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Polyglutamine spinocerebellar ataxias (polyQ SCAs) are a group of neurodegenerative diseases, clinically and genetically heterogeneous, characterized by loss of balance and motor coordination due to dysfunction of the cerebellum and its connections. The diagnosis of each type of polyQ SCA, alongside with genetic tests, includes medical images analysis, and its automation may help specialists to distinguish between each type. Convolutional neural networks (ConvNets or CNNs) have been recently used for medical image processing, with outstanding results. In this work, we present the main clinical and imaging features of polyglutamine SCAs, and the basics of CNNs. Finally, we review studies that have used this approach to automatically process brain medical images and may be applied to SCAs detection. We conclude by discussing the possible limitations and opportunities of using ConvNets for SCAs diagnose in the future., (© 2022. International Federation for Medical and Biological Engineering.)

Most creativity measures are either complex or language-dependent, hindering cross-cultural creativity assessment. We have therefore developed and tested a simple, language-independent insight task based on pictures in the style of the widely used verbal remote associate task (RAT). We demonstrate that the language-independent RAT (LI-RAT) allows assessment of different aspects of insight across large samples with different languages. It also correlates with other creativity and general problem-solving tasks. The entire stimulus set, including its preliminary normative data, is made freely available. This information can be used to select items based on accuracy, mean solution time, likelihood to produce an insight, or conceptual and perceptual similarity between the pictures per item., (© 2022. The Author(s).)

Many fMRI studies have examined the neural mechanisms supporting emotional memory for stimuli that generate emotion rather automatically (e.g., a picture of a dangerous animal or of appetizing food). However, far fewer studies have examined how memory is influenced by emotion related to social and political issues (e.g., a proposal for large changes in taxation policy), which clearly vary across individuals. In order to investigate the neural substrates of affective and mnemonic processes associated with personal opinions, we employed an fMRI task wherein participants rated the intensity of agreement/disagreement to sociopolitical belief statements paired with neural face pictures. Following the rating phase, participants performed an associative recognition test in which they distinguished identical versus recombined face-statement pairs. The study yielded three main findings: behaviorally, the intensity of agreement ratings was linked to greater subjective emotional arousal as well as enhanced high-confidence subsequent memory. Neurally, statements that elicited strong (vs. weak) agreement or disagreement were associated with greater activation of the amygdala. Finally, a subsequent memory analysis showed that the behavioral memory advantage for statements generating stronger ratings was dependent on the medial prefrontal cortex (mPFC). Together, these results both underscore consistencies in neural systems supporting emotional arousal and suggest a modulation of arousal-related encoding mechanisms when emotion is contingent on referencing personal beliefs.

Research on the emotional, cognitive, and social determinants of moral judgment has surged in recent years. The development of moral foundations theory (MFT) has played an important role, demonstrating the breadth of morality. Moral psychology has responded by investigating how different domains of moral judgment are shaped by a variety of psychological factors. Yet, the discipline lacks a validated set of moral violations that span the moral domain, creating a barrier to investigating influences on judgment and how their neural bases might vary across the moral domain. In this paper, we aim to fill this gap by developing and validating a large set of moral foundations vignettes (MFVs). Each vignette depicts a behavior violating a particular moral foundation and not others. The vignettes are controlled on many dimensions including syntactic structure and complexity making them suitable for neuroimaging research. We demonstrate the validity of our vignettes by examining respondents' classifications of moral violations, conducting exploratory and confirmatory factor analysis, and demonstrating the correspondence between the extracted factors and existing measures of the moral foundations. We expect that the MFVs will be beneficial for a wide variety of behavioral and neuroimaging investigations of moral cognition.

Point Distribution Models (PDM) are some of the most popular shape description techniques in medical imaging. However, to create an accurate shape model it is essential to have a representative sample of the underlying population, which is often challenging. This problem is particularly relevant as the dimensionality of the modeled structures increases, and becomes critical when dealing with complex 3D shapes. In this paper, we introduce a new generalized multiresolution hierarchical PDM (GMRH-PDM) able to efficiently address the high-dimension-low-sample-size challenge when modeling complex structures. Unlike previous approaches, our new and general framework extends hierarchical modeling to any type of structure (multi- and single-object shapes) allowing to describe efficiently the shape variability at different levels of resolution. Importantly, the configuration of the algorithm is automatized thanks to the new agglomerative landmark clustering method presented here. Our new and automatic GMRH-PDM framework performed significantly better than classical approaches, and as well as the state-of-the-art with the best manual configuration. Evaluations have been studied for two different cases, the right kidney, and a multi-object case composed of eight subcortical structures.

Point distribution models (PDM) are one of the most extended methods to characterize the underlying population of set of samples, whose usefulness has been demonstrated in a wide variety of applications, including medical imaging. However, one important issue remains unsolved: the large number of training samples required. This problem becomes critical as the complexity of the problem increases, and the modeling of 3D multiobjects/organs represents one of the most challenging cases. Based on the 3D wavelet transform, this paper introduces a multiresolution hierarchical variant of PDM (MRH-PDM) able to efficiently characterize the different inter-object relationships, as well as the particular locality of each element separately. The significant advantage of this new method over two previous approaches in terms of accuracy has been successfully verified for the particular case of 3D subcortical brain structures.

We present a study of linear, quadratic and regularized discriminant analysis (RDA) applied to motor imagery data of three subjects. The aim of the work was to find out which classifier can separate better these two-class motor imagery data: linear, quadratic or some function in between the linear and quadratic solutions. Discriminant analysis methods were tested with two different feature extraction techniques, adaptive autoregressive parameters and logarithmic band power estimates, which are commonly used in brain-computer interface research. Differences in classification accuracy of the classifiers were found when using different amounts of data; if a small amount was available, the best classifier was linear discriminant analysis (LDA) and if enough data were available all three classifiers performed very similar. This suggests that the effort needed to find regularizing parameters for RDA can be avoided by using LDA.

Emotional events tend to be remembered better than nonemotional events. We investigated this phenomenon by measuring two event-related potential (ERP) effects: the emotion effect (more positive ERPs for pleasant or unpleasant stimuli than for neutral stimuli) and the subsequent memory effect (more positive ERPs for subsequently remembered items than for subsequently forgotten items). ERPs were measured while subjects rated the emotional content of pleasant, unpleasant, and neutral pictures. As was expected, subsequent recall was better for pleasant and unpleasant pictures than for neutral pictures. The emotion effect was sensitive to arousal in parietal electrodes and to both arousal and valence in frontocentral electrodes. The subsequent memory effect at centroparietal electrodes was greater for emotional pictures than for neutral pictures during an early epoch (400-600 msec). This result suggests that emotional information has privileged access to processing resources, possibly leading to better memory formation.