@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}
}
@conference {70432,
	title = {MechGNN: A Graph Neural Network Framework for Learning Nanoparticle-Tumor Mechanical Interactions in Targeted Drug Delivery},
	booktitle = {2025 IEEE MIT Undergraduate Research Technology Conference (URTC)},
	year = {2026},
	month = {2026 May 26},
	abstract = {<p>Mechanical heterogeneity and collagen topology in tumor extracellular matrix (ECM) hinder nanoparticle (NP) transport and uptake, motivating models that couple NP mechanics with measured tumor mechanics. Non-invasive stiffness mapping (e.g., MR elastography) and histology-derived stiffness inference (STIFMap) enable learning on patientproximal mechanical maps. We introduce MechGNN, a dualgraph framework with cross-attention that represents NPs (core/shell/ligands) and ECM fiber networks as interacting graphs, and embeds physics-informed edge functions derived from Hertzian contact and receptor-ligand binding kinetics to regularize message passing. Using only public re-sources-STIFMap and open SHG collagen images quantified with CT-FIRE/CurveAlign/TWOMBLI-plus open FEM/BD simulations (FEniCS; Brownian dynamics) and open NP/ligand repositories (caNanoLab, BindingDB, IUPHAR/BPS Guide to Pharmacology), MechGNN achieves a\&nbsp;12-16\%reduction in penetration-depth RMSE relative to the strongest baseline on synthetic-from-real ECM testbeds and sustains an\&nbsp;8-13\%\&nbsp;RMSE gain in leave one tumor type out tests; physics losses reduce constraint violation rates by\&nbsp;≈35\%. The pipeline provides a reproducible bridge from measured ECM mechanics to mechanically designable NP properties.</p>
},
	keywords = {Contacts, drug delivery, Elasticity, Electronic countermeasures, Extracellular Matrix, Graph neural networks, Joining processes, Labeling, Modeling, nanoparticle mechanics, Physics, physics-informed learning, Printing, Textile fibers, Tumors},
	doi = {10.1109/URTC68753.2025.11533032},
	url = {https://doi.org/10.1109/urtc68753.2025.11533032},
	author = {Uppalapati, Khartik and Yimenicioglu, Bora and Abdulkareem, Shakeel and Eftekhari, Adan}
}
