@conference {70433,
	title = {Fcdnets: Surface-Graph Neural Networks for Automated Detection of Focal Cortical Dysplasia on Multi-Contrast Mri Surface Maps},
	booktitle = {2025 IEEE MIT Undergraduate Research Technology Conference (URTC)},
	year = {2026},
	month = {2026 May 26},
	abstract = {<p>Focal cortical dysplasia (FCD) is a leading cause of drug-resistant focal epilepsy, especially in children. However, up to\&nbsp;30-40\%\&nbsp;of FCD lesions are {\textquotedblleft}MRI-negative,{\textquotedblright} eluding visual detection on standard scans. Even expert neuroradiologists miss about one-third of these subtle lesions. Prior automated FCD detection approaches have shown promise but face important limitations. Conventional morphometry pipelines (e.g., FreeSurfer-based thickness or junction maps) rely on hand-tuned thresholds and often struggle with small cohorts and site-specific bias. Voxel-level deep learning methods, including 3D CNN and transformer models, can improve sensitivity but tend to produce excessive false positives and lack interpretability. In this work, we propose FCDNets, a graph neural network (GNN) that learns from multi-contrast MRI cortical surface maps to detect FCD lesions. Trained using only open-access MRI datasets (85 patients and 85 controls from OpenNeuro ds004199; 101 patients and 177 controls from MELD-Public), FCDNets achieves vertex-level AUROC\&nbsp;0.975{\textpm}0.006, lesion Dice\&nbsp;0.64{\textpm}0.12, and 82\% patient sensitivity at\&nbsp;<=5\&nbsp;cm2\&nbsp;false-positive cortex in cross-site five-fold CV. Compared with a 3-D CNN baseline, FCDNets yields +28 percentage points higher sensitivity (82 \% vs. 54 \%) and 45 \% fewer false-positive clusters (0.9 vs. 1.6). On MELD-Public, it maintains AUROC 0.782 and 63\% sensitivity. The surface-based approach produces interpretable lesion probability maps aligned with known FCD imaging biomarkers. This open, multi-center study demonstrates the potential of surface-GNNs to aid in localizing subtle epileptogenic lesions that were previously MRIoccult, which may accelerate diagnosis and surgical treatment for drug-resistant epilepsy.</p>
},
	keywords = {Cortical Surface Analysis, Dies, Epilepsy, Epilepsy Imaging, Equations, Focal Cortical Dysplasia, Graph neural networks, Lesion Detection, Lesions, Magnetic Resonance Imaging, Modeling, Multi-Contrast MRI, Printing, Sensitivity, Signal detection, Surfaces, Vertex-Level Classification},
	doi = {10.1109/URTC68753.2025.11533035},
	url = {https://doi.org/10.1109/urtc68753.2025.11533035.},
	author = {Uppalapati, Khartik and Yimenicioglu, Bora and Tsai, Aaron and Abdulkareem, Shakeel and Hromek-Vaitla, Kaden and Eftekhari, Adan}
}
