Title: Tensor network factorizations: Relationships between brain structural connectomes and traits,
Abstract: Advanced brain imaging techniques make it possible to measure individuals'
structural connectomes in large cohort studies non-invasively. The structural
connectome is initially shaped by genetics and subsequently refined by the
environment. It is extremely interesting to study relationships between
structural connectomes and environment factors or human traits, such as
substance use and cognition. Due to limitations in structural connectome
recovery, previous studies largely focus on functional connectomes. Questions
remain about how well structural connectomes can explain variance in different
human traits. Using a state-of-the-art structural connectome processing
pipeline and a novel dimensionality reduction technique applied to data from
the Human Connectome Project (HCP), we show strong relationships between
structural connectomes and various human traits. Our dimensionality reduction
approach uses a tensor characterization of the connectome and relies on a
generalization of principal components analysis. We analyze over 1100 scans for
1076 subjects from the HCP and the Sherbrooke test-retest data set, as well as
$175$ human traits that measure domains including cognition, substance use,
motor, sensory and emotion. We find that structural connectomes are associated
with many traits. Specifically, fluid intelligence, language comprehension, and
motor skills are associated with increased cortical-cortical brain structural
connectivity, while the use of alcohol, tobacco, and marijuana are associated
with decreased cortical-cortical connectivity.