Partha P. Mitra

This paper introduces scDMGC, a method to analyze high-dimensional transcriptomic data using topological data analysis, revealing cell-type organization and transcriptional gradients in brain datasets.

The paper introduces a method combining deep learning and Discrete Morse techniques to skeletonize neuronal processes from tracer injection data, improving the accuracy of axon length density estimation.

The paper presents a multimodal, multiscale atlas of the mouse brain and head with high-resolution histology and co-registered MRI data, enabling better integration of molecular and connectivity data.

This study creates a 3D histological cell atlas of the developing human brain, providing detailed cell counts, proliferative dynamics, and cell type distribution at 13-14 and 20-21 gestational weeks.

The paper establishes neuroanatomical correspondences between mouse and marmoset brains by analyzing atlas structures and creating a computational tool for comparative analysis.

This paper introduces a network-free AI framework based on data interpolation, demonstrating its effectiveness in generative modeling across ethology, control theory, and mathematics.

The paper uses single nucleus RNA-sequencing to analyze human cortical areas, revealing cell type proportions and gene expression patterns that correlate with functional connectivity and areal specialization.

Whole-brain snRNA-seq paired with Slide-seq assigns cell types to each mouse neuroanatomical structure; most clusters need at least three markers to be defined uniquely.

The paper identifies distinct functional subnetworks in the mouse cortex driven by two types of glutamatergic projection neurons during behavior.

The paper provides a guide to the BRAIN Initiative Cell Census Network (BICCN) data ecosystem, its tools, and resources for analyzing brain cell types across species.

The paper provides a detailed cellular-resolution map of the mouse primary motor cortex, defining cell types and their connectivity.

The paper introduces a hybrid deep learning architecture combining topological priors with encoder-decoder networks to improve semantic segmentation of neuroanatomical data with high precision and recall.

The paper develops genetic tools to dissect glutamatergic neuron subpopulations and their developmental trajectories in the cerebral cortex.

This paper introduces a generative framework to register multimodal, incomplete neuroimaging data into a reference brain atlas, enabling geometric quantification of biological and technical variation.

This paper introduces topological methods to detect and skeletonize neurons and tracer injections, showing improved performance over existing techniques.

The paper investigates the anatomical continuity between koniocellular layers of the dorsal lateral geniculate nucleus and the inferior pulvinar nucleus in common marmosets using immunohistochemistry and neuronal tracing.

This paper presents a neurohistological pipeline and grid-based tracer injection strategy for mesoscale connectivity mapping in common marmosets, enabling high-quality brain-wide connectivity matrices.

This paper develops a variational framework for aligning histology stacks to an atlas, addressing curvature non-identifiability and enabling accurate reconstruction of whole-brain structures.

The paper confirms direct, nonreciprocal auditory projections to V1 in marmoset monkeys, supporting early audiovisual integration in primates.

The paper uses genetic labeling and high-resolution microscopy to reveal fine-grained subtypes of cortical interneurons in mice, showing distinct connectivity patterns.

This paper introduces a topological persistence-based framework for comparing neuronal tree structures, showing it captures more information than traditional morphometric summaries.