Constructing and optimizing 3D atlases from 2D data with application to the developing mouse brain
- University of California, San Francisco, United States
- Agency for Science, Technology and Research, Singapore
Abstract
3D imaging data necessitate 3D reference atlases for accurate quantitative interpretation. Existing computational methods to generate 3D atlases from 2D-derived atlases result in extensive artifacts, while manual curation approaches are labor-intensive. We present a computational approach for 3D atlas construction that substantially reduces artifacts by identifying anatomical boundaries in the underlying imaging data and using these to guide 3D transformation. Anatomical boundaries also allow extension of atlases to complete edge regions. Applying these methods to the eight developmental stages in the Allen Developing Mouse Brain Atlas (ADMBA) led to more comprehensive and accurate atlases. We generated imaging data from fifteen whole mouse brains to validate atlas performance and observed qualitative and quantitative improvement (37% greater alignment between atlas and anatomical boundaries). We provide the pipeline as the MagellanMapper software and the eight 3D reconstructed ADMBA atlases. These resources facilitate whole-organ quantitative analysis between samples and across development.
Data availability
The full 3D generated atlases and wild-type brain images are being deposited with the Human Brain Project EBRAINS data platform. All data analyses are included in the manuscript and supporting files.
-
E11.5 3D Edge-Aware Refined Atlas Derived from the Allen Developing Mouse Brain AtlasEBRAINS , 10.25493/H9A3-GFT.
-
E13.5 3D Edge-Aware Refined Atlas Derived from the Allen Developing Mouse Brain AtlasEBRAINS , 10.25493/YP9K-YMW.
-
E15.5 3D Edge-Aware Refined Atlas Derived from the Allen Developing Mouse Brain AtlasEBRAINS , 10.25493/EXET-XND.
-
E18.5 3D Edge-Aware Refined Atlas Derived from the Allen Developing Mouse Brain AtlasEBRAINS , 10.25493/X4ZT-ARE.
-
P4 3D Edge-Aware Refined Atlas Derived from the Allen Developing Mouse Brain AtlasEBRAINS , 10.25493/QYP4-5VQ.
-
P14 3D Edge-Aware Refined Atlas Derived from the Allen Developing Mouse Brain AtlasEBRAINS , 10.25493/QYP4-5VQ.
-
P28 3D Edge-Aware Refined Atlas Derived from the Allen Developing Mouse Brain AtlasEBRAINS , 10.25493/YW1E-6BW.
-
P56 3D Edge-Aware Refined Atlas Derived from the Allen Developing Mouse Brain AtlasEBRAINS , 10.25493/MYPD-QB8.
Article and author information
Author details
John LR Rubenstein
Funding
Brain and Behavior Research Foundation (NARSAD Young Investigator Grant)
- Stephan J Sanders
National Institute of Mental Health (U01 MH122681)
- Stephan J Sanders
National Institute of Mental Health (R01 MH109901)
- Stephan J Sanders
National Institute of Neurological Disorders and Stroke (R01 NS099099)
- John LR Rubenstein
The authors declare that the funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: All procedures and animal care were approved and performed in accordance with institutional guidelines from the University of California San Francisco Laboratory Animal Research Center (LARC). All animal handling complied with the approved Institutional Animal Care and Use Committee (IACUC) protocol (AN180174-02) at the University of California San Francisco.
Copyright
© 2021, Young et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 4,535
- views
-
- 336
- downloads
-
- 20
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Constructing and optimizing 3D atlases from 2D data with application to the developing mouse brain
eLife 10:e61408.
https://doi.org/10.7554/eLife.61408
Further reading
-
- Computational and Systems Biology
- Neuroscience
Fiber photometry has become a popular technique to measure neural activity in vivo, but common analysis strategies can reduce the detection of effects because they condense within-trial signals into summary measures, and discard trial-level information by averaging across-trials. We propose a novel photometry statistical framework based on functional linear mixed modeling, which enables hypothesis testing of variable effects at every trial time-point, and uses trial-level signals without averaging. This makes it possible to compare the timing and magnitude of signals across conditions while accounting for between-animal differences. Our framework produces a series of plots that illustrate covariate effect estimates and statistical significance at each trial time-point. By exploiting signal autocorrelation, our methodology yields joint 95% confidence intervals that account for inspecting effects across the entire trial and improve the detection of event-related signal changes over common multiple comparisons correction strategies. We reanalyze data from a recent study proposing a theory for the role of mesolimbic dopamine in reward learning, and show the capability of our framework to reveal significant effects obscured by standard analysis approaches. For example, our method identifies two dopamine components with distinct temporal dynamics in response to reward delivery. In simulation experiments, our methodology yields improved statistical power over common analysis approaches. Finally, we provide an open-source package and analysis guide for applying our framework.
-
- Computational and Systems Biology
The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuroscience, it has remained unclear whether fundamental empirical properties of neural network activity can be explained solely based on this normative principle. Here, we derive the structural, coding, and biophysical properties of excitatory-inhibitory recurrent networks of spiking neurons that emerge directly from imposing that the network minimizes an instantaneous loss function and a time-averaged performance measure enacting efficient coding. We assumed that the network encodes a number of independent stimulus features varying with a time scale equal to the membrane time constant of excitatory and inhibitory neurons. The optimal network has biologically plausible biophysical features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and a non-specific excitatory external input. The excitatory-inhibitory recurrent connectivity between neurons with similar stimulus tuning implements feature-specific competition, similar to that recently found in visual cortex. Networks with unstructured connectivity cannot reach comparable levels of coding efficiency. The optimal ratio of excitatory vs inhibitory neurons and the ratio of mean inhibitory-to-inhibitory vs excitatory-to-inhibitory connectivity are comparable to those of cortical sensory networks. The efficient network solution exhibits an instantaneous balance between excitation and inhibition. The network can perform efficient coding even when external stimuli vary over multiple time scales. Together, these results suggest that key properties of biological neural networks may be accounted for by efficient coding.
