Biology subjects

Sivaprakasam, M.

Publications and source records attributed to Sivaprakasam, M..

3 recordsLinked to original sources

bioRxiv2026

ANCHOR : Atlas of Neurochemical Characterization of the Human Brainstem with 3D Reconstruction

The human brainstem is a complex division of the brain comprised of more than 200 nuclei and fiber tracts. The brainstem is essential for the functioning of the entire body. We introduce here the most detailed human brainstem Atlas across the human lifespan: fetus, child, adult. ANCHOR, the Atlas of Neurochemical Characterization of the Human Brainstem, is an online platform that includes more than 800 serial histological sections, stained for Nissl and seven immunochemical (IHC) markers, from the human brainstem of three ages: 25 fetal gestational weeks (GW), 9 years old, and 54 years old. This makes ANCHOR the most comprehensive human brainstem Atlas to date. In these three brainstems, we identified and manually annotated over 200 structures. We further characterized these structures with the seven IHC markers. We specifically describe the catecholaminergic groups in the human brainstem across all three age groups. In addition, we identified the protoplasmic commissural dendrites of the hypoglossal nucleus and we describe the pretectal nuclei in the Nissl-stained fetal 25 GW brainstem. ANCHOR includes an online viewer that integrates multimodal data, from magnetic resonance imaging and block face imaging to Nissl- and IHC-stained serial sections and 3D reconstruction of the entire brainstem. For the 9-year-old specimen, the online viewer allows simultaneous navigation of annotated sections with corresponding IHC, for viewing the specific region-wise cellular features accessible at https://anchor.humanbrain.in/.

neuroscience↗

bioRxiv2024

A three-dimensional histological cell atlas of the developing human brain

The human brain is believed to contain a full complement of neurons by the time of birth together with a substantial amount of the connectivity architecture, even though a significant amount of growth occurs postnatally. The developmental process leading to this outcome is not well understood in humans in comparison with model organisms. Previous magnetic resonance imaging (MRI) studies give three-dimensional coverage but not cellular resolution. In contrast, sparsely sampled histological or spatial omics analyses have provided cellular resolution but not dense whole brain coverage. To address the unmet need to provide a quantitative spatiotemporal map of developing human brain at cellular resolution, we leveraged tape-transfer assisted serial section histology to obtain contiguous histological series and unbiased imaging with dense coverage. Interleaved 20 thick Nissl and H&E series and MRI volumes are co-registered into multimodal reference volumes with 60 isotropic resolution, together with atlas annotations and a stereotactic coordinate system based on skull landmarks. The histological atlas volumes have significantly more contrast and texture than the MRI volumes. We computationally detect cells brain-wide to obtain quantitative characterization of the cytoarchitecture of the developing brain at 13-14 and 20-21 gestational weeks, providing the first comprehensive regional cell counts and characterizing the differential growth of the different brain compartments. Morphological characteristics permit segmentation of cell types from histology. We detected and quantified brain-wide distribution of mitotic figures representing dividing cells, providing an unprecedented spatiotemporal atlas of proliferative dynamics in the developing human brain. Further, we characterized the abundance and distribution of Cajal-Retzius cells, a transient cell population that plays essential roles in organizing glutamatergic cortical neurons into layers. Together, our study provides an unprecedented quantitative window into the developing human brain and the reference volumes and coordinate space should be useful for integrating spatial omics data sets with dense histological context.

neuroscience↗

bioRxiv2020

Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder-decoder deep networks

Understanding of neuronal circuitry at cellular resolution within the brain has relied on tract tracing methods which involve careful observation and interpretation by experienced neuroscientists. With recent developments in imaging and digitization, this approach is no longer feasible with the large scale (terabyte to petabyte range) images. Machine learning based techniques, using deep networks, provide an efficient alternative to the problem. However, these methods rely on very large volumes of annotated images for training and have error rates that are too high for scientific data analysis, and thus requires a significant volume of human-in-the-loop proofreading. Here we introduce a hybrid architecture combining prior structure in the form of topological data analysis methods, based on discrete Morse theory, with the best-in-class deep-net architectures for the neuronal connectivity analysis. We show significant performance gains using our hybrid architecture on detection of topological structure (e.g. connectivity of neuronal processes and local intensity maxima on axons corresponding to synaptic swellings) with precision/recall close to 90% compared with human observers. We have adapted our architecture to a high performance pipeline capable of semantic segmentation of light microscopic whole-brain image data into a hierarchy of neuronal compartments. We expect that the hybrid architecture incorporating discrete Morse techniques into deep nets will generalize to other data domains.

neuroscience↗