Mapping Individualized Dual-Axis Network Topology in Focal Epilepsy: Divergent Alterations in System Integrity, Integration, and Clinical Correlates

Repository Overview This repository stores the raw data, intermediate derivatives, and full analysis outputs for the study: Mapping Individualized Dual-Axis Network Topology in Focal Epilepsy: Divergent Alterations in System Integrity, Integration, and Clinical Correlates The complete analysis codebase is publicly available at:https://github.com/qiruizhangqirui/epilepsy-network-topology This Zenodo record archives the data inputs and generated results necessary to reproduce all primary analyses, normative modeling, statistical inference, and visualization outputs described in the manuscript. Data Directory Documentation This section contains the required input data, subject metadata, and raw neuroimaging derivatives for executing the Epilepsy Network Topology pipeline. Metadata and Configuration Subjects.xlsxMaster clinical and demographic spreadsheet including cohort assignments (TJU, JLH), diagnostic group labels (Focal Epilepsy, TLE, EXE, GGE, SeLECTS, Absence Epilepsy, Healthy Participants), clinical features (duration, onset, pathology), and neurocognitive measures. Noise_atoms_new.txtList of individualized functional network components manually flagged as noise (motion or vascular artifacts), used for sensitivity analyses. Network_Wscore.xlsxIntermediate spreadsheet aggregating regional W-scores and clinical variables for downstream modeling (sCCA and SuStaIn). Atlas and Template Resources NCT_atlases/Canonical atlas definitions used in multi-parcellation correspondence analyses (AS200Y17, EG17, HCP-ICA, MG360J12, TY7, UKB-ICA). Atlases are from Network Correspondence Toolbox (https://github.com/rubykong/cbig_network_correspondence). Subcortical_FLS2mm.nii / Subcortical_8ROI_FLS2mm.niiSubcortical ROI definitions in MNI 2mm space aligned with FreeSurfer ASEG segmentation. schaefer_200x17_conte69.csv / assignment_34.matParcel-to-network mapping files linking fine-grained cortical parcels to large-scale networks. Raw Neuroimaging Derivatives (Per Subject) NCT_Derivatives/ Subject-level CSV matrices quantifying Dice overlap between individualized overlapping functional networks (SPARK-derived) and canonical atlas systems. Used in correspondence analysis and system integrity modeling. SPARK_Derivatives/ Individualized spatial NIfTI maps of functional k-hubness (multi-network participation). Used to quantify system integration alterations. CAT_Derivatives/ Cortical gray matter volume (GMV) metrics or maps processed using CAT12. Used for parallel SuStaIn progression analysis. Results Directory Documentation This section archives all outputs generated by the analysis pipeline (S1–S12), including aggregated measures, normative modeling outputs, statistical results, and figure-ready visualizations. Aggregated Metrics Correspondence_measures.matRaw network correspondence metrics (Normativity, Non-normativity, CNR). Hubness_Measures.matRaw k-hubness values across cortical regions. Correspondence_Wscore.matCovariate-adjusted normative W-scores for system integrity deviations. Hubness_Wscore.matNormative W-scores for system integration alterations. Statistical Results Correspondence_stats.matPermutation-based max-T group-level statistics characterizing correspondence disruptions. Hubness_stats.matStatistical maps identifying functional integration increases or decreases. TJU_GM_stats.matGray matter volume W-scores used for structural progression modeling. Visualization and Validation Outputs correspondence_statistic/ Consensus surface maps of correspondence W-scores across atlases Radar plots summarizing 17-network system integrity changes Global box plots of normativity and non-normativity k-hubness_statistic/ Brain surface maps of integration reconfiguration Network-level bar plots of subtype-related hubness changes Validation/ Spin-test corrected spatial similarity scatter plots (TJU vs JLH) Cross-syndrome heatmaps comparing topology profiles across epilepsy syndromes Advanced Modeling Outputs PySuStaIn/ Correspondence-based progression modeling GMV-based progression modeling Trajectory matrices, subtype probabilities, subject staging assignments Cortical progression visualizations CCA/ Sparse Canonical Correlation Analysis (dual-axis sCCA) outputs Multivariate loading plots linking system integrity, system integration, cognitive domains, and clinical phenotypes This archive preserves all necessary derivative inputs and outputs required to reproduce manuscript figures and statistical findings. If you use this dataset, code or pipeline, please cite: Zhang Q, Dascal A, Javidi SS, Ankeeta A, Sperling MR, Zhang Z, Tracy JI. Mapping Individualized Dual-Axis Network Topology in Focal Epilepsy: Divergent Alterations in System Integrity, Integration, and Clinical Correlates. bioRxiv. 2026:2026.03.17.712432. doi: 10.64898/2026.03.17.712432