PROJECT SUMMARY
Schizophrenia, and other disorders of the psychosis spectrum (PS), most commonly emerge throughout
development and are thought to be caused by disruptions to normative brain maturation occurring during this
time. Critically, deviations from normative neurodevelopment are thought to precede the emergence of clinically
significant PS symptoms by several years, highlighting the profound impact that their discovery would have for
psychiatry research; if we can successfully identify the antecedent brain abnormalities, then we may be able to
intervene early and reduce the risk of individuals developing schizophrenia. Uncovering these antecedent brain
abnormalities requires predictive models built upon recent advances in network neuroscience and machine
learning; moreover, such models must be coupled with large samples of longitudinal neuroimaging and clinical
data to uncover truly prognostic biomarkers. Finally, sex differences are found in both the PS symptoms and
neurodevelopment. Thus, studies that provide a precise understanding of how sex interacts with PS symptoms
and abnormal neurodevelopment are needed. The purpose of the current study is to use advanced tools from
network neuroscience and machine learning coupled with multi-modal neuroimaging to uncover biomarkers that
can predict the emergence of PS symptoms throughout development. To achieve this goal, we will draw on
multiple largescale cross-sectional and longitudinal neurodevelopmental datasets, including the Philadelphia
Neurodevelopmental Cohort, the Healthy Brain Network, and the Adolescent Brain Cognitive Development
study, to study brain structure and connectivity. We use Network Control Theory (NCT) to study connectivity.
NCT treats the brain as a dynamical system allowing us to probe a region's capacity to control changes in brain
states via white matter pathways. Compared to graph theory, NCT is a contemporary approach that posits an
explicit model of how the brain's structure informs and constrains its function, enabling mechanistic insight into
the dysconnectivity associated with the PS. We will quantify developmental abnormalities in NCT metrics using
a nascent machine learning technique known as normative modeling. A normative model builds a growth chart
of brain development that incorporates the expected variation in the relationship between age and the brain into
its predictions. Then, deviations from these growth charts can be understood in terms of what is and what is not
expected in a normative population. Here, we will build cross-sectional (Aim 1) and longitudinal (Aim 2)
normative models of NCT metrics and use multivariate deviations to predict PS symptoms out-of-sample. Finally,
Aim 3 will investigate the extent to which deviations from normative neurodevelopment mediate the relationship
between sex and PS symptoms. The goal of this Pathway to Independence award is to build on my strong
background in psychiatry, multimodal neuroimaging, network neuroscience, and machine learning by expanding
my expertise to developmental psychopathology, NCT, and longitudinal neuroimaging data.