Brain atlases enable the mapping of labeled cells and projections from different brains onto a standard coordinate system. We address two issues in the construction and use of atlases. First, expert neuroanatomists ascertain the fine-scale pattern of brain tissue, the ‘texture’ formed by cellular organization, to define cytoarchitectural borders. We automate the processes of localizing landmark structures and alignment of brains to a reference atlas using machine learning and training data derived from expert annotations. Second, we construct an atlas that is active; that is, augmented with each use. We show that the alignment of new brains to a reference atlas can continuously refine the coordinate system and associated variance. We apply this approach to the adult murine brainstem and achieve a precise alignment of projections in cytoarchitecturally ill-defined regions across brains from different animals.
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All analysis was done following the algorithms detailed in the Methods. The code was written in Python and is available as Supplementary Material and, together with updates, at https://github.com/ActiveBrainAtlas/MouseBrainAtlas through the GNU General Public License (GPL). Organization of the code is in a ReadMe file.
All raw data are publicly available. They may be downloaded, with a listing of files found in Supplementary Table 1 and at https://github.com/ActiveBrainAtlas/MouseBrainAtlas/blob/master/doc/Brain_stack_directories.md, through the Amazon Web Service Storage S3 at the bucket named mousebrainatlas-rawdata.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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The idea for this project was catalyzed at the 2008 meeting on ‘The Architectural Logic of Mammalian CNS’ at the Banbury Center, Cold Spring Harbor Laboratory. We thank N.M. Lindsay for help with annotating the trigeminus, A. Brzozowska-Prechtl and H. Liechty for assistance with the histology, A. Newberry for assistance with coding, and X. Ji and K. Svoboda for timely discussions. This work was funded by NIH BRAIN awards (U01 grant nos. MH105971 and NS0905905 and U19 grant nos. MH114821 and NS107466), a Mathers Charitable Foundation award, and funds from the Dr. George Feher Experimental Biophysics Endowed Chair. We thank L. Enquist and the Center for Neuroanatomy with Neurotropic Viruses (NIH grant no. OD010996) for supplying the PRV.
Integrated supplementary information
Supplementary Figure 1 Reconstruction of one expertly annotated brain out of three used to bootstrap the atlas.
a, Two-dimensional contours mapped to three-dimensional subject space. The gray level volume is shown for reference. b, Reconstructed landmark structures for this particular annotated brain.
The main panel shows the original full-resolution section image and structure contours. The side panels show virtual sections of the reconstructed gray level volume in three orthogonal planes. All panels are synchronized.
The receiver operator characteristic (ROC) curves for the classifiers of all 28 structures, using two annotated brains for training and the other annotated brain for test. The area under an ROC measures the performance of a classifier.
a, Illustration of three annotated brains (blue, green, red) brought in registration in atlas space by global affine transforms. b, Top-down view of the atlas space. Each structure is represented by a color. Circles are instance centroids. Stars are the nominal centroids. Shaded plane is the common mid-sagittal plane. Note the symmetry of the nominal centroids of paired structure with respect to the mid-sagittal plane. c, Reconstructions of three annotated brains with facial motor nucleus (7N) in both sides highlighted. d, An example of the shape estimation for 7N using all six instances of the facial motor nucleus, one from each hemisphere. e, All six instances of the facial motor nucleus aligned using rigid transforms. f, Probabilistic average shape of the facial motor nucleus obtained by voxel averaging.
a, Jaccard index, which ranges from 0 for completely disjoint to 1 for an exact overlap. b,c, The deviation of the centroid of a registered landmark from the expert annotation in absolute units (b) and normalized to the size of the landmark (c). The online source data for this figure include a listing of all data points. Source data
Supplementary Figure 6 Mapping of fluorescent intensities from Neurotrace-blue-stained sections to thionin-stained sections.
Data were obtained from brains with alternate sections stained with Neurotrace blue or thionin. The sections are rigidly aligned by Elastix, with mutual information as criteria. We then randomly sample ten pairs of regions, each 500 µm by 500 µm, from many pairs of adjacent sections and match the intensity histograms of corresponding regions. We match histograms of moderately sized regions rather than entire images because the global tissue content is likely to be different even for adjacent sections, while using regions of limited extent reduces this variance. a, Histogram of the pixel intensity of a region from a thionin section; histology shown as an inset. b, Histogram of the pixel intensity of a region from a Neurotrace blue section at the same level in the brain; histology shown as an inset. c, The estimated nonlinear mapping between the intensities of Neurotrace blue to those of thionin. We collected 1,000 such curves from five brains. The thick black line is for the section in a and b, and the other lines are for other pairs of sections. d, The histogram and image of the Neurotrace blue data in b after correction.
This analysis is over two ChAT brains. a, Jaccard index. b,c, The deviation of the centroid of a registered landmark from the expert annotation in absolute units (b) and normalized to the size of the landmark (c). The online source data for this figure include a listing of all data points. Source data
This analysis is over 13 brains. Only selected landmarks required corrections. a, Corrections in absolute units. b, Corrections normalized to the size of the landmark. The online source data for this figure include a listing of all data points. Source data
a, Landscape of the objective function for a particular registration in one mouse. Magnitude is normalized to yield z-scores. Significance metrics are the z-score of the estimated maximum, and the margin, that is, the distance from the maximum where the z-score drops to unity. b, The z-scores of all structure-specific registrations across 12 mice, as indicated. c,d, Lower bound of all structure-specific registrations in absolute (c) and normalized (d) coordinates across 12 mice, as indicated. e,f, Upper bound of all structure-specific registrations in absolute (e) and normalized (f) coordinates across 12 mice, as indicated. The online source data for this figure include a listing of data points in b–f. Source data
Supplementary Figure 10 The variation in positions of structures around respective nominal centroids.
Different brains are represented by different colors. a, The s.d. in three dimensions across 12 mice, as indicated. The sample-averaged root-mean-square s.d. is 160 ± 40 µm. b–d, The s.d. across 12 mice, as indicated, projected along the medial–lateral (b), dorsal–ventral (c), and rostral–caudal (d) axes. The online source data for this figure include a listing of data points in b–d. Source data
Supplementary Figure 11 Deformation fields derived from global registration and landmark-specific registration for an example section.
Contours are the cross-sections of 0.5-level iso-surfaces of the aligned atlas structures. Grid lines represent the transformed result of a regular grid defined in atlas space. a, Results after global registration. Structures are placed reasonably close to the correct positions, but individual adjustment is still necessary. Grid lines exhibit an affine transformation. b, Results after structure-specific registration. Structure pose and locations are improved. Warped grid lines demonstrate the final deformation field.
Supplementary Figure 12 Results for the area under the receiver operator characteristic curve (ROC) for three different patch sizes.
Classifiers were trained using two brains, MD585 and MD589, and the accuracy was measured against a third brain, MD594. The online source data for this figure include a listing of all data points. Source data