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Results: 9

1.
Figure 6

Figure 6. From: MATLAB Toolbox for Functional Connectivity.

Identified regions of interest used in the analysis: dACC restricted in BA32 (left) and DLPFC restricted in BA9 (right).

Dongli Zhou, et al. Neuroimage. ;47(4):1590-1607.
2.
Figure 8

Figure 8. From: MATLAB Toolbox for Functional Connectivity.

Classification results for 32 subjects. X axis represents the 11 types of association corresponding to Figure 3. Y axis represents the number of subjects classified to each relationship.

Dongli Zhou, et al. Neuroimage. ;47(4):1590-1607.
3.
Figure 7

Figure 7. From: MATLAB Toolbox for Functional Connectivity.

Thirty-six experimental trials for one subject. Blue lines are trials in dACC(BA32) (left panel) and DLPFC(BA9) (right panel). Bold red lines are subject’s mean responses for each ROI.

Dongli Zhou, et al. Neuroimage. ;47(4):1590-1607.
4.
Figure 4

Figure 4. From: MATLAB Toolbox for Functional Connectivity.

Mean results of different FC measures applied to simulations with zero-order relationship (stationary design) between regions. FC measures were computed after smoothing with and without explicitly modeling autocorrelated noise. Results of data with high SNR are shown in red and results of data with low SNR are shown in blue. 1st row: smoothing without modeling autocorrelated noise; 2nd row: smoothing with modeling autocorrelated noise.

Dongli Zhou, et al. Neuroimage. ;47(4):1590-1607.
5.
Figure 5

Figure 5. From: MATLAB Toolbox for Functional Connectivity.

Mean results of different FC measures applied to simulations with zero-order relationship (event-related design) between regions. FC measures were computed after smoothing with and without explicitly modeling autocorrelated noise. Results of data with high SNR are shown in red and results of data with low SNR are shown in blue. 1st row: smoothing without modeling autocorrelated noise; 2nd row: smoothing with modeling autocorrelated noise.

Dongli Zhou, et al. Neuroimage. ;47(4):1590-1607.
6.
Figure 9

Figure 9. From: MATLAB Toolbox for Functional Connectivity.

Mean results of different FC measures between dACC(BA32) and DLPFC(BA9) for three main classified groups. Red dashed lines shown are partial correlation or coherence. ‘pmul’, ‘pmuh’, ‘p_c’, ‘ca_c’ represent partial mutual information over low frequency band, partial mutual information over high frequency band, peak correlation, and functional canonical correlation respectively.

Dongli Zhou, et al. Neuroimage. ;47(4):1590-1607.
7.
Figure 1

Figure 1. From: MATLAB Toolbox for Functional Connectivity.

The plots from top to bottom on the far left show one subject’s whole-waveform-based simulated time-series (140 scans) of two regions (blue lines and red lines) for 4 different patterns of association. Results of cross-correlation and cross-coherence shown here are the overall mean across 50 subjects. mu_l, mu h represent mean mutual information over low frequency band and high frequency band respectively.

Dongli Zhou, et al. Neuroimage. ;47(4):1590-1607.
8.

Figure 3. From: MATLAB Toolbox for Functional Connectivity.

Classification tree of 11 pattern of associations. Here, 1–11 represent: 1. Zero-order relationship unrelated to design; 2. Lagged (lag 4) relationship unrelated to design; 3. Zero-order relationship related to design; 4. Lagged (lag 1) relationship related to design; 5. Relationships among low frequencies unrelated to design; 6. Related peak amplitudes and latencies but variability in higher moments; 7. Related peak amplitudes but unrelated peak latencies; 8. A-1-mode B-2-modes; 9. Relationship of A-peak-amplitude to B-sustained activity; 10. Unrelated peak amplitudes but related peak latencies; and 11. No connectivity. Covariates put in this classification tree were partial zero-order correlation(‘p_zero-order’), partial lag-1 correlation(‘p_lag 1’), partial lag-4 correlation(‘p_lag 4’), partial mutual information over low frequency band(‘pmu_l’), partial mutual information over high frequency band(‘pmu_h’), peak correlation(‘peak’), and functional canonical correlation(‘canonical’).

Dongli Zhou, et al. Neuroimage. ;47(4):1590-1607.
9.
Figure 2

Figure 2. From: MATLAB Toolbox for Functional Connectivity.

The plots from top to bottom on the far left show one subject’s trial-based simulated time-series (20×7 scans) of two regions (blue line and red line) for 11 different patterns of association. Results of FC measures shown here are overall mean across 50 subjects. Red dashed lines shown are partial correlation or coherence. pmul, pmuh, p_c and ca_c represents partial mutual information over low frequency band, partial mutual information over high frequency band, peak correlation, and functional canonical correlation respectively. The shown canonical correlation weight functions of each region (blue line and red line) are the overall mean of weight functions across subjects. Cross-correlation, coherence and mutual information are taken after centering the time-series data from the overall median of each run (20 trials).

Dongli Zhou, et al. Neuroimage. ;47(4):1590-1607.

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