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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

Connectivity metrics with both correlated and anticorrelated components: (A) is the mixture model estimation procedure using “locfdr” of the three components; (B) the original correlations vs. the normalized correlations: the blue line is the posterior probability of the correlated component and anticorrelated component are >0 (“+” sign), and the green line discriminate correlated or anticorrelated posterior probability by using a “−” sign to indicate whether it anticorrelated.
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Figure 8: Connectivity metrics with both correlated and anticorrelated components: (A) is the mixture model estimation procedure using “locfdr” of the three components; (B) the original correlations vs. the normalized correlations: the blue line is the posterior probability of the correlated component and anticorrelated component are >0 (“+” sign), and the green line discriminate correlated or anticorrelated posterior probability by using a “−” sign to indicate whether it anticorrelated.

Mentions: The anticorrelations in rs-fMRI data have drawn attention of many neuroimaging researchers (Fox et al., 2009; Murphy et al., 2009; Weissenbacher et al., 2009; Chai et al., 2012). The discussion has not reached to the agreement whether the anticorrelations are “true positive” or “false positive.” The proposed normalization method provides a pathway to automatically detect the “true positive” anticorrelation component by classifying the “true positives” and “false positives” based on the empirical distribution of connectivity metric. Figure 8 shows that correlated and anticorrelated components can be identified, if existing, we could assign either “+” or “−” sign to anticorrelated connectivity metric depending on different following analyses. Generally, “−” sign suits the regression analysis or statistical tests better, because anticorrelation could be considered as the opposite of correlation. When applying the graph theoretical model based network analysis using normalized connectivity, two separate analyses should be conducted for correlations and anticorrelations (with “+” sign) if both components are detected, with the normalized connectivity metric range of [0, 1] (in Figure 8B). Thus, the results include two parts of inferences: properties of correlated networks and anticorrelated networks. Although in our data example there is no anticorrelation component detected, that normalization method can be also applied to deal with anticorrelations in practical data analysis. Yet, out normalization method could be combined with pre-processing steps (e.g., global signal regression), as the normalized connectivity is probability and shift-invariant.


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

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

Connectivity metrics with both correlated and anticorrelated components: (A) is the mixture model estimation procedure using “locfdr” of the three components; (B) the original correlations vs. the normalized correlations: the blue line is the posterior probability of the correlated component and anticorrelated component are >0 (“+” sign), and the green line discriminate correlated or anticorrelated posterior probability by using a “−” sign to indicate whether it anticorrelated.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 8: Connectivity metrics with both correlated and anticorrelated components: (A) is the mixture model estimation procedure using “locfdr” of the three components; (B) the original correlations vs. the normalized correlations: the blue line is the posterior probability of the correlated component and anticorrelated component are >0 (“+” sign), and the green line discriminate correlated or anticorrelated posterior probability by using a “−” sign to indicate whether it anticorrelated.
Mentions: The anticorrelations in rs-fMRI data have drawn attention of many neuroimaging researchers (Fox et al., 2009; Murphy et al., 2009; Weissenbacher et al., 2009; Chai et al., 2012). The discussion has not reached to the agreement whether the anticorrelations are “true positive” or “false positive.” The proposed normalization method provides a pathway to automatically detect the “true positive” anticorrelation component by classifying the “true positives” and “false positives” based on the empirical distribution of connectivity metric. Figure 8 shows that correlated and anticorrelated components can be identified, if existing, we could assign either “+” or “−” sign to anticorrelated connectivity metric depending on different following analyses. Generally, “−” sign suits the regression analysis or statistical tests better, because anticorrelation could be considered as the opposite of correlation. When applying the graph theoretical model based network analysis using normalized connectivity, two separate analyses should be conducted for correlations and anticorrelations (with “+” sign) if both components are detected, with the normalized connectivity metric range of [0, 1] (in Figure 8B). Thus, the results include two parts of inferences: properties of correlated networks and anticorrelated networks. Although in our data example there is no anticorrelation component detected, that normalization method can be also applied to deal with anticorrelations in practical data analysis. Yet, out normalization method could be combined with pre-processing steps (e.g., global signal regression), as the normalized connectivity is probability and shift-invariant.

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