Overview of Quality Control of structural data with Euler number (A), fMRI head motion scores (B), analysis of behavioral C) and reaction times (D) and overview of the task (E).

Order of the movie clips in the experiment

Slice-based fMRI explanation (A,B), results (C) and subsequent processing steps (D).

In a hypothetical fMRI data acquisition protocol (A), three slices are acquired with TR=2s resulting in Slice-Acquisition Times (SAT) of 0, 0.7, 1.3s etc. Three stimulus trials (T1.RT, T2.RT, T3.RT and red, yellow, green bars) whose onset coincides with each slice results in slices sampling the brain at all timepoints during a 6s epoch relative to the onset of the stimulus. Thus, for a specific voxel on slice 2 (B), after these three stimulus trials, signal intensities at a temporal resolution equal to the ΔSAT (0.7s) are acquired. Statistical models can extract the fMRI signal (B, grey dashed line) at this or at a higher powered binned, but lower temporal resolution. Results from group-level Slice-Based analysis of movie clips at 2s resolution for the separate happy, fear and sad trials (not all timepoints shown). Note results reveal complex increases (hot colors) and decreases (cool colors) of signals across the whole brain during the 26s epoch (C). The slice-based datasets for the happy, fear and sad trials for each subject were then concenated and entered into group spatial ICA for the detection of component maps and timecourses (D).

Results from group spatial ICA with 6 dimensions.

Plotted are both the spatial maps as well as the component time courses averaged across subjects and conditions associated with each IC. Consideration of both spatial and temporal properties of these 6 ICs led to the identification of four ICs as signal (IC0, IC1, IC2, IC4) and two as noise (IC3, IC5). Note the noise components had spatial distributions associated with non-gray-matter regions (CSF, draining veins). Note also the networks are labeled and ordered as explained in the text. Bottom bar denotes the different stages of the behavioral task.

Basic regional functional connectivity in the four large scale networks. Note scale differences between networks.

Black bars denote top 10 regions with highest functional connectivity within each network. All results are bonferroni corrected.

Differences in functional connectivity between IC networks.

Note here we focus on three main contrasts to highlight differences between IC0 and IC1, IC0 and IC2 and between IC2 and IC4.

Overview of the ANOVA table from the statistical modeling of the regional functional connectivity data.

Functional connectivity modulation of emotion conditions across networks.

Note all large scale networks involved in the task are modulated by emotions in all major lobes of the brain. H = Happy, F = Fear, S = Sadness. All results are bonferroni corrected.

Explanation of curvefitting procedure (A), goodness of fit results from Gaussian curvefitting (B), and graphical overview of how peak value (C), time to peak (D) and duration (E) differ between the four IC networks.

Note that goodness of fit values were relatively equal across IC networks, and that there were earlier time to peak values for IC1 compared to IC0. Different letters above bars indicate a significant difference (p < 0.05, bonferroni corrected).

Overview of the ANOVA table from the statistical modeling of the curvefitting data.

Results of the pairwise contrasts of the Happy (H), Sadness (S) and Fear (F) conditions for the peak value (pv), time to peak (ttp) and duration (dur) across all four IC networks.

Only significant (p < 0.05, bonferroni corrected) values are plotted.