31 research outputs found
Quantification of Effective Connectivity in the Brain Using a Measure of Directed Information
Effective connectivity refers to the influence one neural system exerts on another and corresponds to the parameter of a model that tries to explain the observed dependencies. In this sense, effective connectivity corresponds to the intuitive notion of coupling or directed causal influence. Traditional measures to quantify the effective connectivity include model-based methods, such as dynamic causal modeling (DCM), Granger causality (GC), and information-theoretic methods. Directed information (DI) has been a recently proposed information-theoretic measure that captures the causality between two time series. Compared to traditional causality detection methods based on linear models, directed information is a model-free measure and can detect both linear and nonlinear causality relationships. However, the effectiveness of using DI for capturing the causality in different models and neurophysiological data has not been thoroughly illustrated to date. In addition, the advantage of DI compared to model-based measures, especially those used to implement Granger causality, has not been fully investigated. In this paper, we address these issues by evaluating the performance of directed information on both simulated data sets and electroencephalogram (EEG) data to illustrate its effectiveness for quantifying the effective connectivity in the brain
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
Optimal Graph Filters for Clustering Attributed Graphs
Many real-world systems can be represented as graphs where the different
entities are presented by nodes and their interactions by edges. An important
task in studying large datasets is graph clustering. While there has been a lot
of work on graph clustering using the connectivity between the nodes, many
real-world networks also have node attributes. Clustering attributed graphs
requires joint modeling of graph structure and node attributes. Recent work has
focused on graph convolutional networks and graph convolutional filters to
combine structural and content information. However, these methods are mostly
limited to lowpass filtering and do not explicitly optimize the filters for the
clustering task. In this paper, we introduce a graph signal processing based
approach, where we design polynomial graph filters optimized for clustering.
The proposed approach is formulated as a two-step iterative optimization
problem where graph filters that are interpretable and optimal for the given
data are learned while maximizing the separation between different clusters.
The proposed approach is evaluated on attributed networks and compared to the
state-of-the-art graph convolutional network approaches.Comment: 5 pages, 3 figure
From Nano to Macro: Overview of the IEEE Bio Image and Signal Processing Technical Committee
The Bio Image and Signal Processing (BISP) Technical Committee (TC) of the
IEEE Signal Processing Society (SPS) promotes activities within the broad
technical field of biomedical image and signal processing. Areas of interest
include medical and biological imaging, digital pathology, molecular imaging,
microscopy, and associated computational imaging, image analysis, and
image-guided treatment, alongside physiological signal processing,
computational biology, and bioinformatics. BISP has 40 members and covers a
wide range of EDICS, including CIS-MI: Medical Imaging, BIO-MIA: Medical Image
Analysis, BIO-BI: Biological Imaging, BIO: Biomedical Signal Processing,
BIO-BCI: Brain/Human-Computer Interfaces, and BIO-INFR: Bioinformatics. BISP
plays a central role in the organization of the IEEE International Symposium on
Biomedical Imaging (ISBI) and contributes to the technical sessions at the IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP),
and the IEEE International Conference on Image Processing (ICIP). In this
paper, we provide a brief history of the TC, review the technological and
methodological contributions its community delivered, and highlight promising
new directions we anticipate