Biology subjects

Tward, D. J.

Publications and source records attributed to Tward, D. J..

14 recordsLinked to original sources

bioRxiv2026

Automated Neuron Tracing with Imitation Learning

Reconstructing neuronal morphology from large 3D microscopy volumes is essential for quantitative neuro-science but remains challenging due to noise, low contrast, and complex branching geometry. We present an automated neuron tracing method that formulates reconstruction as a sequential decision-making problem and trains a 3D convolutional policy via imitation learning. The key technical contribution is an online expert-action retrieval scheme that derives locally valid continuation directions directly from a gold-standard SWC tree, including at bifurcations. Combined with DAgger-style dataset aggregation, the policy is trained under its own state distribution without requiring handcrafted reward functions or a separate segmentation-to-skeleton postprocessing stage. We package the approach into an end-to-end tracing pipeline with optional path refinement operations and tools for quantitative evaluation and interactive editing. On the BigNeuron Gold166 benchmark, our method achieves state-of-the-art reconstruction accuracy, outperforming all competing challenge methods under both morphology-based aggregate metrics and geometric distance comparisons.

neuroscience↗

bioRxiv2026

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

Large-scale brain mapping increasingly relies on integrating histological imaging datasets within standardized anatomical reference frameworks. To accomplish this, registration techniques are used to align imaging datasets between different modalities or to reference atlases. Deep learning approaches have emerged a fast and scalable framework for approaching this challenge, but such methods typically require large annotated datasets that are unavailable in this setting. To address this, we developed a framework for training a convolutional neural network for this task using entirely simulated data. We show that the same approach can be used for different species (mouse and marmoset), and we provide accuracy validation in terms of Dice and Hasudroff distance between anatomical regions in comparison to an alternative method. This approach has the potential to accelerate large or high throughput studies of brain anatomy.

neuroscience↗

bioRxiv2025

Skeletonization of neuronal processes using Discrete Morse techniques from computational topology

To understand biological intelligence we need to map neuronal networks in vertebrate brains. Mapping mesoscale neural circuitry is done using injections of tracers that label groups of neurons whose axons project to different brain regions. Since many neurons are labeled, it is difficult to follow individual axons. Previous approaches have instead quantified the regional projections using the total label intensity within a region. However, such a quantification is not biologically meaningful. We propose a new approach better connected to the underlying neurons by skeletonizing labeled axon fragments and then estimating a volumetric length density. Our approach uses a combination of deep nets and the Discrete Morse (DM) technique from computational topology. This technique takes into account nonlocal connectivity information and therefore provides noise-robustness. We demonstrate the utility and scalability of the approach on whole-brain tracer injected data. We also define and illustrate an information theoretic measure that quantifies the additional information obtained, compared to the skeletonized tracer injection fragments, when individual axon morphologies are available. Our approach is the first application of the DM technique to computational neuroanatomy. It can help bridge between single-axon skeletons and tracer injections, two important data types in mapping neural networks in vertebrates.

neuroscience↗

bioRxiv2025

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

Brain reference atlases are essential for neuroscience experiments and data integration. However, histological atlases of the mouse brain, crucial in biomedical research, have not kept pace. Autofluorescence-based volumetric brain atlases are increasingly used but lack microscopic histological contrast, cytoarchitectonic information, corresponding MRI datasets, and often have truncated brainstems. Here, we present a multimodal, multiscale atlas of the laboratory mouse brain and head. The new reference brains include the whole head with consecutive Nissl and myelin serial section histology in three planes of section with 0.46 {micro}m in-plane resolution, including intact brainstem, cranial nerves, and associated sensors and musculature. We provide reassembled histological volumes with 20mu isotropic resolution in stereotactic coordinates, determined using co-registered in vivo MRI and CT. In addition to conventional MRI contrasts, we provide diffusion MRI-based in vivo and ex vivo microstructural information, adding a valuable co-registered contrast modality that bridges MRI with cell-resolution histological data. We shift emphasis from compartmental annotations to stereotactic coordinates in the reference brains, offering a basis for evolving annotations over time and resolving conflicting neuroanatomical judgments by different experts. This new reference atlas facilitates integration of molecular cell type data and regional connectivity, serves as a model for similar atlases in other species, and sets a precedent for preserving extra-cranial nervous system structures.

neuroscience↗

bioRxiv2025

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

Accurate localization of white matter pathways using diffusion MRI is critical to investigating brain connectivity, but the accuracy of current methods is not thoroughly understood. A fruitful approach to validating accuracy is to consider microscopy data that have been co-registered with MRI of post mortem samples. In this setting, structure tensor analysis is a standard approach to computing local orientations for validation. However, structure tensor analysis itself has not been well-validated and is subject to uncertainty in its angular resolution, and selectivity to specific spatial scales. In this work, we conducted a simulation study to investigate the accuracy of using structure tensors to estimate the orientations of fibers arranged in configurations with and without crossings. We examined a range of simulated conditions, with a focus on investigating the methods behavior on images with anisotropic resolution, which is particularly common in microscopy data acquisition. We also analyzed 2D and 3D optical microscopy data. Our results show that parameter choice in structure tensor analysis has relatively little effect on accuracy for estimating single orientations, although accuracy decreases with anisotropy. On the other hand, when estimating the orientations of crossing fibers, the choice of parameters becomes critical, and poor choices result in orientation estimates that are essentially random. This work provides a set of recommendations for researchers seeking to apply structure tensor analysis effectively in the study of axonal orientations in brain imaging data and quantifies the methods limitations, particularly in the case of anisotropic data. HighlightsO_LIStructure tensor estimates of homogeneous orientations are robust to parameter choice C_LIO_LIAccuracy in crossing-fiber regions is highly sensitive to parameter choice C_LIO_LIImage anisotropy has a large effect on accuracy in 3D images C_LIO_LISimulations provide guidelines for choosing parameters based on image characteristics C_LI

neuroscience↗

bioRxiv2023

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

Neurodegeneration measured through volumetry in MRI is recognized as a potential Alzheimers Disease (AD) biomarker, but its utility is limited by lack of specificity. Quantifying spatial patterns of neurodegeneration on a whole brain scale rather than locally may help address this. In this work, we turn to network based analyses and extend a graph embedding algorithm to study morphometric connectivity from volume-change correlations measured with structural MRI on the timescale of years. We model our data with the multiple random eigengraphs framework, as well as modify and implement a multigraph embedding algorithm proposed earlier to estimate a low dimensional embedding of the networks. Our version of the algorithm guarantees meaningful finite-sample results and estimates maximum likelihood edge probabilities from population-specific network modes and subject-specific loadings. Furthermore, we propose and implement a novel statistical testing procedure to analyze group differences after accounting for confounders and locate significant structures during AD neurodegeneration. Family-wise error rate is controlled at 5% using permutation testing on the maximum statistic. We show that results from our analysis reveal networks dominated by known structures associated to AD neurodegeneration, indicating the framework has promise for studying AD. Furthermore, we find network-structure tuples that are not found with traditional methods in the field.

neuroscience↗

bioRxiv2022

Amygdalar and ERC Rostral Atrophy and Tau Pathology Reconstruction in Preclinical Alzheimer's Disease

Previous research has emphasized the unique impact of Alzheimers Disease (AD) pathology on the medial temporal lobe (MTL), a reflection that tau pathology is particularly striking in the entorhinal and transentorhinal cortex (ERC, TEC) early in the course of disease. However, other brain regions are affected by AD pathology during its early phases. Here, we use longitudinal diffeomorphometry to measure the atrophy rate from MRI of the amygdala compared with that in the ERC and TEC in controls, individuals who progressed from normal cognition to mild cognitive impairment (MCI), and individuals with MCI who progressed to AD dementia, using a dataset from the Alzheimers Disease Neuroimaging Initiative (ADNI). Our results show significantly higher atrophy rates of the amygdala in both preclinical and MCI converters compared to controls, with rates of volume loss comparable to rates of thickness loss in the ERC and TEC. Using our recently developed method, referred to as Projective LDDMM, we map measures of neurofibrillary tau tangles (NFTs) from digital pathology to MRI atlases and reconstruct, for the first time, dense 3D spatial distributions of NFT density within regions of the MTL. The distribution of NFTs is consistent with the MR atrophy rates, revealing high densities not only in ERC, but in the amygdala and rostral third of the MTL. The similarity of the location of NFTs and shape changes in a well-defined clinical population suggests that amygdalar atrophy rate, as measured through MRI may be a viable biomarker for AD. PACS0000, 1111 2000 MSC0000, 1111 HighlightsO_LIAmygdala atrophy rate estimated from MRIs in preclinical Alzheimers disease (AD) C_LIO_LI3D distributions of neurofibrillary tau tangles (NFTs) reconstructed from 2D histology C_LIO_LINFTs highest in the rostral medial temporal lobe, including amygdala and ERC C_LIO_LIAmygdala atrophy rate is comparable to ERC atrophy rate in preclinical AD C_LIO_LIAmygdala and ERC atrophy rates as potential biomarkers rooted in NFT pathology C_LI

bioengineering↗

bioRxiv2022

Projective LDDMM: Mapping Molecular Digital Pathology with Tissue MRI

Reconstructing dense 3D anatomical coordinates from 2D projective measurements has become a central problem in digital pathology for both animal models and human studies. We describe a new family of diffeomorphic mapping technologies called Projective LDDMM which generate diffeomorphic mappings of dense human MRI atlases at tissue scales onto sparse measurements at micron scales associated with histological and more general optical imaging modalities. We solve the problem of dense mapping surjectively onto histological sections by incorporating new technologies for crossing modalities that use non-linear scattering transforms to represent multiple radiomic-like textures at micron scales and incorporating a Gaussian mixture-model frame-work for modelling tears and distortions associated to each section. We highlight the significance of our method through incorporation of neuropathological measures and MRI, as relevant to the development of biomarkers for Alzheimers disease and one instance of the integration of imaging data across the scales of clinical imaging and digital pathology.

bioengineering↗

bioRxiv2022

Projective LDDMM: Spatially Reconstructing a Story of Rostrally-Dominant Tau in Alzheimer's Disease

Since Braaks initial histological observations, it has been recognized that Alzheimers disease (AD) neurofibrillary tangles (NFTs) appear in the medial temporal lobe (MTL) of the brain very early in the disease course. MRI-based shape diffeomorphometry markers have demonstrated pre-clinical AD changes in the MTL but it has not been possible to confirm that these MRI changes correspond to the presence of NFTs. Here, we present a method termed Projective LDDMM for aligning sparse measurement profiles of AD pathology (i.e., 2D digital histology images) with 3D MRI. We reconstruct measures of 2D NFT density in the dense metric of 3D MRI, using the Mai Paxinos Atlas coordinates for two cases of advanced AD. Analyses reveal the highest levels of NFT density in the rostral third (10-15 mm) of the hippocampus and the adjoining regions of the entorhinal cortex and amygdala. These findings emphasize the selective vulnerability of MTL subregions in AD, and suggest that high resolution MRI methods might benefit from focusing on the rostral MTL to more closely link these MRI images to AD neuropathology.

bioengineering↗

bioRxiv2021

Hierarchical Computational Anatomy: Unifying the Molecular to Tissue Continuum via Measure Representations of the Brain

ObjectiveThe objective of this research is to unify the molecular representations of spatial transcriptomics and cellular scale histology with the tissue scales of Computational Anatomy for brain mapping. Impact statementWe present a unified representation theory for brain mapping based on geometric measures of the micro-scale phenotypes of molecular disease simultaneously with the connectomic scales of complex interacting brain circuits. IntroductionMapping across coordinate systems in computational anatomy allows us to understand structural and functional properties of the brain at the millimeter scale. New measurement technologies in digital pathology and spatial transcriptomics allow us to measure the brain molecule by molecule and cell by cell based on protein and transcriptomic identity. We currently have no mathematical representations for integrating consistently the tissue limits with the molecular particle descriptions. The formalism derived here demonstrates the methodology for transitioning consistently from the molecular scale of quantized particles - as first introduced by Dirac as the class of generalized functions - to the continuum and fluid mechanics scales appropriate for tissue. MethodsWe introduce two methods based on notions of generalized function geometric measures and statistical mechanics. We use generalized functions expanded to include functional geometric descriptions - electrophysiology, transcriptomic, molecular histology - to represent the molecular biology scale integrated with a Boltzman like procedure to pass from the sparse particles to empirical probability laws on the functional state of the tissue. ResultsWe demonstrate a unified mapping methodology for transferring molecular information in the transcriptome and histological scales to the human atlas scales for understanding Alzheimers disease. ConclusionsWe demonstrate a unified brain mapping theory for molecular and tissue scales based on geometric measure representations.

neuroscience↗

bioRxiv2021

CloudReg: Automatic Terabyte-Scale Cross-Modal Brain Volume Registration

Quantifying terabyte-scale multi-modal human and animal imaging data requires scalable analysis tools. We developed CloudReg, an open-source, automatic, terabyte-scale, cloud-based image analysis pipeline that pre-processes and registers cross-modal volumetric datasets with artifacts via spatially-varying polynomial intensity transform. CloudReg accurately registers the following datasets to their respective atlases: in vivo human and ex vivo macaque brain magnetic resonance imaging, ex vivo mouse brain micro-computed tomography, and cleared murine brain light-sheet microscopy.

bioengineering↗

bioRxiv2020

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

A current focus of research in neuroscience is to enumerate, map and annotate neuronal cell types in whole vertebrate brains using different modalities of data acquisition. Mapping these molecular and anatomical datasets into a common reference space remains a key challenge. While several brain-to-atlas mapping workflows exist, they do not adequately address challenges of modern high throughput neuroimaging, including multimodal and multiscale signals, missing data or non reference signals, and geometric quantification of individual variation. Our solution is to implement a generative statistical model that describes the likelihood of imaging data given a sequence of transforms of an atlas image, and a framework for maximum a posteriori estimation of unknown parameters capturing the issues listed above. The key idea in our approach is to minimize the difference between synthetic image volumes and real data over these parameter. Rather than merely using mappings as a "normalization" step, we implement tools for using their local metric changes as an opportunity for geometric quantification of technical and biological sources of variation in an unprecedented manner. While the framework is used to compute pairwise mappings, our approach particularly allows for easy compositions across chains of multimodality datasets. We apply these methods across a broad range of datasets including various combinations of in-vivo and ex-vivo MRI, 3D STP and fMOST data sets, 2D serial histology sections, and brains processed for snRNAseq with tissue partially removed. We show biological utility by quantifying cell density and diffeomorphic characterization of brain shape fluctuations across biological covariates. We note that the magnitude of individual variation is often greater than differences between different sample preparation techniques. To facilitate community accessibility, we implement our algorithm as open source, include a web based framework, and implement input and output dataset standards. Our work establishes a quantitative, scalable and streamlined workflow for unifying a broad spectrum of multi-modal whole-brain light microscopic data volumes into a coordinate-based atlas framework. This work enables large scale integration of whole brain data sets that are essential in modern neuroscience.

neuroscience↗

bioRxiv2018

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

This paper examines the problem of diffeomorphic image mapping in the presence of differing image intensity profiles and missing data. Our motivation comes from the problem of aligning 3D brain MRI with 100 micron isotropic resolution, to histology sections with 1 micron in plane resolution. Multiple stains, as well as damaged, folded, or missing tissue are common in this situation. We overcome these challenges by introducing two new concepts. Cross modality image matching is achieved by jointly estimating polynomial transformations of the atlas intensity, together with pose and deformation parameters. Missing data is accommodated via a multiple atlas selection procedure where several atlases may be of homogeneous intensity and correspond to \"background\" or \"artifact\". The two concepts are combined within an Expectation Maximization algorithm, where atlas selection posteriors and deformation parameters are updated iteratively, and polynomial coefficients are computed in closed form. We show results for 3D reconstruction of digital pathology and MRI in standard atlas coordinates. In conjunction with convolutional neural networks, we quantify the 3D density distribution of tauopathy throughout the medial temporal lobe of an Alzheimers disease postmortem specimen.\n\nAuthor summaryOur work in Alzheimers disease (AD) is attempting to connect histopathology at autopsy and longitudinal clinical magnetic resonance imaging (MRI), combining the strengths of each modality in a common coordinate system. We are bridging this gap by using post mortem high resolution MRI to reconstruct digital pathology in 3D. This image registration problem is challenging because it combines images from different modalities in the presence of missing tissue and artifacts. We overcome this challenge by developing a new registration technique that simultaneously classifies each pixel as \"good data\" / \"missing tissue\" / \"artifact\", learns a contrast transformation between modalities, and computes deformation parameters. We name this technique \"(D)eformable (R)egistration and (I)ntensity (T)ransformation with (M)issing (D)ata\", pronounced as \"Dr. It, M.D.\". In conjunction with convolutional neural networks, we use this technique to map the three dimensional distribution of tau tangles in the medial temporal lobe of an AD postmortem specimen.

neuroscience↗

bioRxiv2018

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

This paper presents a variational framework for dense diffeomorphic atlas-mapping onto high-throughput histology stacks at the 20 m meso-scale. The observed sections are modelled as Gaussian random fields conditioned on a sequence of unknown section by section rigid motions and unknown diffeomorphic transformation of a three-dimensional atlas. To regularize over the high-dimensionality of our parameter space (which is a product space of the rigid motion dimensions and the diffeomorphism dimensions), the histology stacks are modelled as arising from a first order Sobolev space smoothness prior. We show that the joint maximum a-posteriori, penalized-likelihood estimator of our high dimensional parameter space emerges as a joint optimization interleaving rigid motion estimation for histology restacking and large deformation diffeomorphic metric mapping to atlas coordinates. We show that joint optimization in this parameter space solves the classical curvature non-identifiability of the histology stacking problem. The algorithms are demonstrated on a collection of whole-brain histological image stacks from the Mouse Brain Architecture Project.\n\nAuthor SummaryNew developments in neural tracing techniques have motivated the widespread use of histology as a modality for exploring the circuitry of the brain. Automated mapping of pre-labeled atlases onto modern large datasets of histological imagery is a critical step for elucidating the brains neural circuitry and shape. This task is challenging as histological sections are imaged independently and the reconstruction of the unsectioned volume is nontrivial. Typically, neuroanatomists use reference volumes of the same subject (e.g. MRI) to guide reconstruction. However, obtaining reference imagery is often non-standard, as in high-throughput animal models like mouse histology. Others have proposed using anatomical atlases as guides, but have not accounted for the intrinsic nonlinear shape difference from atlas to subject. Our method addresses these limitations by jointly optimizing reconstruction informed by an atlas simultaneously with the nonlinear change of coordinates that encapsulates anatomical variation. This accounts for intrinsic shape differences and enables rigorous, direct comparisons of atlas and subject coordinates. Using simulations, we demonstrate that our method recovers the reconstruction parameters more accurately than atlas-free models and innately produces accurate segmentations from simultaneous atlas mapping. We also demonstrate our method on the Mouse Brain Architecture dataset, successfully mapping and reconstructing over 500 brains.

neuroscience↗