Dataset Information

Computational Image Analysis Reveals Intrinsic Multigenerational Differences between Anterior and Posterior Cerebral Cortex Neural Progenitor Cells.

ABSTRACT: Time-lapse microscopy can capture patterns of development through multiple divisions for an entire clone of proliferating cells. Images are taken every few minutes over many days, generating data too vast to process completely by hand. Computational analysis of this data can benefit from occasional human guidance. Here we combine improved automated algorithms with minimized human validation to produce fully corrected segmentation, tracking, and lineaging results with dramatic reduction in effort. A web-based viewer provides access to data and results. The improved approach allows efficient analysis of large numbers of clones. Using this method, we studied populations of progenitor cells derived from the anterior and posterior embryonic mouse cerebral cortex, each growing in a standardized culture environment. Progenitors from the anterior cortex were smaller, less motile, and produced smaller clones compared to those from the posterior cortex, demonstrating cell-intrinsic differences that may contribute to the areal organization of the cerebral cortex.

SUBMITTER: Winter MR

PROVIDER: S-EPMC4624899 | biostudies-other | 2015 Oct

REPOSITORIES: biostudies-other

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Publications

Computational Image Analysis Reveals Intrinsic Multigenerational Differences between Anterior and Posterior Cerebral Cortex Neural Progenitor Cells.

Winter Mark R MR Liu Mo M Monteleone David D Melunis Justin J Hershberg Uri U Goderie Susan K SK Temple Sally S Cohen Andrew R AR

Stem cell reports 20150903 4

Time-lapse microscopy can capture patterns of development through multiple divisions for an entire clone of proliferating cells. Images are taken every few minutes over many days, generating data too vast to process completely by hand. Computational analysis of this data can benefit from occasional human guidance. Here we combine improved automated algorithms with minimized human validation to produce fully corrected segmentation, tracking, and lineaging results with dramatic reduction in effort ...[more]

PMID: 26344906

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Project description:Working memory is essential for many of our distinctly human abilities, including reasoning, problem solving, and planning. Research spanning many decades has helped to refine our understanding of this high-level function as comprising several hierarchically organized components, some which maintain information in the conscious mind, and others which manipulate and reorganize this information in useful ways. In the neocortex, these processes are likely implemented by a distributed frontoparietal network, with more posterior regions serving to maintain volatile information, and more anterior regions subserving the manipulation of this information. Recent meta-analytic findings have identified the anterior lateral prefrontal cortex, in particular, as being generally engaged by working memory tasks, while the posterior lateral prefrontal cortex was more strongly associated with the cognitive load required by these tasks. These findings suggest specific roles for these regions in the cognitive control processes underlying working memory. To further characterize these regions, we applied three distinct seed-based methods for determining cortical connectivity. Specifically, we employed meta-analytic connectivity mapping across task-based fMRI experiments, resting-state BOLD correlations, and VBM-based structural covariance. We found a frontoparietal pattern of convergence which strongly resembled the working memory networks identified in previous research. A contrast between anterior and posterior parts of the lateral prefrontal cortex revealed distinct connectivity patterns consistent with the idea of a hierarchical organization of frontoparietal networks. Moreover, we found a distributed network that was anticorrelated with the anterior seed region, which included most of the default mode network and a subcomponent related to social and emotional processing. These findings fit well with the internal attention model of working memory, in which representation of information is processed according to an anteroposterior gradient of abstract-to-concrete representations.

Dataset Information

Computational Image Analysis Reveals Intrinsic Multigenerational Differences between Anterior and Posterior Cerebral Cortex Neural Progenitor Cells.

Publications

Computational Image Analysis Reveals Intrinsic Multigenerational Differences between Anterior and Posterior Cerebral Cortex Neural Progenitor Cells.

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