Contextual Feedback to Superficial Layers of V1

Lars Muckli, Federico De Martino, Luca Vizioli, Lucy S. Petro, Fraser W. Smith, Kamil Ugurbil, Rainer Goebel, Essa Yacoub

Research output: Contribution to journalArticlepeer-review

205 Scopus citations


Neuronal cortical circuitry comprises feedforward, lateral, and feedback projections, each of which terminates in distinct cortical layers [1-3]. In sensory systems, feedforward processing transmits signals from the external world into the cortex, whereas feedback pathways signal the brain's inference of the world [4-11]. However, the integration of feedforward, lateral, and feedback inputs within each cortical area impedes the investigation of feedback, and to date, no technique has isolated the feedback of visual scene information in distinct layers of healthy human cortex. We masked feedforward input to a region of V1 cortex and studied the remaining internal processing. Using high-resolution functional brain imaging (0.8 mm3) and multivoxel pattern information techniques, we demonstrate that during normal visual stimulation scene information peaks in mid-layers. Conversely, we found that contextual feedback information peaks in outer, superficial layers. Further, we found that shifting the position of the visual scene surrounding the mask parametrically modulates feedback in superficial layers of V1. Our results reveal the layered cortical organization of external versus internal visual processing streams during perception in healthy human subjects. We provide empirical support for theoretical feedback models such as predictive coding [10, 12] and coherent infomax [13] and reveal the potential of high-resolution fMRI to access internal processing in sub-millimeter human cortex.

Original languageEnglish (US)
Pages (from-to)2690-2695
Number of pages6
JournalCurrent Biology
Issue number20
StatePublished - Oct 19 2015

Bibliographical note

Funding Information:
The institutional review board at the University of Minnesota and the ethics committee at the University of Glasgow approved the studies. We thank Jan Zimmermann for support in data acquisition and Jack Gallant, Kendrick Kay, and Elia Formisano for discussion of encoding analysis. R.G. is supported by ERC grant “ColumnarCodeCracking” (ERC-2010-AdG; grant agreement no. 269853). E.S., K.U., and work conducted in CMRR are supported by NIH grants P41 EB015894 (NIBIB) and P30 NS076408 (NINDS). L.M., L.V., and L.S.P. are supported by ERC grant (ERC StG 2012_311751-“Brain reading of contextual feedback and predictions”) dedicated to L.M.

Publisher Copyright:
© 2015 The Authors.


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