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Puzzle Imaging: Using Large-Scale Dimensionality Reduction Algorithms for Localization.

Glaser JI, Zamft BM, Church GM, Kording KP - PLoS ONE (2015)

Bottom Line: This technique takes many spatially disordered samples, and then pieces them back together using local properties embedded within the sample.We demonstrate the theoretical capabilities of puzzle imaging in three biological scenarios, showing that (1) relatively precise 3-dimensional brain imaging is possible; (2) the physical structure of a neural network can often be recovered based only on the neural connectivity matrix; and (3) a chemical map could be reproduced using bacteria with chemosensitive DNA and conjugative transfer.The ability to reconstruct scrambled images promises to enable imaging based on DNA sequencing of homogenized tissue samples.

View Article: PubMed Central - PubMed

Affiliation: Department of Physical Medicine and Rehabilitation, Northwestern University and Rehabilitation Institute of Chicago, Chicago, Illinois, United States of America.

ABSTRACT
Current high-resolution imaging techniques require an intact sample that preserves spatial relationships. We here present a novel approach, "puzzle imaging," that allows imaging a spatially scrambled sample. This technique takes many spatially disordered samples, and then pieces them back together using local properties embedded within the sample. We show that puzzle imaging can efficiently produce high-resolution images using dimensionality reduction algorithms. We demonstrate the theoretical capabilities of puzzle imaging in three biological scenarios, showing that (1) relatively precise 3-dimensional brain imaging is possible; (2) the physical structure of a neural network can often be recovered based only on the neural connectivity matrix; and (3) a chemical map could be reproduced using bacteria with chemosensitive DNA and conjugative transfer. The ability to reconstruct scrambled images promises to enable imaging based on DNA sequencing of homogenized tissue samples.

No MeSH data available.


Neural Voxel Puzzling Performance.(A) On the left, an example reconstruction of voxel locations using the SDM method. Colors are based on initial locations: those with a larger initial x location are redder, while those with a larger initial y location are bluer. In the middle, a 2-dimensional slice through reconstructed volume. The distance errors are calculated following scaling and rotating the reconstructed volume to match the original volume. On the right, one metric for the accuracy of reconstruction is shown by plotting the reconstructed distances between all points against their true distances for the reconstruction in this panel. The mean plus/minus the standard deviation (shaded) is shown. A perfect reconstruction would be a straight line, corresponding to an R value of 1. (B) Same as panel A, except an example reconstruction using the ULI method. (C) R values for simulations using the SDM method (blue) and ULI method (red), as a function of the voxel size. While voxels were not confined to be cubes, for ease of understanding, we report voxel sizes as the edge length of the cube corresponding to the average voxel size. Error bars represent the standard deviation across simulations in each panel. (D) Mean distance errors in voxels for both methods as a function of the voxel size. (E) Mean distance errors in microns for both methods as a function of the voxel size. (F) Voxels are removed to represent voxels that do not contain location information (such as voxels that contain a single cell body). R values for simulations using both methods are plotted as a function of the percentage of voxels removed. (G) Mean distance errors are plotted as a function of the percentage of voxels removed.
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pone.0131593.g003: Neural Voxel Puzzling Performance.(A) On the left, an example reconstruction of voxel locations using the SDM method. Colors are based on initial locations: those with a larger initial x location are redder, while those with a larger initial y location are bluer. In the middle, a 2-dimensional slice through reconstructed volume. The distance errors are calculated following scaling and rotating the reconstructed volume to match the original volume. On the right, one metric for the accuracy of reconstruction is shown by plotting the reconstructed distances between all points against their true distances for the reconstruction in this panel. The mean plus/minus the standard deviation (shaded) is shown. A perfect reconstruction would be a straight line, corresponding to an R value of 1. (B) Same as panel A, except an example reconstruction using the ULI method. (C) R values for simulations using the SDM method (blue) and ULI method (red), as a function of the voxel size. While voxels were not confined to be cubes, for ease of understanding, we report voxel sizes as the edge length of the cube corresponding to the average voxel size. Error bars represent the standard deviation across simulations in each panel. (D) Mean distance errors in voxels for both methods as a function of the voxel size. (E) Mean distance errors in microns for both methods as a function of the voxel size. (F) Voxels are removed to represent voxels that do not contain location information (such as voxels that contain a single cell body). R values for simulations using both methods are plotted as a function of the percentage of voxels removed. (G) Mean distance errors are plotted as a function of the percentage of voxels removed.

Mentions: We first tested both methods on simulations with voxels with average sides of 5 μm (Fig 3A). The SDM method led to a faithful reconstruction, with the exception that the reconstructed voxels tended to be overrepresented around the outside of the cube and underrepresented in the middle. The ULI method also leads to a faithful reconstruction (Fig 3B).


Puzzle Imaging: Using Large-Scale Dimensionality Reduction Algorithms for Localization.

Glaser JI, Zamft BM, Church GM, Kording KP - PLoS ONE (2015)

Neural Voxel Puzzling Performance.(A) On the left, an example reconstruction of voxel locations using the SDM method. Colors are based on initial locations: those with a larger initial x location are redder, while those with a larger initial y location are bluer. In the middle, a 2-dimensional slice through reconstructed volume. The distance errors are calculated following scaling and rotating the reconstructed volume to match the original volume. On the right, one metric for the accuracy of reconstruction is shown by plotting the reconstructed distances between all points against their true distances for the reconstruction in this panel. The mean plus/minus the standard deviation (shaded) is shown. A perfect reconstruction would be a straight line, corresponding to an R value of 1. (B) Same as panel A, except an example reconstruction using the ULI method. (C) R values for simulations using the SDM method (blue) and ULI method (red), as a function of the voxel size. While voxels were not confined to be cubes, for ease of understanding, we report voxel sizes as the edge length of the cube corresponding to the average voxel size. Error bars represent the standard deviation across simulations in each panel. (D) Mean distance errors in voxels for both methods as a function of the voxel size. (E) Mean distance errors in microns for both methods as a function of the voxel size. (F) Voxels are removed to represent voxels that do not contain location information (such as voxels that contain a single cell body). R values for simulations using both methods are plotted as a function of the percentage of voxels removed. (G) Mean distance errors are plotted as a function of the percentage of voxels removed.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4507868&req=5

pone.0131593.g003: Neural Voxel Puzzling Performance.(A) On the left, an example reconstruction of voxel locations using the SDM method. Colors are based on initial locations: those with a larger initial x location are redder, while those with a larger initial y location are bluer. In the middle, a 2-dimensional slice through reconstructed volume. The distance errors are calculated following scaling and rotating the reconstructed volume to match the original volume. On the right, one metric for the accuracy of reconstruction is shown by plotting the reconstructed distances between all points against their true distances for the reconstruction in this panel. The mean plus/minus the standard deviation (shaded) is shown. A perfect reconstruction would be a straight line, corresponding to an R value of 1. (B) Same as panel A, except an example reconstruction using the ULI method. (C) R values for simulations using the SDM method (blue) and ULI method (red), as a function of the voxel size. While voxels were not confined to be cubes, for ease of understanding, we report voxel sizes as the edge length of the cube corresponding to the average voxel size. Error bars represent the standard deviation across simulations in each panel. (D) Mean distance errors in voxels for both methods as a function of the voxel size. (E) Mean distance errors in microns for both methods as a function of the voxel size. (F) Voxels are removed to represent voxels that do not contain location information (such as voxels that contain a single cell body). R values for simulations using both methods are plotted as a function of the percentage of voxels removed. (G) Mean distance errors are plotted as a function of the percentage of voxels removed.
Mentions: We first tested both methods on simulations with voxels with average sides of 5 μm (Fig 3A). The SDM method led to a faithful reconstruction, with the exception that the reconstructed voxels tended to be overrepresented around the outside of the cube and underrepresented in the middle. The ULI method also leads to a faithful reconstruction (Fig 3B).

Bottom Line: This technique takes many spatially disordered samples, and then pieces them back together using local properties embedded within the sample.We demonstrate the theoretical capabilities of puzzle imaging in three biological scenarios, showing that (1) relatively precise 3-dimensional brain imaging is possible; (2) the physical structure of a neural network can often be recovered based only on the neural connectivity matrix; and (3) a chemical map could be reproduced using bacteria with chemosensitive DNA and conjugative transfer.The ability to reconstruct scrambled images promises to enable imaging based on DNA sequencing of homogenized tissue samples.

View Article: PubMed Central - PubMed

Affiliation: Department of Physical Medicine and Rehabilitation, Northwestern University and Rehabilitation Institute of Chicago, Chicago, Illinois, United States of America.

ABSTRACT
Current high-resolution imaging techniques require an intact sample that preserves spatial relationships. We here present a novel approach, "puzzle imaging," that allows imaging a spatially scrambled sample. This technique takes many spatially disordered samples, and then pieces them back together using local properties embedded within the sample. We show that puzzle imaging can efficiently produce high-resolution images using dimensionality reduction algorithms. We demonstrate the theoretical capabilities of puzzle imaging in three biological scenarios, showing that (1) relatively precise 3-dimensional brain imaging is possible; (2) the physical structure of a neural network can often be recovered based only on the neural connectivity matrix; and (3) a chemical map could be reproduced using bacteria with chemosensitive DNA and conjugative transfer. The ability to reconstruct scrambled images promises to enable imaging based on DNA sequencing of homogenized tissue samples.

No MeSH data available.