Biology subjects

Speckert, A.

Publications and source records attributed to Speckert, A..

4 recordsLinked to original sources

bioRxiv2025

Atlas-independent brain connectome analysis at voxel-level granularity: graph convolutional networks for etiology classification in newborns

Early identification of altered brain networks in neonates at risk for neurodevelopmental impairments is critical for timely intervention and improving outcomes. This study explores the potential of graph convolutional networks (GCNs) applied to structural brain connectomes at the voxel level granularity to classify neonatal connectomes by their underlying etiology: 51 children with congenital heart disease (CHD), 100 children born very preterm (PB), and 43 children with spina bifida aperta (SBA). Leveraging the flexibility of voxel-level parcellation, we captured fine-grained connectomic differences that improved classification performance (F1 = 0.78) compared to both atlas-based methods (F1 = 0.62) and a multilayer perceptron baseline model (F1 = 0.69). This approach enables subject-specific parcellation without the need for predefined anatomical templates, facilitating the analysis of diverse brain morphologies and age ranges. Attribution analysis using integrated gradients provided interpretable insights into etiology-specific connectomic patterns, highlighting regions of potential neurodevelopmental importance, such as the Rolandic operculum, inferior parietal lobule, and inferior frontal gyrus. Lateralized attribution patterns in PB reflected known neurodevelopmental alterations, underscoring the value of interpretable graph learning for understanding etiology-specific connectomic features. This work represents an important step toward atlas-independent connectome analysis, offering a novel framework for studying diverse neonatal populations and advancing our understanding of early brain development.

neuroscience↗

bioRxiv2024

Developmental trajectory of thalamus topography during the late preterm and perinatal period in normal development, after premature birth and in congenital heart defects

The thalamus plays a critical role in neural circuit maturation and brain connectivity. It is well known to show a characteristic topographical organization of local and long-range connectivity, yet developmental changes in this topography remains incompletely understood. Our study aims to first validate the use of diffusion MRI for the topographic delineation of thalamic subdivisions (nuclei), to characterize thalamic topography during development and assess the impact of congenital heart defects (CHD) on thalamus development. We used local fiber orientation distribution functions, derived from diffusion MRI (dMRI) data, to subdivide the thalamus into seven clusters. We first evaluated the within-subject reproducibility of a dMRI based clustering method across 30 adults scan-rescan datasets (n = 30) using K-means clustering, achieving reproducible segmentation of the thalamus into seven parts. This was applied to a large developmental cohort comprising normally developing infants from the term-born dHCP cohort (n = 68, gestational age [GW] at MRI scan ranging from 37.4 to 44.8 weeks, mean {+/-} SD: 41.4 {+/-} 2.1 weeks) and the Zurich cohort (n = 42, GW at MRI ranging from 39.0 to 48.7 weeks, mean {+/-} SD: 42.55 {+/-} 1.96 weeks). The cohort also included preterm-born dHCP cohort (n = 48, GW at MRI scan ranging from 29.3 to 37.4 corrected gestational weeks, mean {+/-} SD: 34.4 {+/-} 2.02 weeks) and one case of a postmortem fetal specimen (n = 1, GW at MRI = 21 weeks). Cluster volumes were analyzed as a function of age using multivariate linear regression. Differences in cluster volumes between control subjects (n = 42, GW at MRI ranging from 39.0 to 48.7 weeks, mean {+/-} SD: 42.55 {+/-} 1.96 weeks) and newborns with congenital heart disease (CHD) (n = 49, GW at MRI ranging from 37.6 to 42.9 weeks, mean {+/-} SD: 40.23 {+/-} 1.30 weeks) were analyzed using variance analysis, controlling for sex and age at MRI scan. The clustering on average had a within-subject overlap of 0.811 (Dice overlap metric). While absolute thalamus volumes increased with age, their relative volumes remained stable in the studied developmental period. CHD infants shown reduced absolute volumes in six of the seven thalamic nuclei, with significant increasing in the relative volume of the cluster overlapping with the mediodorsal nucleus. While the perinatal period comprises rapid developmental events including the maturation of white matter, the topography of thalamic circuitry remains stable. Our study highlights alterations in thalamic topology and potential impacts on brain connectivity in CHD infants. By leveraging local diffusion properties from diffusion MRI, our validated segmentation technique offers a robust, data-centric approach to thalamic delineation.

neuroscience↗

bioRxiv2024

Altered connectome topology in newborns at risk for cognitive developmental delay: a cross-etiologic study

The human brain connectome is characterized by the duality of highly modular structure and efficient integration, supporting information processing. Newborns with congenital heart disease (CHD), prematurity, or spina bifida aperta (SBA) constitute a population at risk for altered brain development and developmental delay (DD). We hypothesize that, independent of etiology, alterations of connectomic organization reflect neural circuitry impairments in cognitive DD. Our study aim is to address this knowledge gap by using a multi-etiologic neonatal dataset to reveal potential commonalities and distinctions in the structural brain connectome and their associations with DD. We used diffusion tensor imaging (DTI) of 187 newborns (42 controls, 51 with CHD, 51 with prematurity, and 43 with SBA). Structural weighted connectomes were constructed using constrained spherical deconvolution based probabilistic tractography and the Edinburgh Neonatal Atlas. Assessment of brain network topology encompassed the analysis of global graph features, network-based statistics, and low-dimensional representation of global and local graph features. The Cognitive Composite Score of the Bayley Scales of Infant and Toddler Development 3rd edition was used as outcome measure at corrected 2 years for the preterm born individuals and SBA patients, and at 1 year for the healthy controls and CHD. We revealed differences in the connectomic structure of newborns across the four groups after visualizing the connectomes in a two-dimensional space defined by network integration and segregation. Further, ANCOVA analyses revealed differences in global efficiency (p < 0.0001), modularity (p < 0.0001), mean rich club coefficient (p = 0.017) and small-worldness (p = 0.016) between groups after adjustment for postmenstrual age at scan and gestational age at birth. Moreover, small-worldness was significantly associated with poorer cognitive outcome, specifically in the CHD cohort (r = -0.41, p = 0.005). Our cross-etiologic study identified divergent structural brain connectome profiles linked to deviations from optimal network integration and segregation in newborns at risk for DD. Small-worldness emerges as a key feature, associating with early cognitive outcomes, especially within the CHD cohort, emphasizing small-worldness crucial role in shaping neurodevelopmental trajectories. Neonatal connectomic alterations associated with DD may serve as a marker identifying newborns at-risk for DD and provide early therapeutic interventions.

neuroscience↗

bioRxiv2024

OSBA: An open neonatal neuroimaging atlas and template for spina bifida aperta

We present the Open Spina Bifida Aperta (OSBA) atlas, an open atlas and set of neuroimaging templates for spina bifida aperta (SBA). Traditional brain atlases may not adequately capture anatomical variations present in pediatric or disease-specific cohorts. The OSBA atlas fills in this gap by representing the computationally averaged anatomy of the neonatal brain with SBA after fetal surgical repair. The OSBA atlas was constructed using structural T2-weighted and diffusion tensor MRI of 28 newborns with SBA who underwent prenatal surgical correction. The corrected gestational age at MRI was 38.1 {+/-} 1.1 weeks (mean {+/-} SD). The OSBA atlas consists of T2-weighted and fractional anisotropy templates, along with 9 tissue prior maps and region of interest (ROI) delineations. The OSBA atlas offers a standardized reference space for spatial normalization and anatomical ROI definition. Our image segmentation and cortical ribbon definition is based on a human-in-the-loop approach including manual segmentation. The precise alignment of the ROIs was achieved by a combination of manual image alignment and automated, non-linear image registration. By providing a dedicated neonatal atlas for SBA, we enable more accurate spatial standardization and support advanced analyses such as diffusion tractography and connectomic studies in newborns affected by this condition.

neuroscience↗