Dataset Information

Classification of Spatiotemporal Neural Activity Patterns in Brain Imaging Data.

ABSTRACT: Various patterns of neural activity are observed in dynamic cortical imaging data. Such patterns may reflect how neurons communicate using the underlying circuitry to perform appropriate functions; thus it is crucial to investigate the spatiotemporal characteristics of the observed neural activity patterns. In general, however, neural activities are highly nonlinear and complex, so it is a demanding job to analyze them quantitatively or to classify the patterns of observed activities in various types of imaging data. Here, we present our implementation of a novel method that successfully addresses the above issues for precise comparison and classification of neural activity patterns. Based on two-dimensional representations of the geometric structure and temporal evolution of activity patterns, our method successfully classified a number of computer-generated sample patterns created from combinations of various spatial and temporal patterns. In addition, we validated our method with voltage-sensitive dye imaging data of Alzheimer's disease (AD) model mice. Our analysis algorithm successfully distinguished the activity data of AD mice from that of wild type with significantly higher performance than previously suggested methods. Our result provides a pragmatic solution for precise analysis of spatiotemporal patterns of neural imaging data.

SUBMITTER: Song M

PROVIDER: S-EPMC5974089 | biostudies-other | 2018 May

REPOSITORIES: biostudies-other

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Publications

Classification of Spatiotemporal Neural Activity Patterns in Brain Imaging Data.

Song Min M Kang Minseok M Lee Hyeonsu H Jeong Yong Y Paik Se-Bum SB

Scientific reports 20180529 1

Various patterns of neural activity are observed in dynamic cortical imaging data. Such patterns may reflect how neurons communicate using the underlying circuitry to perform appropriate functions; thus it is crucial to investigate the spatiotemporal characteristics of the observed neural activity patterns. In general, however, neural activities are highly nonlinear and complex, so it is a demanding job to analyze them quantitatively or to classify the patterns of observed activities in various ...[more]

PMID: 29844346

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Dataset Information

Classification of Spatiotemporal Neural Activity Patterns in Brain Imaging Data.

Publications

Classification of Spatiotemporal Neural Activity Patterns in Brain Imaging Data.

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