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Image Search Results
Journal: bioRxiv
Article Title: Avalanche criticality in individuals, fluid intelligence and working memory
doi: 10.1101/2020.08.24.260588
Figure Lengend Snippet: A. Top panel: The inverted-U trajectory of the MS 〈 r 〉 vs. SE H ( r ). The red dash line represents the quadratic fit of the data ( F =188.758, p <0.001, adjusted R 2 =0.561). Bottom panel: The frequency count for the distribution of MS 〈 r 〉. B. The branching parameters σ vs. 〈 r 〉 for each subject. The green dashed line indicates σ = 1. The Pearson correlation value R and the p value are shown in the figure. The red dashed line represents the linear regression. For further analysis, we selected three representative groups of subjects according to their synchronization level: namely, LMS group (〈 r 〉 = 0.2824 ± 0.0219, blue open circles in A and B), MMS group (〈 r 〉 = 0.5041 ± 0.0042, green open circles in A and B) and HMS group (〈 r 〉 = 0.6304 ± 0.0246, red open circles in A and B). C. Avalanche size distributions for the LMS group, MMS group, and HMS group. To show the difference between these groups, we used gray lines with α = 1.47 to guide the eyes. The corresponding group-aggregated branching parameters are σ LMS = 0.7237 for the LMS group, σ MMS = 1.0123 for the MMS group, and σ HMS = 1.2023 for the HMS group. D. Avalanche duration distributions for three groups in C . To show the difference between these groups, we used gray lines with τ = 1.7 to guide the eyes. E. Scaling relations for the three groups. The blue line and purple line correspond to γ and , respectively. In the inset, indicates the distance to the critical point. F. The dependence of κ on the numbers m of histogram bins for the three groups.
Article Snippet: To estimate the similarity between the binary FC network of each subject and the group-aggregated binary SC network, we computed the
Techniques:
Journal: bioRxiv
Article Title: Avalanche criticality in individuals, fluid intelligence and working memory
doi: 10.1101/2020.08.24.260588
Figure Lengend Snippet: A. Pearson correlation between anatomical and functional networks as a function of MS 〈 r 〉. The link density in the FC network ρ FC = 0.7 and threshold in the group-aggregated SC network THR SC =40 (corresponding to ρ SC = 0.4836) are shown in the figure. Red dashed line: quadratic fitting ( F = 36.997, p < 0.001, adjusted R 2 = 0.197), which is better than linear fitting ( F = 39.346, p < 0.001, adjusted R 2 = 0.115). B. Hamming distance HD ( FC − SC ) between anatomical and functional networks as a function of MS 〈 r 〉. Red dashed line: quadratic fitting ( F = 36.997, p < 0.001, adjusted R 2 =0.197), which is better than linear fitting ( F = 39.346, p < 0.001, adjusted R 2 = 0.115). C. The Pearson correlation between anatomical and functional networks as a function of FC density ρ FC ( THR SC = 40 corresponding to ρ SC = 0.4826) for the HMS, MMS, and LMS groups. D . The Hamming distance between anatomical and functional networks as a function of FC density ρ FC ( THR SC = 40 corresponding to ρ SC = 0.4826) for the HMS, MMS, and LMS groups. In C-D , the stars indicate significant differences between the HMS and MMS groups (two-tails two-sample t-test, p < 0.05, uncorrected); the open triangles indicate significant differences between the LMS and MMS groups (two-tails two-sample t-test, p < 0.05, uncorrected).
Article Snippet: To estimate the similarity between the binary FC network of each subject and the group-aggregated binary SC network, we computed the
Techniques: Functional Assay
Journal: bioRxiv
Article Title: Avalanche criticality in individuals, fluid intelligence and working memory
doi: 10.1101/2020.08.24.260588
Figure Lengend Snippet: The color bar indicated the Pearson correlation value between regional SE and PMAT. The cortical and subcortical regions were defined by the Human Brainnetome Atlas ( http://atlas.brainnetome.org/bnatlas.html ). Data was visualized using BrainNet Viewer .
Article Snippet: To estimate the similarity between the binary FC network of each subject and the group-aggregated binary SC network, we computed the
Techniques: