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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

The regions showed higher correlations in children with ASD, compared to the TD group (q < 0.05, corrected for multiple comparisons). No pairs of regions showed higher connectivity in the TD than the ASD group.
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Figure 7: The regions showed higher correlations in children with ASD, compared to the TD group (q < 0.05, corrected for multiple comparisons). No pairs of regions showed higher connectivity in the TD than the ASD group.

Mentions: We apply the normalization algorithm to all 4005 connectivity metrics for each individual, and no subject in this data set is detected with anticorrelation component of the mixture model. Figure 6 shows the distribution of correlations for one subject as well as the corresponding empirical Bayes normalization function. Next, we conduct Wilcoxon signed-rank tests toward all 4005 original correlations and normalized correlations between 90 ROIs for TC vs. TSD. We then perform local fdr for multiple testing control. Unlike the simulation study, the ground truth of the false positives and false negatives of the data example is unknown. Comparing to the simulation testing results, it seems that the difference between test results of original and normalized correlations has the similar pattern: the normalized connectivity test results include small p-values scattered randomly. Because 4005 tests are performed simultaneously, the multiple testing correction methods including local fdr and Network Based Statistics (NBS) performed for both empirical Bayes normalized correlations and original correlations (Efron, 2004; Zalesky et al., 2010a). No significant feature or network is identified after the correction for the original correlations (q-value 0.1 as threshold for local fdr and permutation p-value 0.05 for NBS). In contrast, the analysis based on empirical Bayes normalized connectivity metrics shows significant connectivity differences between the ASD and TC groups, and 44 connectivity features have fdr q-values less than 0.1. We demonstrate the results in Figure 7. The ASD group show higher function connectivity between pairs of ROIs for all the 44 features than the TC group. Most of these significantly expressed connectivity are between distant ROIs, which are across the the functional subsystems of primary sensory, subcortical, limbic, paralimbic, and association areas defined by Mesulam (1998) and Supekar et al. (2013). We further perform bootstrap analysis to evaluate the reliability of the findings. From 3000 resamples, the 44 features are detected on average 78.6% (with sd 11.3%). As comparison, we detect no connectivity between or within any of these subsystems showing greater connectivity in the TD group, compared with the ASD group. These results suggest that hyper-connectivity in ASD spans multiple functional subsystems of the human brain. The revealed results are consistent with the recent findings of brain hyper-connectivity of ASD children by Supekar et al. (2013), which include multiple studies from three image data acquisition sites in the U.S.


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

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

The regions showed higher correlations in children with ASD, compared to the TD group (q < 0.05, corrected for multiple comparisons). No pairs of regions showed higher connectivity in the TD than the ASD group.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 7: The regions showed higher correlations in children with ASD, compared to the TD group (q < 0.05, corrected for multiple comparisons). No pairs of regions showed higher connectivity in the TD than the ASD group.
Mentions: We apply the normalization algorithm to all 4005 connectivity metrics for each individual, and no subject in this data set is detected with anticorrelation component of the mixture model. Figure 6 shows the distribution of correlations for one subject as well as the corresponding empirical Bayes normalization function. Next, we conduct Wilcoxon signed-rank tests toward all 4005 original correlations and normalized correlations between 90 ROIs for TC vs. TSD. We then perform local fdr for multiple testing control. Unlike the simulation study, the ground truth of the false positives and false negatives of the data example is unknown. Comparing to the simulation testing results, it seems that the difference between test results of original and normalized correlations has the similar pattern: the normalized connectivity test results include small p-values scattered randomly. Because 4005 tests are performed simultaneously, the multiple testing correction methods including local fdr and Network Based Statistics (NBS) performed for both empirical Bayes normalized correlations and original correlations (Efron, 2004; Zalesky et al., 2010a). No significant feature or network is identified after the correction for the original correlations (q-value 0.1 as threshold for local fdr and permutation p-value 0.05 for NBS). In contrast, the analysis based on empirical Bayes normalized connectivity metrics shows significant connectivity differences between the ASD and TC groups, and 44 connectivity features have fdr q-values less than 0.1. We demonstrate the results in Figure 7. The ASD group show higher function connectivity between pairs of ROIs for all the 44 features than the TC group. Most of these significantly expressed connectivity are between distant ROIs, which are across the the functional subsystems of primary sensory, subcortical, limbic, paralimbic, and association areas defined by Mesulam (1998) and Supekar et al. (2013). We further perform bootstrap analysis to evaluate the reliability of the findings. From 3000 resamples, the 44 features are detected on average 78.6% (with sd 11.3%). As comparison, we detect no connectivity between or within any of these subsystems showing greater connectivity in the TD group, compared with the ASD group. These results suggest that hyper-connectivity in ASD spans multiple functional subsystems of the human brain. The revealed results are consistent with the recent findings of brain hyper-connectivity of ASD children by Supekar et al. (2013), which include multiple studies from three image data acquisition sites in the U.S.

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