235 research outputs found
The Statistical Analysis of Functional MRI Data
Abstracts not available for BookReview
Wavelet analysis of the multivariate fractional Brownian motion
The work developed in the paper concerns the multivariate fractional Brownian
motion (mfBm) viewed through the lens of the wavelet transform. After recalling
some basic properties on the mfBm, we calculate the correlation structure of
its wavelet transform. We particularly study the asymptotic behavior of the
correlation, showing that if the analyzing wavelet has a sufficient number of
null first order moments, the decomposition eliminates any possible long-range
(inter)dependence. The cross-spectral density is also considered in a second
part. Its existence is proved and its evaluation is performed using a von
Bahr-Essen like representation of the function \sign(t) |t|^\alpha. The
behavior of the cross-spectral density of the wavelet field at the zero
frequency is also developed and confirms the results provided by the asymptotic
analysis of the correlation
Fractal analysis of resting state functional connectivity of the brain
A variety of resting state neuroimaging data tend to exhibit fractal behavior
where its power spectrum follows power-law scaling. Resting state functional
connectivity is significantly influenced by fractal behavior which may not
directly originate from neuronal population activities of the brain. To
describe the fractal behavior, we adopted the fractionally integrated process
(FIP) model instead of the fractional Gaussian noise (FGN) since the FIP model
covers more general aspects of fractality than the FGN model. We also introduce
a novel concept called the nonfractal connectivity which is defined as the
correlation of short memory independent of fractal behavior, and compared it
with the fractal connectivity which is an asymptotic wavelet correlation. We
propose several wavelet-based estimators of fractal connectivity and nonfractal
connectivity for a multivariate fractionally integrated noise (mFIN). The
performance of these estimators was evaluated through simulation studies and
the analyses of resting state functional MRI data of the rat brain.Comment: The 2012 International Joint Conference on Neural Network
Efficiency and Cost of Economical Brain Functional Networks
Brain anatomical networks are sparse, complex, and have economical small-world properties. We investigated the efficiency and cost of human brain functional networks measured using functional magnetic resonance imaging (fMRI) in a factorial design: two groups of healthy old (N = 11; mean age = 66.5 years) and healthy young (N = 15; mean age = 24.7 years) volunteers were each scanned twice in a no-task or “resting” state following placebo or a single dose of a dopamine receptor antagonist (sulpiride 400 mg). Functional connectivity between 90 cortical and subcortical regions was estimated by wavelet correlation analysis, in the frequency interval 0.06–0.11 Hz, and thresholded to construct undirected graphs. These brain functional networks were small-world and economical in the sense of providing high global and local efficiency of parallel information processing for low connection cost. Efficiency was reduced disproportionately to cost in older people, and the detrimental effects of age on efficiency were localised to frontal and temporal cortical and subcortical regions. Dopamine antagonism also impaired global and local efficiency of the network, but this effect was differentially localised and did not interact with the effect of age. Brain functional networks have economical small-world properties—supporting efficient parallel information transfer at relatively low cost—which are differently impaired by normal aging and pharmacological blockade of dopamine transmission
Wavelet-Based and Fourier-Based Multivariate Whittle Estimation: multiwave
Multivariate time series with long-dependence are observed in many applications such as finance, geophysics or neuroscience. Many packages provide estimation tools for univariate settings but few are addressing the problem of long-dependence estimation for multivariate settings. The package multiwave is providing efficient estimation procedures for multivariate time series. Two semi-parametric estimation methods of the long-memory exponents and long-run covariance matrix of time series are implemented. The first one is the Fourier-based estimation proposed by Shimotsu (2007) and the second one is a wavelet-based estimation described in Achard and Gannaz (2016). The objective of this paper is to provide an overview of the R package multiwave with its practical application perspectives
Graph analysis of functional brain networks: practical issues in translational neuroscience
The brain can be regarded as a network: a connected system where nodes, or
units, represent different specialized regions and links, or connections,
represent communication pathways. From a functional perspective communication
is coded by temporal dependence between the activities of different brain
areas. In the last decade, the abstract representation of the brain as a graph
has allowed to visualize functional brain networks and describe their
non-trivial topological properties in a compact and objective way. Nowadays,
the use of graph analysis in translational neuroscience has become essential to
quantify brain dysfunctions in terms of aberrant reconfiguration of functional
brain networks. Despite its evident impact, graph analysis of functional brain
networks is not a simple toolbox that can be blindly applied to brain signals.
On the one hand, it requires a know-how of all the methodological steps of the
processing pipeline that manipulates the input brain signals and extract the
functional network properties. On the other hand, a knowledge of the neural
phenomenon under study is required to perform physiological-relevant analysis.
The aim of this review is to provide practical indications to make sense of
brain network analysis and contrast counterproductive attitudes
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