Spectral density-based clustering algorithms for complex networks.

Introduction: Clustering is usually the first exploratory analysis step in empirical data. When the data set comprises graphs, the most common approaches focus on clustering its vertices. In this work, we are interested in grouping networks with similar connectivity structures together instead of grouping vertices of the graph. We could apply this approach to functional brain networks (FBNs) for identifying subgroups of people presenting similar functional connectivity, such as studying a mental disorder. The main problem is that real-world networks present natural fluctuations, which we should consider. Methods: In this context, spectral density is an exciting feature because graphs generated by different models present distinct spectral densities, thus presenting different connectivity structures. We introduce two clustering methods: k-means for graphs of the same size and gCEM, a model-based approach for graphs of different sizes. We evaluated their performance in toy models. Finally, we applied them to FBNs of monkeys under anesthesia and a dataset of chemical compounds. Results: We show that our methods work well in both toy models and real-world data. They present good results for clustering graphs presenting different connectivity structures even when they present the same number of edges, vertices, and degree of centrality. Discussion: We recommend using k-means-based clustering for graphs when graphs present the same number of vertices and the gCEM method when graphs present a different number of vertices.

Correction to: How do you perceive threat? It's all in your pattern of brain activity.

The author found a mistake in their published article. They observed that Fig. 2 presented some mistakes as follow.

How do you perceive threat? It's all in your pattern of brain activity.

Whether subtle differences in the emotional context during threat perception can be detected by multi-voxel pattern analysis (MVPA) remains a topic of debate. To investigate this question, we compared the ability of pattern recognition analysis to discriminate between patterns of brain activity to a threatening versus a physically paired neutral stimulus in two different emotional contexts (the stimulus being directed towards or away from the viewer). The directionality of the stimuli is known to be an important factor in activating different defensive responses. Using multiple kernel learning (MKL) classification models, we accurately discriminated patterns of brain activation to threat versus neutral stimuli in the directed towards context but not during the directed away context. Furthermore, we investigated whether it was possible to decode an individual's subjective threat perception from patterns of whole-brain activity to threatening stimuli in the different emotional contexts using MKL regression models. Interestingly, we were able to accurately predict the subjective threat perception index from the pattern of brain activation to threat only during the directed away context. These results show that subtle differences in the emotional context during threat perception can be detected by MVPA. In the directed towards context, the threat perception was more intense, potentially producing more homogeneous patterns of brain activation across individuals. In the directed away context, the threat perception was relatively less intense and more variable across individuals, enabling the regression model to successfully capture the individual differences and predict the subjective threat perception.

Measuring abnormal brains: building normative rules in neuroimaging using one-class support vector machines.

Pattern recognition methods have demonstrated to be suitable analyses tools to handle the high dimensionality of neuroimaging data. However, most studies combining neuroimaging with pattern recognition methods focus on two-class classification problems, usually aiming to discriminate patients under a specific condition (e.g., Alzheimer's disease) from healthy controls. In this perspective paper we highlight the potential of the one-class support vector machines (OC-SVM) as an unsupervised or exploratory approach that can be used to create normative rules in a multivariate sense. In contrast with the standard SVM that finds an optimal boundary separating two classes (discriminating boundary), the OC-SVM finds the boundary enclosing a specific class (characteristic boundary). If the OC-SVM is trained with patterns of healthy control subjects, the distance to the boundary can be interpreted as an abnormality score. This score might allow quantification of symptom severity or provide insights about subgroups of patients. We provide an intuitive description of basic concepts in one-class classification, the foundations of OC-SVM, current applications, and discuss how this tool can bring new insights to neuroimaging studies.

An fMRI normative database for connectivity networks using one-class support vector machines.

The application of functional magnetic resonance imaging (fMRI) in neuroscience studies has increased enormously in the last decade. Although primarily used to map brain regions activated by specific stimuli, many studies have shown that fMRI can also be useful in identifying interactions between brain regions (functional and effective connectivity). Despite the widespread use of fMRI as a research tool, clinical applications of brain connectivity as studied by fMRI are not well established. One possible explanation is the lack of normal patterns and intersubject variability-two variables that are still largely uncharacterized in most patient populations of interest. In the current study, we combine the identification of functional connectivity networks extracted by using Spearman partial correlation with the use of a one-class support vector machine in order construct a normative database. An application of this approach is illustrated using an fMRI dataset of 43 healthy subjects performing a visual working memory task. In addition, the relationships between the results obtained and behavioral data are explored.

The impact of functional connectivity changes on support vector machines mapping of fMRI data.

Functional magnetic resonance imaging (fMRI) is currently one of the most widely used methods for studying human brain function in vivo. Although many different approaches to fMRI analysis are available, the most widely used methods employ so called "mass-univariate" modeling of responses in a voxel-by-voxel fashion to construct activation maps. However, it is well known that many brain processes involve networks of interacting regions and for this reason multivariate analyses might seem to be attractive alternatives to univariate approaches. The current paper focuses on one multivariate application of statistical learning theory: the statistical discrimination maps (SDM) based on support vector machine, and seeks to establish some possible interpretations when the results differ from univariate approaches. In fact, when there are changes not only on the activation level of two conditions but also on functional connectivity, SDM seems more informative. We addressed this question using both simulations and applications to real data. We have shown that the combined use of univariate approaches and SDM yields significant new insights into brain activations not available using univariate methods alone. In the application to a visual working memory fMRI data, we demonstrated that the interaction among brain regions play a role in SDM's power to detect discriminative voxels.

Estímulos emocionais: processamento sensorial e respostas motoras / Emotional stimuli: sensory processing and motor responses

A emoçäo pode ser funcionalmente considerada como uma disposiçäo à açäo que prepara o organismo para comportamentos relacionados à aproximaçäo e esquiva. Para preparar uma saída motora apropriada, o organismo tem que ser eficiente na codificaçäo de estímulos relevantes. Neste trabalho, apresentamos evidências a partir de estudos de neuroimagem que revelam que a visualizaçäo de imagens emocionais promove uma maior ativaçäo do córtex visual do que a observaçäo de figuras neutras. Além desta facilitaçäo do processamento sensorial, os estímulos emocionais desencadeiam reaçöes somáticas e vegetativas. Registros da dinâmica postural e da freqüência cardíaca enquanto voluntários assistiam a um bloco de figuras desagradáveis revelou uma reduçäo significativa na oscilaçäo corporal e bradicardia. Uma investigaçäo paralela mostrou que o tempo de reaçäo também lentifica após a visualizaçäo de figuras negativas. Este conjunto de respostas - imobilidade, bradicardia e tempo de reaçäo mais lento - pode refletir o engajamento do sistema defensivo, similar às reaçöes defensivas desencadeadas em ambiente natural por estímulos ameaçadores distantes. Em resumo, o sistema afetivo influencia um nível precoce de codificaçäo sensorial e a saída motora favorecendo, portanto, disposiçöes para as açöes apropriadas

[Emotional stimuli: sensory processing and motor responses]. / Estímulos emocionais: processamento sensorial e respostas motoras.

Emotion can be functionally considered as action dispositions preparing the organism for either avoidance- or approach- related behaviors. In order to prepare an appropriate behavioral output, the organism has to be efficient in the encoding of relevant stimuli. We herein present evidence from neuroimaging studies that seeing emotional and arousing pictures leads to greater activation in visual cortex than seeing neutral ones. In addition to this facilitation of sensory processing, emotional stimuli prompt somatic and vegetative reactions. Recordings of postural oscillations and heart rate while participants visualized a block of unpleasant pictures, revealed a significant reduction of body sway and bradycardia. A parallel investigation showed that reaction time also slows down after the visualization of negative pictures. Taken together, immobility, bradycardia and slower reaction time in the laboratory experimental set may reflect the engagement of the defensive system, resembling the defensive reactions to distant threatening stimuli in natural contexts. In summary, the affective system operates at an early level of sensory encoding and at the motor output favoring dispositions for appropriate actions.

The neural correlates of moral sensitivity: a functional magnetic resonance imaging investigation of basic and moral emotions.

Humans are endowed with a natural sense of fairness that permeates social perceptions and interactions. This moral stance is so ubiquitous that we may not notice it as a fundamental component of daily decision making and in the workings of many legal, political, and social systems. Emotion plays a pivotal role in moral experience by assigning human values to events, objects, and actions. Although the brain correlates of basic emotions have been explored, the neural organization of "moral emotions" in the human brain remains poorly understood. Using functional magnetic resonance imaging and a passive visual task, we show that both basic and moral emotions activate the amygdala, thalamus, and upper midbrain. The orbital and medial prefrontal cortex and the superior temporal sulcus are also recruited by viewing scenes evocative of moral emotions. Our results indicate that the orbital and medial sectors of the prefrontal cortex and the superior temporal sulcus region, which are critical regions for social behavior and perception, play a central role in moral appraisals. We suggest that the automatic tagging of ordinary social events with moral values may be an important mechanism for implicit social behaviors in humans.