  In mental health research, it has proven difficult to find measures of brain
function that provide reliable indicators of mental health and well-being,
including susceptibility to mental health disorders. Recently, a family of
data-driven analyses have provided such reliable measures when applied to
large, population-level datasets. In the current pre-registered replication
study, we show that the canonical correlation analysis (CCA) methods previously
developed using resting-state MRI functional connectivity and subject measures
of cognition and behavior from healthy adults are also effective in measuring
well-being (a "positive-negative axis") in an independent developmental
dataset. Our replication was successful in two out of three of our
pre-registered criteria, such that a primary CCA mode's weights displayed a
significant positive relationship and explained a significant amount of
variance in both functional connectivity and subject measures. The only
criteria that was not successful was that compared to other modes the magnitude
of variance explained by the primary CCA mode was smaller than predicted, a
result which could indicate a developmental trajectory of a primary mode. This
replication establishes a signature neurotypical relationship between
connectivity and phenotype, opening new avenues of research in neuroscience
with clear clinical applications.
