By computerized analysis of cortical activity recorded via fMRI for pediatric epilepsy patients, we implement algorithmic localization of epileptic seizure focus within one of eight cortical lobes. Our innovative machine learning techniques involve intensive analysis of large matrices of mutual information coefficients between pairs of anatomically identified cortical regions. Drastic selection of pairs of regions with biologically significant inter-connectivity provides efficient inputs for our multi-layer perceptron (MLP) classifier. By imposing rigorous parameter parsimony to avoid overfitting, we construct a small-size MLP with very good percentages of successful classification.

中文翻译：

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通过对fMRI记录的计算机分析来确定癫痫发作的局限性

通过计算机分析的功能性磁共振成像记录的小儿癫痫患者的皮质活动，我们实现了癫痫发作重点的算法定位在八个皮质叶之一。我们创新的机器学习技术涉及深入分析在成对解剖学识别的皮层区域之间的互信息系数的大矩阵。对具有生物学上显着互连性的区域对的大量选择为我们的多层感知器（MLP）分类器提供了有效的输入。通过施加严格的参数简约性以避免过度拟合，我们构建了具有成功分类百分比很高的小型MLP。