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An empirical Bayes normalization method for connectivity metrics in resting state fMRI.

Chen S, Kang J, Wang G - Front Neurosci (2015)

Bottom Line: Moreover, the normalization function maps the original connectivity metrics to values between zero and one, which is well-suited for the graph theory based network analysis and avoids the information loss due to the (negative value) hard thresholding step.We apply the normalization method to a simulation study and the simulation results show that our normalization method effectively improves the robustness and reliability of the quantification of brain functional connectivity and provides more powerful group difference (biomarkers) detection.We illustrate our method on an analysis of a rs-fMRI dataset from the Autism Brain Imaging Data Exchange (ABIDE) study.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Biostatistics, University of Maryland College Park, MD, USA.

ABSTRACT
Functional connectivity analysis using resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a powerful technique for investigating functional brain networks. The functional connectivity is often quantified by statistical metrics (e.g., Pearson correlation coefficient), which may be affected by many image acquisition and preprocessing steps such as the head motion correction and the global signal regression. The appropriate quantification of the connectivity metrics is essential for meaningful and reproducible scientific findings. We propose a novel empirical Bayes method to normalize the functional brain connectivity metrics on a posterior probability scale. Moreover, the normalization function maps the original connectivity metrics to values between zero and one, which is well-suited for the graph theory based network analysis and avoids the information loss due to the (negative value) hard thresholding step. We apply the normalization method to a simulation study and the simulation results show that our normalization method effectively improves the robustness and reliability of the quantification of brain functional connectivity and provides more powerful group difference (biomarkers) detection. We illustrate our method on an analysis of a rs-fMRI dataset from the Autism Brain Imaging Data Exchange (ABIDE) study.

No MeSH data available.


Related in: MedlinePlus

Empirical distribution of pairwise correlations between 90 random white noise vectors.
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Figure 1: Empirical distribution of pairwise correlations between 90 random white noise vectors.

Mentions: We first introduce the distribution of 4005 pairwise correlations between time courses from 90 nodes. Each time course is a randomly simulated white noise vector (including 50 data points) with mean 0, and variance 1 and all time courses are generated independently. The resulting connectivity metric distribution is shown in Figure 1. The correlations range between (-0.63, 0.66) and are centered around 0. The 4005 sampling correlations (the calculated statistics) are used to quantify the connectivity expressions, and among those correlations there are many values close to 1 or -1 that are often considered as “false positively” correlated or anticorrelated. We denote the distribution in Figure 1 as the distribution.


An empirical Bayes normalization method for connectivity metrics in resting state fMRI.

Chen S, Kang J, Wang G - Front Neurosci (2015)

Empirical distribution of pairwise correlations between 90 random white noise vectors.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4584951&req=5

Figure 1: Empirical distribution of pairwise correlations between 90 random white noise vectors.
Mentions: We first introduce the distribution of 4005 pairwise correlations between time courses from 90 nodes. Each time course is a randomly simulated white noise vector (including 50 data points) with mean 0, and variance 1 and all time courses are generated independently. The resulting connectivity metric distribution is shown in Figure 1. The correlations range between (-0.63, 0.66) and are centered around 0. The 4005 sampling correlations (the calculated statistics) are used to quantify the connectivity expressions, and among those correlations there are many values close to 1 or -1 that are often considered as “false positively” correlated or anticorrelated. We denote the distribution in Figure 1 as the distribution.

Bottom Line: Moreover, the normalization function maps the original connectivity metrics to values between zero and one, which is well-suited for the graph theory based network analysis and avoids the information loss due to the (negative value) hard thresholding step.We apply the normalization method to a simulation study and the simulation results show that our normalization method effectively improves the robustness and reliability of the quantification of brain functional connectivity and provides more powerful group difference (biomarkers) detection.We illustrate our method on an analysis of a rs-fMRI dataset from the Autism Brain Imaging Data Exchange (ABIDE) study.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Biostatistics, University of Maryland College Park, MD, USA.

ABSTRACT
Functional connectivity analysis using resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a powerful technique for investigating functional brain networks. The functional connectivity is often quantified by statistical metrics (e.g., Pearson correlation coefficient), which may be affected by many image acquisition and preprocessing steps such as the head motion correction and the global signal regression. The appropriate quantification of the connectivity metrics is essential for meaningful and reproducible scientific findings. We propose a novel empirical Bayes method to normalize the functional brain connectivity metrics on a posterior probability scale. Moreover, the normalization function maps the original connectivity metrics to values between zero and one, which is well-suited for the graph theory based network analysis and avoids the information loss due to the (negative value) hard thresholding step. We apply the normalization method to a simulation study and the simulation results show that our normalization method effectively improves the robustness and reliability of the quantification of brain functional connectivity and provides more powerful group difference (biomarkers) detection. We illustrate our method on an analysis of a rs-fMRI dataset from the Autism Brain Imaging Data Exchange (ABIDE) study.

No MeSH data available.


Related in: MedlinePlus