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Pediatric epilepsy due to drug-resistant Focal Cortical Dysplasia (FCD) presents significant healthcare challenges. Precise preoperative identification of FCD lesions is imperative for surgical planning and patient outcomes. This paper presents a proof-of-concept for an integrated methodology that combines Electroencephalogram (EEG)-based functional connectivity analysis with Magnetic Resonance Imaging (MRI)-derived cortical thickness measurements to identify FCD lesions in pediatric epileptic patients. We examined a single-case clinical scenario from Oregon Health Science and University, consistently identifying the Caudal Middle Frontal (cMFG) region across both EEG and MRI modalities, a finding that was confirmed in the postoperative MRI scan. This cross-validation underscores the potential of the precision of our approach in pinpointing the surgical target region. Despite being constrained by its preliminary nature, our research offers a valuable foundation for a personalized, rigorous method of detecting the location of the FCD lesions. It holds significant clinical implications for managing FCD-related epilepsy. It also portends broader applications in neurology and precision medicine. Nonetheless, further large-scale studies are needed to validate and fine-tune our methodology. Clinical Relevance - This study offers clinicians an advanced, integrated approach to preoperative assessment of FCD lesions, potentially improving the precision of surgical planning in pediatric epilepsy. 

Deep Neural Networks (DNNs) trained for classification tasks are vulnerable to adversarial attacks. But not all the classes are equally vulnerable. Adversarial training does not make all classes or groups equally robust as well. For example, in classification tasks with long-tailed distributions, classes are asymmetrically affected during adversarial training, with lower robust accuracy for less frequent classes. In this regard, we propose a provable robustness method by leveraging the continuous piecewise-affine (CPA) nature of DNNs. Our method can impose linearity constraints on the decision boundary, as well as the DNN CPA partition, without requiring any adversarial training. Using such constraints, we show that the margin between the decision boundary and minority classes can be increased in a provable manner. We also present qualitative and quantitative validation of our method for class-specific robustness. Our code is available at https: //github.com/Josuelmet/CROP