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EN
Verbal phonological and semantic fluencies were investigated in 24 patients with unilateral prefrontal lesions and 10 normal control subjects. Lesions were limited to small areas within either the dorsolateral (Brodmann's area 46/9) or ventromedial (posterior part of the gyrus rectus) cortices. In a phonological fluency task, patients with lesions to the left dorsolateral region were impaired. In semantic fluency, not only the left dorsolateral group but also the two right frontal damaged groups performed worse than the control group. In agreement with previous studies, our results show that the phonological fluency is mediated by the left dorsolateral prefrontal cortex. In contrast to this, performance on the semantic fluency task depends on a wider portion of the prefrontal cortex involving the left and right dorsolateral and the right ventromedial areas.
EN
Glioma detection and classification is an critical step to diagnose and select the correct treatment for the brain tumours. There has been advances in glioma research and Magnetic Resonance Imaging (MRI) is the most accurate non-invasive medical tool to localize and analyse brain cancer.The scientific global community has been organizing challenges of open data analysis to push forward automatic algorithms to tackle this task. In this paper we analyse part of such challenge data, the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS), with novel algorithms using partial learning to test an active learning methodology and tensor-based image modelling methods to deal with the fusion of the multimodal MRI data into one space. A Random Forest classifier is used for pixel classification. Our results show an error rates of 0.011 up to 0.057 for intra-subject classification. These results are promising compared to other studies. We plan to extend this method to use more than 3 MRI modalities and present a full active learning approach.
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