Estimation of shape and growth brain network atlases for connectomic brain mapping in developing infants

Abstract

In vivo brain connectomics have heavily relied on using functional and diffusion Magnetic Resonance Imaging (MRI) modalities to examine functional and structural relationships between pairs of anatomical regions in the brain. However, research work on brain morphological (i.e., shape-to-shape) connections, which can be derived from T1-w and T2-w MR images, in both typical and atypical development or ageing is very scarce. Furthermore, the brain cannot be only regarded as a static shape, since it is a dynamic complex system that changes at functional, structural and morphological levels. Hence, examining the ‘connection’ between brain shape and its changes with time (e.g., growth) may help advance our understanding of connectomic brain dynamics as well as disorders that may affect it. To address these limitations, we unprecedentedly introduce two population-based shape and growth connectivity analysis tools that further extend the field of connectomics to brain morphology and dynamics: the morphome and the kinectome. Specifically, for a population of anatomically labelled shapes, the morphome identifies a network of anatomical shape regions that are connected when morphologically similar at a single timepoint, whereas the kinectome identifies anatomical shape regions that elicit similar evolution dynamics across successive timepoints. These proposed generic tools can be easily invested to examine how a baseline shape influences its deformation trajectory at later timepoints using any longitudinal shape data. We evaluated these tools on 23 infants, with right and left cortical surfaces reconstructed at birth, 3, 6, 9 and 12 months of age. Investigating the relationship between the neonatal morphome and the postnatal kinectome (from birth to 1 year of age) gave insights into brain connectivity at birth and how it develops over time.

Joint functional brain network atlas estimation and feature selection for neurological disorder diagnosis with application to autism

“…However, the connectional aspect of the brain, captured by the wiring of its functional and structural neural connections, was overlooked in the field of building population-based brain maps. A few recent landmark works (Rekik et al, 2017(Rekik et al, , 2018bDhifallah Figure 1: Conventional dimensional reduction methods for neurological biomarker discovery and proposed network atlas-guided feature selection method. While typical feature selection (FS) methods aim to identify the most discriminative features in the original feature space for the target classification task, feature extraction (FE) methods cannot track the original features as they extract new discriminative features via projection.…”

Do Baby Brain Cortices that Look Alike at Birth Grow Alike During the First Year of Postnatal Development?

“…At a local scale, we examine shape-velocity correlations in very distinctive cortical regions. Finally, at a connectional scale, inspired from [5], we further propose a novel integral brain 'shape-growth' graph representation to identify neonatal cortical regions that are similar in morphology, but also grow similarly in respectively the left and the right hemispheres.…”