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      Learning Effective Connectivity Network Structure from fMRI Data Based on Artificial Immune Algorithm

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      Author(s): 1 , 1 , 2 , * , 3
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      Publication date (Electronic):
      Journal: PLoS ONE
      Publisher: Public Library of Science

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          Abstract

          Many approaches have been designed to extract brain effective connectivity from functional magnetic resonance imaging (fMRI) data. However, few of them can effectively identify the connectivity network structure due to different defects. In this paper, a new algorithm is developed to infer the effective connectivity between different brain regions by combining artificial immune algorithm (AIA) with the Bayes net method, named as AIAEC. In the proposed algorithm, a brain effective connectivity network is mapped onto an antibody, and four immune operators are employed to perform the optimization process of antibodies, including clonal selection operator, crossover operator, mutation operator and suppression operator, and finally gets an antibody with the highest K2 score as the solution. AIAEC is then tested on Smith’s simulated datasets, and the effect of the different factors on AIAEC is evaluated, including the node number, session length, as well as the other potential confounding factors of the blood oxygen level dependent (BOLD) signal. It was revealed that, as contrast to other existing methods, AIAEC got the best performance on the majority of the datasets. It was also found that AIAEC could attain a relative better solution under the influence of many factors, although AIAEC was differently affected by the aforementioned factors. AIAEC is thus demonstrated to be an effective method for detecting the brain effective connectivity.

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          Network modelling methods for FMRI.

          Stephen Smith, Karla L. Miller, Gholamreza Salimi-Khorshidi … (2011)
          There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution. Copyright © 2010 Elsevier Inc. All rights reserved.
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            Dynamic causal modelling.

            L. Harrison, W. Penny, W Penny (2003)
            In this paper we present an approach to the identification of nonlinear input-state-output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate the influence of extrinsic inputs on the states, (2) parameters that mediate intrinsic coupling among the states, and (3) [bilinear] parameters that allow the inputs to modulate that coupling. Identification proceeds in a Bayesian framework given known, deterministic inputs and the observed responses of the system. We developed this approach for the analysis of effective connectivity using experimentally designed inputs and fMRI responses. In this context, the coupling parameters correspond to effective connectivity and the bilinear parameters reflect the changes in connectivity induced by inputs. The ensuing framework allows one to characterise fMRI experiments, conceptually, as an experimental manipulation of integration among brain regions (by contextual or trial-free inputs, like time or attentional set) that is revealed using evoked responses (to perturbations or trial-bound inputs, like stimuli). As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling (cf., psychophysiologic interactions). However, unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic.
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              A MATLAB toolbox for Granger causal connectivity analysis.

              Anil K. Seth (2010)
              Assessing directed functional connectivity from time series data is a key challenge in neuroscience. One approach to this problem leverages a combination of Granger causality analysis and network theory. This article describes a freely available MATLAB toolbox--'Granger causal connectivity analysis' (GCCA)--which provides a core set of methods for performing this analysis on a variety of neuroscience data types including neuroelectric, neuromagnetic, functional MRI, and other neural signals. The toolbox includes core functions for Granger causality analysis of multivariate steady-state and event-related data, functions to preprocess data, assess statistical significance and validate results, and to compute and display network-level indices of causal connectivity including 'causal density' and 'causal flow'. The toolbox is deliberately small, enabling its easy assimilation into the repertoire of researchers. It is however readily extensible given proficiency with the MATLAB language. Copyright 2009 Elsevier B.V. All rights reserved.
              Article 0 comments Cited 199 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Juan Zhou: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date Collection: 2016
                Publication date (Electronic): 5 April 2016
                Volume: 11
                Issue: 4
                Electronic Location Identifier: e0152600
                Affiliations
                [1 ]Beijing Municipal Key Laboratory of Multimedia and Intelligent Software, College of Computer Science and Technology, Beijing University of Technology, Beijing, China
                [2 ]Beijing Key Lab of MRI and Brain Informatics, Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
                [3 ]Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, United States of America
                Duke-NUS Graduate Medical School, SINGAPORE
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: JZJ PPL. Performed the experiments: JDL. Analyzed the data: JZJ JDL PPL. Contributed reagents/materials/analysis tools: JZJ JDL PPL. Wrote the paper: JZJ JDL PPL ADZ.

                Article
                Publisher ID: PONE-D-15-53941
                DOI: 10.1371/journal.pone.0152600
                PMC ID: 4821460
                PubMed ID: 27045295
                SO-VID: f0be0614-1f89-4305-afda-e3dc15a11b85
                Copyright © © 2016 Ji et al
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 13 December 2015
                Date accepted : 16 March 2016
                Page count
                Figures: 9, Tables: 8, Pages: 32
                Funding
                This work was partly supported by the NSFC Research Program (61375059, 61332016, 61473196), the National “973” Key Basic Research Program of China (2014CB744601), the Specialized Research Fund for the Doctoral Program of Higher Education (20121103110031), and the Beijing Municipal Education Research Plan key project (Beijing Municipal Fund Class B) (KZ201410005004).
                Categories
                Subject: Research Article
                Subject: Physical Sciences
                Subject: Mathematics
                Subject: Applied Mathematics
                Subject: Algorithms
                Subject: Research and Analysis Methods
                Subject: Simulation and Modeling
                Subject: Algorithms
                Subject: Biology and Life Sciences
                Subject: Physiology
                Subject: Immune Physiology
                Subject: Antibodies
                Subject: Medicine and Health Sciences
                Subject: Physiology
                Subject: Immune Physiology
                Subject: Antibodies
                Subject: Biology and Life Sciences
                Subject: Immunology
                Subject: Immune System Proteins
                Subject: Antibodies
                Subject: Medicine and Health Sciences
                Subject: Immunology
                Subject: Immune System Proteins
                Subject: Antibodies
                Subject: Biology and Life Sciences
                Subject: Biochemistry
                Subject: Proteins
                Subject: Immune System Proteins
                Subject: Antibodies
                Subject: Engineering and Technology
                Subject: Electronics Engineering
                Subject: Charge-Coupled Devices
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Brain Mapping
                Subject: Functional Magnetic Resonance Imaging
                Subject: Medicine and Health Sciences
                Subject: Diagnostic Medicine
                Subject: Diagnostic Radiology
                Subject: Magnetic Resonance Imaging
                Subject: Functional Magnetic Resonance Imaging
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Diagnostic Radiology
                Subject: Magnetic Resonance Imaging
                Subject: Functional Magnetic Resonance Imaging
                Subject: Medicine and Health Sciences
                Subject: Radiology and Imaging
                Subject: Diagnostic Radiology
                Subject: Magnetic Resonance Imaging
                Subject: Functional Magnetic Resonance Imaging
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Neuroimaging
                Subject: Functional Magnetic Resonance Imaging
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Neuroimaging
                Subject: Functional Magnetic Resonance Imaging
                Subject: Physical Sciences
                Subject: Mathematics
                Subject: Optimization
                Subject: Biology and Life Sciences
                Subject: Immunology
                Subject: Immune Response
                Subject: Clonal Selection
                Subject: Medicine and Health Sciences
                Subject: Immunology
                Subject: Immune Response
                Subject: Clonal Selection
                Subject: Computer and Information Sciences
                Subject: Neural Networks
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Neural Networks
                Subject: Biology and Life Sciences
                Subject: Molecular Biology
                Subject: Molecular Biology Techniques
                Subject: Cloning
                Subject: Research and Analysis Methods
                Subject: Molecular Biology Techniques
                Subject: Cloning
                Custom metadata
                Data Availability Data are available from the website " http://www.fmrib.ox.ac.uk/analysis/netsim/index.html." The dataset is offered by Stephen Smith from “Smith S M, Miller K L, Salimi-Khorshidi G, Webster M, Beckmann C F, Nichols T E, et al. Network modelling methods for FMRI. Neuroimage. 2011; 54(2): 875-891.”

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