This application aims to develop a novel representation and computational tools that enable a better understanding of human brain development. Such methods are in high demand as previously introduced techniques for adult brain analysis are either incomplete for such purposes or are not directly transferable to infants. Given the dramatic changes in neuroanatomy during the first two years of life, the candidate proposes to explicitly incorporate age into her quantitative image analysis tools. She will define and construct a four dimensional brain atlas that will summarize central tendencies and variations over time in normal infants. This atlas will then be used to compare groups of control and prematurely born subjects and describe pathological development processes. The candidate will also introduce computational tools that may incorporate information from such an atlas into a template-based segmentation and registration algorithm. The former assists in assigning anatomical labels in the images, and the latter relies on previously accumulated image statistics when establishing spatial correspondences among newly observed data. As the most rapid period of myelination occurs in the first two years of life, information about the white matter will be invaluable for this technique. The candidate will therefore rely heavily on diffusion weighted MR images to compliment structural image information. During the Mentored Phase of the award, an age-dependent representation of the developing brain will be constructed relying on neurodevelopmental hypothesis and multi-modal image acquisitions from infant data sets. In the Independent Phase, image processing tools will be introduced that are specifically designed to work with infant data, and the new model will be used to describe and compare normal and disrupted brain development. This project is consistent with the long-term career goal of the candidate, which is to establish a competitive and independent research program in quantitatively modeling human brain development by the analysis of multi-modal medical acquisitions. The project will also facilitate the candidate's short-term goal of becoming knowledgeable in pediatric neuroscience and pediatric MR imaging. The Mentored Phase of this work is to be performed at the MGH/Harvard/MIT Martinos Center for Biomedical Imaging, where the candidate will take advantage of the cutting-edge imaging facilities, imaging expertise, as well as the world-class educational opportunities at its collaborating institutions. Her career development plan includes training in pediatric neuroanatomy, the physics of MR;coursework in neuroscience, and participation in seminars and scientific conferences.

Public Health Relevance

Relevance: The investigation of brain development in infants has been greatly hindered by the lack of robust automated computational tools infant neuroimaging data. The goal of the proposed project is to establish a better understanding of normal brain development and to examine the effects of premature birth by establishing an age- dependent 4D atlas and analytic tools specialized for this underserved population. As such, it has the potential to have a large impact on understanding the development of human brain in both health and disease.

National Institute of Health (NIH)
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Career Transition Award (K99)
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Special Emphasis Panel (ZHD1-MRG-C (ZL))
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Freund, Lisa S
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Massachusetts General Hospital
United States
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Narayanan, Priya Lakshmi; Warton, Christopher; Rosella Boonzaier, Natalie et al. (2016) Improved segmentation of cerebellar structures in children. J Neurosci Methods 262:1-13
de Macedo Rodrigues, Katyucia; Ben-Avi, Emma; Sliva, Danielle D et al. (2015) A FreeSurfer-compliant consistent manual segmentation of infant brains spanning the 0-2 year age range. Front Hum Neurosci 9:21
Kolasinski, James; Takahashi, Emi; Stevens, Allison A et al. (2013) Radial and tangential neuronal migration pathways in the human fetal brain: anatomically distinct patterns of diffusion MRI coherence. Neuroimage 79:412-22
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