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

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.

Left mid-ventral temporal cortex interacts with early visual cortex and the anterior temporal lobe to support word individuation

“…Thus, individuation of real words in the lmFG may not be a result of solely visual processing occurring in this region, but rather a local reflection of a network-level computation. A similar timing pattern has been reported for face individuation in the fusiform gyrus, where early activity in response to faces is coarse and later activity is individuated 47 and may reflect network-level interactions 48 . This suggests that a similar dynamic process is conserved between both word and face stimuli and may reflect a general principle of visual processing.…”

Posterior and Mid-Fusiform Contribute to Distinct Stages of Facial Expression Processing

“…Face sensitive activity in this time window has been shown to be sensitive to face familiarity and to attention 39,40 . Previous studies and the results presented here show that face identity can be decoded from the activity in this later time window in mid-fusiform 8,41 and reflects a distributed code for identity among regions of the face processing network 42 . Additionally, the previously mentioned face adaptation study showed that activity in this window reflects the subjectively perceived facial expression after adaptation 38 .…”

Endogenous activity modulates stimulus and circuit-specific neural tuning and predicts perceptual behavior

“…Specifically, this method can be used to examine the effects of any multivariate signal on local discriminant information on a trial-by-trial basis. For example, this algorithm could be used to examine how activity in one region modulates discriminant information in another region, a form of multivariate functional connectivity 58 , 59 . Much like MI here, using this method for multivariate functional connectivity would yield a trail-by-trial measure of how much one region influences the representation in another region.…”