My objective for the K25 award is to establish myself as an independent neuroimaging statistician, with
expertise in whole-brain network analyses and an integral member of multidisciplinary research teams devoted
to addressing diseases of the brain. Attaining these goals will require didactic training and research guidance.
Research
We will develop new methodology to improve whole-brain dynamic connectivity analyses of normal and
abnormal brain function, which is vital for understanding various brain disorders, such as Alzheimer’s Disease,
and may help identify biomarkers and inform early prevention and treatment. Previous studies are largely
based on one average network constructed using data from an entire brain scan (i.e., static connectivity), but
emerging evidence suggests network topology exhibits meaningful variations on the second to minute scale,
creating a gap in understanding unless these variations are quantified. While several methods have been
proposed to address this new direction in the field, there does not yet exist a unifying framework that
accurately estimates whole-brain networks, as well as the dynamics of network change across a functional
magnetic resonance imaging (fMRI) experiment, while a) accounting for variables of interest and motion-
induced artifacts and b) allowing for individual estimates of dynamics. The novel methods proposed here will
address these needs and provide a set of tools for future dynamic brain network analysis research. This
research, along with my proposed training plan, will facilitate my progression toward becoming an independent
neuroimaging statistician with expertise in brain network analysis.
Training
The proposed training program involves four components: 1) career guidance and neuroscience and network
analysis training from a mentoring committee; 2) an educational component to establish fundamental
knowledge in computational neuroscience and image analysis; 3) performing innovative research using the
skills gained from the proposed training plan and; 4) participating in the exchange of knowledge and ideas with
other statisticians and neuroscientists through workshops, conferences, seminar series, and journal clubs. The
training will enable me to shift from an early career statistician to an established, independent, neuroimaging
statistician with expertise in whole-brain network analyses. The training in computational neuroscience and
image analysis will allow me to become a multidisciplinary research team scientist dedicated to studying the
human brain. The growth gained through this 5-year period will lead to a skill set, and a confidence, that allows
me to be more well-versed in the neuroscience and biology behind the data I am analyzing. This will ultimately
lead to more effective communication with neuroscientists and clinicians, improved study design, more
informed statistical analyses, and a more comprehensive interpretation of the results in my future work.