Biology subjects

Bergmans, T.

Publications and source records attributed to Bergmans, T..

3 recordsLinked to original sources

bioRxiv2025

NeuroCarta: An Automated and Quantitative Approach to Mapping Cellular Networks in the Mouse Brain

Understanding the structural organization of the brain is essential for deciphering how complex functions emerge from neural circuits. The Allen Mouse Brain Connectivity Atlas (AMBCA) has revolutionized our ability to quantify anatomical connectivity at a mesoscale resolution, bridging the gap between microscopic cellular interactions and macroscopic network organization. To leverage AMBCA for automated network construction and analysis, here we introduce NeuroCarta, an open-source MATLAB toolbox designed to extract, process, and analyze brain-wide connectivity networks. NeuroCarta generates directed and weighted connectivity graphs, computes key network metrics, and visualizes topological features of brain circuits. As an application example, using NeuroCarta on viral tracer data from the AMBCA, we demonstrate that the mouse brain exhibits a densely connected architecture, with a degree of separation of approximately four synapses, suggesting an optimized balance between local specialization and global integration. We identify attractor nodes that may serve as key convergence points in brain-wide neural computations and show that NeuroCarta facilitates comparative network analyses, revealing regional variations in projection patterns. While the toolbox is currently constrained by the resolution and coverage of the AMBCA dataset, it provides a scalable and customizable framework for investigating brain network topology, interregional communication, and anatomical constraints on mesoscale circuit organization.

neuroscience↗

bioRxiv2023

Where top-down meets bottom-up: Cell-type specific connectivity map of the whisker system

Sensorimotor computation integrates bottom-up world state information with top-down knowledge and task goals to form action plans. In the rodent whisker system, a prime model of active sensing, evidence shows neuromodulatory neurotransmitters shape whisker control, affecting whisking frequency and amplitude. Since neuromodulatory neurotransmitters are mostly released from subcortical nuclei and have long-range projections that reach the rest of the central nervous system, mapping the circuits of top-down neuromodulatory control of sensorimotor nuclei will help to systematically address the mechanisms of active sensing. Therefore, we developed a neuroinformatic target discovery pipeline to mine the Allen Institutes Mouse Brain Connectivity Atlas. Using network connectivity analysis, we identified new putative connections along the whisker system and anatomically confirmed the existence of 42 previously unknown monosynaptic connections. Using this data, we updated the sensorimotor connectivity map of the mouse whisker system and developed the first cell-type-specific map of the network. The map includes 157 projections across 18 principal nuclei of the whisker system and neuro-modulatory neurotransmitter-releasing. Performing a graph network analysis of this connectome, we identified cell-type specific hubs, sources, and sinks, provided anatomical evidence for monosynaptic inhibitory projections into all stages of the ascending pathway, and showed that neuromodulatory projections improve network-wide connectivity. These results argue that beyond the modulatory chemical contributions to information processing and transfer in the whisker system, the circuit connectivity features of the neuromodulatory networks position them as nodes of sensory and motor integration.

neuroscience↗

bioRxiv2022

TITAN: A Toolbox for Information-Theoretic Analysis of Molecular Networks

Biological systems are naturally described as networks, spanning molecular interactions, cellular circuits, and brain-wide functional connectivity. Despite the ubiquity of network data, workflows for inferring network structure and then applying comparable graph analyses across modalities remain fragmented. We present NETSCOPE, an open-source, multi-platform toolbox for information-theoretic network inference and analysis. NETSCOPE estimates pairwise statistical dependence with mutual information (MI), derives weighted adjacency matrices, removes likely spurious edges using shuffle-based thresholds, and prunes indirect connections using the data processing inequality (DPI). A key feature is the conversion of MI-based similarity into a metric space via (normalized) variation of information (VI), enabling weighted shortest-path and centrality analyses that require distance-like edge weights. We validate the toolbox on synthetic data with known ground-truth topology and by reconstructing published molecular networks in Saccharomyces cerevisiae. We further demonstrate cross-domain use cases in single-cell transcriptomic networks, cell-level anatomical maps, EEG connectivity, and resting-state fMRI. NETSCOPE runs in Python and MATLAB/Octave, and is compatible with Jupyter/Colab workflows.

bioinformatics↗