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The need to make timely and accurate diagnoses of brain diseases has posed challenges to computer-aided diagnosis systems. In this field, advances in deep learning techniques play an important role, as they carry out processes to extract relevant anatomical and functional characteristics of the tissues to classify them. In this paper, the study of various architectures of convolutional neural networks (CNN) is presented, with the aim of classifying three types of brain tumors in high-contrast magnetic resonance (MR) images. The architectures of the present study were VGG16, ResNet50, Xception, whose implementations are defined in the Keras framework. The evaluation of these architectures were preceded by data augmentation techniques and transfer learning, which improved the effectiveness of the training process, thanks to the use of pre-trained models with the ImageNet dataset. The VGG16 architecture was the one with the best performance, with an accuracy of 98.04%, followed by ResNet50 with 94.89%, and finally, Xception with 92.18%.

Humans can effortlessly extract the affective meaning of touch delivered to another person`s skin as well as their own. Interpersonal touch communication conveys discrete emotion and intention. For example, we can easily imagine experiencing a warm feeling when observing a person being hugged. Previous findings about simple touch observation suggested that this phenomenon could be linked to somatosensory resonance and the theory of mind (ToM). Yet, concerning more complex interpersonal affective touch, our understanding of how such mechanisms work is still limited. Thus, in the current study we generated a novel socio-affective touch database of 39 stimulus videos, covering both pleasant (e.g., hugging a person) and unpleasant (e.g. slapping a person) touch scenarios, and investigated how the human brain processes different types of interpersonal affective touch during passive observation. First, 21 participants evaluated pleasantness and arousal of each touch video. Subsequently, the same participants watched the same videos in the scanner. Importantly, we also provided the participants with both positive and negative touch stimulation in the scanner to capture actual touch sensitive cortices which we used as parts of regions of interest (ROI) along with social brain regions. Using correlational multivariate pattern analysis (MVPA) methods, neural spaces of affective touch were obtained in ROIs, followed by multiple regression analysis between the group neural matrix in each ROI and affective ratings. The results suggest that both actual touch sensitive cortices and social brain regions represent valence information after controlling the effects of arousal and other visual factors. Our findings highlight the involvement of social understanding and a mirror somatosensory system during observation of other`s affective touch interactions in the absence of actual touch. ispartof: International Association for the Study of Affective Touch location:Liverpool, UK date:1 Sep - 3 Sep 2017 status: published

Having the means to share research data openly is essential to modern science. For human research, a key aspect in this endeavor is obtaining consent from participants, not just to take part in a study, which is a basic ethical principle, but also to share their data with the scientific community. To ensure that the participants' privacy is respected, national and/or supranational regulations and laws are in place. It is, however, not always clear to researchers what the implications of those are, nor how to comply with them. The Open Brain Consent (https://open-brain-consent.readthedocs.io) is an international initiative that aims to provide researchers in the brain imaging community with information about data sharing options and tools. We present here a short history of this project and its latest developments, and share pointers to consent forms, including a template consent form that is compliant with the EU general data protection regulation. We also share pointers to an associated data user agreement that is not only useful in the EU context, but also for any researchers dealing with personal (clinical) data elsewhere.

Human physiology changes in predictable ways in anticipation of and after exposure to emotional visual stimuli. In a series of experiments reported by Radin (1997), it was found that even when stimuli were adequately randomized, so that the upcoming stimuli could not be inferred, that anticipatory responses (as measured by changes in skin conductance) before exposure to emotional pictures were significantly larger than before exposure to calm pictures. In three subsequent expreriments, the first and third close replications and the second a conceptual replication of Radin s studies, Bierman confirmed this so called "presentiment" or pre-feeling effect. Bierman sub-sequently decided to see whether these anomalies observed in physiological baseline measurements could also be found in data from studies published previously in the main stream literature. Two datasets were found and reanalyzed. The first dataset as from a study on the speed with which fear arises in animal phobic participants vs. controls by the German group of Hamm. The second study was concerned with the difference in anticipatory responses prior to choosing cards from risky vs. non-risky decks of cards in a gambling task by the US group of Damasio. The combined result showed a significant anomalous difference similar to the effects found in the original studies by Radin and Bierman. A new development in presentiment research is the measurement of brain images rather than skin conductance preceding the presentation of randomized neutral and emotional stimuli. Preliminary results suggest that the anomaly can be located in the brain. This would allow for more detailed inspection of differences between different types of emotional stimuli like violent and erotic.

Using advanced neuroimaging to communicate with individuals in a vegetative state, medical science is developing a richer understanding of 'disorders of consciousness'. Join Adrian Owen, Canada Excellence Research Chair in Neuroscience and Imaging at Western, as he shares pioneering insights into our deepening knowledge of some of the most challenging questions about how the brain functions and the very nature of consciousness.

Attention deficit and hyperactivity disorder (ADHD) is a medical condition that affects approximately 7% of children worldwide. The diagnosis of ADHD can be done using psychological tests and electroencephalography (EEG). However, the variability and complexity of EEG signals affects its diagnostic utility. The purpose of this work is to identify relevant features of EEG signals from children diagnosed with ADHD and control cases for their classification. A total of 47 children were included in the study (22 with ADHD and 25 in the control group). EEG of cognitive evoked potentials were preprocessed using wavelet filtering and synchronized averaging. Then, fourteen features were calculated in signals from four channels (F3, AF3, F4 and AF4), including evoked potentials, power spectrum, entropy, chaos, bicoherence measures, and prominent peaks. For feature selection, the algorithms PCA, hybrid stepwise regression, ridge regression, and correlation values were evaluated. It was evidenced that evoked potentials have a relative high level of importance, as well as the prominent peaks. On the other hand, the values of chaos and bicoherence measures, along with the gender, are the least representative features. These results are consistent among the four feature selection algorithms. In conclusion, 9 of the 14 features are representative of the data set and were used for the classification stage of this work.

Contains fulltext : 139156.pdf (Publisher’s version ) (Open Access) Inaugurele rede, 26 september 2013 22 p.