Abstract #3550

Personalized functional network topographic mapping in human neonates

Jianlong Zhao^1,2,3, Yuehua Xu^1,2,3, Zaixu Cui⁴, Hongming Li^5,6, Lianglong Sun^1,2,3, Xinyuan Liang^1,2,3, Meizhen Han^1,2,3, Zilong Zeng^1,2,3, Qiongling Li^1,2,3, Tengda Zhao^1,2,3, and Yong He^1,2,3,4

¹State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, ²Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China, ³IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China, ⁴Chinese Institute for Brain Research, Beijing, China, ⁵Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, United States, ⁶Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States

Synopsis

Keywords: Functional Connectivity, fMRI (resting state), Personalized functional network parcellation

Motivation: The neonatal brain already possesses rich functional layouts that are crucial for future behavior development. However, how the individualized functional topography refines at birth, supports neurodevelopmental outcomes, and is impacted by prematurity remain largely unclear.

Goal(s): We aim to delineate the typical and atypical refining pattern of functional topography in the neonatal brain.

Approach: We leveraged advances in machine learning and large fMRI datasets covering term neonatal, preterm and adult population.

Results: Boundaries of primary networks could significantly predict neonatal brain maturity and neurodevelopmental outcomes at 18 months. Association networks could significantly identified preterm brain and reflect accelerated maturation in preterm.

Impact: Our results highlight how neonatal brain function architecture organizes, develops and supports the emerge of diverse behaviors.

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