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Approximately 30% of epilepsy patients are medically intractable. Epilepsy surgery may offer cure or palliation, and neuromodulation and direct drug delivery are being developed as alternatives. Successful treatment requires correct localization of seizure onset zones and understanding surrounding functional cortex to avoid iatrogenic disability. Several neurophysiologic and imaging localization techniques have inherent individual weaknesses which can be overcome by multimodal analysis. We review common noninvasive techniques, then illustrate the value of multimodal analysis to localize seizure onset for targeted treatment.

Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain representative features from the images play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is especially important in the early diagnosis of dementia. In this work, we present a technique that allows using specific time series analysis techniques with 3D images. This is achieved by sampling the image using a fractal-based method which preserves the spatial relationship among voxels. In addition, a method called Empirical functional PCA (EfPCA) is presented, which combines Empirical Mode Decomposition (EMD) with functional PCA to express an image in the space spanned by a basis of empirical functions, instead of using components computed by a predefined basis as in Fourier or Wavelet analysis. The devised technique has been used to classify images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Parkinson Progression Markers Initiative (PPMI), achieving accuracies up to 93% and 92% differential diagnosis tasks (AD versus controls and PD versus Controls, respectively). The results obtained validate the method, proving that the information retrieved by our methodology is significantly linked to the diseases.