  We propose two methodologies in order to better understand the
inter-individual variability of resting-state functional Magnetic Resonance
Imaging (fMRI) brain data. The aim of the study was to quantify whether the
average dendrogram is representative of the initial population and to identify
its possible sources of instability. The average dendrogram is based on the
Pearson correlation between resting-state networks. The first method identifies
networks that can lead to unstable partitions of the average dendrogram. The
second method identified homogeneous sub-samples of participants for whom their
associated average dendrograms were more stable than that of the whole sample.
The two suggested methods have shown significant quantifiable behavioral data
results with regards to detecting an unstable network or presence of
subpopulations when the noise level does not conceal the structure of the data.
These two methods have been successfully applied to establish a cerebral atlas
for late adulthood. The first method made it clear that there was no unstable
network among the atlas networks. The second method highlighted the presence of
two distinct sub-populations with different age-related brain organizations.

Medical Physics