A scalable computational framework for large-scale imaging of neural circuits
Presented During:
Saturday, June 28, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
M4 (Mezzanine Level)
Poster No:
1788
Submission Type:
Abstract Submission
Authors:
Yael Balbastre1, Kabilar Gunalan2, Aaron Kanzer2, Elissa Bell3, Nathan Blanke3, Malte Casper4,5, Kaidong Chai3, Ting Gong3, Julia Lehman6, Chiara Maffei3, Emine Özen4,5, Wenze Li4,5, Gabriel Ramos Llorden3, Richard Schalek7, Jasmine Shao3, David Stansby1, Shruti Varade3, Jingjing Wu3, Susie Huang8, Peter Lee1, Jeff Lichtman7, Claire Walsh1, Hui Wang3, Zhuhao Wu9, Suzanne Haber10, Elizabeth Hillman4, Satra Ghosh2, Anastasia Yendiki3
Institutions:
1University College London, London, Greater London, 2Massachusetts Institute of Technology, Cambridge, MA, 3Massachusetts General Hospital, Charlestown, MA, 4Department of Biomedical Engineering, Columbia University in the City of New York, New York City, NY, 5Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University in the City of New York, New York City, NY, 6University of Rochester, Rochester, NY, 7Harvard University, Cambridge, MA, 8Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical, Boston, MA, 9Weill Cornell Medical College, New York City, NY, 10Rochester University, Rochester, NY
First Author:
Co-Author(s):
Malte Casper
Department of Biomedical Engineering, Columbia University in the City of New York|Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University in the City of New York
New York City, NY|New York City, NY
Department of Biomedical Engineering, Columbia University in the City of New York|Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University in the City of New York
New York City, NY|New York City, NY
Emine Özen
Department of Biomedical Engineering, Columbia University in the City of New York|Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University in the City of New York
New York City, NY|New York City, NY
Department of Biomedical Engineering, Columbia University in the City of New York|Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University in the City of New York
New York City, NY|New York City, NY
Wenze Li
Department of Biomedical Engineering, Columbia University in the City of New York|Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University in the City of New York
New York City, NY|New York City, NY
Department of Biomedical Engineering, Columbia University in the City of New York|Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University in the City of New York
New York City, NY|New York City, NY
Susie Huang
Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical
Boston, MA
Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical
Boston, MA
Elizabeth Hillman
Department of Biomedical Engineering, Columbia University in the City of New York
New York City, NY
Department of Biomedical Engineering, Columbia University in the City of New York
New York City, NY
Introduction:
The NIH BRAIN Initiative Connectivity across Scales (CONNECTS) program was launched in 2023 to produce wiring diagrams of mammalian brains at unprecedented resolutions. Here we report on data standardization, integration, and visualization efforts in the center for Large-scale Imaging of Neural Circuits (LINC), a CONNECTS-funded consortium focused on the macaque and human brain. The LINC center will scale up novel optical and X-ray microscopy techniques to image a large brain volume (~12/360 cc in macaque/human) that contains cortico-subcortical projections targeted by neuromodulation for motor and psychiatric disorders. This volume will be imaged with: polarization-sensitive optical coherence tomography (PS-OCT; Liu, 2023) at 6μm; lightsheet microscopy (LSM; Voleti, 2019) at 0.6μm; and hierarchical phase-contrast tomography (HiP-CT; Walsh, 2021) at 0.87-17μm. Mesoscopic diffusion MRI (dMRI; Huang, 2021) in the same brains will provide the link to noninvasive neuroimaging.
Methods:
The computational ecosystem for data of this scale is still nascent. A human brain imaged at 1μm resolution would yield petascale data, prohibitive for the file formats and software packages used in human neuroimaging. In particular, such data (1) can be loaded in memory only in chunks; (2) must be compressed efficiently; (3) cannot be duplicated. Thus data must be stored on a cloud-based, distributed framework, in a format that allows efficient access to data chunks at different resolution levels suitable for different tasks. The electron-microscopy community has already adopted formats and tools to handle petascale data (Shapson-Coe, 2024). This includes Zarr, a chunked, compressed, distributed file format that allows efficient partial reads and writes, and OME-Zarr, an extension that supports multiscale data and spatial metadata (Moore, 2021). The Neuroglancer viewer supports OME-Zarr and operates purely client-side-data streams from source to client, bypassing the webserver, which is thus very lightweight. However, it lacks key features of desktop neuroimaging viewers (spatial transformations, image fusion, integrated annotation and visualization of common volume, surface, and streamline formats). Our approach for the LINC project is to bridge these worlds, combining the scalability of OME-Zarr and Neuroglancer with the usability of human neuroimaging tools.
Results:
For data storage, we have adopted the Distributed Archives for Neurophysiology Data Integration (DANDI; Rübel, 2022), a cloud-based platform that enforces the BIDS standard, supports OME-Zarr, and enables byte-range access via HTTP requests, so that data can be mounted and accessed by virtual file systems. As the Zarr specification does not handle general affine transforms, or other NIfTI-style metadata, we have drafted NIfTI-Zarr, an extension that preserves NIfTI header fields (github.com/neuroscales/nifti-zarr). We have developed a library for converting the native file formats of our data-generating sites to NIfTI-Zarr (Fig.1), while auto-populating BIDS metadata (github.com/lincbrain/linc-convert). Our cloud-based platform can open data in Neuroglancer for visualization or Webknossos (Boergens, 2017) for annotation (Fig.2). We are extending Neuroglancer with (1) support for visualizing tractography streamlines (github.com/lincbrain/neuroglancer) and (2) a library for easy setup of multi-modal Neuroglancer scenes that can import neuroimaging formats and transforms (github.com/neuroscales/ngtools).
Conclusions:
We have prototyped a scalable framework to bridge the whole-brain coverage of noninvasive neuroimaging and the single-axon resolution of microscopy. Future efforts will focus on interactive annotation of tractography streamlines. A key implication for the neuroimaging software stack is that files can no longer be assumed to live on the local filesystem. Developers must support abstract file systems so that code can operate seamlessly on local or remote files.
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 1
Neuroinformatics and Data Sharing:
Workflows 2
Novel Imaging Acquisition Methods:
Diffusion MRI
Multi-Modal Imaging
Keywords:
Cross-Species Homologues
Data Organization
Open Data
Open-Source Software
Tractography
White Matter
1|2Indicates the priority used for review
Abstract Information
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Liu, C. J., Ammon, W., Jones, R. J., Nolan, J. C., Gong, D., Maffei, C., ... & Wang, H. (2023). Quantitative imaging of three-dimensional fiber orientation in the human brain via two illumination angles using polarization-sensitive optical coherence tomography. bioRxiv, 2023-10.
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