Biology subjects

Knirsch, W.

Publications and source records attributed to Knirsch, W..

6 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↗

bioRxiv2023

Characterization of dynamic patterns of human fetal to neonatal brain asymmetry with deformation-based morphometry

Despite established knowledge on the morphological and functional asymmetries in the human brain, the understanding of how brain asymmetry patterns change during late fetal to neonatal life remains incomplete. The goal of this study was to characterize the dynamic patterns of inter-hemispheric brain asymmetry over this critically important developmental stage using longitudinally acquired MRI scans. Super-resolution reconstructed T2-weighted MRI of 20 neurotypically developing participants were used, and for each participant fetal and neonatal MRI was acquired. To quantify brain morphological changes, deformation-based morphometry (DBM) on the longitudinal MRI scans was utilized. Two registration frameworks were evaluated and used in our study: (A) fetal to neonatal image registration and (B) registration through a mid-time template. Developmental changes of cerebral asymmetry were characterized as (A) the inter-hemispheric differences of the Jacobian determinant (JD) of fetal to neonatal morphometry change and the (B) time-dependent change of the JD capturing left-right differences at fetal or neonatal time points. Left-right and fetal-neonatal differences were statistically tested using multivariate linear models, corrected for participants age and sex and using threshold-free cluster enhancement. Fetal to neonatal morphometry changes demonstrated asymmetry in the temporal pole and left-right asymmetry differences between fetal and neonatal timepoints revealed temporal changes in the temporal pole, likely to go from right dominant in fetal to a bilateral morphology in neonatal timepoint. Furthermore, the analysis revealed right-dominant subcortical grey matter in neonates and three clusters of increased JD values in the left hemisphere from fetal to neonatal timepoints. While these findings provide evidence that morphological asymmetry gradually emerges during development, discrepancies between registration frameworks require careful considerations when using DBM for longitudinal data of early brain development.

neuroscience↗

bioRxiv2020

Delayed maturation of the structural brain connectome in neonates with congenital heart disease

There is emerging evidence for delayed brain development in neonates with congenital heart disease (CHD). We hypothesize that the perioperative development of the structural brain connectome is a proxy to such delays. Therefore, we set out to quantify the alterations and longitudinal pre- to postoperative changes in the connectome in CHD neonates and assess risk factors for disturbed perioperative network development relative to healthy term newborns. In this prospective cohort study, 114 term neonates with CHD underwent cardiac surgery at the University Childrens Hospital Zurich. Forty-six healthy term newborns were included as controls. Pre- and postoperative structural connectomes were derived from mean fractional anisotropy values of fibre pathways traced using diffusion tractography. Graph theory parameters calculated across a range of proportional cost thresholds were compared between groups by multi-threshold permutation correction adjusting for con-founders. Network based statistic was calculated for edgewise network comparison. White matter injury (WMI) volume was quantified on 3D T1-weighted images. Random coefficient mixed models with interaction terms of (i) CHD subtype and (ii) WMI volume with postmenstrual age at MRI respectively were built to assess modifying effects on network development. Pre- and postoperatively, at the global level, efficiency, indicative of network integration, was higher in controls compared to CHD neonates. In contrast, local efficiency and transitivity, indicative of network segregation, were higher in CHD neonates compared to controls (all p<0.025 for one-sided t-tests). Preoperatively these group differences were also found across multiple widespread nodes (all p<0.025, accounting for multiple comparison), whereas postoperatively nodal differences were not evident. At the edge-level, the majority of weaker connections in CHD neonates compared to controls involved interhemispheric connections (66.7% preoperatively; 54.5% postoperatively). A trend showing a more rapid pre- to postoperative decrease in local efficiency was found in class I CHD neonates compared to controls. In CHD neonates, larger WMI volume was associated with lower strength (p=0.0026) and global efficiency (p=0.0097). The maturation of the structural connectome is delayed in neonates with CHD, with a pattern of lower structural integration and higher segregation compared to healthy controls. Trend-level evidence indicated that normalized postoperative cardiac physiology in class I CHD neonates might improve structural network topology. In contrast, the degree of WMI burden negatively impacts network strength and integration. Further research is needed to elucidate how aberrant structural network development in CHD represents neural correlates of later neurodevelopmental impairments.

neuroscience↗

bioRxiv2018

Left planum temporale growth predicts language development in newborns with congenital heart disease

Congenital heart diseases (CHD) are the most common congenital anomalies, accounting for a third of all congenital anomaly cases. While surgical correction dramatically improved survival rates, the lag behind normal neurodevelopment appears to persist. Deficits of higher cognitive functions are particularly common, including developmental delay in communication and oral-motor apraxia. It remains unclear whether the varying degree of cognitive developmental delay is reflected in variability in brain growth patterns. To answer this question, we aimed to investigate whether the rate of regional brain growth is correlated with later life neurodevelopment. 44 newborns were included in our study, out of whom 33 were diagnosed with dextro-transposition of the great arteries (d-TGA) and 11 with other forms of severe CHD. During the first month of life, neonates underwent corrective or palliative cardiovascular bypass surgery, pre- and postoperative cerebral MRI were performed 18.7 {+/-} 7.03 days apart. MRI was performed in natural sleep on a 3.0T scanner using an 8-channel head coil, fast spin-echo T2-weighted anatomical sequences were acquired in three planes. Based on the principles of deformation based morphometry, we calculated brain growth rate maps that reflected the rate of non-linear deformation that occurs between pre- and post-operative brain images. An explorative, whole-brain, threshold-free cluster enhancement analysis revealed strong correlation between the growth rate of the left planum temporale and the posterior operculum of the left frontal lobe and language score at 12 months of age, corrected for demographic variables (p=0.018, t=5.656). No significant correlation was found between brain growth rates and motor or cognitive scores. Post hoc analysis showed that the length of hospitalization interacts with this correlation with longer hospitalization stay results in faster enlargement of the internal cerebro-spinal fluid spaces. Our study provides evidence to the early importance of left-dominant perisylvian regions in language development even before the direct postnatatal exposure to native language. In CHD patients, the perioperative period results in a critical variability of brain growth rate in this region, which is a reliable neural correlate of language development at one year of age.

neuroscience↗