1,721,037 research outputs found
Mapping the Voxel-Wise Effective Connectome in Resting State fMRI
Full text linkA network approach to brain and dynamics opens new perspectives towards understanding of its function. The functional connectivity from functional MRI recordings in humans is widely explored at large scale, and recently also at the voxel level. The networks of dynamical directed connections are far less investigated, in particular at the voxel level. To reconstruct full brain effective connectivity network and study its topological organization, we present a novel approach to multivariate Granger causality which integrates information theory and the architecture of the dynamical network to efficiently select a limited number of variables. The proposed method aggregates conditional information sets according to community organization, allowing to perform Granger causality analysis avoiding redundancy and overfitting even for high-dimensional and short datasets, such as time series from individual voxels in fMRI. We for the first time depicted the voxel-wise hubs of incoming and outgoing information, called Granger causality density (GCD), as a complement to previous repertoire of functional and anatomical connectomes. Analogies with these networks have been presented in most part of default mode network; while differences suggested differences in the specific measure of centrality. Our findings could open the way to a new description of global organization and information influence of brain function. With this approach is thus feasible to study the architecture of directed networks at the voxel level and individuating hubs by investigation of degree, betweenness and clustering coefficient
A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data
No full textA great improvement to the insight on brain function that we can get from fMRI data can come from
effective connectivity analysis, in which the flow of information between even remote brain regions is
inferred by the parameters of a predictive dynamical model. As opposed to biologically inspired models,
some techniques as Granger causality (GC) are purely data-driven and rely on statistical prediction and
temporal precedence. While powerful and widely applicable, this approach could suffer from two main
limitations when applied to BOLD fMRI data: confounding effect of hemodynamic response function
(HRF) and conditioning to a large number of variables in presence of short time series. For task-related
fMRI, neural population dynamics can be captured by modeling signal dynamics with explicit exogenous
inputs; for resting-state fMRI on the other hand, the absence of explicit inputs makes this task more difficult,
unless relying on some specific prior physiological hypothesis. In order to overcome these issues
and to allow a more general approach, here we present a simple and novel blind-deconvolution technique
for BOLD-fMRI signal. In a recent study it has been proposed that relevant information in resting-state
fMRI can be obtained by inspecting the discrete events resulting in relatively large amplitude BOLD signal
peaks. Following this idea, we consider resting fMRI as ‘spontaneous event-related’, we individuate point
processes corresponding to signal fluctuations with a given signature, extract a region-specific HRF and
use it in deconvolution, after following an alignment procedure. Coming to the second limitation, a fully
multivariate conditioning with short and noisy data leads to computational problems due to overfitting.
Furthermore, conceptual issues arise in presence of redundancy. We thus apply partial conditioning to a
limited subset of variables in the framework of information theory, as recently proposed. Mixing these
two improvements we compare the differences between BOLD and deconvolved BOLD level effective networks
and draw some conclusions
Recovering Directed Networks in Neuroimaging Datasets Using Partially Conditioned Granger Causality
Full text linkRecovering directed pathways of information transfer between brain areas is an important issue in neuroscience and
helps to shed light on the brain function in several physiological and cognitive states. Granger causality (GC) analysis
is a valuable tool to detect directed dynamical connectivity, and it is being increasingly used. Unfortunately, this
approach encounters some limitations in particularly when applied to neuroimaging datasets, often consisting in
short and noisy data and for which redundancy plays an important role. In this article, we address one of these limitations,
namely, the computational and conceptual problems arising when conditional GC, necessary to disambiguate
direct and mediated influences, is used on short and noisy datasets of many variables, as it is typically the case
in some electroencephalography (EEG) protocols and in functional magnetic resonance imaging (fMRI). We show
that considering GC in the framework of information theory we can limit the conditioning to a limited number of
variables chosen as the most informative, obtaining more stable and reliable results both in EEG and fMRI data
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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