\u003eJama-J Am Med Assoc\u003c/em\u003e \u003cstrong\u003e307\u003c/strong\u003e, 922-930, doi:10.1001/jama.2012.220 (2012).\u003c/li\u003e\n\u003cli\u003eFoldvary, N.\u003cem\u003e et al.\u003c/em\u003e Seizure outcome after temporal lobectomy for temporal lobe epilepsy - A Kaplan-Meier survival analysis. \u003cem\u003eNeurology\u003c/em\u003e \u003cstrong\u003e54\u003c/strong\u003e, 630-634, doi:Doi 10.1212/Wnl.54.3.630 (2000).\u003c/li\u003e\n\u003cli\u003eJanszky, J.\u003cem\u003e et al.\u003c/em\u003e Temporal lobe epilepsy with hippocampal sclerosis: predictors for long-term surgical outcome. \u003cem\u003eBrain\u003c/em\u003e \u003cstrong\u003e128\u003c/strong\u003e, 395-404, doi:10.1093/brain/awh358 (2005).\u003c/li\u003e\n\u003cli\u003e\u0026Ouml;zkara, C.\u003cem\u003e et al.\u003c/em\u003e Surgical outcome of patients with mesial temporal lobe epilepsy related to hippocampal sclerosis. \u003cem\u003eEpilepsia\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, 696-699, doi:10.1111/j.1528-1167.2007.01503.x (2008).\u003c/li\u003e\n\u003cli\u003eJeha, L. 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T.\u003cem\u003e et al.\u003c/em\u003e The organization of the human cerebral cortex estimated by intrinsic functional connectivity. \u003cem\u003eJ Neurophysiol\u003c/em\u003e \u003cstrong\u003e106\u003c/strong\u003e, 1125-1165, doi:10.1152/jn.00338.2011 (2011).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"functional magnetic resonance imaging, diffusion tensor imaging, temporal lobe epilepsy, surgical outcomes","lastPublishedDoi":"10.21203/rs.3.rs-4758395/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4758395/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe purpose of the current study was to assess the change characteristics of structural network connection strength (SNCS), functional network connection strength (FNCS), and structural-functional correlation (SFC) of preoperative disconnected networks in temporal lobe epilepsy (TLE) and the capability of these measures to predict postoperative seizure outcomes. This study enrolled 39 TLE patients with 29 healthy controls (HCs). Patients were divided into seizure-free (SF) and non-seizure-free (NSF) groups. Each individual network was divided into four-tier subnets. FNCS was significantly lower in the 1st level (p\u0026thinsp;=\u0026thinsp;0.007/p\u0026thinsp;=\u0026thinsp;0.009), 2nd level (p\u0026thinsp;=\u0026thinsp;0.002/p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 3rd level disconnect networks (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001/p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in SF and NSF(27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4, 16 men; 30.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9, 8 men). SNCS was significantly lower in SF than HCs (33.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9, 15 men) and NSF (p\u0026thinsp;=\u0026thinsp;0.008/p\u0026thinsp;=\u0026thinsp;0.037) in the 1st level disconnected network. Further, significant SFC in the 1st level (p\u0026thinsp;=\u0026thinsp;0.026/R\u0026sup2;=0.169), 2nd level (p\u0026thinsp;=\u0026thinsp;0.002/R\u0026sup2;=0.283), and 3rd level disconnected networks (p\u0026thinsp;=\u0026thinsp;0.011/R\u0026sup2;=0.213) in HCs. Meanwhile, the SF group showed a positive SFC at the 2nd level disconnected network (p\u0026thinsp;=\u0026thinsp;0.039/R\u0026sup2;=0.159). Our study demonstrated that the consistency in SNCS and FNCS of the regions indirectly affected by surgery is associated with lower risk of post-surgical seizure. Preoperative multimodal magnetic resonance imaging may serve as an important tool to guide surgical planning.\u003c/p\u003e","manuscriptTitle":"Association Between Disconnected Networks and Surgical Outcome in Temporal Lobe Epilepsy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-23 21:15:20","doi":"10.21203/rs.3.rs-4758395/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ad557147-74d1-49f8-8354-524266aa5e1f","owner":[],"postedDate":"August 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":35834580,"name":"Biological sciences/Neuroscience/Diseases of the nervous system"},{"id":35834581,"name":"Biological sciences/Neuroscience/Diseases of the nervous system/Epilepsy"},{"id":35834582,"name":"Biological sciences/Structural biology/Nmr spectroscopy"},{"id":35834583,"name":"Health sciences/Medical research/Outcomes research"},{"id":35834584,"name":"Health sciences/Medical research/Study design/Clinical trials"},{"id":35834585,"name":"Health sciences/Neurology/Neurological disorders"},{"id":35834586,"name":"Health sciences/Medical research"},{"id":35834587,"name":"Health sciences/Neurology"}],"tags":[],"updatedAt":"2024-09-27T12:38:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-23 21:15:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4758395","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4758395","identity":"rs-4758395","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}