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BrainAligner: 3D registration atlases of Drosophila brains.

Peng H, Chung P, Long F, Qu L, Jenett A, Seeds AM, Myers EW, Simpson JH - Nat. Methods (2011)

Bottom Line: Analyzing Drosophila melanogaster neural expression patterns in thousands of three-dimensional image stacks of individual brains requires registering them into a canonical framework based on a fiducial reference of neuropil morphology.Using a neuropil marker (the antibody nc82) as a reference of the brain morphology and a target brain that is itself a statistical average of data for 295 brains, we achieved a registration accuracy of 2 μm on average, permitting assessment of stereotypy, potential connectivity and functional mapping of the adult fruit fly brain.We used BrainAligner to generate an image pattern atlas of 2954 registered brains containing 470 different expression patterns that cover all the major compartments of the fly brain.

View Article: PubMed Central - PubMed

Affiliation: Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA. pengh@janelia.hhmi.org

ABSTRACT
Analyzing Drosophila melanogaster neural expression patterns in thousands of three-dimensional image stacks of individual brains requires registering them into a canonical framework based on a fiducial reference of neuropil morphology. Given a target brain labeled with predefined landmarks, the BrainAligner program automatically finds the corresponding landmarks in a subject brain and maps it to the coordinate system of the target brain via a deformable warp. Using a neuropil marker (the antibody nc82) as a reference of the brain morphology and a target brain that is itself a statistical average of data for 295 brains, we achieved a registration accuracy of 2 μm on average, permitting assessment of stereotypy, potential connectivity and functional mapping of the adult fruit fly brain. We used BrainAligner to generate an image pattern atlas of 2954 registered brains containing 470 different expression patterns that cover all the major compartments of the fly brain.

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Schematic illustration of the BrainAligner algorithm. (a) BrainAligner performs a global alignment (G) followed by nonlinear local alignments (L) using landmarks. Scale bars: 50 µm. (b) The Reliable Landmark Matching (RLM) algorithm for detecting corresponding feature points in subject and target images. Dots of the same color indicate the matching landmarks; PT, a target brain landmark position; PS, a subject brain landmark; PMI, PINT, PCC, the best matching positions based on mutual information (MI), voxel intensity (INT), and correlation coefficient (CC) of local image patches. In the tetrahedron-pruning step, the landmarks in a subject image that clearly violate the relative position relationships of the target are discarded.
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Figure 2: Schematic illustration of the BrainAligner algorithm. (a) BrainAligner performs a global alignment (G) followed by nonlinear local alignments (L) using landmarks. Scale bars: 50 µm. (b) The Reliable Landmark Matching (RLM) algorithm for detecting corresponding feature points in subject and target images. Dots of the same color indicate the matching landmarks; PT, a target brain landmark position; PS, a subject brain landmark; PMI, PINT, PCC, the best matching positions based on mutual information (MI), voxel intensity (INT), and correlation coefficient (CC) of local image patches. In the tetrahedron-pruning step, the landmarks in a subject image that clearly violate the relative position relationships of the target are discarded.

Mentions: In global alignment, we sequentially optimized the displacement, scaling and rotation parameters of an affine transform from subject to target to maximize the correlation of voxel intensities between the two images (Fig. 2a and Methods). We visually examined the transformed brains after the global alignment, and found no transformation errors in over 99% of our samples. The failure cases typically corresponded to poorly dissected brains that were either damaged structurally or for which excess tissues were present.


BrainAligner: 3D registration atlases of Drosophila brains.

Peng H, Chung P, Long F, Qu L, Jenett A, Seeds AM, Myers EW, Simpson JH - Nat. Methods (2011)

Schematic illustration of the BrainAligner algorithm. (a) BrainAligner performs a global alignment (G) followed by nonlinear local alignments (L) using landmarks. Scale bars: 50 µm. (b) The Reliable Landmark Matching (RLM) algorithm for detecting corresponding feature points in subject and target images. Dots of the same color indicate the matching landmarks; PT, a target brain landmark position; PS, a subject brain landmark; PMI, PINT, PCC, the best matching positions based on mutual information (MI), voxel intensity (INT), and correlation coefficient (CC) of local image patches. In the tetrahedron-pruning step, the landmarks in a subject image that clearly violate the relative position relationships of the target are discarded.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3104101&req=5

Figure 2: Schematic illustration of the BrainAligner algorithm. (a) BrainAligner performs a global alignment (G) followed by nonlinear local alignments (L) using landmarks. Scale bars: 50 µm. (b) The Reliable Landmark Matching (RLM) algorithm for detecting corresponding feature points in subject and target images. Dots of the same color indicate the matching landmarks; PT, a target brain landmark position; PS, a subject brain landmark; PMI, PINT, PCC, the best matching positions based on mutual information (MI), voxel intensity (INT), and correlation coefficient (CC) of local image patches. In the tetrahedron-pruning step, the landmarks in a subject image that clearly violate the relative position relationships of the target are discarded.
Mentions: In global alignment, we sequentially optimized the displacement, scaling and rotation parameters of an affine transform from subject to target to maximize the correlation of voxel intensities between the two images (Fig. 2a and Methods). We visually examined the transformed brains after the global alignment, and found no transformation errors in over 99% of our samples. The failure cases typically corresponded to poorly dissected brains that were either damaged structurally or for which excess tissues were present.

Bottom Line: Analyzing Drosophila melanogaster neural expression patterns in thousands of three-dimensional image stacks of individual brains requires registering them into a canonical framework based on a fiducial reference of neuropil morphology.Using a neuropil marker (the antibody nc82) as a reference of the brain morphology and a target brain that is itself a statistical average of data for 295 brains, we achieved a registration accuracy of 2 μm on average, permitting assessment of stereotypy, potential connectivity and functional mapping of the adult fruit fly brain.We used BrainAligner to generate an image pattern atlas of 2954 registered brains containing 470 different expression patterns that cover all the major compartments of the fly brain.

View Article: PubMed Central - PubMed

Affiliation: Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA. pengh@janelia.hhmi.org

ABSTRACT
Analyzing Drosophila melanogaster neural expression patterns in thousands of three-dimensional image stacks of individual brains requires registering them into a canonical framework based on a fiducial reference of neuropil morphology. Given a target brain labeled with predefined landmarks, the BrainAligner program automatically finds the corresponding landmarks in a subject brain and maps it to the coordinate system of the target brain via a deformable warp. Using a neuropil marker (the antibody nc82) as a reference of the brain morphology and a target brain that is itself a statistical average of data for 295 brains, we achieved a registration accuracy of 2 μm on average, permitting assessment of stereotypy, potential connectivity and functional mapping of the adult fruit fly brain. We used BrainAligner to generate an image pattern atlas of 2954 registered brains containing 470 different expression patterns that cover all the major compartments of the fly brain.

Show MeSH
Related in: MedlinePlus