190 research outputs found

    Generative Models and Learning Algorithms for Core-Periphery Structured Graphs

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    We consider core-periphery structured graphs, which are graphs with a group of densely and sparsely connected nodes, respectively, referred to as core and periphery nodes. The so-called core score of a node is related to the likelihood of it being a core node. In this paper, we focus on learning the core scores of a graph from its node attributes and connectivity structure. To this end, we propose two classes of probabilistic graphical models: affine and nonlinear. First, we describe affine generative models to model the dependence of node attributes on its core scores, which determine the graph structure. Next, we discuss nonlinear generative models in which the partial correlations of node attributes influence the graph structure through latent core scores. We develop algorithms for inferring the model parameters and core scores of a graph when both the graph structure and node attributes are available. When only the node attributes of graphs are available, we jointly learn a core-periphery structured graph and its core scores. We provide results from numerical experiments on several synthetic and real-world datasets to demonstrate the efficacy of the developed models and algorithms

    Machine Learning for Neuroimaging with Scikit-Learn

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    Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g. resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.Comment: Frontiers in neuroscience, Frontiers Research Foundation, 2013, pp.1

    Causal Mapping of Emotion Networks in the Human Brain: Framework and Initial Findings

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    Emotions involve many cortical and subcortical regions, prominently including the amygdala. It remains unknown how these multiple network components interact, and it remains unknown how they cause the behavioral, autonomic, and experiential effects of emotions. Here we describe a framework for combining a novel technique, concurrent electrical stimulation with fMRI (es-fMRI), together with a novel analysis, inferring causal structure from fMRI data (causal discovery). We outline a research program for investigating human emotion with these new tools, and provide initial findings from two large resting-state datasets as well as case studies in neurosurgical patients with electrical stimulation of the amygdala. The overarching goal is to use causal discovery methods on fMRI data to infer causal graphical models of how brain regions interact, and then to further constrain these models with direct stimulation of specific brain regions and concurrent fMRI. We conclude by discussing limitations and future extensions. The approach could yield anatomical hypotheses about brain connectivity, motivate rational strategies for treating mood disorders with deep brain stimulation, and could be extended to animal studies that use combined optogenetic fMRI

    Information Flow in Computational Systems

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    We develop a theoretical framework for defining and identifying flows of information in computational systems. Here, a computational system is assumed to be a directed graph, with "clocked" nodes that send transmissions to each other along the edges of the graph at discrete points in time. We are interested in a definition that captures the dynamic flow of information about a specific message, and which guarantees an unbroken "information path" between appropriately defined inputs and outputs in the directed graph. Prior measures, including those based on Granger Causality and Directed Information, fail to provide clear assumptions and guarantees about when they correctly reflect information flow about a message. We take a systematic approach---iterating through candidate definitions and counterexamples---to arrive at a definition for information flow that is based on conditional mutual information, and which satisfies desirable properties, including the existence of information paths. Finally, we describe how information flow might be detected in a noiseless setting, and provide an algorithm to identify information paths on the time-unrolled graph of a computational system.Comment: Significantly revised version which was accepted for publication at the IEEE Transactions on Information Theor

    A Review on Dependence Measures in Exploring Brain Networks from fMRI Data

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    Functional magnetic resonance imaging (fMRI) technique allows us to capture activities occurring in a human brain via signals from blood flow, known as BOLD (blood oxygen level-dependent) signals. Exploring a relationship among brain regions inside human brains from fMRI data is an active and challenging research topic. Relationships or associations between brain regions are commonly referred to as brain connectivity or brain network. This connectivity can be divided into two groups, the functional connectivity which describes the statistical information among brain regions and the effective connectivity which specifies how one region interacts with others by a causal model. This survey paper provides a review on learning brain connectivities via fMRI data, mathematical definitions or dependence measures of such connectivities. These well-known measures include correlation, partial correlation, conditional independence, dynamical causal modeling, Granger causality, and structural equation modeling, which all can be translated in terms of mathematical conditions of model parameters. We also discusses about relevant estimation techniques that have been widely used in the problems of fMRI modeling. Understanding a rigorous definition on relationships in human brain allows us to interpret or compare the results in the context of learning brain network more clearly

    Neural mechanisms of social cognition – the mirror neuron system and beyond

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    In my PhD thesis, I present three functional magnetic resonance imaging studies aimed at investigating neurobiological mechanisms underlying social cognition. My thesis focuses on fast and automatic processes that are proposed to build the basis of social understanding, and might be activated in parallel to more effortful deliberate mechanisms. The proposed neural substrate of fast and automatic processes are mirror neurons, which according to the theory of embodied simulation allow humans to understand other individuals’ actions, and even emotions and intentions. Since non-invasive techniques cannot be applied to measure mirror neurons, but only neural populations assumed to constitute the mirror neuron system, experimental paradigms and analysis routines that allow approximation of mirror neuron functions need to be developed. In study 1, I demonstrated that different social cognitive skills, including imitation, affective empathy and theory of mind share a common neural basis, located in regions associated with the mirror neuron system. In addition to standard analyses, a shared voxel analysis was applied that revealed common activation for social-cognitive processes not only across, but also within participants. Study 2 was set up to investigate whether the mirror neuron system can distinguish the valence of facial configurations. The use of a functional magnetic resonance imaging adaptation paradigm allowed to determine neural populations sensitive to emotional valence. While the fusiform gyrus was sensitive to changes from fearful to smiling faces and also from smiling to fearful faces, Brodmann area 44 reaching into insula, and superior temporal sulcus, i.e. regions more commonly associated with the mirror neuron system and with the so called mentalizing network, showed particularly increased activation for switches from smiling to fearful faces. Study 3 was dedicated to the investigation of decision making in the context of ambiguous facial configurations. While probabilistic decision making on these facial configurations lead to activation in the executive control network, final decisions for an emotion resulted in nucleus accumbens activation. In addition, perceiving fear in a face lead to higher nucleus accumbens activation during final decisions than perceiving happiness. This finding can be linked to salience processing in the nucleus accumbens. In conclusion, all three studies show an involvement of fast and automatic processing regions for different social-cognitive processes. Study 3 additionally examined the interaction with slower and more deliberate processes, as involved in probabilistic decision making on ambiguous faces. The mirror neuron system seems to be critically involved in different social-cognitive tasks and also sensitive to emotional valence. In cases when automatic processing is not possible, as when presented with ambiguous facial configurations, brain regions commonly associated with probabilistic decision making assist, and the nucleus accumbens, possibly by directing salience, is involved in the final decision. These results deepen the understanding of the mechanisms of social cognition and encourage the use of sophisticated methods in experimental paradigms and analysis

    Precis of neuroconstructivism: how the brain constructs cognition

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    Neuroconstructivism: How the Brain Constructs Cognition proposes a unifying framework for the study of cognitive development that brings together (1) constructivism (which views development as the progressive elaboration of increasingly complex structures), (2) cognitive neuroscience (which aims to understand the neural mechanisms underlying behavior), and (3) computational modeling (which proposes formal and explicit specifications of information processing). The guiding principle of our approach is context dependence, within and (in contrast to Marr [1982]) between levels of organization. We propose that three mechanisms guide the emergence of representations: competition, cooperation, and chronotopy; which themselves allow for two central processes: proactivity and progressive specialization. We suggest that the main outcome of development is partial representations, distributed across distinct functional circuits. This framework is derived by examining development at the level of single neurons, brain systems, and whole organisms. We use the terms encellment, embrainment, and embodiment to describe the higher-level contextual influences that act at each of these levels of organization. To illustrate these mechanisms in operation we provide case studies in early visual perception, infant habituation, phonological development, and object representations in infancy. Three further case studies are concerned with interactions between levels of explanation: social development, atypical development and within that, developmental dyslexia. We conclude that cognitive development arises from a dynamic, contextual change in embodied neural structures leading to partial representations across multiple brain regions and timescales, in response to proactively specified physical and social environment
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