Biology subjects

Kottke, R.

Publications and source records attributed to Kottke, R..

8 recordsLinked to original sources

bioRxiv2026

Integrin-identity shapes and mechanotemporally encodes the fibroblast transcriptome

Fibroblast transcriptomic states reflect physiological and pathological tissue contexts, yet the upstream determinants that stabilize these states remain poorly defined. Integrins mediate extracellular matrix (ECM) adhesion and biochemical signaling, but whether they encode mechanical constraints into stable transcriptomic programs is unclear. Using engineered mouse fibroblasts, bulk and single-cell transcriptomics, and controlled micromechanical confinement, we show that integrins can shape the transcriptomic landscape. The bulk transcriptome of fibroblasts expressing V- and {beta}1-class indicates a shared mechanosensitive baseline, except when these integrin classes are expressed individually. We also found integrin-specific gene clusters, including {beta}1-class integrin-dependent enrichment of Areg, Epha7, Lhpp and Igf2r, which regulate development, regeneration and disease, and altered YAP1 targeted gene expression. At single-cell resolution under confinement, {beta}1-class integrins sustain a progenitor-associated program, whereas their loss or V-class enrichment promotes a constitutively activated state linked to injury repair and wound healing. Mechanical confinement and confinement duration further reshapes these states in an integrin-identity-dependent manner. Our findings establish integrin-identity as a determinant of how fibroblasts transduce mechanotemporal inputs from the cell surface to the nucleus.

Cell Biology↗

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

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↗

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↗

bioRxiv2017

Intra-voxel incoherent motion magnetic resonance imaging of the living human fetus: the technique and within-subject reproducibility

Our purpose was to test the within-subject repeatability of the perfusion fraction, diffusion coefficient and pseudo diffusion coefficient measurements in various fetal organs and in the placenta based on the intra-voxel incoherent motion imaging principle. In utero diffusion-weighted magnetic resonance imaging was performed on 1.5T and 3.0T clinical scanners with b-factors ranging from 0 to 900 s/mm2 in 16 steps and a tetrahedral diffusion-weighting encoding scheme. Data from 16 pregnant women (maternal age: 34 {+/-} 4.9 years, range: 24.6 -40.8) were included in this pilot study. For 15 cases, IVIM was repeated (maternal age: 33.7 {+/-} 5.2 years, range: 24.6 - 40.8). A bi-exponential model was fitted on the volume-averaged diffusion values and the perfusion fraction (f), diffusion coefficient (d) and pseudo diffusion coefficient (D*) were calculated. Within-subject repeatability was given as the test-retest variability of the IVIM parameters in the fetal frontal cortex, frontal white matter, cerebellum, lungs, kidneys, liver and in the placenta. An in-house developed image processing script was utilized to remove the image frames with excessive motion, and to perform motion correction by using non-linear freeform deformations. For the fetal lungs, liver and the placenta, within-subject variability ranged from 14.4% to 20.4% for f, 12.2% to 14.1% for d and 16.8% to 25.3% for D*. The diffusion coefficients of the investigated brain regions were moderately to highly reproducible (4.8% to 15.2%), however, f and D* showed inferior reproducibility compared to corresponding measures derived for the lungs, liver and placenta. The IVIM-based parameters of the fetal kidney were revealed to be highly variable across scans. Our results indicate that in utero intra-voxel incoherent motion magnetic resonance imaging potentially provides a novel method for examining microvascular perfusion and diffusion in the developing human fetus. The reproducible quantification of the perfusion and diffusion parameters depend greatly upon data quality, fetal and maternal movements, and image post processing to detect and remove corrupted data before calculating the IVIM model.

bioinformatics↗