Hierarchical reorganization of brain Functional Networks in Mesial Temporal Lobe Epilepsy

Hierarchical reorganization of brain Functional Networks in Mesial 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 Research Article Hierarchical reorganization of brain Functional Networks in Mesial Temporal Lobe Epilepsy Kanlin Lin, Xuesa Li, Hui Li, Yaling Chen, Lidan Lin, Sifan Qiu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8204963/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 Background Mesial temporal lobe epilepsy (mTLE) is a common recurrent neurological disorder, which timely surgical intervention is often required in drug-resistant cases. Although previous neuroimaging studies have explored brain abnormalities associated with mTLE, the alterations in brain network functional activities remain far from be elucidated. Methods Preoperative resting-state fMRI data were collected from 47 patients with drug-resistant mTLE, divided into c groups based on postoperative outcomes. Data from 32 age- and sex-matched healthy controls (HC) were included for comparison. Gradient analysis was used to investigate the changes in the hierarchy of brain networks in mTLE patients. Results Both SF and NSF groups exhibited disruptions in principal gradient organization compared to HC. Significant differences in gradient scores were observed between the SF and NSF groups, particularly within the default mode network (DMN) and somatomotor network (SMN). In the DMN, significant differences were observed in regions such as the left middle temporal gyrus and the left inferior temporal gyrus. In the SMN, significant differences were found in regions such as the right precentral gyrus and the right postcentral gyrus. Conclusion Alterations in brain networks hierarchy within the SMN and DMN may reflect distinct patterns of network reorganization associated with postoperative seizure outcomes, which provide new insights into prognosis and therapeutic strategies in mTLE. Mesial temporal lobe epilepsy gradient hierarchy functional magnetic resonance imaging treatment outcome Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Mesial temporal lobe epilepsy (mTLE) is the most prevalent drug-resistant epilepsy, characterized by abnormal functional activities, including the hippocampus and amygdala of mesial temporal lobe 1 , 2 . The anterior temporal lobectomy (ATL) is the effective surgical intervention, achieving short-term seizure-free (SF) rates of approximately 67% in patients with drug resistance 3 – 5 . However, with some patients experiencing seizure recurrence and classified as non-seizure-free (NSF). NSF patients not only endure persistent seizures but also face significant diminished quality of life, therefore we need reliable preoperative biomarkers to predict surgical outcomes 6 – 8 . Gradient analysis, a robust tool for mapping macroscale functional network organization, has proven valuable in the study of various neurological disorders. Some studies have utilized gradient analysis to investigate abnormal functional connectivity and network reorganization in conditions such as Alzheimer's disease, major depression, and schizophrenia. This approach reduces the complexity of functional network analysis by projecting connectivity patterns into low-dimensional gradient spaces, revealing a spectrum that ranges from primary sensory-motor systems to higher-order cognitive networks 9 . Recent studies have shown that network alterations, such as reduced information flow and increased hypersynchronization within the epileptogenic zone, are strongly associated with postoperative seizure recurrence 10 . While extensive neural network reorganization is present in mTLE, gradient analysis has yet to fully elucidate the differences in brain networks between NSF and SF patients. This study aims to investigate preoperative gradient changes in mTLE patients using resting-state functional magnetic resonance imaging (rs-fMRI). We explored the differences in brain network hierarchy between NSF and SF patients. This study would provide new insights o n the neuropathological changes of mTLE and its early diagnosis. 2. Materials and methods 2.1. Subjects This study was approved by the Ethics Committee of the 900th Hospital of PLA Joint Logistic Support Force (Approval Number: 2019-005). Written informed consent was obtained from all participants and their families in accordance with the Declaration of Helsinki. A total of 47 patients with drug-resistant mTLE who underwent epileptogenic zone resection and 32 age- and sex-matched healthy controls (HC) were included. All surgical patients completed a standardized 12-month postoperative follow-up and met the following inclusion criteria: (1) right-handedness; and (2) a diagnosis of mTLE based on the 2017 International League Against Epilepsy (ILAE) criteria 11 . Surgical outcomes were categorized using the Engel classification system into two subgroups: SF (Class Ia) and NSF (Classes Ib-IV). Exclusion criteria for the mTLE patients were as follows: (1) Age under 16 years; (2) Epilepsy localized outside the mesial temporal lobe (e.g., extra-mesial temporal or extratemporal origin); (3) History of cranial trauma or neurosurgical procedures; (4) Structural intracranial abnormalities; (5) Severe systemic comorbidities or concurrent neuropsychiatric disorders; (6) Contraindications for MRI. 2.2. Image acquisition All MRI acquisition were performed on a 3.0 T Siemens Magnetom Trio Tim superconducting system equipped with a 12-channel phased-array coil. Participants were positioned supine with ear protection and foam padding to minimize head movement. They were instructed to keep their eyes closed, avoid intentional cognitive activity, and remain as still as possible throughout the scan. The imaging protocol included 3D-T2-weighted images for structural screening, followed by rs-fMRI and 3D-T1-weighted images. Rs-fMRI employed an echo-planar imaging sequence with parameters: TR = 2000 ms, TE = 21 ms, flip angle = 90°, slice thickness = 4.0 mm, interslice gap = 0.8 mm, FOV = 240 × 240 mm 2 , 33 slices, 240 time points. Anatomical imaging used a 3D gradient-echo sequence with parameters: TR = 1900 ms, TE = 2.5 ms, flip angle = 9°, slice thickness = 1.0 mm, 160 slices, FOV = 240 × 240 mm 2 . 2.3. Resting-state fMRI preprocessing Resting-state fMRI data preprocessing were conducted using Statistical Parametric Mapping (SPM12; https://www.fil.ion.ucl.ac.uk/spm/ ) and the Data Processing & Analysis for Brain Imaging toolbox (DPABI-V8.2; https://rfmri.org/DPABI ) based on MATLAB 12 . The first 10 time points were discarded to ensure signal stabilization, followed by slice timing correction and head motion correction. Functional images were then spatially normalized to a standard template with resampling to 4 mm isotropic voxels, and spatial smoothing was applied using a Gaussian kernel with a full width at half maximum (FWHM) of 6 mm. Linear detrending was performed, and nuisance covariates, including signals from white matter, cerebrospinal fluid (CSF), and global mean signals, were regressed out. Band-pass filtering (0.01–0.1 Hz) was applied to reduce noise and remove confounding signals. Subsequently, time series data were extracted using the 400-region functional parcellation template proposed by Schaefer et al 13 . Functional connectivity matrices were computed based on Pearson correlation coefficients, followed by Fisher Z-transformation to normalize the data distribution for further analysis. 2.4 Gradient Processing Pipeline Cortical gradients were derived following the methodology described by Vos de Wael et al. 9 , with group-averaged functional connectivity matrices computed from Fisher Z-transformed individual matrices across participants. Dimensionality reduction was performed using diffusion embedding implemented in the BrainSpace toolbox, enabling the extraction of two principal gradients. To ensure consistency with established neurocognitive frameworks, group-level gradients were aligned to a subset of the Human Connectome Project (HCP) dataset using Procrustes rotation, as outlined by Vos de Wael et al 14 . Gradient scores of seven functional networks were quantified by averaging parcel values based on established neurocognitive atlases, facilitating a comprehensive characterization of cortical organization across groups 15 . 2.5 Sub-regional Analysis Further comparisons of brain areas within SMN and DMN were conducted to explore these differences in greater detail. Gradient scores were compared across the SF group, NSF group, and HC group to investigate the impact of epilepsy on the specific brain regions. 2.6 Statistical Analysis Statistical analysi s were performed using SPSS 24.0 software. The normality of continuous variables was assessed using the Shapiro-Wilk test. Variables with a normal distribution were expressed as mean ± standard deviation (SD) and compared across the three groups (SF, NSF, and HC) using one-way analysis of variance (ANOVA), with multiple comparisons conducted using the least significant difference (LSD) method. For variables that did not meet normality, non-parametric tests were applied, and post-hoc pairwise comparisons were performed with Bonferroni correction. Gender were analyzed using the χ² test. Statistical significance was defined as P <0.05. 3. Result 3.1. Demographic information and clinical characteristics There were no significant differences in gender and age among SF, NSF and HC groups. There was no significant difference in age of onset between SF and NSF patients (Table 1 ). Table 1 Demographic Information and Clinical Characteristics. SF (n = 26) NSF (n = 21) HC (n = 32) Statistic P value Age(year) 28.50 (24.75 ~ 37.75) 30.00 (27.00 ~ 37.00) 26.50 (23.25 ~ 39.50) 1.449 0.485 Gender(M/F) 10/16 7/14 11/21 0.160 0.923 Age of onset(year) 19.04 ± 10.761 19.38 ± 12.391 - -0.101 0.920 Note: SF, Seizure-free group; NSF, Non-seizure-free group; HC, Healthy controls; F, Female; M,Male. H for Age (Kruskal-Wallis), χ² for Male/Female (Chi-square test), t-value for Age of onset (Independent Samples t-test). 3.2. Selection of research gradients Aligned with previous research on cortical gradients, this study focused on the first 19 gradient eigenvalues, emphasizing gradients with higher eigenvalues due to their greater significance (Fig. 1 a-c). Results demonstrated a progressively decreasing trend in eigenvalues, with the principal gradient exhibiting the highest eigenvalue, accounting for the largest proportion of data variance 9 . The distribution of eigenvalues further validated the selection of the principal gradient, which offered superior statistical power and scientific rigor while requiring fewer components. Importantly, the principal gradient effectively captured the cortical spatial hierarchical organization (Fig. 1 d-f) 16 . Therefore, the analysis in this study specifically concentrated on the principal gradient. 3.3. Distribution patterns of principal gradient across groups In all three groups (HC, SF, NSF), transmodal regions like the default mode network showed similar gradient values, while unimodal sensory regions such as the visual network consistently appeared at the opposite end of the principal gradient (Fig. 2 ). 3.4. Comparison of network gradients across groups The violin plots (Fig. 3 ) revealed significant differences in gradient scores of the somatomotor network (SMN) and default mode network (DMN) among the HC, SF, and NSF groups. In the DMN, the SF group demonstrated significantly lower gradient scores compared to both the HC and NSF groups. In the SMN, both the SF and HC groups demonstrated significantly higher gradient scores than the NSF group. 3.5. Comparison of gradients in brain area across groups The bar plots (Fig. 4 ) illustrated the brain area gradient score differences within the DMN and SMN among the HC, SF, and NSF groups ( P <0.05, Bonferroni-corrected).Within the DMN, the SF group exhibited significantly lower gradient scores compared to the NSF group in the left middle temporal gyrus (Temporal_Mid_L, P = 0.043), the left inferior temporal gyrus (Temporal_Inf_L, P = 0.009), the left orbital part of the inferior frontal gyrus (Frontal_Inf_Orb_2_L, P = 0.006), and the left superior frontal gyrus (Frontal_Sup_2_L, P = 0.030). Furthermore, the SF group demonstrated significantly lower gradient scores compared to the HC group in the left middle temporal gyrus (Temporal_Mid_L, P = 0.012), the left superior frontal gyrus (Frontal_Sup_2_L, P <0.001), and the right superior frontal gyrus (Frontal_Sup_2_R, P = 0.027). Within the SMN, the SF group exhibited significantly higher gradient scores than the NSF group in the left precentral gyrus (Precentral_L, P = 0.003), the left postcentral gyrus (Postcentral_L, P = 0.005), the left superior parietal gyrus (Parietal_Sup_L, P = 0.004), the right precentral gyrus (Precentral_R, P = 0.005), the right postcentral gyrus (Postcentral_R, P <0.001), and the right paracentral lobule (Paracentral_Lobule_R, P = 0.049). Furthermore, the NSF group demonstrated significantly lower gradient scores compared to the HC group in the left Rolandic operculum (Rolandic_Oper_L, P = 0.048), the left postcentral gyrus (Postcentral_L, P = 0.007), the left superior parietal gyrus (Parietal_Sup_L, P = 0.009), the right Rolandic operculum (Rolandic_Oper_R, P = 0.025), the right precentral gyrus (Precentral_R, P = 0.032), the right postcentral gyrus (Postcentral_R, P < 0.001), and the right paracentral lobule (Paracentral_Lobule_R, P = 0.09). 4. Discussion This study investigated preoperative alterations in functional network gradients among mTLE patients. Specifically, in the DMN, the SF group exhibited significantly lower gradient scores compared to both the NSF and HC groups. Within the SMN, the SF and HC groups demonstrated significantly higher gradient scores compared to the NSF group. After identifying significant changes in the network gradient, we proceeded to explore changes in regions within the DMN and SMN. In the DMN, the SF group showed lower gradient scores compared to the NSF group in the middle and inferior temporal gyrus, the orbital part of the inferior frontal gyrus, and the superior frontal gyrus. When compared to the HC group, the SF group also exhibited lower gradient scores in the middle temporal gyrus and superior frontal gyrus. In the SMN, the SF group demonstrated higher gradient scores than the NSF group in the precentral and postcentral gyrus, superior parietal gyrus, and paracentral lobule. The NSF group showed lower gradient scores compared to the HC group in the rolandic operculum, precentral and postcentral gyrus, and paracentral lobule. Gradient analysis has been widely applied in neurological and psychiatric research, revealing disease-specific disruptions in brain network hierarchy across conditions such as subthreshold depression, chronic pain, and schizophrenia 17 – 19 . In this study, there were significant differences in the gradient scores of SF and NSF group in the DMN and SMN. Previous study has shown that NSF patients exhibited significantly higher SFC in DMN compared to SF patients 20 . Abnormal coupling of DMN regions could trigger epileptic seizures, thereby leading to adverse consequences. This indicated that abnormal changes in the DMN are closely related to the recurrence of epilepsy in mTLE patients, which is consistent with our study. A longitudinal study reported that the functional connectivity of the SMN was significantly increased after ATL 21 . This is consistent with the result we found that the gradient scores of SMN was higher in the SF group, indicating that enhanced SMN connectivity may play an important role in achieving seizure freedom after surgery. Within the DMN, the SF group exhibited significantly lower gradient scores compared to the NSF group in the left middle temporal gyrus, inferior temporal gyrus, orbital part of the inferior frontal gyrus, and superior frontal gyrus. In contrast, within the SMN, the SF group demonstrated higher gradient scores than the NSF group in the precentral gyrus, postcentral gyrus, superior parietal gyrus, and paracentral lobule. The left middle temporal gyrus is responsible for semantic-phonological integration and lexical retrieval, with its white matter pathways directly receiving output fibers from the hippocampus and parahippocampal cortex, making it susceptible to abnormal discharges in mTLE 22 . Previous study found that patients with TLE exhibited significantly increased degree centrality in the left middle temporal gyrus, indicating that it functions by enhancing functional connectivity and abnormal network integration 23 . In the present study, the NSF group exhibited higher gradients in middle temporal gyrus, indicating that broader cross-network integration in the left middle temporal gyrus may be associated with poorer postoperative outcomes. The left inferior temporal gyrus is susceptible to disruptions in temporal lobe network function caused by mTLE. Previous study found that inferior temporal gyrus activation was significantly associated with postoperative naming decline 24 . Previous study showed that mTLE patients exhibited negative activation in the left inferior frontal gyrus, suggesting impaired functional connectivity 25 .In our study, the gradient scores of the inferior temporal gyrus and inferior frontal gyrus were significantly lower in the SF group compared to the NSF group, indicating more localized functional connectivity in SF patients. The superior frontal gyrus is a critical region involved in higher-order cognitive functions, which are frequently disrupted in mTLE due to functional network alterations 26 . Studies indicated that increased DC or hyperconnectivity in the superior frontal gyrus reflected compensatory mechanisms or maladaptive network reorganization, which was often associated with worse cognitive outcomes 27 . Our study revealed that the SF group exhibited lower gradient scores in the inferior frontal gyrus compared to the NSF group, which may reflect a more streamlined and efficient functional network organization, minimizing unnecessary functional differentiation or hyperconnectivity. Previous study showed that functional connectivity between the hippocampus and the rolandic operculum was reduced three months after ATL 28 . Our study revealed lower gradient scores in the rolandic operculum in the NSF group compared to HC group, which may impaired functional integration in this region. As the key affected regions in TLE, the precentral gyrus is primarily involved in motor planning and execution, and the postcentral gyrus is responsible for somatosensory processing 29 , 30 . Previous study showed that TLE patients had significantly lower gradient in the left precentral and postcentral gyrus compared to HC 31 . Our study demonstrated that NSF patients exhibited significantly lower gradients in both the precentral and postcentral gyrus compared to SF patients and HC. These disruptions are associated with impairments in attention and executive control. The superior parietal gyrus plays a crucial role in spatial attention, working memory, and sensory integration, and its disruption might lead to cognitive deficits in mTLE patients 32 . Previous study showed as the disease progresses, functional connectivity in the superior parietal gyrus were decreasedin TLE patients 33 . Our study found that the NSF group exhibited significantly decreased gradient scores in the left superior parietal gyrus compared to the SF group and HC, suggesting that impaired region may underlie the sensory and cognitive impairment in mTLE. This study had several limitations. First, the sample size may limit the generalizability of the findings. Future studies with larger, multicenter cohorts are needed to validate these results. Second, the cross-sectional design precludes the assessment of longitudinal changes in network gradients, particularly in relation to postoperative recovery and long-term outcomes.Incorporating longitudinal follow-up data could provide deeper insights into the dynamic reorganization of functional networks following surgery. Lastly, integrating multimodal imaging data, such as diffusion tensor imaging and task-based fMRI, may further elucidate the structural-functional interplay underlying mTLE. In summary, this study compared gradient features across SF, NSF, and HC groups, providing novel insights into the relationship between functional network reorganization and surgical outcomes. These methodological advancements not only extend our understanding of mTLE pathophysiology but also establish a foundation for future research on networks in neurological disorders. Declarations Author contributions Kanlin Lin, Xuesa Li: Conceptualization, Methodology, Software, Investigation, Formal Analysis, Visualization, Writing - Original Draft. Hui Li, Yaling Chen: Methodology, Writing - Original Draft. Lidan Lin, Sifan Qiu, Ligang Song: Data Curation, Writing - Original Draft. Xiaoyang Wang, Shangwen Xu: Conceptualization, Funding Acquisition, Resources, Supervision, Writing - Review & Editing. Funding This study was supported by grants from the Fujian Province guided project (Grant No.2023Y0066) and the Joint Funds for the innovation of science and Technology, Fujian province (Grant No.2024Y9647). Data availability All data generated or analyzed during this study are available from the corresponding author on reasonable request. Ethics approval and consent to participate This study was approved by the Ethics Committee of the 900th Hospital of PLA Joint Logistic Support Force (Approval Number: 2019-005). 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00:26:43","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":74287,"visible":true,"origin":"","legend":"","description":"","filename":"5912382e2f31499cb4c30ce03cefdb101structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8204963/v1/89b3319df578a9a0bd216de1.xml"},{"id":97745796,"identity":"3598e244-50ea-4177-b5f4-294772d6efd8","added_by":"auto","created_at":"2025-12-09 00:26:43","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":83024,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8204963/v1/fab1042bd7a3c4b3f0efa05d.html"},{"id":97745784,"identity":"8251e421-3118-4ec3-8f71-c21fafd1bd3a","added_by":"auto","created_at":"2025-12-09 00:26:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":206887,"visible":true,"origin":"","legend":"\u003cp\u003e19 eigenvalues of HC (a) group, SF (b) group and NSF (c) group.The higher eigenvalues indicates greater importance. Gradient eigenvalue distribution plot of the first two gradients in HC (d) group, SF (e) group and NSF (f) group.\u003c/p\u003e","description":"","filename":"Onlinefloatimage111.png","url":"https://assets-eu.researchsquare.com/files/rs-8204963/v1/1febce5f001d7348cbc8b1aa.png"},{"id":97896678,"identity":"0e19a42e-5297-4ad7-9ccd-a8099a308c9b","added_by":"auto","created_at":"2025-12-10 15:36:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":218080,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional connectome gradient mapping in HC, SF, and NSF groups.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8204963/v1/d9be9216cf98735debc1af4d.png"},{"id":97745786,"identity":"290edd72-3d4f-44ec-8178-f3169350275d","added_by":"auto","created_at":"2025-12-09 00:26:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38944,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plots of the network gradient scores among the HC, SF, and NSF groups (*: \u003cem\u003eP\u003c/em\u003e＜0.05).\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8204963/v1/b479ddbb774cd819951f79d3.png"},{"id":97896295,"identity":"f0e6841d-7b4f-44ab-84f7-560e36b2e648","added_by":"auto","created_at":"2025-12-10 15:36:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":62646,"visible":true,"origin":"","legend":"\u003cp\u003eBar plots comparing the gradient scores of brain regions within DMN and SMN among the HC, SF, and NSF groups.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8204963/v1/1238a75c6046857985886a0e.png"},{"id":100689981,"identity":"c54aac16-3ff9-42cd-a543-9d1784f37b41","added_by":"auto","created_at":"2026-01-20 13:48:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1196671,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8204963/v1/1b96ee17-69e1-473a-bb55-234f8862a043.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hierarchical reorganization of brain Functional Networks in Mesial Temporal Lobe Epilepsy","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMesial temporal lobe epilepsy (mTLE) is the most prevalent drug-resistant epilepsy, characterized by abnormal functional activities, including the hippocampus and amygdala of mesial temporal lobe\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The anterior temporal lobectomy (ATL) is the effective surgical intervention, achieving short-term seizure-free (SF) rates of approximately 67% in patients with drug resistance\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. However, with some patients experiencing seizure recurrence and classified as non-seizure-free (NSF). NSF patients not only endure persistent seizures but also face significant diminished quality of life, therefore we need reliable preoperative biomarkers to predict surgical outcomes\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGradient analysis, a robust tool for mapping macroscale functional network organization, has proven valuable in the study of various neurological disorders. Some studies have utilized gradient analysis to investigate abnormal functional connectivity and network reorganization in conditions such as Alzheimer's disease, major depression, and schizophrenia. This approach reduces the complexity of functional network analysis by projecting connectivity patterns into low-dimensional gradient spaces, revealing a spectrum that ranges from primary sensory-motor systems to higher-order cognitive networks\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Recent studies have shown that network alterations, such as reduced information flow and increased hypersynchronization within the epileptogenic zone, are strongly associated with postoperative seizure recurrence\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. While extensive neural network reorganization is present in mTLE, gradient analysis has yet to fully elucidate the differences in brain networks between NSF and SF patients.\u003c/p\u003e\u003cp\u003eThis study aims to investigate preoperative gradient changes in mTLE patients using resting-state functional magnetic resonance imaging (rs-fMRI). We explored the differences in brain network hierarchy between NSF and SF patients. This study would provide new insights o\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003en\u003c/span\u003e the neuropathological changes of mTLE and its early diagnosis.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Subjects\u003c/h2\u003e\u003cp\u003e This study was approved by the Ethics Committee of the 900th Hospital of PLA Joint Logistic Support Force (Approval Number: 2019-005). Written informed consent was obtained from all participants and their families in accordance with the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003eA total of 47 patients with drug-resistant mTLE who underwent epileptogenic zone resection and 32 age- and sex-matched healthy controls (HC) were included. All surgical patients completed a standardized 12-month postoperative follow-up and met the following inclusion criteria: (1) right-handedness; and (2) a diagnosis of mTLE based on the 2017 International League Against Epilepsy (ILAE) criteria\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Surgical outcomes were categorized using the Engel classification system into two subgroups: SF (Class Ia) and NSF (Classes Ib-IV).\u003c/p\u003e\u003cp\u003eExclusion criteria for the mTLE patients were as follows: (1) Age under 16 years; (2) Epilepsy localized outside the mesial temporal lobe (e.g., extra-mesial temporal or extratemporal origin); (3) History of cranial trauma or neurosurgical procedures; (4) Structural intracranial abnormalities; (5) Severe systemic comorbidities or concurrent neuropsychiatric disorders; (6) Contraindications for MRI.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Image acquisition\u003c/h2\u003e\u003cp\u003eAll MRI acquisition were performed on a 3.0 T Siemens Magnetom Trio Tim superconducting system equipped with a 12-channel phased-array coil. Participants were positioned supine with ear protection and foam padding to minimize head movement. They were instructed to keep their eyes closed, avoid intentional cognitive activity, and remain as still as possible throughout the scan. The imaging protocol included 3D-T2-weighted images for structural screening, followed by rs-fMRI and 3D-T1-weighted images. Rs-fMRI employed an echo-planar imaging sequence with parameters: TR\u0026thinsp;=\u0026thinsp;2000 ms, TE\u0026thinsp;=\u0026thinsp;21 ms, flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg;, slice thickness\u0026thinsp;=\u0026thinsp;4.0 mm, interslice gap\u0026thinsp;=\u0026thinsp;0.8 mm, FOV\u0026thinsp;=\u0026thinsp;240 \u0026times; 240 mm\u003csup\u003e2\u003c/sup\u003e, 33 slices, 240 time points. Anatomical imaging used a 3D gradient-echo sequence with parameters: TR\u0026thinsp;=\u0026thinsp;1900 ms, TE\u0026thinsp;=\u0026thinsp;2.5 ms, flip angle\u0026thinsp;=\u0026thinsp;9\u0026deg;, slice thickness\u0026thinsp;=\u0026thinsp;1.0 mm, 160 slices, FOV\u0026thinsp;=\u0026thinsp;240 \u0026times; 240 mm\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Resting-state fMRI preprocessing\u003c/h2\u003e\u003cp\u003eResting-state fMRI data preprocessing were conducted using Statistical Parametric Mapping (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 the Data Processing \u0026amp; Analysis for Brain Imaging toolbox (DPABI-V8.2; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rfmri.org/DPABI\u003c/span\u003e\u003cspan address=\"https://rfmri.org/DPABI\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) based on MATLAB\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The first 10 time points were discarded to ensure signal stabilization, followed by slice timing correction and head motion correction. Functional images were then spatially normalized to a standard template with resampling to 4 mm isotropic voxels, and spatial smoothing was applied using a Gaussian kernel with a full width at half maximum (FWHM) of 6 mm. Linear detrending was performed, and nuisance covariates, including signals from white matter, cerebrospinal fluid (CSF), and global mean signals, were regressed out. Band-pass filtering (0.01\u0026ndash;0.1 Hz) was applied to reduce noise and remove confounding signals. Subsequently, time series data were extracted using the 400-region functional parcellation template proposed by Schaefer et al\u003csup\u003e13\u003c/sup\u003e. Functional connectivity matrices were computed based on Pearson correlation coefficients, followed by Fisher Z-transformation to normalize the data distribution for further analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Gradient Processing Pipeline\u003c/h2\u003e\u003cp\u003eCortical gradients were derived following the methodology described by Vos de Wael et al.\u003csup\u003e9\u003c/sup\u003e, with group-averaged functional connectivity matrices computed from Fisher Z-transformed individual matrices across participants. Dimensionality reduction was performed using diffusion embedding implemented in the BrainSpace toolbox, enabling the extraction of two principal gradients. To ensure consistency with established neurocognitive frameworks, group-level gradients were aligned to a subset of the Human Connectome Project (HCP) dataset using Procrustes rotation, as outlined by Vos de Wael et al\u003csup\u003e14\u003c/sup\u003e. Gradient scores of seven functional networks were quantified by averaging parcel values based on established neurocognitive atlases, facilitating a comprehensive characterization of cortical organization across groups\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Sub-regional Analysis\u003c/h2\u003e\u003cp\u003eFurther comparisons of brain areas within SMN and DMN were conducted to explore these differences in greater detail. Gradient scores were compared across the SF group, NSF group, and HC group to investigate the impact of epilepsy on the specific brain regions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analysi\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003es\u003c/span\u003e were performed using SPSS 24.0 software. The normality of continuous variables was assessed using the Shapiro-Wilk test. Variables with a normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and compared across the three groups (SF, NSF, and HC) using one-way analysis of variance (ANOVA), with multiple comparisons conducted using the least significant difference (LSD) method. For variables that did not meet normality, non-parametric tests were applied, and post-hoc pairwise comparisons were performed with Bonferroni correction. Gender were analyzed using the χ\u0026sup2; test. Statistical significance was defined as \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Demographic information and clinical characteristics\u003c/h2\u003e\u003cp\u003eThere were no significant differences in gender and age among SF, NSF and HC groups. There was no significant difference in age of onset between SF and NSF patients (Table\u0026nbsp;\u003cspan refid=\"Tab1\" 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 Information and Clinical Characteristics.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSF (n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNSF (n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStatistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\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\u003eAge(year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.50 (24.75\u0026thinsp;~\u0026thinsp;37.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.00 (27.00\u0026thinsp;~\u0026thinsp;37.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.50 (23.25\u0026thinsp;~\u0026thinsp;39.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.485\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender(M/F)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10/16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7/14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11/21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.923\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge of onset(year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.04\u0026thinsp;\u0026plusmn;\u0026thinsp;10.761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.38\u0026thinsp;\u0026plusmn;\u0026thinsp;12.391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.920\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: SF, Seizure-free group; NSF, Non-seizure-free group; HC, Healthy controls; F, Female; M,Male. H for Age (Kruskal-Wallis), χ\u0026sup2; for Male/Female (Chi-square test), t-value for Age of onset (Independent Samples t-test).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Selection of research gradients\u003c/h2\u003e\u003cp\u003eAligned with previous research on cortical gradients, this study focused on the first 19 gradient eigenvalues, emphasizing gradients with higher eigenvalues due to their greater significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea-c). Results demonstrated a progressively decreasing trend in eigenvalues, with the principal gradient exhibiting the highest eigenvalue, accounting for the largest proportion of data variance\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The distribution of eigenvalues further validated the selection of the principal gradient, which offered superior statistical power and scientific rigor while requiring fewer components. Importantly, the principal gradient effectively captured the cortical spatial hierarchical organization (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed-f)\u003csup\u003e16\u003c/sup\u003e. Therefore, the analysis in this study specifically concentrated on the principal gradient.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Distribution patterns of principal gradient across groups\u003c/h2\u003e\u003cp\u003eIn all three groups (HC, SF, NSF), transmodal regions like the default mode network showed similar gradient values, while unimodal sensory regions such as the visual network consistently appeared at the opposite end of the principal gradient (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Comparison of network gradients across groups\u003c/h2\u003e\u003cp\u003eThe violin plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) revealed significant differences in gradient scores of the somatomotor network (SMN) and default mode network (DMN) among the HC, SF, and NSF groups. In the DMN, the SF group demonstrated significantly lower gradient scores compared to both the HC and NSF groups. In the SMN, both the SF and HC groups demonstrated significantly higher gradient scores than the NSF group.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Comparison of gradients in brain area across groups\u003c/h2\u003e\u003cp\u003eThe bar plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) illustrated the brain area gradient score differences within the DMN and SMN among the HC, SF, and NSF groups (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, Bonferroni-corrected).Within the DMN, the SF group exhibited significantly lower gradient scores compared to the NSF group in the left middle temporal gyrus (Temporal_Mid_L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043), the left inferior temporal gyrus (Temporal_Inf_L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), the left orbital part of the inferior frontal gyrus (Frontal_Inf_Orb_2_L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), and the left superior frontal gyrus (Frontal_Sup_2_L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030). Furthermore, the SF group demonstrated significantly lower gradient scores compared to the HC group in the left middle temporal gyrus (Temporal_Mid_L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012), the left superior frontal gyrus (Frontal_Sup_2_L, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), and the right superior frontal gyrus (Frontal_Sup_2_R, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027).\u003c/p\u003e\u003cp\u003eWithin the SMN, the SF group exhibited significantly higher gradient scores than the NSF group in the left precentral gyrus (Precentral_L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), the left postcentral gyrus (Postcentral_L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), the left superior parietal gyrus (Parietal_Sup_L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), the right precentral gyrus (Precentral_R, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), the right postcentral gyrus (Postcentral_R, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), and the right paracentral lobule (Paracentral_Lobule_R, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049). Furthermore, the NSF group demonstrated significantly lower gradient scores compared to the HC group in the left Rolandic operculum (Rolandic_Oper_L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048), the left postcentral gyrus (Postcentral_L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), the left superior parietal gyrus (Parietal_Sup_L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), the right Rolandic operculum (Rolandic_Oper_R, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025), the right precentral gyrus (Precentral_R, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032), the right postcentral gyrus (Postcentral_R, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the right paracentral lobule (Paracentral_Lobule_R, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study investigated preoperative alterations in functional network gradients among mTLE patients. Specifically, in the DMN, the SF group exhibited significantly lower gradient scores compared to both the NSF and HC groups. Within the SMN, the SF and HC groups demonstrated significantly higher gradient scores compared to the NSF group. After identifying significant changes in the network gradient, we proceeded to explore changes in regions within the DMN and SMN. In the DMN, the SF group showed lower gradient scores compared to the NSF group in the middle and inferior temporal gyrus, the orbital part of the inferior frontal gyrus, and the superior frontal gyrus. When compared to the HC group, the SF group also exhibited lower gradient scores in the middle temporal gyrus and superior frontal gyrus. In the SMN, the SF group demonstrated higher gradient scores than the NSF group in the precentral and postcentral gyrus, superior parietal gyrus, and paracentral lobule. The NSF group showed lower gradient scores compared to the HC group in the rolandic operculum, precentral and postcentral gyrus, and paracentral lobule.\u003c/p\u003e\u003cp\u003eGradient analysis has been widely applied in neurological and psychiatric research, revealing disease-specific disruptions in brain network hierarchy across conditions such as subthreshold depression, chronic pain, and schizophrenia\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In this study, there were significant differences in the gradient scores of SF and NSF group in the DMN and SMN. Previous study has shown that NSF patients exhibited significantly higher SFC in DMN compared to SF patients\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Abnormal coupling of DMN regions could trigger epileptic seizures, thereby leading to adverse consequences. This indicated that abnormal changes in the DMN are closely related to the recurrence of epilepsy in mTLE patients, which is consistent with our study. A longitudinal study reported that the functional connectivity of the SMN was significantly increased after ATL\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. This is consistent with the result we found that the gradient scores of SMN was higher in the SF group, indicating that enhanced SMN connectivity may play an important role in achieving seizure freedom after surgery.\u003c/p\u003e\u003cp\u003eWithin the DMN, the SF group exhibited significantly lower gradient scores compared to the NSF group in the left middle temporal gyrus, inferior temporal gyrus, orbital part of the inferior frontal gyrus, and superior frontal gyrus. In contrast, within the SMN, the SF group demonstrated higher gradient scores than the NSF group in the precentral gyrus, postcentral gyrus, superior parietal gyrus, and paracentral lobule. The left middle temporal gyrus is responsible for semantic-phonological integration and lexical retrieval, with its white matter pathways directly receiving output fibers from the hippocampus and parahippocampal cortex, making it susceptible to abnormal discharges in mTLE\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Previous study found that patients with TLE exhibited significantly increased degree centrality in the left middle temporal gyrus, indicating that it functions by enhancing functional connectivity and abnormal network integration\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In the present study, the NSF group exhibited higher gradients in middle temporal gyrus, indicating that broader cross-network integration in the left middle temporal gyrus may be associated with poorer postoperative outcomes. The left inferior temporal gyrus is susceptible to disruptions in temporal lobe network function caused by mTLE. Previous study found that inferior temporal gyrus activation was significantly associated with postoperative naming decline\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Previous study showed that mTLE patients exhibited negative activation in the left inferior frontal gyrus, suggesting impaired functional connectivity\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.In our study, the gradient scores of the inferior temporal gyrus and inferior frontal gyrus were significantly lower in the SF group compared to the NSF group, indicating more localized functional connectivity in SF patients. The superior frontal gyrus is a critical region involved in higher-order cognitive functions, which are frequently disrupted in mTLE due to functional network alterations\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Studies indicated that increased DC or hyperconnectivity in the superior frontal gyrus reflected compensatory mechanisms or maladaptive network reorganization, which was often associated with worse cognitive outcomes\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Our study revealed that the SF group exhibited lower gradient scores in the inferior frontal gyrus compared to the NSF group, which may reflect a more streamlined and efficient functional network organization, minimizing unnecessary functional differentiation or hyperconnectivity.\u003c/p\u003e\u003cp\u003ePrevious study showed that functional connectivity between the hippocampus and the rolandic operculum was reduced three months after ATL\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Our study revealed lower gradient scores in the rolandic operculum in the NSF group compared to HC group, which may impaired functional integration in this region. As the key affected regions in TLE, the precentral gyrus is primarily involved in motor planning and execution, and the postcentral gyrus is responsible for somatosensory processing\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Previous study showed that TLE patients had significantly lower gradient in the left precentral and postcentral gyrus compared to HC\u003csup\u003e31\u003c/sup\u003e. Our study demonstrated that NSF patients exhibited significantly lower gradients in both the precentral and postcentral gyrus compared to SF patients and HC. These disruptions are associated with impairments in attention and executive control. The superior parietal gyrus plays a crucial role in spatial attention, working memory, and sensory integration, and its disruption might lead to cognitive deficits in mTLE patients\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Previous study showed as the disease progresses, functional connectivity in the superior parietal gyrus were decreasedin TLE patients\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Our study found that the NSF group exhibited significantly decreased gradient scores in the left superior parietal gyrus compared to the SF group and HC, suggesting that impaired region may underlie the sensory and cognitive impairment in mTLE.\u003c/p\u003e\u003cp\u003eThis study had several limitations. First, the sample size may limit the generalizability of the findings. Future studies with larger, multicenter cohorts are needed to validate these results. Second, the cross-sectional design precludes the assessment of longitudinal changes in network gradients, particularly in relation to postoperative recovery and long-term outcomes.Incorporating longitudinal follow-up data could provide deeper insights into the dynamic reorganization of functional networks following surgery. Lastly, integrating multimodal imaging data, such as diffusion tensor imaging and task-based fMRI, may further elucidate the structural-functional interplay underlying mTLE.\u003c/p\u003e\u003cp\u003eIn summary, this study compared gradient features across SF, NSF, and HC groups, providing novel insights into the relationship between functional network reorganization and surgical outcomes. These methodological advancements not only extend our understanding of mTLE pathophysiology but also establish a foundation for future research on networks in neurological disorders.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKanlin Lin, Xuesa Li: Conceptualization, Methodology, Software, Investigation, Formal Analysis, Visualization, Writing - Original Draft. Hui Li, Yaling Chen: Methodology, Writing - Original Draft. Lidan Lin, Sifan Qiu, Ligang Song: Data Curation, Writing - Original Draft. Xiaoyang Wang, Shangwen Xu: Conceptualization, Funding Acquisition, Resources, Supervision, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the Fujian Province guided project (Grant No.2023Y0066) and the Joint Funds for the innovation of science and Technology, Fujian province (Grant No.2024Y9647).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the 900th Hospital of PLA Joint Logistic Support Force (Approval Number: 2019-005). Written informed consent was obtained from all participants and their families in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMuzumdar D, et al. Mesial temporal lobe epilepsy - An overview of surgical techniques. Int J Surg. 2016;36:411\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKohlhase K, Z\u0026ouml;llner JP, Tandon N, Strzelczyk A, Rosenow F. Comparison of minimally invasive and traditional surgical approaches for refractory mesial temporal lobe epilepsy: A systematic review and meta-analysis of outcomes. 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NeuroReport. 2021;32:1009\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\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":"Mesial temporal lobe epilepsy, gradient, hierarchy, functional magnetic resonance imaging, treatment outcome","lastPublishedDoi":"10.21203/rs.3.rs-8204963/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8204963/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMesial temporal lobe epilepsy (mTLE) is a common recurrent neurological disorder, which timely surgical intervention is often required in drug-resistant cases. Although previous neuroimaging studies have explored brain abnormalities associated with mTLE, the alterations in brain network functional activities remain far from be elucidated.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePreoperative resting-state fMRI data were collected from 47 patients with drug-resistant mTLE, divided into c groups based on postoperative outcomes. Data from 32 age- and sex-matched healthy controls (HC) were included for comparison. Gradient analysis was used to investigate the changes in the hierarchy of brain networks in mTLE patients.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBoth SF and NSF groups exhibited disruptions in principal gradient organization compared to HC. Significant differences in gradient scores were observed between the SF and NSF groups, particularly within the default mode network (DMN) and somatomotor network (SMN). In the DMN, significant differences were observed in regions such as the left middle temporal gyrus and the left inferior temporal gyrus. In the SMN, significant differences were found in regions such as the right precentral gyrus and the right postcentral gyrus.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAlterations in brain networks hierarchy within the SMN and DMN may reflect distinct patterns of network reorganization associated with postoperative seizure outcomes, which provide new insights into prognosis and therapeutic strategies in mTLE.\u003c/p\u003e","manuscriptTitle":"Hierarchical reorganization of brain Functional Networks in Mesial Temporal Lobe Epilepsy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-09 00:26:38","doi":"10.21203/rs.3.rs-8204963/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":"d297457f-40b6-4eb4-9bd9-1037ebceffb7","owner":[],"postedDate":"December 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-20T11:35:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-09 00:26:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8204963","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8204963","identity":"rs-8204963","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}