29,985 research outputs found
Towards a Multi-Subject Analysis of Neural Connectivity
Directed acyclic graphs (DAGs) and associated probability models are widely
used to model neural connectivity and communication channels. In many
experiments, data are collected from multiple subjects whose connectivities may
differ but are likely to share many features. In such circumstances it is
natural to leverage similarity between subjects to improve statistical
efficiency. The first exact algorithm for estimation of multiple related DAGs
was recently proposed by Oates et al. 2014; in this letter we present examples
and discuss implications of the methodology as applied to the analysis of fMRI
data from a multi-subject experiment. Elicitation of tuning parameters requires
care and we illustrate how this may proceed retrospectively based on technical
replicate data. In addition to joint learning of subject-specific connectivity,
we allow for heterogeneous collections of subjects and simultaneously estimate
relationships between the subjects themselves. This letter aims to highlight
the potential for exact estimation in the multi-subject setting.Comment: to appear in Neural Computation 27:1-2
Movement-Efficient Sensor Deployment in Wireless Sensor Networks With Limited Communication Range.
We study a mobile wireless sensor network (MWSN) consisting of multiple
mobile sensors or robots. Three key factors in MWSNs, sensing quality, energy
consumption, and connectivity, have attracted plenty of attention, but the
interaction of these factors is not well studied. To take all the three factors
into consideration, we model the sensor deployment problem as a constrained
source coding problem. %, which can be applied to different coverage tasks,
such as area coverage, target coverage, and barrier coverage. Our goal is to
find an optimal sensor deployment (or relocation) to optimize the sensing
quality with a limited communication range and a specific network lifetime
constraint. We derive necessary conditions for the optimal sensor deployment in
both homogeneous and heterogeneous MWSNs. According to our derivation, some
sensors are idle in the optimal deployment of heterogeneous MWSNs. Using these
necessary conditions, we design both centralized and distributed algorithms to
provide a flexible and explicit trade-off between sensing uncertainty and
network lifetime. The proposed algorithms are successfully extended to more
applications, such as area coverage and target coverage, via properly selected
density functions. Simulation results show that our algorithms outperform the
existing relocation algorithms
Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches
In the past two decades, functional Magnetic Resonance Imaging has been used
to relate neuronal network activity to cognitive processing and behaviour.
Recently this approach has been augmented by algorithms that allow us to infer
causal links between component populations of neuronal networks. Multiple
inference procedures have been proposed to approach this research question but
so far, each method has limitations when it comes to establishing whole-brain
connectivity patterns. In this work, we discuss eight ways to infer causality
in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality,
Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and
Transfer Entropy. We finish with formulating some recommendations for the
future directions in this area
Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity
Functional brain networks are well described and estimated from data with
Gaussian Graphical Models (GGMs), e.g. using sparse inverse covariance
estimators. Comparing functional connectivity of subjects in two populations
calls for comparing these estimated GGMs. Our goal is to identify differences
in GGMs known to have similar structure. We characterize the uncertainty of
differences with confidence intervals obtained using a parametric distribution
on parameters of a sparse estimator. Sparse penalties enable statistical
guarantees and interpretable models even in high-dimensional and low-sample
settings. Characterizing the distributions of sparse models is inherently
challenging as the penalties produce a biased estimator. Recent work invokes
the sparsity assumptions to effectively remove the bias from a sparse estimator
such as the lasso. These distributions can be used to give confidence intervals
on edges in GGMs, and by extension their differences. However, in the case of
comparing GGMs, these estimators do not make use of any assumed joint structure
among the GGMs. Inspired by priors from brain functional connectivity we derive
the distribution of parameter differences under a joint penalty when parameters
are known to be sparse in the difference. This leads us to introduce the
debiased multi-task fused lasso, whose distribution can be characterized in an
efficient manner. We then show how the debiased lasso and multi-task fused
lasso can be used to obtain confidence intervals on edge differences in GGMs.
We validate the techniques proposed on a set of synthetic examples as well as
neuro-imaging dataset created for the study of autism
Brain covariance selection: better individual functional connectivity models using population prior
Spontaneous brain activity, as observed in functional neuroimaging, has been
shown to display reproducible structure that expresses brain architecture and
carries markers of brain pathologies. An important view of modern neuroscience
is that such large-scale structure of coherent activity reflects modularity
properties of brain connectivity graphs. However, to date, there has been no
demonstration that the limited and noisy data available in spontaneous activity
observations could be used to learn full-brain probabilistic models that
generalize to new data. Learning such models entails two main challenges: i)
modeling full brain connectivity is a difficult estimation problem that faces
the curse of dimensionality and ii) variability between subjects, coupled with
the variability of functional signals between experimental runs, makes the use
of multiple datasets challenging. We describe subject-level brain functional
connectivity structure as a multivariate Gaussian process and introduce a new
strategy to estimate it from group data, by imposing a common structure on the
graphical model in the population. We show that individual models learned from
functional Magnetic Resonance Imaging (fMRI) data using this population prior
generalize better to unseen data than models based on alternative
regularization schemes. To our knowledge, this is the first report of a
cross-validated model of spontaneous brain activity. Finally, we use the
estimated graphical model to explore the large-scale characteristics of
functional architecture and show for the first time that known cognitive
networks appear as the integrated communities of functional connectivity graph.Comment: in Advances in Neural Information Processing Systems, Vancouver :
Canada (2010
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