Journal: Brain and Behavior

Article Title: Exposure to attachment narratives dynamically modulates cortical arousal during the resting state in the listener

doi: 10.1002/brb3.1007

Figure Lengend Snippet: (a) Linear regressions for the ratio of percentage of subjects in high to low vigilance stages between all resting states. (b) Temporal evolution of the percentage of subjects in high (red) versus low (blue) vigilance stages for each TR following the three narratives: insecure‐dismissing (left), insecure‐preoccupied (middle), and secure (right). Linear regressions for both high and low vigilance stages are plotted as black lines. The coefficients for the intercept and slope of the ANCOVA model equations differed between narrative conditions: insecure‐dismissing: y = 76.1−0.151x+ε; insecure‐preoccupied: y = 68.8−0.077x+ε; secure: y = 69.6−0.074x+ε and revealed significant differences between the slopes of the regression in insecure‐dismissing versus insecure‐preoccupied as well as insecure‐dismissing versus secure

Article Snippet: Linear regressions for high vigilance stages were added to compare the slope and intercept between narratives in an ANCOVA (aoctool in MATLAB) using a Bonferroni adjustment to correct for multiple comparisons (multcompare in MATLAB).

Techniques:

Journal: Brain and Behavior

Article Title: Exposure to attachment narratives dynamically modulates cortical arousal during the resting state in the listener

doi: 10.1002/brb3.1007

Figure Lengend Snippet: ANCOVA model coefficient estimates for all narrative conditions

Article Snippet: Linear regressions for high vigilance stages were added to compare the slope and intercept between narratives in an ANCOVA (aoctool in MATLAB) using a Bonferroni adjustment to correct for multiple comparisons (multcompare in MATLAB).

Techniques:

Journal: Brain and Behavior

Article Title: Exposure to attachment narratives dynamically modulates cortical arousal during the resting state in the listener

doi: 10.1002/brb3.1007

Figure Lengend Snippet: Results of the post hoc tests on ANCOVA results

Article Snippet: Linear regressions for high vigilance stages were added to compare the slope and intercept between narratives in an ANCOVA (aoctool in MATLAB) using a Bonferroni adjustment to correct for multiple comparisons (multcompare in MATLAB).

Techniques:

Journal: Developmental Cognitive Neuroscience

Article Title: Deficits in neural encoding of speech in preterm infants

doi: 10.1016/j.dcn.2023.101259

Figure Lengend Snippet: Clustering of the first two principal component coefficients of the 11 FFR measures . (A) The PC coefficients for each measure are plotted as empty circles connected with the lines to the centroid of their cluster that is plotted as a filled circle. Abbreviations: SN – FFR SNR, NR – Noise RMS, PL – Power LOW, PM – Power MID, mI – maxITPC, RC – Response Consistency, PS – Pitch Strength, IL – ITPC LOW, IM – ITPC MID, dL – Low/High Power Ratio, dM – Mid/High Power Ratio. AU – arbitrary units. (B) Main effects in ANCOVA analysis, representing an effect of one factor on the response from changing the factor value while averaging out the effects of the other factors . The factors are listed along the y-axes. The bars indicate the confidence intervals. The significant factors (i.e., those whose confidence intervals do not include zero) are marked with asterisks. ( C) Synchronization and ( D ) Power findings of the two experimental groups controlled for Age, Tone, and Maternal Education. Non-parametric Wilcoxon rank-sum tests demonstrated that Synchronization ( Z = − 1.88, P = 0.030) but not Power ( Z = − 0.97, P = 0.17) was impaired by prematurity.

Article Snippet: The scores of the two principal components and individual measures were fed into a 4-way fixed-effects ANCOVA (MATLAB function fitlm with method anova ) with two discrete factors of Prematurity (preterm versus term) and Tone (ga2, ga3, or ga4) and two continuous factors of Corrected Age at FFR data collection (in months) and Maternal Education (in years).

Techniques:

Journal: Developmental Cognitive Neuroscience

Article Title: Deficits in neural encoding of speech in preterm infants

doi: 10.1016/j.dcn.2023.101259

Figure Lengend Snippet: Results of the 3-way fixed-effects ANCOVA for individual measures (FDR-corrected across measures and factors).

Article Snippet: The scores of the two principal components and individual measures were fed into a 4-way fixed-effects ANCOVA (MATLAB function fitlm with method anova ) with two discrete factors of Prematurity (preterm versus term) and Tone (ga2, ga3, or ga4) and two continuous factors of Corrected Age at FFR data collection (in months) and Maternal Education (in years).

Techniques: