PROJECT SUMMARY
The choroid plexus (ChP) protrudes into the lumen of the four cerebral ventricles and is the principal source of
cerebrospinal fluid (CSF), which together play an important role in neuronal patterning, brain physiology,
buoyancy, and maintaining homeostasis by providing physical, enzymatic, and immunological barriers to the
brain. Neuroimaging studies have observed ChP morphological changes with aging, neurodevelopmental and
neuropsychiatric disorders, which suggests that the ChP may play a role in development and brain disorders.
Despite this growing evidence, the ChP has not been the focus of commonly used neuroimaging tools, which
causes it to be poorly segmented, mislabeled, and incorrectly quantified. Therefore, there is a critical need to
more accurately segment the ChP. The overall objectives for this project are to develop a novel, fast, reliable,
generalizable, and dedicated open-source deep learning method for accurate ChP segmentation to understand
how the ChP changes across the lifespan and differs among brain disorders. Samples for this study will come
from high resolution [Human Connectome Project (HCP) and Connectome Related Human Disease (CRHD)]
and conventional (inclusive of high risk for psychosis, first episode and chronic psychosis, bipolar disorder, and
Alzheimer’s disease) neuroimaging datasets totaling over 22,000 brains. The rationale for the proposed research
is to provide an open-source segmentation tool that will allow for more robust analyses into the ChP’s role in
various brain disorders and a better foundational understanding of the how the ChP changes over time with age.
To attain the overall objectives, the following three specific aims are proposed: (1) develop and validate a deep-
learning method for the accurate segmentation of the ChP; (2) generate ChP volume data across the lifespan
that can be used to characterize longitudinal changes and morphological differences across a variety of
neuropsychiatric disorders; (3) establish reliability, generalizability, and fairness for broad distribution of
FastPlex. To accomplish these aims, a total of 700 brains will be manually segmented – accounting for scanner
type and image resolution that is balanced for age, sex, ethnicity/race, socioeconomic status, and brain disorder
– to serve as training, validation, and testing labels for the deep-learning tool. Lasty, reliability and generalizability
will be established to produce a tool that will be broadly distributed with the research community. The proposed
research is innovative and significant because it will focus on an innovative comprehensive ChP segmentation
tool (lateral, temporal horn, 3rd, and 4th ventricles) that also estimates partial volume effects and provides super
resolution ChP labels, which together will enhance foundational knowledge on ChP neurodevelopmental and
neuropsychiatric changes. The results of this research are expected to contribute meaningfully to the
understanding of pathologic mechanisms underlying these disorders and to the development of novel strategies
targeting specific disease processes.