Non-negative data-driven mapping of structural connections with application to the neonatal brain

Abstract

Mapping connections in the neonatal brain can provide insight into the crucial early stages of neurodevelopment that shape brain organisation and lay the foundations for cognition and behaviour. Diffusion MRI and tractography provide unique opportunities for such explorations, through estimation of white matter bundles and brain connectivity. Atlas-based tractography protocols, i.e. apriori defined sets of masks and logical operations in a template space, have been commonly used in the adult brain to drive such explorations. However, rapid growth and maturation of the brain during early development make it challenging to ensure correspondence and validity of such atlas-based tractography approaches in the developing brain. An alternative can be provided by data-driven methods, which do not depend on predefined regions of interest. Here, we develop a novel data-driven framework to extract white matter bundles and their associated grey matter networks from neonatal tractography data, based on non-negative matrix factorisation that is inherently suited to the non-negative nature of structural connectivity data. We also develop a non-negative dual regression framework to map group-level components to individual subjects. Using in-silico simulations, we evaluate the accuracy of our approach in extracting connectivity components and compare with an alternative data-driven method, independent component analysis. We apply non-negative matrix factorisation to whole-brain connectivity obtained from publicly available datasets from the Developing Human Connectome Project, yielding grey matter components and their corresponding white matter bundles. We assess the validity and interpretability of these components against traditional tractography results and greymatter networks obtained from resting-state fMRI in the same subjects. We subsequently use them to generate a parcellation of the neonatal cortex using data from 323 new-born babies and we assess the robustness and reproducibility of this connectivity-driven parcellation.

Structural and Functional Connectivity Substrates of Cognitive Impairment in Multiple Sclerosis

“…These measures help unveil the topological features of brain structural networks and can be used to study the relationship with cognitive functions ( 30 ). In contrast to graph analysis, data-driven mapping approaches such as independent component analysis (ICA), a multivariate method identifying single brain structural networks ( 31 , 32 ), and nonnegative matrix factorization (NMF), an unsupervised technique based on structural network parcellations from DTI data ( 33 ), may be used, by providing a different way of assessing disrupted SC in pathological conditions ( 31 , 32 ) (see Box 2 for strengths and limitations of SC assessment).…”

“…Nonnegative matrix factorization: An unsupervised technique for extracting connectivity components from diffusion MRI data, both at the group and individual level ( 33 )…”

Concurrent brain parcellation and connectivity estimation via co‐clustering of resting state fMRI data: A novel approach

“…Connectional topography plays important role in brain functions and behaviors, and can provide insight into neurodevelopment, too (Phillips, Fish, Kambi, Redinbaugh, & Saalmann, 2019;Thompson, Mohammadi-Nejad, Robinson, Andersson, & Sotiropoulos, 2020;Wu, Calhoun, Jung, & Caprihan, 2015). Many neuropsychiatric disorders, such as schizophrenia, have been manifested by disrupted communications for connections between distributed brain networks (Gong et al, 2019;Jiang et al, 2019;Kim et al, 2020;Rolls et al, 2020;Sheffield, Rogers, Blackford, Heckers, & Woodward, 2020).…”

Concurrent mapping of brain ontogeny and phylogeny within a common connectivity space

“…To achieve this, we first developed and validated a novel library of tractography protocols for reconstructing white matter bundles in the neonate brain. Previous studies have developed such neonatal protocols 23,27,59–64 , however, none have been developed where correspondence across diverse brains is explicit. The protocols developed here are defined analogously with protocols for the adult human and macaque brain 20 .…”