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Principal Investigator: Pew-Thian Yap
Organization: UNIV OF NORTH CAROLINA CHAPEL HILL
Fiscal Year: 2024
Award: $428,568
Funding agency: National Institute of Mental Health
Computational Diffusion MRI for Studying Early Human Brain Development
Abstract
In the first years of life, the human brain develops dynamically in both structure and function. Many neurodevel-
opmental disorders are associated with aberrations from normative growth during this critical period of early brain
development. The increasing availability of longitudinal baby MRI data, such as those acquired through the Baby
Connectome Project (BCP), affords unprecedented opportunity for precise charting of early brain developmental
trajectories in order to understand normative and aberrant growth. Dedicated computational tools are needed for
accurate processing and analysis of baby MR images, which typically exhibit dynamic heterogeneous changes
across time. The goal of this project is to equip brain researchers with computational tools effective for studying
the early developing human brain in terms of tissue microstructure and white matter pathways using diffusion
MRI.
We propose three aims. In Aim 1, we will develop computational tools for effective estimation of white matter
pathways in the baby brain via diffusion tractography. We will tackle the challenge of tracking through regions
with low diffusion anisotropy owing to ongoing myelination in the developing brain. Our tools will allow proper
characterization of complex white matter pathway patterns such as fanning and bending. This will allow solving
the gyral bias problem ubiquitous in existing tractography algorithms with fiber streamlines terminating predomi-
nantly at gyral crowns but not sulcal banks. Our tools will allow tracing of cortico-cortical and cortico-subcortical
pathways with more uniform coverage of the cortex. In Aim 2, we will develop microstructural analysis meth-
ods that are unconfounded by complex fiber configurations, such as crossing, bending, branching, kissing, and
fanning, allowing more accurate and specific characterization of changes in tissue microarchitecture during early
brain development. In Aim 3, we will develop techniques that will allow diffusion MRI data collected at multiple
sites, which are very common in the era of big data, to be harmonized to mitigate the negative effects of inter-site
variability. Unlike existing methods that harmonize derived quantities such as fractional anisotropy, our method
can be applied directly to the diffusion-weighted images, allowing measurements based on microstructure and
connectivity to be subsequently computed for consistent analysis. We will also develop deep learning tools for
multi-shell data prediction so that diffusion MRI data collected with different numbers of shells can be harmonized.
Successful completion of this project will empower the neuroscience community with computational tools to better
chart the normative early development of the human brain using diffusion MRI. The developed tools will also
enable quantitative brain examinations of children who are affected by neurological developmental disorders.
Terms: <0-11 years old><21+ years old><Address><Adult><Adult Human><Affect><Algorithms><Anatomic Sites><Anatomic structures><Anatomy><Anisotropy><Award><Axon><Behavior><Big Data><BigData><Body Tissues><Brain><Brain Nervous System><Cell Communication and Signaling><Cell Signaling><Cell-Extracellular Matrix><Child><Child Youth><Children (0-21)><Communities><Complex><Computational toolkit><Crowns><DWI (diffusion weighted imaging)><DWI-MRI><Data><Dedications><Dendrites><Development><Diffusion><Diffusion MRI><Diffusion Magnetic Resonance Imaging><Diffusion Weighted MRI><Diffusion weighted imaging><Diffusion-weighted Magnetic Resonance Imaging><ECM><Encephalon><Environment><Exhibits><Extracellular Matrix><Fiber><Generalized Growth><Geometry><Goals><Growth><Human><Image><Infant><Intracellular Communication and Signaling><Investigators><Life><MR Imaging><MR Tomography><MRI><MRIs><Magnetic Resonance Imaging><Measurement><Medical Imaging, Magnetic Resonance / Nuclear Magnetic Resonance><Methods><Minnesota><Modern Man><Myelin><NMR Imaging><NMR Tomography><Nature><Neurites><Neurodevelopmental Disorder><Neurologic><Neurological><Neurological Development Disorder><Neurosciences><North Carolina><Nuclear Magnetic Resonance Imaging><Pathway interactions><Pattern><Process><Property><Protocol><Protocols documentation><Research><Research Personnel><Researchers><Signal Transduction><Signal Transduction Systems><Signaling><Site><Structure><Techniques><Time><Tissue Growth><Tissues><Universities><Zeugmatography><adulthood><biological signal transduction><brain tissue><computational toolbox><computational tools><computational toolset><computerized tools><connectome><critical period><dMRI><data harmonization><deep learning><deep learning method><deep learning strategy><design><designing><developmental><developmental disease><developmental disorder><diffused><diffuses><diffusing><diffusion anisotropy><diffusion tensor imaging><diffusions><empowerment><harmonized data><image-based method><imaging><imaging method><imaging modality><improved><kids><myelination><neurodevelopmental disease><ontogeny><pathway><substantia alba><tool><tractography><white matter><youngster>