Supplementary Materials
Supplementary Methods
Here we conducted analyses to determine how differences in statistical thresholding may have affected
our results. We also conducted an analysis to identify the degree of overlap between results from the
present study and the previously established network of brain regions contributing to motor learning as
identified through ALE meta-analysis (Hardwick et al., 2013).
Unthresholded Analyses
The network with the largest overall volume was identified as the RS network (381,166mm3). We then
reduced the threshold of the SC and MACM maps in an attempt to match the overall volumes identified
by each analysis. However, the thresholded RS map still identified a greater volume than the
unthresholded SC and MACM maps. We therefore compared the results of the RS connectivity map with
those of the unthresholded SC and MACM maps. This allowed us to gain further understanding of the
effects of differences in thresholding on the results obtained for each analysis.
Comparison with Meta-Analysis of Motor Learning
The left dPMC seed region was taken from a meta-analysis of motor learning, where it was the region
most specifically associated with motor learning across different analyses (Hardwick et al., 2013). Here
we applied a mask representing the main effect from that meta-analysis to the results of the functional
connectivity analyses. This analysis allowed the identification of brain regions showing consistent taskbased activation during motor learning (i.e. the results of the meta-analysis), as well as functional
connectivity (i.e. RS, SC, MACM, or a combination of all three) with the dPMC as a core region for this
process.
Unthresholded Analyses
Supplementary Figure 1: The RSFC analysis compared to the unthresholded MACM and SC maps
We examined the unthresholded SC and MACM analyses to further examine whether differences in
statistical power contributed to differences between the networks identified. Results indicated a similar
pattern to those of the main analyses; RSFC and liberal MACM identified similar networks of prefrontal,
premotor and parietal regions, while the SC map consistent of predominantly widespread frontal
connectivity. Notably, the unthresholded MACM maps implicated several subcortical structures as
having connectivity with the seed, including the thalamus, basal ganglia, and cerebellum.
Comparison with Meta-analysis of Motor Learning
Supplementary Figure 2: Results of the three functional connectivity analyses masked by the previous
meta-analysis of motor learning (Hardwick et al., 2013).
As with the analyses above, the left dPMC region is included by design. Masking the results of the
resting state analysis with the results of our previous meta-analysis yielded further clusters in the SMA,
left superior parietal lobule and precuneus and left thalamus. Masking the MACM analysis gave a
similar, focal pattern of results with clusters in SMA and the left superior parietal lobule. Masking the
results of the SC analysis resulted in a more restricted group of clusters, consisting of left primary motor
cortex and the SMA. Finally, the most restrictive analysis, a conjunction between the motor learning
meta-analysis and the combined conjunction analysis of functional connectivity analyses (i.e. RS, SC and
MACM) left a single cluster in the supplementary motor area.