Work

Complementary axes of functional system integrity and integration with divergent clinical correlates characterize focal epilepsy

Year: 2026

Type: dataset

Abstract: Repository Overview This repository stores the raw data, intermediate derivatives, and full analysis outputs for the study: Complementary axes of functional system integrity and integration with divergent clinical correlates characterize focal epilepsy 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 directory contains the required input data, subject metadata, and raw neuroimaging derivatives for executing the Epilepsy Network Topology pipeline. File Overview Metadata & Configuration Subjects.xlsx: The master clinical and demographic spreadsheet. It contains subject metadata, cohort assignments (TJU or JLH), grouping labels (e.g., Focal Epilepsy, TLE, EXE, GGE, SeLECTS, Absence Epilepsy, Healthy Participants), clinical features (duration, onset, pathology), and detailed neurocognitive scores. Noise_atoms_new.txt: A text file listing individualized functional network components (atoms) manually flagged as noise (e.g., motion or vascular artifacts) to support sensitivity analyses. Network_Wscore.xlsx: An intermediate spreadsheet aggregating pre-computed regional W-scores and clinical features, tailored for downstream advanced modeling (sCCA and SuStaIn). Atlas & Templates NCT_atlases/: Contains standard definitions of the canonical functional and structural atlases used for the multi-parcellation correspondence analysis (e.g., AS200Y17, EG17, HCP-ICA, MG360J12, TY7, UKB-ICA). Subcortical_FLS2mm.nii / Subcortical_8ROI_FLS2mm.nii: Subcortical ROI definitions in MNI space (2mm), aligned with the FreeSurfer ASEG segmentation used for volumetric correspondence and integration metrics. AS17network_34ROIs_sorted.nii.gz: A NIfTI file containing the 34 subcortical and cortical ROI definitions sorted for the AS17 functional network, used in functional hubness mapping and structural gray matter volume pipelines. FSLMNI2mm.nii: A brain gray matter mask in FSL MNI 2mm space, used to mask regions when calculating overlap Dice coefficients in S1_Org_outputs.m. schaefer_200x17_conte69.csv / assignment_34.mat: Label mapping files associating granular parcels with large-scale cortical networks. tian2aseg.mat: A subcortical mapping matrix (Tian subcortical atlas parcellations mapped to FreeSurfer ASEG labels) used in S6_TJU_GM_pipeline.m. Raw Derivatives (per Subject) The pipeline expects pre-processed, single-subject derivatives organized within the following subdirectories: NCT_Derivatives/ (Network Correspondence Topology) Contains subject-specific CSV matrices quantifying the pairwise overlap (Dice coefficients) between individualized overlapping functional networks (derived via SPARK) and canonical atlases. Ingested by S1_Org_outputs.m and parsed by S2_correspondence_analysis.m. SPARK_Derivatives/ Contains spatial NIfTI maps of individualized functional k-hubness (representing multi-network participation). Specifically analyzed by S5_hubmess_pipeline.m. CAT_Derivatives/ Contains Cortical Gray Matter Volume (GMV) metrics or raw maps processed via CAT12 or equivalent structural pipelines. Analyzed by S6_TJU_GM_pipeline.m to compute normative structural deviations needed for GMV-based parallel SuStaIn modeling. Results Directory Documentation This directory archives all generated outputs from the analysis pipeline (S1-S12). It includes aggregated metrics, individualized normative models, statistical assessments, and final visualizations mimicking the paper figures. Top-Level Files Aggregated Metrics All_derivatives_struct.mat: Consolidated MATLAB structure containing all subjects' raw network correspondence (Dice overlap matrices), noise indices, and subcortical overlaps, generated by S1_Org_outputs.m as a pipeline cache. Correspondence_measures.mat: Raw correspondence measures (e.g., CNR, Normativity, Non-normativity) organized by site, atlas, and subject. Hubness_Measures.mat: Raw k-hubness (multi-network participation) metrics for all subjects. TJU_GM_Measures.mat: Raw structural Gray Matter Volumes (GMV) extracted across regions and atlases, generated by S6_TJU_GM_pipeline.m. Correspondence_Wscore.mat: Normative W-scores (covariate-adjusted Z-scores) mapping individualized deviations in network correspondence relative to cohort-matched healthy participants. Hubness_Wscore.mat: Normative W-scores for functional k-hubness alterations. TJU_GM_Wscore.mat: Normative W-scores for structural Gray Matter Volumes, representing gray matter volumetric deviations compared to control participants, generated by S6_TJU_GM_pipeline.m. Statistical Results Correspondence_stats.mat: Group-level inferential statistics (e.g., permutation-based max-T p-values) characterizing correspondence disruptions. Hubness_stats.mat: Group-level statistics delineating regions of functional integration gain or loss. Subdirectories Visualization & Validation Demographics/: Contains clinical demographics visualizations such as age distribution density plots and stacked bar charts for clinical characteristics (epilepsy type, pathology, focus lateralization, FBTCS status). GLM/: Contains General Linear Model (GLM) mapping outputs including FDR-corrected correlation heatmaps of network metrics (correspondence, hubness) against clinical and cognitive variables, and the comprehensive stats summary spreadsheet (GLM_stats_summary.csv). correspondence_statistic/: Consensus Brain Maps: Surface visualizations (.tiff/.nii) plotting consensus correspondence W-scores across multiple atlases for specific patient subgroups (e.g., TLE, EXE). Radar Plots: *_Radar17.tiff – System-level plots displaying correspondence alterations across the canonical 17 resting-state networks. Box Plots: Global distribution plots detailing mean Normativity and Non-normativity shifts. hubness_statistic/: Brain Surface Visualizations: Maps pinpointing multi-network integration reconfigurations. Bar Plots: Network and regional correlation bar charts parsing subtype-driven hubness differences. Validation/ (Independent Cross-Cohort Analysis): Scatter Plots: Validation scatter maps measuring spatial similarity (Spin-test corrected) between TJU discovery and JLH validation effect size maps. Cross-Syndrome Heatmaps: Matrices quantifying network topological correlation (correspondence and hubness vectors combined) across the full epilepsy spectrum (Focal, GGE, SeLECTS, Absence). Advanced Modeling PySuStaln/: Separate subdirectories (Correspondence/, GMV/) housing the outputs from the respective progression models. Contains inferred trajectory matrices, subtype probability models, assigned subject staging sequence data (.csv), and cortical progression renderings (.tiff). PySuStaln_statistic/: Contains box plots (Box_*_*.png) and stacked bar charts (StackedBar_*_*.png) describing clinical variables (onset age, disease duration, epilepsy type, pathology, lateralization) stratified across SuStaIn-inferred subtypes, along with subtype-specific progression staged CSV/PNG files. CCA/: Outputs from the dual-axis Sparse Canonical Correlation Analysis (S8). Features component loading bar plots and matrices charting the multivariate associations between system integrity/integration and cognition/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. Complementary axes of functional system integrity and integration with divergent clinical correlates characterize focal epilepsy. Nat Commun (2026). https://doi.org/10.1038/s41467-026-79018-2

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.18808543