ArticlePloS one2016
Learning Effective Connectivity Network Structure from fMRI Data Based on Artificial Immune Algorithm.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm.Bioengineering (Basel, Switzerland) · 2023Article
- Brain effective connectome based on fMRI and DTI data: Bayesian causal learning and assessment.PloS one · 2023Article
- An Adaptive Shrinking Grid Search Chaotic Wolf Optimization Algorithm Using Standard Deviation Updating Amount.Computational intelligence and neuroscience · 2020Article
- Increasing robustness of pairwise methods for effective connectivity in magnetic resonance imaging by using fractional moment series of BOLD signal distributions.Network neuroscience (Cambridge, Mass.) · 2019Article
- ACOEC-FD: Ant Colony Optimization for Learning Brain Effective Connectivity Networks From Functional MRI and Diffusion Tensor Imaging.Frontiers in neuroscience · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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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