4,816 research outputs found
Identification of Dynamic functional brain network states Through Tensor Decomposition
With the advances in high resolution neuroimaging, there has been a growing
interest in the detection of functional brain connectivity. Complex network
theory has been proposed as an attractive mathematical representation of
functional brain networks. However, most of the current studies of functional
brain networks have focused on the computation of graph theoretic indices for
static networks, i.e. long-time averages of connectivity networks. It is
well-known that functional connectivity is a dynamic process and the
construction and reorganization of the networks is key to understanding human
cognition. Therefore, there is a growing need to track dynamic functional brain
networks and identify time intervals over which the network is
quasi-stationary. In this paper, we present a tensor decomposition based method
to identify temporally invariant 'network states' and find a common topographic
representation for each state. The proposed methods are applied to
electroencephalogram (EEG) data during the study of error-related negativity
(ERN).Comment: 2014 IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP
Dynamic Tensor Clustering
Dynamic tensor data are becoming prevalent in numerous applications. Existing
tensor clustering methods either fail to account for the dynamic nature of the
data, or are inapplicable to a general-order tensor. Also there is often a gap
between statistical guarantee and computational efficiency for existing tensor
clustering solutions. In this article, we aim to bridge this gap by proposing a
new dynamic tensor clustering method, which takes into account both sparsity
and fusion structures, and enjoys strong statistical guarantees as well as high
computational efficiency. Our proposal is based upon a new structured tensor
factorization that encourages both sparsity and smoothness in parameters along
the specified tensor modes. Computationally, we develop a highly efficient
optimization algorithm that benefits from substantial dimension reduction. In
theory, we first establish a non-asymptotic error bound for the estimator from
the structured tensor factorization. Built upon this error bound, we then
derive the rate of convergence of the estimated cluster centers, and show that
the estimated clusters recover the true cluster structures with a high
probability. Moreover, our proposed method can be naturally extended to
co-clustering of multiple modes of the tensor data. The efficacy of our
approach is illustrated via simulations and a brain dynamic functional
connectivity analysis from an Autism spectrum disorder study.Comment: Accepted at Journal of the American Statistical Associatio
Tensor Analysis and Fusion of Multimodal Brain Images
Current high-throughput data acquisition technologies probe dynamical systems
with different imaging modalities, generating massive data sets at different
spatial and temporal resolutions posing challenging problems in multimodal data
fusion. A case in point is the attempt to parse out the brain structures and
networks that underpin human cognitive processes by analysis of different
neuroimaging modalities (functional MRI, EEG, NIRS etc.). We emphasize that the
multimodal, multi-scale nature of neuroimaging data is well reflected by a
multi-way (tensor) structure where the underlying processes can be summarized
by a relatively small number of components or "atoms". We introduce
Markov-Penrose diagrams - an integration of Bayesian DAG and tensor network
notation in order to analyze these models. These diagrams not only clarify
matrix and tensor EEG and fMRI time/frequency analysis and inverse problems,
but also help understand multimodal fusion via Multiway Partial Least Squares
and Coupled Matrix-Tensor Factorization. We show here, for the first time, that
Granger causal analysis of brain networks is a tensor regression problem, thus
allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI
recordings shows the potential of the methods and suggests their use in other
scientific domains.Comment: 23 pages, 15 figures, submitted to Proceedings of the IEE
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
Brain connectivity analysis: a short survey
This short survey the reviews recent literature on brain connectivity studies. It encompasses all forms of static and dynamic
connectivity whether anatomical, functional, or effective. The last decade has seen an ever increasing number of studies devoted
to deduce functional or effective connectivity, mostly from functional neuroimaging experiments. Resting state conditions have
become a dominant experimental paradigm, and a number of resting state networks, among them the prominent default mode
network, have been identified. Graphical models represent a convenient vehicle to formalize experimental findings and to closely
and quantitatively characterize the various networks identified. Underlying these abstract concepts are anatomical networks, the
so-called connectome, which can be investigated by functional imaging techniques as well. Future studies have to bridge the gap between anatomical neuronal connections and related functional or effective connectivities
Relationship Between Structure and Functional Connectivity Within the Default Mode Network
We proposed a novel measure of conceptualizing dynamic functional network connectivity (FNC) in the human brain using flexibility of functional connectivity (fFC), which captures the variance of functional connectivity across time. In task-free fMRI scans (N = 122), this measure was demonstrated to correspond to the underlying structural connectivity (SC) within the default mode network (DMN), while static functional connectivity (sFC) did so to a relatively low degree. As SC likely does not develop to facilitate task-free brain function, but rather to integrate information during cognitive engagement, we argue that fFC can estimate the potential functional connectivity exhibited outside of the task-free setting to a greater degree than sFC, and is better suited for examining behavioral correlates of FNC. In support of this, we showed that SC-fFC coupling was related to intelligence levels, while SC-sFC coupling was not. Further, we found that the DMN existed in a functionally disconnected state during a large portion of the scan, raising questions about whether sFC is a meaningful quantifier of functional connectivity in the absence of a task, and scrutinizing its extrapolative power to real-world, cognitively engaging scenarios. Given that fFC is based on FNC variability across time rather than its average, it is largely unaffected by such contaminants
- …