3 Experimental procedures and data analysis
3.2.6 fMRI data analysis
Image analysis was performed using MATLAB (Mathworks Inc., Natick, MA) and SPM5 (Wellcome Department of Cognitive Neurology, University College London). Images of each condition were corrected for head movements by alignment to the mean image. The data for each subject were normalized using coregistration to the individual anatomical image and segmented into MNI standard coordinate space. The data were also smoothed by a Gaussian filter (8mm FWHM).
At the single-subject level, we applied a high-pass filter (453 s) to remove baseline drifts. The regressors of interest for the all conditions (see below) were entered into a general linear model (GLM) as boxcar functions convolved with the hemodynamic response function. In the action execution experiment, the baseline condition was not explicitly modelled. In the action observation experiment, all control conditions were modelled since we had 2 control conditions for every agent (see Figure 14e, f, i, j). In both experiments, head motion parameters were included in the analysis as regressors of no interest. For the action observation experiment, images of parameter estimates for the contrasts of interest were created for each subject. These contrasts were: grasping tool, grasping food, grasping block, pointing to tool, pointing to food, baseline with objects, and baseline without objects for robot and for human respectively.
Conjunction between action execution and action observation
The SPM conjunction null method [203] was used to assess activation common to two experiments for the conditions “execution of grasping food&tool minus baseline” and
“observation of a human agent grasping food&tool minus baseline with objects.” To restrict
the activations only to areas with both motor and visual properties, we defined the ROIs as voxels in the conjunction analysis which overlap with the anatomical regions of interest from the Wake Forest University Pick Atlas [188]: the left/right IPL, left pIFG15, and the bilateral premotor cortex. These regions have been reported to be associated with the MNS in most studies (see Figure 3) [210]. The so created “ROI mask” was used to mask the activations presented in the results section. In the random effects analysis, voxels exceeding a statistical threshold of p < 0.05 (FDR-corrected for multiple comparisons) are presented. In the figures, significant voxels are overlaid on a single subject MNI template. The nomenclature of
15
50 anatomical structures lying outside the ROIs follows the Harvard–Oxford structural atlas and the Jülich histological atlas [90].
SPM ANOVAS
The second-stage (random effects) group analysis was used to create images for displaying the activations of the MNS areas depending on the observed action type, the goal of the action and the agent (for overview see
Table 4). To this end, the individual contrast images (created in the first-level analysis) were
entered into 3 different ANOVAs. The first ANOVA (action ANOVA I) was performed on the factors action type (grasping/pointing), agent (human/robot) and goal (grasping tool, food) and aimed at investigating how the activity in the MNS areas is modulated depending on the type of action, but also the nature of the agent, and the goal. Since grasping, but not pointing was additionally targeted to the geometric shape (block) the second ANOVA (goal ANOVA I) with factors goal (grasping tool, food, block) and agent (human/robot) was used to test for the differences in activation between grasping specific everyday objects (tool and food) and an abstract shape (block). To investigate whether the difference in the activity when comparing different agents might be based on the superficial difference in their appearance, a third ANOVA with factors baseline type (with objects/without objects) and agent (human/robot) was also performed (agent ANOVA I) .
Table 4: Main ANOVAS and their results. The depicted ANOVAS were used to investigate the influence of
observed action-type, goal of action and agent on the MNS activity. Post-hoc ANOVAS were performed to clarify the nature of interactions described in the text.
Action Agent
Grasping (G)>Pointing(P) Tool(T)>Food(F) Tool (T) >Block(B) Food(F)>Block(B) Robot(R)>Human(H)
action ANOVA I action type×goal×agent for H and R in rIPL, lIPL, PMC goal ANOVA I goal×agent for H and R in rIPL, lIPL, PCM for H and R in rIPL, lIPL, PCM, pIFG for H and R in
rIPL, lIPL, PCM, pIFG rIPL, lIPL, PCM
agent ANOVA I baseline type×agent
no difference for static pictures of R and H
action ANOVA II
region×action
post hoc action ANOVA II
action type×goal×agent
for H and R in rIPL, lIPL, PMC
for G but not P in rIPL, lIPL, PMC
goal ANOVA II
region×goal×agent
post hoc goal ANOVA I
goal×agent for H and R in rIPL, lIPL, PCM for H and R in rIPL, lIPL, PCM, pIFG for H and R in
rIPL, lIPL, PCM, pIFG rIPL, lIPL, PMC
agent ANOVAII
region×agent×state
post hoc agent ANOVAII
agent×state
for G but not for static in rIPL, lIPL, PMC
SPM ACTIVATIONS
PERCENT SIGNAL CHANGE
Goal
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Percent signal change ANOVAS
To investigate the effect of goal, action type and agent depending on the brain region, we calculated the mean percent signal change in every ROI (right IPL, left IPL, PMC, left IFG) for each condition and subject. To this end we used the mean intensity (beta values of the voxels) in that region in comparison to the mean intensity over all brain voxels. Individual mean percentage signal change values for each ROI in each condition were averaged across subjects and entered in three different repeated-measures ANOVAs. The purpose of percent signal change ANOVAs was to determine whether in each ROI there were significant differences in mean signal strength as function of action type (grasping/pointing), agent (robot/human), goal (tool, food, block). Similar to the ANOVA design for the contrast images (see above), but with the additional differentiation between the different ROIs, the first ANOVA was performed on the factors region (right IPL, left IPL, PMC, left IFG), agent (human/robot), action type (grasping/pointing) and goal (tool, food) (action ANOVA II). To see the goal-dependent activations for tool and food items vs. geometric shape, the second ANOVA was performed only for the grasping action and contained all three types of objects (goal ANOVA II). It was performed on factors region (right IPL, left IPL, PMC, left IFG),
goal (grasping tool, food, block) and agent (human/robot). The third ANOVA was aimed at
investigating whether solely the difference in the agents’ appearance may lead to differential activations in the ROIs (agent ANOVA II). In this ANOVA we therefore included both static conditions and grasping conditions (we call this factor state) for both agents resulting in an ANOVA with the factors region (right IPL, left IPL, PMC, left IFG), state (grasping/static), and agent (human/robot). When these three main ANOVAs showed significant main effects or interaction effects, post-hoc ANOVAs and t-tests were performed to determine whether and how a given condition significantly differed from other conditions.