Biology subjects

Augustinack, J.

Publications and source records attributed to Augustinack, J..

11 recordsLinked to original sources

bioRxiv2026

The Infant Brainstem - A Multimodal Multiscale Postmortem Imaging Pipeline

Brainstem disorders in human infants - including sudden infant death syndrome (SIDS), the leading cause of postnatal infant mortality in the United States - are characterized by cellular and molecular abnormalities that conventional neuroimaging cannot detect. A substantial challenge arises from the fact that the immature myelination of the infant brain severely degrades MRI contrast, leaving the discrete nuclei and white matter tracts of the brainstem poorly resolved at the scale where pathology occurs. We introduce a postmortem imaging pipeline that bridges this gap by integrating four spatially registered modalities: whole-brain magnetic resonance imaging (MRI) (550 m), brainstem-specific MRI (150 m), polarization-sensitive optical coherence tomography (PSOCT, 10 m), and histology with immunohistochemistry (1.88 m). Our central finding is that PSOCT provides excellent tissue contrast independent of myelination state - directly overcoming the principal limitation of MRI in the infant brain -while enabling three-dimensional visualization of nuclei and tracts at resolutions 15 to 55 times finer than MRI alone. Histology provides cellular-level ground truth and validates the optical contrasts. Applied here to a normative 34-day-old infant brainstem, this pipeline establishes a generalizable framework for studying infant brainstem neuroanatomy in three dimensions, with particular relevance to disorders such as SIDS where gross anatomy is intact but cellular abnormalities remain the target of investigation.

neuroscience↗

bioRxiv2026

Hierarchical X-ray microscopy and mesoscopic diffusion MRI in the same brain reveal the human connectome across scales

We present a multimodal pipeline for 3D imaging of cerebral white-matter archi-tecture across scales, from whole-brain axonal projections down to individual myelinated axons. After diffusion MRI, an adult ex vivo human hemisphere undergoes label-free imaging with Hierarchical Phase-Contrast Tomography (HiP-CT) from 20 {micro}m/voxel in the whole hemisphere to 2 {micro}m/voxel in areas of interest, with intrinsic cross-scale alignment. A 4 cm tissue block extracted from the hemisphere is reimaged with HiP-CT at 0.857 {micro}m/voxel, enabling direct visualisation of single myelinated axons. After osmium staining, micro-CT at 0.364 {micro}m/voxel and electron microscopy at 4 nm/voxel are acquired in biop-sies from the tissue block to validate the presence of myelinated axons in the label-free HiP-CT contrast. Spanning three orders of magnitude in resolution, these co-registered multimodal datasets bridge microscopic wiring and macro-scopic brain organisation, providing a foundation for anatomically grounded whole-brain connectomics.

neuroscience↗

bioRxiv2026

The modular nature of long-association pathways in the human brain

Human brain functions are organized within large-scale networks, yet the long-range structural pathways that support them remain only partly understood. Diffusion MRI tractography has enabled non-invasive mapping of these connections but its low resolution has also contributed to a view of white matter association bundles as monolithic entities linking distant cortical regions. This contrasts with anatomical studies in non-human primates and humans, which show these bundles act as conduits for complex systems of short- and long-range projections. Here we investigate the dorsal branch of the superior longitudinal fasciculus (SLF-I), the proposed structural backbone of the dorsal attention network, whose anatomy in humans is debated. Using anatomic tracing, polarization-sensitive optical coherence tomography, and in vivo and ex vivo diffusion MRI, we reconstruct the SLF-I across species, modalities, and scales. High resolution tractography (<750 m), consistent with tracer and PS-OCT data, reveals multiple fiber systems of varying lengths rather than uniform parieto-frontal connections observed at more conventional resolution (1.5 mm). Comparative analyses show a consistent organization: the SLF-I carries short-range cortico-cortical projections rostral to area 6 and longer, layered connections extending to parietal regions, while direct parieto-frontal connectivity is mostly supported by the adjacent cingulum bundle. These findings reconcile our understanding of the SLF-I across the scales of non-invasive neuroimaging and invasive anatomic studies and provide an updated framework for future investigations of large-scale networks in health and disease.

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bioRxiv2025

Fast segmentation with the NextBrain histological atlas

Structural brain analysis at the subregion level offers critical insights into healthy aging and neurodegenerative diseases. The NextBrain histological atlas was recently introduced to support such fine-grained investigations, but its existing Bayesian segmentation framework remains computationally prohibitive, particularly for large-scale studies. We present a new, open-source tool that dramatically accelerates segmentation using a hybrid approach combining: machine learning, contrast-adaptive segmentation; target-specific image synthesis; and fast diffeomorphic registration (all three with GPU support). Our method enables highly granular segmentation of brain MRI scans of any resolution and contrast (in vivo or ex vivo) at a fraction of the computational cost of the original method (<5 minutes on a GPU). We validate our tool on four different modalities (in vivo MRI, ex vivo MRI, HiP-CT, and photography) across a total of approximately 4,000 brain scans. Our results demonstrate that the accelerated approach achieves comparable accuracy to the original method in terms of Dice scores, while reducing runtime by over an order of magnitude. This work enables high-resolution anatomical analysis at unprecedented scale and flexibility, providing a practical solution for large neuroimaging studies. Our tool is publicly available in FreeSurfer (https://surfer.nmr.mgh.harvard.edu/fswiki/HistoAtlasSegmentation).

neuroscience↗

bioRxiv2025

Harmonized Protocol for Subfield Segmentation in the Hippocampal Body on High-Resolution in vivo MRI from the Hippocampal Subfields Group (HSG)

Hippocampal subfields differentially develop and age, and they vary in vulnerability to neurodegenerative diseases. Innovation in high-resolution imaging has accelerated clinical research on human hippocampal subfields, but substantial differences in segmentation protocols impede comparisons of results across laboratories. The Hippocampal Subfields Group (HSG) is an international organization seeking to address this issue by developing a histologically-valid, reliable, and freely available segmentation protocol for high-resolution T2-weighted 3 tesla MRI (http://www.hippocampalsubfields.com). Here, we report the first portion of the protocol focused on subfields in the hippocampal body; protocols for the head and tail are in development. The body protocol includes definitions of the internal boundaries between subiculum, Cornu Ammonis (CA) 1-3 subfields, and dentate gyrus, in addition to the external boundaries of the hippocampus apart from surrounding white matter and cerebrospinal fluid. The segmentation protocol is based on a novel histological reference data set labeled by multiple expert neuroanatomists. With broad participation of the research community, we voted on the segmentation protocol via online survey, which included detailed protocol information, feasibility testing, demonstration videos, example segmentations, and labeled histology. All boundary definitions were rated as having high clarity and reached consensus agreement by Delphi procedure. The harmonized body protocol yielded high inter- and intra-rater reliability. In the present paper we report the procedures to develop and test the protocol, as well as the detailed procedures for manual segmentation using the harmonized protocol. The harmonized protocol will significantly facilitate cross-study comparisons and provide increased insight into the structure and function of hippocampal subfields across the lifespan and in neurodegenerative diseases.

neuroscience↗

bioRxiv2024

A next-generation, histological atlas of the human brain and its application to automated brain MRI segmentation

Magnetic resonance imaging (MRI) is the standard tool to image the human brain in vivo. In this domain, digital brain atlases are essential for subject-specific segmentation of anatomical regions of interest (ROIs) and spatial comparison of neuroanatomy from different subjects in a common coordinate frame. High-resolution, digital atlases derived from histology (e.g., Allen atlas [7], BigBrain [13], Julich [15]), are currently the state of the art and provide exquisite 3D cytoarchitectural maps, but lack probabilistic labels throughout the whole brain. Here we present NextBrain, a next - generation probabilistic atlas of human brain anatomy built from serial 3D histology and corresponding highly granular delineations of five whole brain hemispheres. We developed AI techniques to align and reconstruct [~]10,000 histological sections into coherent 3D volumes with joint geometric constraints (no overlap or gaps between sections), as well as to semi-automatically trace the boundaries of 333 distinct anatomical ROIs on all these sections. Comprehensive delineation on multiple cases enabled us to build the first probabilistic histological atlas of the whole human brain. Further, we created a companion Bayesian tool for automated segmentation of the 333 ROIs in any in vivo or ex vivo brain MRI scan using the NextBrain atlas. We showcase two applications of the atlas: automated segmentation of ultra-high-resolution ex vivo MRI and volumetric analysis of Alzheimers disease and healthy brain ageing based on [~]4,000 publicly available in vivo MRI scans. We publicly release: the raw and aligned data (including an online visualisation tool); the probabilistic atlas; the segmentation tool; and ground truth delineations for a 100 m isotropic ex vivo hemisphere (that we use for quantitative evaluation of our segmentation method in this paper). By enabling researchers worldwide to analyse brain MRI scans at a superior level of granularity without manual effort or highly specific neuroanatomical knowledge, NextBrain holds promise to increase the specificity of MRI findings and ultimately accelerate our quest to understand the human brain in health and disease.

neuroscience↗

bioRxiv2023

Segmentation of supragranular and infragranular layers in ultra-high resolution 7T ex vivo MRI of the human cerebral cortex

Accurate labeling of specific layers in the human cerebral cortex is crucial for advancing our understanding of neurodevelopmental and neurodegenerative disorders. Lever-aging recent advancements in ultra-high resolution ex vivo MRI, we present a novel semi-supervised segmentation model capable of identifying supragranular and infragranular layers in ex vivo MRI with unprecedented precision. On a dataset consisting of 17 whole-hemisphere ex vivo scans at 120 {micro}m, we propose a multi-resolution U-Nets framework (MUS) that integrates global and local structural information, achieving reliable segmentation maps of the entire hemisphere, with Dice scores over 0.8 for supra- and infragranular layers. This enables surface modeling, atlas construction, anomaly detection in disease states, and cross-modality validation, while also paving the way for finer layer segmentation. Our approach offers a powerful tool for comprehensive neuroanatomical investigations and holds promise for advancing our mechanistic understanding of progression of neurodegenerative diseases.

neuroscience↗

bioRxiv2023

Sustaining wakefulness: Brainstem connectivity in human consciousness

Consciousness is comprised of arousal (i.e., wakefulness) and awareness. Substantial progress has been made in mapping the cortical networks that modulate awareness in the human brain, but knowledge about the subcortical networks that sustain arousal is lacking. We integrated data from ex vivo diffusion MRI, immunohistochemistry, and in vivo 7 Tesla functional MRI to map the connectivity of a subcortical arousal network that we postulate sustains wakefulness in the resting, conscious human brain, analogous to the cortical default mode network (DMN) that is believed to sustain self-awareness. We identified nodes of the proposed default ascending arousal network (dAAN) in the brainstem, hypothalamus, thalamus, and basal forebrain by correlating ex vivo diffusion MRI with immunohistochemistry in three human brain specimens from neurologically normal individuals scanned at 600-750 {micro}m resolution. We performed deterministic and probabilistic tractography analyses of the diffusion MRI data to map dAAN intra-network connections and dAAN-DMN internetwork connections. Using a newly developed network-based autopsy of the human brain that integrates ex vivo MRI and histopathology, we identified projection, association, and commissural pathways linking dAAN nodes with one another and with cortical DMN nodes, providing a structural architecture for the integration of arousal and awareness in human consciousness. We release the ex vivo diffusion MRI data, corresponding immunohistochemistry data, network-based autopsy methods, and a new brainstem dAAN atlas to support efforts to map the connectivity of human consciousness. One sentence summaryWe performed ex vivo diffusion MRI, immunohistochemistry, and in vivo 7 Tesla functional MRI to map brainstem connections that sustain wakefulness in human consciousness.

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bioRxiv2021

A multimodal imaging and analysis pipeline for creating a cellular census of the human cerebral cortex

Cells are not uniformly distributed in the human cerebral cortex. Rather, they are arranged in a regional and laminar fashion that span a range of scales. Here we demonstrate an innovative imaging and analysis pipeline to construct a reliable cell census across the human cerebral cortex. Magnetic resonance imaging (MRI) is used to establish a macroscopic reference coordinate system of laminar and cytoarchitectural boundaries. Cell counting is obtained with both traditional immunohistochemistry, to provide a stereological gold-standard, and with a custom-made inverted confocal light-sheet fluorescence microscope (LSFM) for 3D imaging at cellular resolution. Finally, mesoscale optical coherence tomography (OCT) enables the registration of the distorted histological cell typing obtained with LSFM to the MRI-based atlas coordinate system.

neuroscience↗

bioRxiv2021

Post mortem validation of DSI compressed sensing with optical imaging

While many useful microstructural indices, as well as orientation distribution functions, can be obtained from multi-shell dMRI data, there is growing interest in exploring the richer set of microstructural features that can be extracted from the full ensemble average propagator (EAP). The EAP can be readily computed from diffusion spectrum imaging (DSI) data, at the cost of a very lengthy acquisition. Compressed sensing (CS) has been used to make DSI more practical by reducing its acquisition time. CS applied to DSI (CS-DSI) attempts to reconstruct the EAP from significantly undersampled q-space data. We present a post mortem validation study where we evaluate the ability of CS-DSI to approximate not only fully sampled DSI but also multi-shell acquisitions with high fidelity. Human brain samples are imaged with high-resolution DSI at 9.4T and with polarization-sensitive optical coherence tomography (PSOCT). The latter provides direct measurements of axonal orientations at microscopic resolutions, allowing us to evaluate the mesoscopic orientation estimates obtained from diffusion MRI, in terms of their angular error and the presence of spurious peaks. We test two fast, dictionary-based, L2-regularized algorithms for CS-DSI reconstruction. We find that, for a CS acceleration factor of R=3, i.e., an acquisition with 171 gradient directions, one of these methods is able to achieve both low angular error and low number of spurious peaks. With a scan length similar to that of high angular resolution multi-shell acquisition schemes, this CS-DSI approach is able to approximate both fully sampled DSI and multi-shell data with high accuracy. Thus it is suitable for orientation reconstruction and microstructural modeling techniques that require either grid- or shell-based acquisitions. We find that the signal-to-noise ratio (SNR) of the training data used to construct the dictionary can have an impact on the accuracy of CS-DSI, but that there is substantial robustness to loss of SNR in the test data. Finally, we show that, as the CS acceleration factor increases beyond R=3, the accuracy of these reconstruction methods degrade, either in terms of the angular error, or in terms of the number of spurious peaks. Our results provide useful benchmarks for the future development of even more efficient q-space acceleration techniques.

neuroscience↗

bioRxiv2019

7 Tesla MRI of the ex vivo human brain at 100 micron resolution

We present an ultra-high resolution MRI dataset of an ex vivo human brain specimen. The brain specimen was donated by a 58-year-old woman who had no history of neurological disease and died of non-neurological causes. After fixation in 10% formalin, the specimen was imaged on a 7 Tesla MRI scanner at 100 m isotropic resolution using a custom-built 31-channel receive array coil. Single-echo multi-flip Fast Low-Angle SHot (FLASH) data were acquired over 100 hours of scan time (25 hours per flip angle), allowing derivation of a T1 parameter map and synthesized FLASH volumes. This dataset provides an unprecedented view of the three-dimensional neuroanatomy of the human brain. To optimize the utility of this resource, we warped the dataset into standard stereotactic space. We now distribute the dataset in both native space and stereotactic space to the academic community via multiple platforms. We envision that this dataset will have a broad range of investigational, educational, and clinical applications that will advance understanding of human brain anatomy in health and disease.\n\n\n\nO_TBL View this table:\norg.highwire.dtl.DTLVardef@17d0265org.highwire.dtl.DTLVardef@285366org.highwire.dtl.DTLVardef@17b53c6org.highwire.dtl.DTLVardef@1b90277org.highwire.dtl.DTLVardef@150ea14_HPS_FORMAT_FIGEXP M_TBL C_TBL

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