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Neuroimage. 2017 Nov 15;162:32-44. doi: 10.1016/j.neuroimage.2017.08.033. Epub 2017 Aug 13.

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

Author information

1
Center for the Neural Basis of Cognition, Carnegie Mellon University and University of Pittsburgh, USA; Program in Neural Computation, Carnegie Mellon University and University of Pittsburgh, USA; Department of Neurological Surgery, University of Pittsburgh, USA. Electronic address: ynli@cmu.edu.
2
Center for the Neural Basis of Cognition, Carnegie Mellon University and University of Pittsburgh, USA; Department of Neurological Surgery, University of Pittsburgh, USA.
3
Center for the Neural Basis of Cognition, Carnegie Mellon University and University of Pittsburgh, USA; Program in Neural Computation, Carnegie Mellon University and University of Pittsburgh, USA; Department of Neurological Surgery, University of Pittsburgh, USA.

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.

KEYWORDS:

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

PMID:
28813643
PMCID:
PMC5705443
[Available on 2018-11-15]
DOI:
10.1016/j.neuroimage.2017.08.033
[Indexed for MEDLINE]

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