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NeuroImage v.162, 2017년, pp.32 - 44   SCI SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

Multi-Connection Pattern Analysis: Decoding the representational content of neural communication

Li, Yuanning    (Center for the Neural Basis of Cognition, Carnegie Mellon University and University of Pittsburgh, USA   ); Richardson, Robert Mark    (Center for the Neural Basis of Cognition, Carnegie Mellon University and University of Pittsburgh, USA   ); Ghuman, Avniel Singh    (Center for the Neural Basis of Cognition, Carnegie Mellon University and University of Pittsburgh, USA  );
  • 초록  

    Abstract The lack of multivariate methods for decoding the representational content of interregional neural communication has left it difficult to know what information is represented in distributed brain circuit interactions. Here we present Multi-Connection Pattern Analysis (MCPA), which works by learning mappings between the activity patterns of the populations as a factor of the information being processed. These maps are used to predict the activity from one neural population based on the activity from the other population. Successful MCPA-based decoding indicates the involvement of distributed computational processing and provides a framework for probing the representational structure of the interaction. Simulations demonstrate the efficacy of MCPA in realistic circumstances. In addition, we demonstrate that MCPA can be applied to different signal modalities to evaluate a variety of hypothesis associated with information coding in neural communications. We apply MCPA to fMRI and human intracranial electrophysiological data to provide a proof-of-concept of the utility of this method for decoding individual natural images and faces in functional connectivity data. We further use a MCPA-based representational similarity analysis to illustrate how MCPA may be used to test computational models of information transfer among regions of the visual processing stream. Thus, MCPA can be used to assess the information represented in the coupled activity of interacting neural circuits and probe the underlying principles of information transformation between regions. Highlights MCPA allows for multivariate single trial classification of functional connectivity. Decodes the representational content of interregional neural communication. Extracts the discriminant information in the shared activity between populations. A general framework that can be extended and applied to different signal modalities.


  • 주제어

    Functional connectivity .   Multivariate statistical analysis .   Decoding .   Representation similarity analysis .   Functional magnetic resonance imaging (fMRI) .   Intracranial electroencephalography (iEEG).  

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