Approximating Cellular Densities from High-Resolution Neuroanatomical Imaging Data

LaGrow, Theodore J.; Moore, Michael G.; Prasad, Judy A.; Davenport, Mark A.; Dyer, Eva L.

doi:10.1109/embc.2018.8512220

Cited by 9 publications

(8 citation statements)

References 9 publications

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“…For this factor we will need to adapt additional computational tools for use in our workflow. One potential tool is spatial point process analysis, which has successfully been used to extract spatially distributed counts of cells and synapses from modalities such as Nissl-stained brain images (LaGrow et al 2018;Anton-Sanchez et al 2014).…”

Section: Discussionmentioning

confidence: 99%

Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data

2020

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Reconstructing brain connectivity at sufficient resolution for computational models designed to study the biophysical mechanisms underlying cognitive processes is extremely challenging. For such a purpose, a mesoconnectome that includes laminar and cell-class specificity would be a major step forward. We analyzed the ability of gene expression patterns to predict cell-class and layer-specific projection patterns and assessed the functional annotations of the most predictive groups of genes. To achieve our goal we used publicly available volumetric gene expression and connectivity data and we trained computational models to learn and predict cell-class and layer-specific axonal projections using gene expression data. Predictions were done in two ways, namely predicting projection strengths using the expression of individual genes and using the co-expression of genes organized in spatial modules, as well as predicting binary forms of projection. For predicting the strength of projections, we found that ridge (L2-regularized) regression had the highest cross-validated accuracy with a median r2 score of 0.54 which corresponded for binarized predictions to a median area under the ROC value of 0.89. Next, we identified 200 spatial gene modules using a dictionary learning and sparse coding approach. We found that these modules yielded predictions of comparable accuracy, with a median r2 score of 0.51. Finally, a gene ontology enrichment analysis of the most predictive gene groups resulted in significant annotations related to postsynaptic function. Taken together, we have demonstrated a prediction workflow that can be used to perform multimodal data integration to improve the accuracy of the predicted mesoconnectome and support other neuroscience use cases.

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Section: Discussionmentioning

confidence: 99%

Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data

2020

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“…For this factor we will need to adapt additional computational tools for use in our pipeline. One potential tool is spatial point process analysis, which has successfully been used to extract spatially distributed counts of cells and synapses from modalities such as Nissl-stained brain images (LaGrow et al, 2018;Anton-Sanchez et al, 2014). Translational neuroscientists could benefit from the use of our predictive workflow.…”

Section: Discussionmentioning

confidence: 99%

Prediction of a cell-type specific mouse mesoconnectome using gene expression data

Timonidis

Bakker

Tiesinga

2019

Preprint

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Reconstructing brain connectivity at sufficient resolution for computational models designed to study the biophysical mechanisms underlying cognitive processes is extremely challenging. For such a purpose, a mesoconnectome that includes laminar and cell-type specificity would be a major step forward. We analysed the ability of gene expression patterns to predict cell-type and laminar specific projection patterns and analyzed the biological context of the most predictive groups of genes. To achieve our goal, we used publicly available volumetric gene expression and connectivity data and pre-processed it for prediction by averaging across brain regions, imputing missing values and rescaling. Afterwards, we predicted the strength of axonal projections and their binary form using expression patterns of individual genes and co-expression patterns of spatial gene modules. For predicting projection strength, we found that ridge (L2-regularized) regression had the highest cross-validated accuracy with a median r 2 score of 0.54 which corresponded for binarized predictions to a median area under the ROC value of 0.89. Next, we identified 200 spatial gene modules using the dictionary learning and sparse coding approach. We found that these modules yielded predictions of comparable accuracy, with a median r 2 score of 0.51. Finally, a gene ontology enrichment analysis of the most predictive gene groups resulted in significant annotations related to post-

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“…While our initial workflows focus on XRM and EM datasets, many of these methods can be easily deployed to other modalities such as light microscopy [ 47 ], and the overall framework is appropriate for problems in many domains. These include other scientific data analysis tasks as varied as machine learning for processing noninvasive medical imaging data or statistical analysis of population data.…”

Section: Potential Implicationsmentioning

confidence: 99%

Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets

et al. 2020

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Background Emerging neuroimaging datasets (collected with imaging techniques such as electron microscopy, optical microscopy, or X-ray microtomography) describe the location and properties of neurons and their connections at unprecedented scale, promising new ways of understanding the brain. These modern imaging techniques used to interrogate the brain can quickly accumulate gigabytes to petabytes of structural brain imaging data. Unfortunately, many neuroscience laboratories lack the computational resources to work with datasets of this size: computer vision tools are often not portable or scalable, and there is considerable difficulty in reproducing results or extending methods. Results We developed an ecosystem of neuroimaging data analysis pipelines that use open-source algorithms to create standardized modules and end-to-end optimized approaches. As exemplars we apply our tools to estimate synapse-level connectomes from electron microscopy data and cell distributions from X-ray microtomography data. To facilitate scientific discovery, we propose a generalized processing framework, which connects and extends existing open-source projects to provide large-scale data storage, reproducible algorithms, and workflow execution engines. Conclusions Our accessible methods and pipelines demonstrate that approaches across multiple neuroimaging experiments can be standardized and applied to diverse datasets. The techniques developed are demonstrated on neuroimaging datasets but may be applied to similar problems in other domains.

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Approximating Cellular Densities from High-Resolution Neuroanatomical Imaging Data

Cited by 9 publications

References 9 publications

Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data

Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data

Prediction of a cell-type specific mouse mesoconnectome using gene expression data

Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets

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