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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

(A) Histograms of correlations and correlations with systematic shifts for a subject in the control group; (B) Histograms of the change of the shift.
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Figure 4: (A) Histograms of correlations and correlations with systematic shifts for a subject in the control group; (B) Histograms of the change of the shift.

Mentions: In addition, we compare the raw correlations (without and with subject systematic shifts) with the normalized connectivity expressions to investigate the effects of normalization in connectivity metric quantification and differentially expressed connectivity detection. Different levels of subject systematic shifts (different σ2) are also included. We first evaluate the effects of normalization on random shifts. If there is a random shift from the random measurement error, Figure 4A demonstrates the histograms of the original correlations (red) and systematically (with randomness) shifted correlations (blue) for a subject in the control group. Figure 4B illustrates the impact of the systematic shifts on the non-normalized and the normalized connectivity expression. The red histogram in Figure 4B shows the difference of original correlations and shifted correlations. Thus, if there is a systematic shift the connectivity will be affected with consistent bias, which may cause invalid group level inferences. The blue histogram in Figure 4B shows the differences of normalized original correlations and normalized shifted correlations which are distributed around 0. Clearly, the normalized connectivity metric is almost invariant to the systematic shifts, therefore the normalization algorithm improves the robustness of the connectivity metrics to systematic shifts/noises.


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

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

(A) Histograms of correlations and correlations with systematic shifts for a subject in the control group; (B) Histograms of the change of the shift.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 4: (A) Histograms of correlations and correlations with systematic shifts for a subject in the control group; (B) Histograms of the change of the shift.
Mentions: In addition, we compare the raw correlations (without and with subject systematic shifts) with the normalized connectivity expressions to investigate the effects of normalization in connectivity metric quantification and differentially expressed connectivity detection. Different levels of subject systematic shifts (different σ2) are also included. We first evaluate the effects of normalization on random shifts. If there is a random shift from the random measurement error, Figure 4A demonstrates the histograms of the original correlations (red) and systematically (with randomness) shifted correlations (blue) for a subject in the control group. Figure 4B illustrates the impact of the systematic shifts on the non-normalized and the normalized connectivity expression. The red histogram in Figure 4B shows the difference of original correlations and shifted correlations. Thus, if there is a systematic shift the connectivity will be affected with consistent bias, which may cause invalid group level inferences. The blue histogram in Figure 4B shows the differences of normalized original correlations and normalized shifted correlations which are distributed around 0. Clearly, the normalized connectivity metric is almost invariant to the systematic shifts, therefore the normalization algorithm improves the robustness of the connectivity metrics to systematic shifts/noises.

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