Classification of Spatiotemporal Neural Activity Patterns in Brain Imaging Data

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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.
Publisher
NATURE PUBLISHING GROUP
Issue Date
2018-05
Language
English
Article Type
Article
Keywords

MONKEY VISUAL-CORTEX; CORTICAL ACTIVITY; OPTICAL RECORDINGS; TRAVELING-WAVES; CEREBRAL-CORTEX; BARREL CORTEX; IN-VIVO; DYNAMICS; CIRCUITS; RESPONSES

Citation

SCIENTIFIC REPORTS, v.8, pp.8231

ISSN
2045-2322
DOI
10.1038/s41598-018-26605-z
URI
http://hdl.handle.net/10203/242635
Appears in Collection
BiS-Journal Papers(저널논문)
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