Development of a mesoscale neural circuitry map of the common marmoset is an essential task due to the ideal characteristics of the marmoset as a URLanism for neuroscience research . To facilitate this development there is a need for new computational tools to cross-register multi-modal data sets containing MRI volumes as well as multiple histological series , and to register the combined data set to a common reference atlas. We present a fully automatic pipeline for same-subject-MRI guided reconstruction of image volumes from a series of histological sections of different modalities, followed by diffeomorphic mapping to a reference atlas. We show registration results for Nissl, myelin, CTB, and fluorescent tracer images using a same-subject ex-vivo MRI as our reference and show that our method achieves accurate registration and eliminates artifactual warping that may be result from the absence of a reference MRI data set. Examination of the determinant of the local metric tensor of the diffeomorphic mapping between each subject's ex-vivo MRI and resultant Nissl reconstruction allows an unprecedented local quantification of geometrical distortions resulting from the histological processing, showing a slight shrinkage, a median linear\pm  scale change of ~-1\% in going from the ex-vivo MRI to the tape-transfer generated histological image data\pm  .
Whole brain neuroanatomy using tera-voxel light-microscopic data sets is of much current interest. A fundamental problem in this field is the mapping of individual brain data sets to a reference space. Previous work has not rigorously quantified the distortions in brain geometry from in-vivo to ex-vivo brains due to the tissue processing, which will be important when computing properties such as local cell and process densities at the voxel level in creating reference brain maps. Further, existing approaches focus on registering uni-modal volumetric data; however, given the increasing interest in the marmoset model for neuroscience research , it is necessary to cross-register multi-modal data sets including MRIs and multiple histological series that can help address individual variations in brain architecture. Here we present a computational approach for same-subject multimodal MRI guided reconstruction of a histological series, jointly with diffeomorphic mapping to a reference atlas. We quantify the scale change during the different stages of histological processing of the brains using the Jacobian determinant of the diffeomorphic transformations involved. There are two major steps in the histology process with associated scale distortions (a) brain perfusion (b) histological sectioning and reassembly. By mapping the final image stacks to the ex-vivo post fixation MRI, we show that tape-transfer histology can be reassembled accurately into 3D volumes with a local scale change of 2.0\pm 0.4\% per axis dimension. In contrast, the perfusion step, as assessed by mapping the in-vivo MRIs to the ex-vivo post fixation MRIs, shows a larger local scale change of 6.9\pm 2.1\% per axis dimension. This is the first systematic quantification of the local metric distortions associated with whole-brain histological processing, and we expect that the results will generalize to other species .
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Development of a mesoscale neural circuitry map of the common marmoset is an essential task
Whole brain neuroanatomy using tera-voxel light-microscopic data sets is of much current interest. A fundamental problem in this field is the mapping of individual brain data sets to a reference space. Previous work has not rigorously quantified the distortions in brain geometry from in-vivo to ex-vivo brains
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ideal characteristics of the marmoset as a URLanism
tissue processing, which will be important when computing properties such as local cell and process densities at the voxel level in creating reference brain maps. Further, existing approaches focus on registering uni-modal volumetric data; however, given the increasing interest in the marmoset model
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. To facilitate this development there is a need for new computational tools
, it is necessary
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containing MRI volumes as well as
including MRIs and
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, and to register the combined data set to a common reference atlas. We present a fully automatic pipeline for same-subject-MRI
that can help address individual variations in brain architecture. Here we present a computational approach for same-subject multimodal MRI
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image volumes from a series of histological sections of different modalities, followed by
a histological series, jointly with
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show registration results for Nissl, myelin, CTB, and fluorescent tracer images using a same-subject ex-vivo MRI as our reference and show that our method achieves accurate registration and eliminates artifactual warping that may be result from the absence of a reference MRI data set. Examination of the
quantify the scale change during the different stages of histological processing of the brains using the Jacobian
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local metric tensor of the diffeomorphic mapping between each subject's
diffeomorphic transformations involved. There are two major steps in the histology process with associated scale distortions (a) brain perfusion (b) histological sectioning and reassembly. By mapping the final image stacks to the
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MRI and resultant Nissl reconstruction allows an unprecedented local quantification of geometrical distortions resulting from the histological processing, showing a slight shrinkage, a median linear
post fixation MRI, we show that tape-transfer histology can be reassembled accurately into 3D volumes with a local scale change of 2.0
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0.4\% per axis dimension. In contrast, the perfusion step, as assessed by mapping the in-vivo MRIs to the ex-vivo post fixation MRIs, shows a larger local
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~-1\% in going from the ex-vivo MRI to the tape-transfer generated histological image data
6.9
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2.1\% per axis dimension. This is the first systematic quantification of the local metric distortions associated with whole-brain histological processing, and we expect that the results will generalize to other species