Daniel Jacob Tward

This paper introduces an automated neuron tracing method using imitation learning, achieving state-of-the-art accuracy on a benchmark dataset.

The paper develops a deep learning framework for cross-modality histological brain section registration across species using synthetic images, achieving accuracy comparable to alternative methods.

This study evaluates structure tensor analysis for estimating fiber orientations in brain imaging, showing parameter sensitivity in crossing fibers and anisotropic data.

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 introduces a pipeline to profile dendritic morphology of genetically-defined single neurons in the mouse brain, revealing morphological modules and age- or HD-associated changes.

This paper introduces STalign, a method for aligning spatial transcriptomics data using diffeomorphic metric mapping, improving alignment accuracy over affine methods.

This paper applies joint graph embedding to analyze volumetric MRI data and identifies AD neurodegeneration patterns with novel network structures.

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 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 develops tools to track synaptic plasticity in vivo by imaging endogenous AMPA receptors and generates a detailed spatiotemporal map of synaptic changes during behavior.

The paper introduces an optimized tissue clearing protocol for rat brains and develops an open-source platform for high-throughput analysis.

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 develops a registration technique for aligning 3D MRI and histology images in Alzheimer’s disease, handling intensity differences and missing data, and applies it to map tau tangles in brain tissue.

The paper introduces an EM-LDDMM algorithm for registering 3D atlases to 2D sparse target sections, handling uncertainties through hidden variables and diffeomorphic mappings.

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.