A computational framework for characterizing normative development of structural brain connectivity in the perinatal stage

• Published in: bioRxiv
• Main Authors: Wu, Yihan, Gholipour, Ali, Vasung, Lana, Karimi, Davood
• Format: Journal Article Paper
• Language: English
• Published: United States Cold Spring Harbor Laboratory Press 11.03.2023; Cold Spring Harbor Laboratory
• Edition: 1.1
• Subjects: Bioengineering; Computational neuroscience; Neural networks; Neuroimaging; Trends; diffusion MRI; neonatal brain; structural brain connectivity
• ISSN: 2692-8205; 2692-8205
• DOI: 10.1101/2023.03.10.532142

Quantitative assessment of the brain’s structural connectivity in the perinatal stage is useful for studying normal and abnormal neurodevelopment. However, estimation of the structural connectome from diffusion MRI data involves a series of complex and ill-posed computations. For the perinatal period, this analysis is further challenged by the rapid brain development and difficulties of imaging subjects at this stage. These factors, along with high inter-subject variability, have made it difficult to chart the normative development of the structural connectome. Hence, there is a lack of baseline trends in connectivity metrics that can be used as reliable references for assessing normal and abnormal brain development at this critical stage. In this paper we propose a computational framework, based on spatio-temporal atlases, for determining such baselines. We apply the framework on data from 169 subjects between 33 and 45 postmenstrual weeks. We show that this framework can unveil clear and strong trends in the development of structural connectivity in the perinatal stage. Some of our interesting findings include that connection weighting based on neurite density produces more consistent trends and that the trends in global efficiency, local efficiency, and characteristic path length are more consistent than in other metrics.