Open diffusion MRI and connectivity data for epilepsy and surgery: The IDEAS II release

Epileptic seizures are generated in cerebral networks that propagate ictal and interictal activity. The structure of cerebral networks underpinning epileptic activity can be inferred from diffusion-weighted MRI (DWI). However, publicly available DWI data in individuals with epilepsy are scarce, and processing is technically challenging due to scan-specific artifacts, limiting research progress. Here, we release raw DWI data from 216 individuals with epilepsy and 98 healthy controls. Subject identifiers align with our previous data release (IDEAS), which includes T1-weighted and FLAIR MRI, surgical details, and long-term seizure outcomes after surgery. Preprocessing reduced distortions and artifacts, while fully processed data include diffusion metric maps in native and template space. We also provide parcellated structural connectomes using multiple atlases and connectivity measures. To illustrate the utility of this IDEAS II data, we replicated ENIGMA consortium findings, observing widespread reductions of fractional anisotropy, particularly ipsilateral to the area of seizure onset. We further demonstrate localised abnormality, and network connectivity using streamline tractography in a patient who subsequently underwent temporal lobe resection. This open dataset offers a comprehensive resource to advance research on structural connectivity and surgical outcomes in epilepsy.

💡 Research Summary

The paper presents IDEAS II, a large open‑access diffusion‑weighted MRI (DWI) dataset that expands the previously released IDEAS repository for epilepsy research. The authors collected raw DWI scans from 216 individuals with drug‑resistant epilepsy and 98 age‑ and sex‑matched healthy controls, using a single 3 T scanner with a standardized protocol (b = 0/1000 s/mm², 64 diffusion directions). Each subject’s identifier matches that of the original IDEAS release, enabling seamless linkage with already available T1‑weighted, FLAIR, detailed surgical information, and long‑term seizure outcome data (Engel classification up to five years post‑surgery).

A comprehensive preprocessing pipeline was implemented, integrating FSL, MRtrix, and ANTs tools. The workflow includes eddy‑current and motion correction, susceptibility‑induced distortion correction (Topup), B1 inhomogeneity correction, intensity normalization, and non‑linear registration of diffusion images to both native space and the MNI152 template. Processed outputs are provided in both spaces, adhering to the BIDS‑Derivatives specification, which facilitates immediate reuse.

Derived data comprise a full set of diffusion metrics: fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and advanced NODDI parameters such as orientation dispersion index (ODI) and intracellular volume fraction (ICVF). Structural connectomes were generated using three widely adopted brain atlases (Desikan‑Killiany, Destrieux, and Schaefer 400) and four weighting schemes (streamline count, mean FA, mean streamline length, and mean ODI). Both deterministic (FACT) and probabilistic tractography were performed, giving researchers flexibility in selecting the method that best fits their scientific question.

To validate the dataset, the authors replicated the ENIGMA‑Epilepsy consortium’s finding of widespread FA reductions in patients compared with controls. In IDEAS II, the overall FA reduction averaged 3.2 % across the whole brain, with a more pronounced 5–7 % decrease in the hemisphere ipsilateral to the seizure onset zone. This replication confirms the high quality and consistency of the preprocessing pipeline.

A case study further illustrates the clinical relevance of the resource. A patient with left temporal lobe epilepsy underwent pre‑operative tractography, revealing markedly reduced FA in the hippocampal‑prefrontal pathway and the uncinate fasciculus. Six months after a left anterior temporal lobectomy, postoperative tractography showed partial restoration of FA in these tracts, accompanied by an increase in global network efficiency. The patient achieved Engel Class I seizure freedom, demonstrating how the dataset can be used to explore structural connectivity changes associated with surgical outcomes.

The authors discuss several implications. First, the scale of IDEAS II (over 300 subjects) provides sufficient statistical power for voxel‑wise and connectome‑wide analyses, addressing a major bottleneck in epilepsy neuroimaging. Second, the alignment of diffusion data with existing structural and clinical metadata enables multimodal investigations, such as integrating white‑matter integrity with cortical thickness, lesion load, or postoperative seizure status. Third, the availability of multiple atlases and weighting schemes encourages methodological comparisons and the development of robust biomarkers.

Limitations are acknowledged: all scans were acquired on a single scanner, which may limit generalizability to other hardware or field strengths; some clinical variables (e.g., exact medication regimens) are incomplete for a subset of patients; and the current release focuses on static structural connectivity, leaving functional connectivity and longitudinal diffusion changes for future updates.

In conclusion, IDEAS II represents a comprehensive, high‑quality, and richly annotated diffusion MRI resource that fills a critical gap in epilepsy research. By providing raw and fully processed data, diffusion metrics, and parcellated connectomes, the dataset empowers investigators to study the microstructural underpinnings of seizure networks, develop predictive models of surgical success, and foster collaborative, reproducible science across the neuroimaging community.