Biology subjects

Ram, K.

Publications and source records attributed to Ram, K..

5 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↗

bioRxiv2020

Detection and skeletonization of single neurons and tracer injections using topological methods

Neuroscientific data analysis has traditionally relied on linear algebra and stochastic process theory. However, the tree-like shapes of neurons cannot be described easily as points in a vector space (the subtraction of two neuronal shapes is not a meaningful operation), and methods from computational topology are better suited to their analysis. Here we introduce methods from Discrete Morse (DM) Theory to extract the tree-skeletons of individual neurons from volumetric brain image data, and to summarize collections of neurons labelled by tracer injections. Since individual neurons are topologically trees, it is sensible to summarize the collection of neurons using a consensus tree-shape that provides a richer information summary than the traditional regional connectivity matrix approach. The conceptually elegant DM approach lacks hand-tuned parameters and captures global properties of the data as opposed to previous approaches which are inherently local. For individual skeletonization of sparsely labelled neurons we obtain substantial performance gains over state-of-the-art non-topological methods (over 10% improvements in precision and faster proofreading). The consensus-tree summary of tracer injections incorporates the regional connectivity matrix information, but in addition captures the collective collateral branching patterns of the set of neurons connected to the injection site, and provides a bridge between single-neuron morphology and tracer-injection data.

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↗

bioRxiv2018

A High-throughput Neurohistological Pipeline for Brain-Wide Mesoscale Connectivity Mapping of the Common Marmoset

Understanding the connectivity architecture of entire vertebrate brains is a fundamental but difficult task. MRI based methods offer whole brain coverage, but remain indirect in the approach to connectivity mapping. Recent progress has been made in directly mapping whole-brain connectivity architecture in the mouse at the mesoscopic scale. The basic approach uses tracer injections systematically placed on a grid of locations spanning the brain and computational analysis of the resulting whole brain data sets. Scaling this approach to bigger primate brains poses nontrivial technical challenges. Here we present an integrated neurohistological pipeline as well as a grid-based tracer injection strategy for systematic mesoscale connectivity mapping in the common Marmoset (Callithrix jacchus). Individual brains are sectioned into [~]1700 20{micro}m sections using the tape transfer technique, permitting high quality 3D reconstruction of a series of histochemical stains (Nissl, myelin) interleaved with tracer labelled sections. Combining the resulting 3D volumes, containing informative cytoarchitectonic markers, with in-vivo and ex-vivo MRI, and using an integrated computational pipeline, we are able to overcome the significant individual variation exhibited by Marmosets to obtain routine and high quality maps to a common atlas framework. This will facilitate the systematic assembly of a mesoscale connectivity matrix together with unprecedented 3D reconstructions of brain-wide projection patterns in a primate brain. While component instruments or protocols may be available from previous work, we believe that this is the first detailed systems-level presentation of the methodology required for high-throughput neuroanatomy in a model primate.

neuroscience↗

bioRxiv2018

High precision automated detection of labelednuclei in terabyte-scale whole-brain volumetricimage data of mouse

There is a need in modern neuroscience for accurate and automated image processing techniques for analyzing the large volume of neuroanatomical data. For e.g., the use of light microscopy to image whole mouse brains in a mesoscopic scale produces individual neuroanatomical data volumes in the TerraByte range.. A fundamental task involves the detection and quantification of objects of a given type, e.g. neuronal nuclei or somata, in whole mouse brains. Traditionally this quantification is performed by human visual inspection with high accuracy, that is not scalable.. When state-of-the-art CNN and SVM-based methods are used to solve this classification problem, they achieve accuracy levels between 85 - 92%. However, higher rates of precision and recall, close to that of humans are necessary. In this paper, we describe an unsupervised, iterative algorithm, which provides a high close to human performance for a specific problem of broad interest, i.e. detection of Green Fluorescent Protein labeled nuclei in whole mouse brains. The algorithm judiciously combines classical computer vision (CV) techniques and is focused on the complex problem of decomposing strong overlapped objects (nuclei). Our proposed iterative method uses features detected on ridge lines over distance transformation and an arc based iterative spatial-filling method to solve the problem. We demonstrate our results on two whole mouse brain data sets of Gigabyte resolution and compare it with manual annotation of the brains. Our results show that an aptly designed CV algorithm with classical feature extractors when tailored to this problem of interest achieves near-ideal human-like performance. Quantitative analysis, when compared with the manually annotated ground truth, reveals that our approach performs better on whole mouse brain scans than general purpose machine learning (including deep CNN) methods.

neuroscience↗