Daniel Jacob Tward

Automated Neuron Tracing with Imitation Learning

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

Deep Learning Based Cross-Modality Histological Brain Section Registration in Multiple Species Using Synthetic Images

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.

Validation of Structure Tensor Analysis for Orientation Estimation in Brain Tissue Microscopy

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

Skeletonization of neuronal processes using Discrete Morse techniques from computational topology

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.

3D multimodal histological atlas and coordinate framework for the mouse brain and head

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.

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

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.

Dendritome Mapping Unveils Spatial Organization of Striatal D1/D2-Neuron Morphology

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.

Alignment of spatial transcriptomics data using diffeomorphic metric mapping

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

Applying joint graph embedding to study Alzheimer's neurodegeneration patterns in volumetric data

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

The cell type composition of the adult mouse brain revealed by single cell and spatial genomics

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 BRAIN Initiative Cell Census Data Ecosystem: A User's Guide

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.

Visualizing synaptic plasticity in vivo by large-scale imaging of endogenous AMPA receptors

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.

An optimized tissue clearing protocol for rat brain labeling, imaging, and high throughput analysis

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

Solving the where problem in neuroanatomy: a generative framework with learned mappings to register multimodal, incomplete data into a reference brain

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.

Diffeomorphic registration with intensity transformation and missing data: Application to 3D digital pathology of Alzheimer’s disease

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.

EM-LDDMM for 3D to 2D registration

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

On variational solutions for whole brain serial-section histology using the computational anatomy random orbit model

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.